From c5816cef34220ccac8672f3a940e80db558e1116 Mon Sep 17 00:00:00 2001 From: Janardan Singh Kavia Date: Wed, 20 May 2026 11:18:50 -0400 Subject: [PATCH 01/30] fix: upgrade dependencies for security fixes (#13218) * chore: upgrade dependencies for security fixes - Upgrade authlib from 1.7.0 to 1.7.2 - Update Docker build to use Node.js 22.x (includes npm 10.9+) - Update uv.lock with latest dependency versions * chore: run npm audit fix run npm audit fix --------- Co-authored-by: Janardan S Kavia Co-authored-by: Adam Aghili --- docker/build_and_push.Dockerfile | 2 +- src/frontend/package-lock.json | 81 +++++++++----------------------- uv.lock | 6 +-- 3 files changed, 26 insertions(+), 63 deletions(-) diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile index 62d28c8198..a7797f38fa 100644 --- a/docker/build_and_push.Dockerfile +++ b/docker/build_and_push.Dockerfile @@ -34,7 +34,7 @@ RUN apt-get update \ # gcc gcc \ curl \ - && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \ + && curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \ && apt-get install -y nodejs \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index 7d80a96912..36cafec97b 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -217,7 +217,6 @@ "integrity": "sha512-CGOfOJqWjg2qW/Mb6zNsDm+u5vFQ8DxXfbM09z69p5Z6+mE1ikP2jUXw+j42Pf1XTYED2Rni5f95npYeuwMDQA==", "dev": true, "license": "MIT", - "peer": true, "dependencies": { "@babel/code-frame": "^7.29.0", "@babel/generator": "^7.29.0", @@ -1083,7 +1082,6 @@ "resolved": "https://registry.npmjs.org/@chakra-ui/styled-system/-/styled-system-2.9.2.tgz", "integrity": "sha512-To/Z92oHpIE+4nk11uVMWqo2GGRS86coeMmjxtpnErmWRdLcp1WVCVRAvn+ZwpLiNR+reWFr2FFqJRsREuZdAg==", "license": "MIT", - "peer": true, "dependencies": { "@chakra-ui/shared-utils": "2.0.5", "csstype": "^3.1.2", @@ -1095,7 +1093,6 @@ 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sys_platform != 'darwin')" }, { name = "datasets" }, { name = "ddgs" }, { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, @@ -5957,7 +6765,8 @@ all = [ { name = "dspy-ai" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, - { name = "faiss-cpu" }, + { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, + { name = "faiss-cpu", version = "1.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" }, { name = "fake-useragent" }, { name = "fastavro", version = "1.9.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" }, { name = "fastavro", version = "1.12.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" }, @@ -5970,8 +6779,8 @@ all = [ { name = "graph-retriever" }, { name = "huggingface-hub" }, { name = "ibm-cloud-sdk-core" }, - { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11'" }, - { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11'" }, + { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, + { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, { name = "jigsawstack" }, { name = "json-repair" }, { name = "kubernetes" }, @@ -5995,12 +6804,12 @@ all = [ { name = "langchain-nvidia-ai-endpoints" }, { name = "langchain-ollama" }, { name = "langchain-openai" }, - { name = "langchain-pinecone" }, + { name = "langchain-pinecone", marker = "python_full_version < '3.14'" }, { name = "langchain-sambanova" }, { name = "langchain-unstructured" }, - { name = "langfuse" }, + { name = "langfuse", marker = "python_full_version < '3.14'" }, { name = "langsmith" }, - { name = "langwatch" }, + { name = "langwatch", marker = "python_full_version < '3.14'" }, { name = "lark" }, { name = "litellm" }, { name = "markdown" }, @@ -6027,7 +6836,7 @@ all = [ { name = "pytube" }, { name = "qdrant-client" }, { name = "qianfan" }, - { name = "ragstack-ai-knowledge-store" }, + { name = "ragstack-ai-knowledge-store", marker = "python_full_version < '3.13'" }, { name = "redis" }, { name = "scrapegraph-py", version = "1.47.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, { name = "scrapegraph-py", version = "2.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, @@ -6048,13 +6857,14 @@ all = [ { name = "zep-python" }, ] altk = [ - { name = "agent-lifecycle-toolkit", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, + { name = "agent-lifecycle-toolkit", marker = "(python_full_version < '3.14' and platform_machine != 'x86_64') or (python_full_version < '3.14' and sys_platform != 'darwin')" }, ] anthropic = [ { name = "langchain-anthropic" }, ] apify = [ - { name = "apify-client" }, + { name = "apify-client", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "apify-client", version = "3.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, ] arize = [ { name = "arize-phoenix-otel" }, @@ -6099,9 +6909,10 @@ cohere = [ ] complete = [ { name = "ag2" }, - { name = "agent-lifecycle-toolkit", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, + { name = "agent-lifecycle-toolkit", marker = "(python_full_version < '3.14' and platform_machine != 'x86_64') or (python_full_version < '3.14' and sys_platform != 'darwin')" }, { name = "aioboto3" }, - { name = "apify-client" }, + { name = "apify-client", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "apify-client", version = "3.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "arize-phoenix-otel" }, { name = "assemblyai" }, { name = "astrapy" }, @@ -6115,7 +6926,7 @@ complete = [ { name = "composio" }, { name = "composio-langchain" }, { name = "couchbase" }, - { name = "cuga", marker = "platform_machine == 'arm64' or sys_platform != 'darwin'" }, + { name = "cuga", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" }, { name = "datasets" }, { name = "ddgs" }, { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, @@ -6123,7 +6934,8 @@ complete = [ { name = "dspy-ai" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, - { name = "faiss-cpu" }, + { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, + { name = "faiss-cpu", version = "1.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" }, { name = "fake-useragent" }, { name = "fastavro", version = "1.9.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" }, { name = "fastavro", version = "1.12.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" }, @@ -6136,8 +6948,8 @@ complete = [ { name = "graph-retriever" }, { name = "huggingface-hub" }, { name = "ibm-cloud-sdk-core" }, - { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11'" }, - { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11'" }, + { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, + { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, { name = "jigsawstack" }, { name = "json-repair" }, { name = "kubernetes" }, @@ -6161,12 +6973,12 @@ complete = [ { name = "langchain-nvidia-ai-endpoints" }, { name = "langchain-ollama" }, { name = "langchain-openai" }, - { name = "langchain-pinecone" }, + { name = "langchain-pinecone", marker = "python_full_version < '3.14'" }, { name = "langchain-sambanova" }, { name = "langchain-unstructured" }, - { name = "langfuse" }, + { name = "langfuse", marker = "python_full_version < '3.14'" }, { name = "langsmith" }, - { name = "langwatch" }, + { name = "langwatch", marker = "python_full_version < '3.14'" }, { name = "lark" }, { name = "litellm" }, { name = "markdown" }, @@ -6193,7 +7005,7 @@ complete = [ { name = "pytube" }, { name = "qdrant-client" }, { name = "qianfan" }, - { name = "ragstack-ai-knowledge-store" }, + { name = "ragstack-ai-knowledge-store", marker = "python_full_version < '3.13'" }, { name = "redis" }, { name = "scrapegraph-py", version = "1.47.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, { name = "scrapegraph-py", version = "2.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, @@ -6224,7 +7036,7 @@ ctransformers = [ { name = "ctransformers" }, ] cuga = [ - { name = "cuga", marker = "platform_machine == 'arm64' or sys_platform != 'darwin'" }, + { name = "cuga", marker = "(python_full_version < '3.14' and platform_machine == 'arm64') or (python_full_version < '3.14' and sys_platform != 'darwin')" }, ] datasets = [ { name = "datasets" }, @@ -6250,7 +7062,8 @@ elasticsearch = [ { name = "langchain-elasticsearch" }, ] faiss = [ - { name = "faiss-cpu" }, + { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, + { name = "faiss-cpu", version = "1.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" }, ] fake-useragent = [ { name = "fake-useragent" }, @@ -6293,8 +7106,8 @@ huggingface = [ ] ibm-watsonx-clients = [ { name = "ibm-cloud-sdk-core" }, - { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11'" }, - { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11'" }, + { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, + { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" }, ] jigsawstack = [ { name = "jigsawstack" }, @@ -6315,13 +7128,13 @@ langchain-unstructured = [ { name = "langchain-unstructured" }, ] langfuse = [ - { name = "langfuse" }, + { name = "langfuse", marker = "python_full_version < '3.14'" }, ] langsmith = [ { name = "langsmith" }, ] langwatch = [ - { name = "langwatch" }, + { name = "langwatch", marker = "python_full_version < '3.14'" }, ] lark = [ { name = "lark" }, @@ -6406,7 +7219,7 @@ pgvector = [ { name = "pgvector" }, ] pinecone = [ - { name = "langchain-pinecone" }, + { name = "langchain-pinecone", marker = "python_full_version < '3.14'" }, ] postgresql = [ { name = "sqlalchemy", extra = ["postgresql-psycopg", "postgresql-psycopg2binary"] }, @@ -6427,7 +7240,7 @@ qianfan = [ { name = "qianfan" }, ] ragstack = [ - { name = "ragstack-ai-knowledge-store" }, + { name = "ragstack-ai-knowledge-store", marker = "python_full_version < '3.13'" }, ] redis = [ { name = "redis" }, @@ -6521,7 +7334,7 @@ dev = [ [package.metadata] requires-dist = [ { name = "ag2", marker = "extra == 'ag2'", specifier = ">=0.1.0" }, - { name = "agent-lifecycle-toolkit", marker = "(platform_machine != 'x86_64' and extra == 'altk') or (sys_platform != 'darwin' and extra == 'altk')", specifier = ">=0.10.1,<1.0" }, + { name = "agent-lifecycle-toolkit", marker = "(python_full_version < '3.14' and platform_machine != 'x86_64' and extra == 'altk') or (python_full_version < '3.14' and sys_platform != 'darwin' and extra == 'altk')", specifier = ">=0.10.1,<1.0" }, { name = "aioboto3", marker = "extra == 'aioboto3'", specifier = ">=15.2.0,<16.0.0" }, { name = "aiofile", specifier = ">=3.9.0,<4.0.0" }, { name = "aiofiles", specifier = ">=24.1.0,<25.0.0" }, @@ -6550,14 +7363,14 @@ requires-dist = [ { name = "cryptography", specifier = ">=46.0.7" }, { name = "ctransformers", marker = "extra == 'ctransformers'", specifier = ">=0.2.10" }, { name = "ctransformers", marker = "extra == 'local'", specifier = ">=0.2" }, - { name = "cuga", marker = "platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'cuga'", specifier = ">=0.2.20,<0.3.0" }, - { name = "cuga", marker = "sys_platform != 'darwin' and extra == 'cuga'", specifier = ">=0.2.20,<0.3.0" }, + { name = "cuga", marker = "python_full_version < '3.14' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'cuga'", specifier = ">=0.2.20,<0.3.0" }, + { name = "cuga", marker = "python_full_version < '3.14' and sys_platform != 'darwin' and extra == 'cuga'", specifier = ">=0.2.20,<0.3.0" }, { name = "datasets", marker = "extra == 'datasets'", specifier = ">2.14.7,<5.0.0" }, { name = "ddgs", marker = "extra == 'duckduckgo'", specifier = ">=9.0.0" }, { name = "defusedxml", specifier = ">=0.7.1,<1.0.0" }, { name = "diskcache", specifier = ">=5.6.3,<6.0.0" }, { name = "docling", marker = "(platform_machine != 'x86_64' and extra == 'docling') or (sys_platform != 'darwin' and extra == 'docling')", specifier = ">=2.36.1,<3.0.0" }, - { name = "docling-core", marker = "extra == 'docling'", specifier = ">=2.36.1,<3.0.0" }, + { name = "docling-core", marker = "extra == 'docling'", specifier = ">=2.77.0,<3.0.0" }, { name = "docstring-parser", specifier = ">=0.16,<1.0.0" }, { name = "dspy-ai", marker = "extra == 'dspy'", specifier = "==2.5.41" }, { name = "duckdb", specifier = ">=1.0.0,<2.0.0" }, @@ -6568,7 +7381,8 @@ requires-dist = [ { name = "elevenlabs", marker = "python_full_version == '3.12.*'", specifier = "==1.58.1" }, { name = "email-validator", specifier = ">=2.0.0" }, { name = "emoji", specifier = ">=2.12.0,<3.0.0" }, - { name = "faiss-cpu", marker = "extra == 'faiss'", specifier = "==1.9.0.post1" }, + { name = "faiss-cpu", marker = "python_full_version >= '3.14' and extra == 'faiss'", specifier = ">=1.13.2" }, + { name = "faiss-cpu", marker = "python_full_version < '3.14' and extra == 'faiss'", specifier = "==1.9.0.post1" }, { name = "fake-useragent", marker = "extra == 'fake-useragent'", specifier = ">=2.0.0" }, { name = "fastapi", specifier = ">=0.135.0,<1.0.0" }, { name = "fastapi-pagination", specifier = ">=0.13.1,<1.0.0" }, @@ -6579,20 +7393,20 @@ requires-dist = [ { name = "filelock", specifier = ">=3.20.1,<4.0.0" }, { name = "firecrawl-py", specifier = ">=1.0.16,<2.0.0" }, { name = "gassist", marker = "sys_platform == 'win32' and extra == 'gassist'", specifier = ">=0.0.1" }, - { name = "gitpython", marker = "extra == 'gitpython'", specifier = "==3.1.47" }, + { name = "gitpython", marker = "extra == 'gitpython'", specifier = ">=3.1.50" }, { name = "google-api-python-client", marker = "extra == 'google'", specifier = "~=2.161" }, { name = "google-api-python-client", marker = "extra == 'google-api'", specifier = "~=2.161" }, { name = "google-search-results", marker = "extra == 'serpapi'", specifier = ">=2.4.1,<3.0.0" }, { name = "grandalf", specifier = ">=0.8.0,<1.0.0" }, { name = "graph-retriever", marker = "extra == 'graph-retriever'", specifier = "==0.8.0" }, { name = "greenlet", specifier = ">=3.1.1,<4.0.0" }, - { name = "gunicorn", specifier = ">=25.3.0,<26.0.0" }, + { name = "gunicorn", specifier = ">=25.3.0,<27.0.0" }, { name = "httpx", extras = ["http2"], specifier = ">=0.27,<1.0.0" }, { name = "huggingface-hub", extras = ["inference"], marker = "extra == 'huggingface'", specifier = ">=1.0.0,<2.0.0" }, { name = "ibm-cloud-sdk-core", marker = "extra == 'ibm-watsonx-clients'", specifier = "~=3.24.4" }, - { name = "ibm-watsonx-ai", specifier = ">=1.3.1,<2.0.0" }, - { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11' and extra == 'ibm-watsonx-clients'", specifier = "~=2.8.0" }, - { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11' and extra == 'ibm-watsonx-clients'", specifier = "~=2.8.0" }, + { name = "ibm-watsonx-ai", marker = "python_full_version < '3.14'", specifier = ">=1.3.1,<2.0.0" }, + { name = "ibm-watsonx-orchestrate-clients", marker = "python_full_version >= '3.11' and python_full_version < '3.14' and extra == 'ibm-watsonx-clients'", specifier = "~=2.8.0" }, + { name = "ibm-watsonx-orchestrate-core", marker = "python_full_version >= '3.11' and python_full_version < '3.14' and extra == 'ibm-watsonx-clients'", specifier = "~=2.8.0" }, { name = "jaraco-context", specifier = ">=6.1.0" }, { name = "jigsawstack", marker = "extra == 'jigsawstack'", specifier = "==0.2.7" }, { name = "jq", marker = "sys_platform != 'win32'", specifier = ">=1.7.0,<2.0.0" }, @@ -6608,7 +7422,7 @@ requires-dist = [ { name = "langchain-chroma", marker = "extra == 'chroma'", specifier = "~=0.2.6" }, { name = "langchain-cohere", marker = "extra == 'cohere'", specifier = "~=0.5.0" }, { name = "langchain-community", specifier = "~=0.4.1" }, - { name = "langchain-core", specifier = ">=1.2.28,<2.0.0" }, + { name = "langchain-core", specifier = ">=1.3.3,<2.0.0" }, { name = "langchain-elasticsearch", marker = "extra == 'elasticsearch'", specifier = "~=1.0.0" }, { name = "langchain-experimental", specifier = "~=0.4.1" }, { name = "langchain-google-calendar-tools", marker = "extra == 'google'", specifier = "~=0.0.1" }, @@ -6618,7 +7432,7 @@ requires-dist = [ { name = "langchain-graph-retriever", marker = "extra == 'graph-retriever'", specifier = "==0.8.0" }, { name = "langchain-groq", marker = "extra == 'groq'", specifier = "~=1.1.1" }, { name = "langchain-huggingface", marker = "(platform_machine != 'x86_64' and extra == 'langchain-huggingface') or (sys_platform != 'darwin' and extra == 'langchain-huggingface')", specifier = "~=1.2.0" }, - { name = "langchain-ibm", specifier = "~=1.0.2" }, + { name = "langchain-ibm", marker = "python_full_version < '3.14'", specifier = "~=1.0.2" }, { name = "langchain-mcp-adapters", marker = "extra == 'langchain-mcp-adapters'", specifier = ">=0.1.14,<0.2.0" }, { name = "langchain-milvus", marker = "extra == 'milvus'", specifier = "~=0.3.2" }, { name = "langchain-mistralai", marker = "extra == 'mistral'", specifier = "~=1.1.1" }, @@ -6626,7 +7440,7 @@ requires-dist = [ { name = "langchain-nvidia-ai-endpoints", marker = "extra == 'nvidia'", specifier = "~=1.0.0" }, { name = "langchain-ollama", marker = "extra == 'ollama'", specifier = "~=0.3.10" }, { name = "langchain-openai", marker = "extra == 'openai'", specifier = ">=1.1.6" }, - { name = "langchain-pinecone", marker = "extra == 'pinecone'", specifier = "~=0.2.13" }, + { name = "langchain-pinecone", marker = "python_full_version < '3.14' and extra == 'pinecone'", specifier = "~=0.2.13" }, { name = "langchain-sambanova", marker = "extra == 'sambanova'", specifier = "~=1.0.0" }, { name = "langchain-unstructured", marker = "extra == 'langchain-unstructured'", specifier = "~=1.0.0" }, { name = "langchainhub", specifier = "~=0.1.15" }, @@ -6730,12 +7544,12 @@ requires-dist = [ { name = "langflow-base", extras = ["yfinance"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["youtube"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["zep"], marker = "extra == 'complete'" }, - { name = "langfuse", marker = "extra == 'langfuse'", specifier = "~=3.8" }, + { name = "langfuse", marker = "python_full_version < '3.14' and extra == 'langfuse'", specifier = "~=3.8" }, { name = "langgraph-checkpoint", specifier = ">4.0.0,<5.0.0" }, { name = "langsmith", marker = "extra == 'langsmith'", specifier = ">=0.3.42,<1.0.0" }, - { name = "langwatch", marker = "extra == 'langwatch'", specifier = "~=0.10.0" }, + { name = "langwatch", marker = "python_full_version < '3.14' and extra == 'langwatch'", specifier = "~=0.10.0" }, { name = "lark", marker = "extra == 'lark'", specifier = "==1.2.2" }, - { name = "lfx", editable = "../../lfx" }, + { name = "lfx", specifier = "~=0.4.3" }, { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.83.0" }, { name = "llama-cpp-python", marker = "extra == 'llama-cpp'", specifier = "~=0.2.0" }, { name = "llama-cpp-python", marker = "extra == 'local'", specifier = ">=0.2.0" }, @@ -6756,7 +7570,8 @@ requires-dist = [ { name = "networkx", specifier = ">=3.4.2,<4.0.0" }, { name = "nltk", marker = "extra == 'nltk'", specifier = ">=3.9.4" }, { name = "numexpr", marker = "extra == 'numexpr'", specifier = "==2.10.2" }, - { name = "onnxruntime", specifier = ">=1.20,<1.24" }, + { name = "onnxruntime", marker = "python_full_version < '3.14'", specifier = ">=1.20,<1.24" }, + { name = "onnxruntime", marker = "python_full_version >= '3.14'", specifier = ">=1.26" }, { name = "openai", marker = "extra == 'openai'", specifier = ">=1.68.2,<3.0.0" }, { name = "openinference-instrumentation-langchain", marker = "extra == 'openinference'", specifier = ">=0.1.29" }, { name = "openlayer", marker = "extra == 'openlayer'", specifier = ">=0.9.0,<1.0.0" }, @@ -6775,7 +7590,8 @@ requires-dist = [ { name = "pip", specifier = ">=26.0.0,<27.0.0" }, { name = "platformdirs", specifier = ">=4.2.0,<5.0.0" }, { name = "prometheus-client", specifier = ">=0.20.0,<1.0.0" }, - { name = "pyarrow", marker = "extra == 'pyarrow'", specifier = ">=19.0.1,!=22.0.0" }, + { name = "protobuf", specifier = ">=6.33.6,<7.0.0" }, + { name = "pyarrow", marker = "extra == 'pyarrow'", specifier = ">=23.0.1,<24.0.0" }, { name = "pyasn1", specifier = ">=0.6.3,<0.7.0" }, { name = "pydantic", specifier = "~=2.12.5" }, { name = "pydantic-settings", specifier = ">=2.2.0,<3.0.0" }, @@ -6790,7 +7606,7 @@ requires-dist = [ { name = "pytube", marker = "extra == 'pytube'", specifier = "==15.0.0" }, { name = "qdrant-client", marker = "extra == 'qdrant'", specifier = "==1.9.2" }, { name = "qianfan", marker = "extra == 'qianfan'", specifier = "==0.3.5" }, - { name = "ragstack-ai-knowledge-store", marker = "extra == 'ragstack'", specifier = "==0.2.1" }, + { name = "ragstack-ai-knowledge-store", marker = "python_full_version < '3.13' and extra == 'ragstack'", specifier = "==0.2.1" }, { name = "redis", marker = "extra == 'redis'", specifier = ">=7.4.0,<8.0.0" }, { name = "rich", specifier = ">=13.7.0,<14.0.0" }, { name = "scipy", specifier = ">=1.15.2,<2.0.0" }, @@ -6840,7 +7656,7 @@ dev = [ { name = "httpx", extras = ["http2"], specifier = ">=0.27" }, { name = "ipykernel", specifier = ">=6.29.0" }, { name = "pre-commit", specifier = ">=3.7.0" }, - { name = "pytest", specifier = ">=8.2.0" }, + { name = "pytest", specifier = ">=9.0.3" }, { name = "pytest-asyncio", specifier = ">=0.23.0" }, { name = "pytest-cov", specifier = 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git \ - # npm - npm \ # gcc gcc \ curl \ && curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \ && apt-get install -y nodejs \ + && corepack enable \ + && corepack prepare npm@11 --activate \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 69b6311956..658c975f42 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -46,7 +46,7 @@ dependencies = [ "pillow>=12.1.1,<13.0.0", "docstring-parser>=0.16,<1.0.0", "PyJWT>=2.12.1", - "pandas==2.2.3", + "pandas>=2.2.3", "multiprocess>=0.70.14,<1.0.0", "duckdb>=1.0.0,<2.0.0", "python-docx>=1.1.0,<2.0.0", @@ -276,7 +276,7 @@ metaphor = ["metaphor-python==0.1.23"] metal = ["metal_sdk==2.5.1"] markup = ["MarkupSafe==3.0.2"] aws = ["boto3>=1.34.162,<2.0.0", "langchain-aws~=1.1.0"] -numexpr = ["numexpr==2.10.2"] +numexpr = ["numexpr>=2.10.4"] qianfan = ["qianfan==0.3.5"] pgvector = ["pgvector>=0.4.2"] litellm = 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"https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "loguru" }, { name = "opencv-python" }, - { name = "pandas" }, + { name = "pandas", version = "2.2.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pillow" }, { name = "pydantic" }, { name = "pydantic-core" }, @@ -18104,7 +18243,8 @@ dependencies = [ { name = "multitasking" }, { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, - { name = "pandas" }, + { name = "pandas", version = "2.2.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "peewee" }, { name = "platformdirs" }, { name = "pytz" }, From 7f8bd2d4320a2de5ae86717f35e8791e15b2d8b2 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Fri, 22 May 2026 16:24:03 -0400 Subject: [PATCH 05/30] chore: update versions and revert dockerfile npm --- docker/build_and_push.Dockerfile | 2 + pyproject.toml | 4 +- src/backend/base/pyproject.toml | 4 +- src/backend/base/uv.lock | 465 +++++++++++++++++++++---------- src/lfx/pyproject.toml | 2 +- src/sdk/pyproject.toml | 2 +- uv.lock | 14 +- 7 files changed, 328 insertions(+), 165 deletions(-) diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile index e6c1fab085..a08456e4fd 100644 --- a/docker/build_and_push.Dockerfile +++ b/docker/build_and_push.Dockerfile @@ -29,6 +29,8 @@ RUN apt-get update \ # deps for building python deps build-essential \ git \ + # npm + npm \ # gcc gcc \ curl \ diff --git a/pyproject.toml b/pyproject.toml index 93e217c803..d2f2411bfa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "langflow" -version = "1.9.3" +version = "1.9.4" description = "A Python package with a built-in web application" requires-python = ">=3.10,<3.15" license = "MIT" @@ -17,7 +17,7 @@ maintainers = [ ] # Define your main dependencies here dependencies = [ - "langflow-base[complete]>=0.9.3", + "langflow-base[complete]>=0.9.4", ] diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 658c975f42..38a381916e 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "langflow-base" -version = "0.9.3" +version = "0.9.4" description = "A Python package with a built-in web application" requires-python = ">=3.10,<3.15" license = "MIT" @@ -17,7 +17,7 @@ maintainers = [ ] dependencies = [ - "lfx~=0.4.3", + "lfx~=0.4.4", "fastapi>=0.135.0,<1.0.0", "httpx[http2]>=0.27,<1.0.0", "aiofile>=3.9.0,<4.0.0", diff --git a/src/backend/base/uv.lock b/src/backend/base/uv.lock index f951ba5813..b2d181f149 100644 --- a/src/backend/base/uv.lock +++ b/src/backend/base/uv.lock @@ -3,18 +3,21 @@ revision = 3 requires-python = ">=3.10, <3.15" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -175,7 +178,8 @@ name = "aiofile" version = "3.9.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -193,15 +197,17 @@ version = "3.11.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -516,7 +522,8 @@ name = "apify-client" version = "2.5.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -537,15 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'3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -682,7 +691,8 @@ name = "async-timeout" version = "4.0.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -696,7 +706,8 @@ name = "async-timeout" version = "5.0.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -2402,7 +2413,7 @@ name = "cuda-bindings" version = "13.2.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "cuda-pathfinder", marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (python_full_version < 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"nvidia-cufft", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] cufile = [ { name = "nvidia-cufile", marker = "sys_platform == 'linux'" }, ] cupti = [ - { name = "nvidia-cuda-cupti", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] curand = [ - { name = "nvidia-curand", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] cusolver = [ - { name = "nvidia-cusolver", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] cusparse = [ - { name = "nvidia-cusparse", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] nvjitlink = [ - { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] nvrtc = [ - { name = "nvidia-cuda-nvrtc", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] nvtx = [ - { name = "nvidia-nvtx", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" }, + { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] [[package]] @@ -3172,7 +3183,8 @@ version = 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sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -3294,15 +3308,19 @@ version = "1.9.0.post1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -3340,8 +3358,7 @@ version = "1.13.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", ] dependencies = [ { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" }, @@ -3612,12 +3629,15 @@ version = "1.9.7" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -3649,10 +3669,10 @@ version = "1.12.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] sdist = { url = "https://files.pythonhosted.org/packages/6e/5b/ccb338db71f347e3bc031d268bf6dc41e5ead63b6997b8e72af92f05e18e/fastavro-1.12.2.tar.gz", hash = "sha256:3c79502d56cf6b76210032e1c53494ddfbc73c140bccf2ef4092b3f0825323ab", size = 1030127, upload-time = "2026-04-24T14:36:01.269Z" } @@ -5122,7 +5142,8 @@ name = "ibm-watsonx-ai" version = "1.3.42" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -5149,12 +5170,15 @@ version = "1.5.12" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -5485,7 +5509,8 @@ name = "ipython" version = "8.39.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -5513,15 +5538,17 @@ version = "9.13.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -5659,14 +5686,14 @@ wheels = [ [[package]] name = "jinxed" -version = "2.0.0" +version = "2.0.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ansicon", marker = "sys_platform == 'win32'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/cb/eb/8821ce6e7386e96355f2c6be83944925b4a0870572896fd33a4e61b8aa5a/jinxed-2.0.0.tar.gz", hash = "sha256:64b960b8f8d9966e1b2e7cb57ea2b1aa0a8d7e68045b96dc7ef58201e43a1209", size = 127118, upload-time = "2026-05-08T21:25:25.917Z" } +sdist = { url = "https://files.pythonhosted.org/packages/3e/b0/76ee7c88752e69d6582a099672309e49801a56a607f7a89573ccbfb5a3d6/jinxed-2.0.1.tar.gz", hash = "sha256:5220778f130e7b9502055dbefaef9cb92ea5c892e72ff2c8e514c8be4d9af9ca", size = 127988, upload-time = "2026-05-22T18:12:03.816Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b3/00/b61668fd3b1e43b445979ec9a9e0af4781bf06884937d1e906f6a1be6dff/jinxed-2.0.0-py2.py3-none-any.whl", hash = "sha256:b3df1be5262a37145ef42875a8bbf918f1a563fbd035359650dd9fc0bb2b9294", size = 95364, upload-time = "2026-05-08T21:25:24.536Z" }, + { url = "https://files.pythonhosted.org/packages/36/1a/d760e6033c3cd760ec1b2c6a56b5b330d3c37ebfb3bc63bdb322edfb7057/jinxed-2.0.1-py2.py3-none-any.whl", hash = "sha256:436ace63cc670cc08127bb388d23ac19418f2c3965efd60d578f1462065a18a5", size = 95364, upload-time = "2026-05-22T18:12:02.176Z" }, ] [[package]] @@ -5778,15 +5805,19 @@ version = "1.0.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -5801,8 +5832,7 @@ version = "1.1.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", ] sdist = { url = "https://files.pythonhosted.org/packages/d3/59/322338183ecda247fb5d1763a6cbe46eff7222eaeebafd9fa65d4bf5cb11/jmespath-1.1.0.tar.gz", hash = "sha256:472c87d80f36026ae83c6ddd0f1d05d4e510134ed462851fd5f754c8c3cbb88d", size = 27377, upload-time = "2026-01-22T16:35:26.279Z" } wheels = [ @@ -6620,7 +6650,7 @@ wheels = [ [[package]] name = "langflow-base" -version = "0.9.3" +version = "0.9.4" source = { editable = "." } dependencies = [ { name = "aiofile", version = "3.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, @@ -7534,7 +7564,7 @@ requires-dist = [ { name = "langsmith", marker = "extra == 'langsmith'", specifier = ">=0.3.42,<1.0.0" }, { name = "langwatch", marker = "python_full_version < '3.14' and extra == 'langwatch'", specifier = "~=0.10.0" }, { name = "lark", marker = "extra == 'lark'", specifier = "==1.2.2" }, - { name = "lfx", specifier = "~=0.4.3" }, + { name = "lfx", editable = "../../lfx" }, { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.83.0" }, { name = "llama-cpp-python", marker = "extra == 'llama-cpp'", specifier = "~=0.2.0" }, { name = "llama-cpp-python", marker = "extra == 'local'", specifier = ">=0.2.0" }, @@ -7834,8 +7864,8 @@ wheels = [ [[package]] name = "lfx" -version = "0.4.3" -source = { registry = "https://pypi.org/simple" } +version = "0.4.4" +source = { editable = "../../lfx" } dependencies = [ { name = "ag-ui-protocol" }, { name = "aiofile", version = "3.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, @@ -7884,8 +7914,69 @@ dependencies = [ { name = "validators" }, { name = "wheel" }, ] -wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/ba/432372c0b5b56e4e80e1e5ea48ab0035e392198d595e8a479fcb3acf84f7/lfx-0.4.3-py3-none-any.whl", hash = "sha256:382dc084f6ec6265cb864c50ccea1248a06bbe00834851e4c2b25e4a5794eab2", size = 2064790, upload-time = "2026-05-15T18:24:16.213Z" }, + +[package.metadata] +requires-dist = [ + { name = "ag-ui-protocol", specifier = ">=0.1.10" }, + { name = "aiofile", specifier = ">=3.8.0,<4.0.0" }, + { name = "aiofiles", specifier = ">=24.1.0,<25.0.0" }, + { name = "asyncer", specifier = 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size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" }, @@ -9511,15 +9648,19 @@ version = "1.23.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -9563,8 +9704,7 @@ version = "1.26.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", ] dependencies = [ { name = "flatbuffers", marker = "python_full_version >= '3.14'" }, @@ -10757,7 +10897,8 @@ name = "pandas" version = "2.2.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -10811,15 +10952,17 @@ version = "2.3.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -10939,7 +11082,7 @@ name = "pexpect" version = "4.9.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "ptyprocess", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" }, + { name = "ptyprocess" }, ] sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } wheels = [ @@ -14092,7 +14235,8 @@ name = "scikit-image" version = "0.25.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14137,15 +14281,17 @@ version = "0.26.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14217,7 +14363,8 @@ name = "scikit-learn" version = "1.7.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14267,15 +14414,17 @@ version = "1.8.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14330,7 +14479,8 @@ name = "scipy" version = "1.15.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14392,15 +14542,17 @@ version = "1.17.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14476,10 +14628,12 @@ name = "scrapegraph-py" version = "1.47.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -14502,13 +14656,14 @@ version = "2.1.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] dependencies = [ @@ -14525,8 +14680,8 @@ name = "secretstorage" version = "3.5.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "cryptography", marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" }, - { name = "jeepney", marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" }, + { name = "cryptography", marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'win32')" }, + { name = "jeepney", marker = "(python_full_version >= '3.14' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'win32')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/1c/03/e834bcd866f2f8a49a85eaff47340affa3bfa391ee9912a952a1faa68c7b/secretstorage-3.5.0.tar.gz", hash = "sha256:f04b8e4689cbce351744d5537bf6b1329c6fc68f91fa666f60a380edddcd11be", size = 19884, upload-time = "2025-11-23T19:02:53.191Z" } wheels = [ @@ -15174,7 +15329,8 @@ name = "tifffile" version = "2025.5.10" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -15191,7 +15347,8 @@ name = "tifffile" version = "2026.3.3" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -15209,13 +15366,14 @@ version = "2026.5.15" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] dependencies = [ @@ -15332,15 +15490,19 @@ version = "2.2.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version == '3.12.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform != 'darwin'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.13.*' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.13.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and sys_platform != 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine != 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.11' and sys_platform != 'darwin'", + "python_full_version < '3.11' and sys_platform == 'win32'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'win32'", "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine != 'arm64' and sys_platform == 'darwin'", ] @@ -15385,8 +15547,7 @@ version = "2.4.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", ] sdist = { url = "https://files.pythonhosted.org/packages/22/de/48c59722572767841493b26183a0d1cc411d54fd759c5607c4590b6563a6/tomli-2.4.1.tar.gz", hash = "sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f", size = 17543, upload-time = "2026-03-25T20:22:03.828Z" } wheels = [ diff --git a/src/lfx/pyproject.toml b/src/lfx/pyproject.toml index 1520a970cf..56ba4142a7 100644 --- a/src/lfx/pyproject.toml +++ b/src/lfx/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "lfx" -version = "0.4.3" +version = "0.4.4" description = "Langflow Executor - A lightweight CLI tool for executing and serving Langflow AI flows" readme = "README.md" authors = [ diff --git a/src/sdk/pyproject.toml b/src/sdk/pyproject.toml index e641f75798..ca47ba1eea 100644 --- a/src/sdk/pyproject.toml +++ b/src/sdk/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "langflow-sdk" -version = "0.1.3" +version = "0.1.4" 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{ name = "aiofile", version = "3.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, @@ -8194,7 +8194,7 @@ dev = [ [[package]] name = "langflow-sdk" -version = "0.1.3" +version = "0.1.4" source = { editable = "src/sdk" } dependencies = [ { name = "httpx", extra = ["http2"] }, @@ -8400,7 +8400,7 @@ wheels = [ [[package]] name = "lfx" -version = "0.4.3" +version = "0.4.4" source = { editable = "src/lfx" } dependencies = [ { name = "ag-ui-protocol" }, From 4ee1fecb28467d2d7a26fc8ef82d2e6487c3b83a Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Fri, 22 May 2026 16:24:46 -0400 Subject: [PATCH 06/30] chore: revert dockerfile corepack changes --- docker/build_and_push.Dockerfile | 2 -- 1 file changed, 2 deletions(-) diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile index a08456e4fd..a7797f38fa 100644 --- a/docker/build_and_push.Dockerfile +++ b/docker/build_and_push.Dockerfile @@ -36,8 +36,6 @@ RUN apt-get update \ curl \ && curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \ && apt-get install -y nodejs \ - && corepack enable \ - && corepack prepare npm@11 --activate \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* From fca26ff79f080a1b079e9e7602cb015680c7b52f Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Fri, 22 May 2026 17:53:35 -0400 Subject: [PATCH 07/30] chore: address node and jquery issues --- docker/build_and_push.Dockerfile | 15 ++- pyproject.toml | 2 +- uv.lock | 161 ++++++------------------------- 3 files changed, 38 insertions(+), 140 deletions(-) diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile index a7797f38fa..18ee99d4d0 100644 --- a/docker/build_and_push.Dockerfile +++ b/docker/build_and_push.Dockerfile @@ -29,13 +29,17 @@ RUN apt-get update \ # deps for building python deps build-essential \ git \ - # npm - npm \ # gcc gcc \ curl \ - && curl -fsSL https://deb.nodesource.com/setup_22.x | bash - \ - && apt-get install -y nodejs \ + && ARCH=$(dpkg --print-architecture) \ + && if [ "$ARCH" = "amd64" ]; then NODE_ARCH="x64"; \ + elif [ "$ARCH" = "arm64" ]; then NODE_ARCH="arm64"; \ + else NODE_ARCH="$ARCH"; fi \ + && NODE_VERSION="22.14.0" \ + && curl -fsSL "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${NODE_ARCH}.tar.xz" \ + | tar -xJ -C /usr/local --strip-components=1 \ + && npm install -g npm@latest \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* @@ -94,7 +98,8 @@ RUN ARCH=$(dpkg --print-architecture) \ | head -1) \ && if [ -z "$NODE_VERSION" ]; then echo "ERROR: Could not determine Node.js version" && exit 1; fi \ && curl -fsSL "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${NODE_ARCH}.tar.xz" \ - | tar -xJ -C /usr/local --strip-components=1 + | tar -xJ -C /usr/local --strip-components=1 \ + && npm install -g npm@latest RUN useradd user -u 1000 -g 0 --no-create-home --home-dir /app/data COPY --from=builder --chown=1000 /app/.venv /app/.venv diff --git 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langchain_mongodb import MongoDBAtlasVectorSearch from pymongo.collection import Collection from pymongo.operations import SearchIndexModel diff --git a/uv.lock b/uv.lock index 122a888487..ed25c85c55 100644 --- a/uv.lock +++ b/uv.lock @@ -6816,20 +6816,22 @@ wheels = [ [[package]] name = "langchain-mongodb" -version = "0.7.0" +version = "0.11.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "langchain" }, + { name = "langchain-classic" }, { name = "langchain-core" }, { name = "langchain-text-splitters" }, { name = "lark" }, { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pymongo" }, + { name = "pymongo-search-utils" }, ] -sdist = { url = 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QdrantVectorStoreComponent.display_name == "Qdrant" except ImportError as e: - if "qdrant_client" in str(e) or "langchain_community" in str(e): + if "qdrant_client" in str(e) or "langchain_qdrant" in str(e): pytest.skip("Qdrant dependencies not installed (expected in test environment)") pytest.fail(f"Failed to import QdrantVectorStoreComponent: {e}") except AttributeError as e: diff --git a/src/lfx/pyproject.toml b/src/lfx/pyproject.toml index 998b1dfa29..0711c98a22 100644 --- a/src/lfx/pyproject.toml +++ b/src/lfx/pyproject.toml @@ -98,6 +98,7 @@ integration = [ "langchain-openai>=1.1.12,<2.0.0", "langchain-community>=0.4.1,<1.0.0", "langchain-mongodb>=0.11.0", + "langchain-qdrant>=1.1.0", "beautifulsoup4>=4.12.0", "lxml>=5.0.0", ] diff --git a/src/lfx/src/lfx/components/qdrant/qdrant.py b/src/lfx/src/lfx/components/qdrant/qdrant.py index ab7b838fc4..9ccbbbc434 100644 --- a/src/lfx/src/lfx/components/qdrant/qdrant.py +++ b/src/lfx/src/lfx/components/qdrant/qdrant.py @@ -1,5 +1,5 @@ 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"name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -58988,7 +58988,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -59864,11 +59864,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -61064,7 +61064,7 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "lfx", @@ -61731,7 +61731,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -63818,7 +63818,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "pydantic", @@ -63834,11 +63834,11 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "openai", - "version": "2.33.0" + "version": "2.38.0" } ], "total_dependencies": 6 @@ -64200,11 +64200,11 @@ "dependencies": [ { "name": "tiktoken", - "version": "0.12.0" + "version": "0.13.0" }, { "name": "docling_core", - "version": "2.74.1" + "version": "2.77.0" }, { "name": "lfx", @@ -64509,7 +64509,7 @@ }, { "name": "docling_core", - "version": "2.74.1" + "version": "2.77.0" } ], "total_dependencies": 2 @@ -64899,7 +64899,7 @@ }, { "name": "docling_core", - "version": "2.74.1" + "version": "2.77.0" }, { "name": "pydantic", @@ -65284,7 +65284,7 @@ "dependencies": [ { "name": "docling_core", - "version": "2.74.1" + "version": "2.77.0" }, { "name": "lfx", @@ -65530,7 +65530,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -65694,7 +65694,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "langchain_elasticsearch", @@ -67482,7 +67482,7 @@ "dependencies": [ { "name": "numpy", - "version": "2.4.4" + "version": "2.4.6" }, { "name": "lfx", @@ -67745,7 +67745,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "metaphor_python", @@ -68206,7 +68206,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -68214,7 +68214,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 4 @@ -68890,11 +68890,11 @@ "dependencies": [ { "name": "chromadb", - "version": "1.5.8" + "version": "1.5.9" }, { "name": "cryptography", - "version": "47.0.0" + "version": "48.0.0" }, { "name": "langchain_chroma", @@ -68914,7 +68914,7 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "langchain_huggingface", @@ -68934,7 +68934,7 @@ }, { "name": "langchain_ibm", - "version": "1.0.7" + "version": "1.0.11" } ], "total_dependencies": 12 @@ -69141,11 +69141,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "cryptography", - "version": "47.0.0" + "version": "48.0.0" }, { "name": "langchain_chroma", @@ -69161,11 +69161,11 @@ }, { "name": "numpy", - "version": "2.4.4" + "version": "2.4.6" }, { "name": "chromadb", - "version": "1.5.8" + "version": "1.5.9" } ], "total_dependencies": 7 @@ -69493,15 +69493,15 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -69517,7 +69517,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 7 @@ -72150,7 +72150,7 @@ }, { "name": "git", - "version": "3.1.47" + "version": "3.1.50" }, { "name": "lfx", @@ -72316,7 +72316,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -72558,7 +72558,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -72907,11 +72907,11 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "langchain_google_community", @@ -73064,7 +73064,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "langchain_google_genai", @@ -73322,7 +73322,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "lfx", @@ -73596,7 +73596,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "pydantic", @@ -73973,7 +73973,7 @@ }, { "name": "google_auth_oauthlib", - "version": "1.3.1" + "version": "1.4.0" }, { "name": "lfx", @@ -74271,7 +74271,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -74778,11 +74778,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -74961,11 +74961,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -75127,11 +75127,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "pydantic", @@ -75300,7 +75300,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "tenacity", @@ -75791,7 +75791,7 @@ "dependencies": [ { "name": "langchain_ibm", - "version": "1.0.7" + "version": "1.0.11" }, { "name": "pydantic", @@ -76344,11 +76344,11 @@ "dependencies": [ { "name": "ibm_watsonx_ai", - "version": "1.5.10" + "version": "1.5.12" }, { "name": "langchain_ibm", - "version": "1.0.7" + "version": "1.0.11" }, { "name": "pydantic", @@ -76594,7 +76594,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "lfx", @@ -77089,11 +77089,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -79869,7 +79869,7 @@ }, { "name": "langchain_experimental", - "version": "0.4.1" + "version": "0.4.2" } ], "total_dependencies": 2 @@ -80453,7 +80453,7 @@ }, { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" } ], "total_dependencies": 2 @@ -80600,11 +80600,11 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -80757,7 +80757,7 @@ }, { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -80765,7 +80765,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" } ], "total_dependencies": 4 @@ -81015,7 +81015,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -81143,7 +81143,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -81271,7 +81271,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -81279,7 +81279,7 @@ }, { "name": "langchain", - "version": "1.2.17" + "version": "1.2.18" } ], "total_dependencies": 3 @@ -81392,7 +81392,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -81895,11 +81895,11 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -82333,11 +82333,11 @@ }, { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -82873,7 +82873,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -83097,7 +83097,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -83304,11 +83304,11 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -83669,11 +83669,11 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "sqlalchemy", - "version": "2.0.49" + "version": "2.0.50" }, { "name": "lfx", @@ -83774,11 +83774,11 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -83980,7 +83980,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -84176,11 +84176,11 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "langchain_experimental", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -84740,11 +84740,11 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -85132,7 +85132,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -85290,7 +85290,7 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "lfx", @@ -85551,11 +85551,11 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -86245,7 +86245,7 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "pydantic", @@ -86257,7 +86257,7 @@ }, { "name": "openai", - "version": "2.33.0" + "version": "2.38.0" } ], "total_dependencies": 5 @@ -88519,7 +88519,7 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "lfx", @@ -88527,7 +88527,7 @@ }, { "name": "openai", - "version": "2.33.0" + "version": "2.38.0" } ], "total_dependencies": 4 @@ -88856,7 +88856,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -89133,7 +89133,7 @@ "dependencies": [ { "name": "mem0", - "version": "1.0.11" + "version": "2.0.2" }, { "name": "lfx", @@ -89911,7 +89911,7 @@ "dependencies": [ { "name": "langchain_mistralai", - "version": "1.1.2" + "version": "1.1.4" }, { "name": "pydantic", @@ -90141,7 +90141,7 @@ "dependencies": [ { "name": "langchain_mistralai", - "version": "1.1.2" + "version": "1.1.4" }, { "name": "pydantic", @@ -90575,7 +90575,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -91885,7 +91885,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -92585,265 +92585,6 @@ } }, "tool_mode": false - }, - "policies": { - "base_classes": [ - "Tool" - ], - "beta": true, - "conditional_paths": [], - "custom_fields": {}, - "description": "Component for building tool protection code from textual business policies and instructions.\nPowered by [ALTK ToolGuard](https://github.com/AgentToolkit/toolguard )", - "display_name": "Policies", - "documentation": "https://github.com/AgentToolkit/toolguard", - "edited": false, - "field_order": [ - "enabled", - "mode", - "project", - "in_tools", - "policies", - "model", - "api_key" - ], - "frozen": false, - "icon": "shield-check", - "legacy": false, - "metadata": { - "code_hash": "3fe07c8c9934", - "dependencies": { - "dependencies": [ - { - "name": "toolguard", - "version": "0.2.17" - }, - { - "name": "lfx", - "version": null - } - ], - "total_dependencies": 2 - }, - "keywords": [ - "model", - "llm", - "language model", - "large language model" - ], - "module": "lfx.components.models_and_agents.policies_component.PoliciesComponent" - }, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Guarded Tools", - "group_outputs": false, - "method": "guard_tools", - "name": "guarded_tools", - "selected": "Tool", - "tool_mode": true, - "types": [ - "Tool" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "api_key": { - "_input_type": "SecretStrInput", - "advanced": true, - "display_name": "API Key", - "dynamic": false, - "info": "Model Provider API key", - "input_types": [], - "load_from_db": true, - "name": "api_key", - "override_skip": false, - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "import os\nimport re\nimport shutil\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, cast\n\nfrom toolguard.buildtime import (\n PolicySpecOptions,\n ToolGuardsCodeGenerationResult,\n ToolGuardSpec,\n generate_guard_specs,\n generate_guards_code,\n)\nfrom toolguard.extra.langchain_to_oas import langchain_tools_to_openapi\nfrom toolguard.runtime import load_toolguards, load_toolguards_from_memory\nfrom toolguard.runtime.runtime import RESULTS_FILENAME\n\nfrom lfx.base.models import LCModelComponent\nfrom lfx.base.models.unified_models import (\n get_language_model_options,\n get_llm,\n update_model_options_in_build_config,\n)\nfrom lfx.components.models_and_agents.policies.guard_sync_utils import sync_generated_guard_code_inputs\nfrom lfx.components.models_and_agents.policies.guarded_tool import GuardedTool\nfrom lfx.components.models_and_agents.policies.llm_wrapper import LangchainModelWrapper\nfrom lfx.components.models_and_agents.policies.module_utils import unload_module\nfrom lfx.field_typing import LanguageModel, Tool\nfrom lfx.io import (\n BoolInput,\n HandleInput,\n ModelInput,\n MultilineInput,\n Output,\n SecretStrInput,\n StrInput,\n TabInput,\n)\nfrom lfx.log.logger import logger\n\nif TYPE_CHECKING:\n from lfx.inputs.inputs import InputTypes\n\n\nTOOLGUARD_WORK_DIR = Path(os.getenv(\"TOOLGUARD_WORK_DIR\") or \"tmp_toolguard\")\nBUILDTIME_MODELS = [\"gpt-5\", \"claude-sonnet\"] # currently inactive, we recommend but do not enforce\nSTEP1 = \"Step_1\"\nSTEP2 = \"Step_2\"\nMODE_GENERATE = \"🛠️ Generate\"\nMODE_GUARD = \"🛡️ Guard\"\nGENERATED_GUARD_INFO_PREFIX = \"Auto-generated ToolGuard code for \"\n\n\nclass PoliciesComponent(LCModelComponent):\n \"\"\"Component for building tool protection code from textual business policies and instructions.\n\n This component uses ToolGuard to generate and apply policy-based guards to tools,\n ensuring that tool execution complies with defined business policies.\n Powered by ALTK ToolGuard (https://github.com/AgentToolkit/toolguard).\n \"\"\"\n\n display_name = \"Policies\"\n description = \"\"\"Component for building tool protection code from textual business policies and instructions.\nPowered by [ALTK ToolGuard](https://github.com/AgentToolkit/toolguard )\"\"\"\n documentation: str = \"https://github.com/AgentToolkit/toolguard\"\n icon = \"shield-check\"\n name = \"policies\"\n beta = True\n\n inputs = cast(\n \"list[InputTypes]\",\n [\n BoolInput(\n name=\"enabled\",\n display_name=\"Enabled\",\n info=\"If `true` - guards tool calls. If `false`, skip policy validation.\",\n value=True,\n ),\n TabInput(\n name=\"mode\",\n display_name=\"Activity\",\n options=[MODE_GENERATE, MODE_GUARD],\n info=(\n \"Generate new guard code or apply existing guard. \"\n \"Review generated files in the details panel on the right.\"\n ),\n value=MODE_GENERATE,\n real_time_refresh=True,\n tool_mode=True,\n ),\n MultilineInput(\n name=\"project\",\n display_name=\"Policies Project\",\n info=\"Folder name of the generated code\",\n value=\"my_project\",\n # required=True,\n ),\n HandleInput(\n name=\"in_tools\",\n display_name=\"Tools\",\n input_types=[\"Tool\"],\n is_list=True,\n required=True,\n info=\"These are the tools that the agent can use to help with tasks.\",\n ),\n StrInput(\n name=\"policies\",\n display_name=\"Policies\",\n info=\"One or more clear, well-defined and self-contained business policies\",\n is_list=True,\n tool_mode=True,\n placeholder=\"Add business policy...\",\n list_add_label=\"Add Policy\",\n # input_types=[],\n ),\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=(\n \"Select LLM for Policies buildtime. We recommend using \"\n \"Anthropic Claude-Sonnet series for this task.\"\n ),\n real_time_refresh=True,\n required=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Model Provider API key\",\n required=False,\n advanced=True,\n ),\n ],\n )\n outputs = [\n Output(\n display_name=\"Guarded Tools\",\n type_=Tool,\n name=\"guarded_tools\",\n method=\"guard_tools\",\n # group_outputs=True,\n ),\n ]\n\n @property\n def work_dir(self) -> Path:\n return TOOLGUARD_WORK_DIR / self._to_snake_case(self.project)\n\n def build_model(self) -> LanguageModel:\n llm_model = get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=self.api_key,\n stream=False,\n )\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n return llm_model\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n \"\"\"Dynamically update build config with user-filtered model options.\"\"\"\n updated_build_config = update_model_options_in_build_config(\n component=self,\n build_config=build_config,\n cache_key_prefix=\"language_model_options\",\n get_options_func=get_language_model_options,\n field_name=field_name,\n field_value=field_value,\n )\n py_module = self._to_snake_case(self.project)\n return sync_generated_guard_code_inputs(\n build_config=updated_build_config,\n work_dir=self.work_dir,\n step2_subdir=STEP2,\n project_name=py_module,\n )\n\n async def _generate_guard_specs(self) -> list[ToolGuardSpec]:\n logger.debug(\"Starting step 1\")\n logger.debug(f\"model = {self.model}\")\n llm = LangchainModelWrapper(self.build_model())\n out_dir = self.work_dir / STEP1\n if out_dir.exists():\n shutil.rmtree(out_dir)\n policy_text = \"\\n * \".join(self.policies)\n open_api = langchain_tools_to_openapi(self.in_tools)\n\n options = PolicySpecOptions(example_number=4)\n specs = await generate_guard_specs(\n policy_text=policy_text, tools=open_api, llm=llm, work_dir=out_dir, options=options\n )\n logger.debug(\"Step 1 Done\")\n return specs\n\n async def _generate_guard_code(self, specs: list[ToolGuardSpec]) -> ToolGuardsCodeGenerationResult:\n logger.debug(\"Starting step 2\")\n out_dir = self.work_dir / STEP2\n if out_dir.exists():\n shutil.rmtree(out_dir)\n llm = LangchainModelWrapper(self.build_model())\n app_name = self._to_snake_case(self.project)\n open_api = langchain_tools_to_openapi(self.in_tools)\n\n gen_result = await generate_guards_code(\n tools=open_api, tool_specs=specs, work_dir=out_dir, llm=llm, app_name=app_name\n )\n logger.debug(\"Step 2 Done\")\n return gen_result\n\n def in_recommended_models(self, model_name: str):\n return any(recommended in model_name for recommended in BUILDTIME_MODELS)\n\n def validate_before_generate(self) -> None:\n \"\"\"Validate required inputs before generating guard code.\"\"\"\n if not self.project:\n msg = \"Policies: project cannot be empty!\"\n raise ValueError(msg)\n\n if not any(self.policies):\n msg = \"Policies: policies cannot be empty!\"\n raise ValueError(msg)\n\n if not self.in_tools:\n msg = \"Policies: in_tools cannot be empty!\"\n raise ValueError(msg)\n\n if not self.model or not self.api_key:\n msg = \"Policies: model or api_key cannot be empty!\"\n raise ValueError(msg)\n\n # uncomment if willing to enforce certain models for buildtime\n # if not self.in_recommended_models(self.model[0][\"name\"]):\n # msg = f\"Policies: model {self.model[0]['name']} is not in recommended models: {BUILDTIME_MODELS}\"\n # raise ValueError(msg)\n\n async def generate(self):\n specs = await self._generate_guard_specs()\n res = await self._generate_guard_code(specs)\n\n # if there was a previous version of the guard, remove it from python cache\n unload_module(res.domain.app_name)\n\n def _verify_cached_guards(self, code_dir: Path) -> None:\n # Validate cache exists before attempting to load\n if not code_dir.exists():\n msg = (\n f\"Policies: Cache directory not found at '{code_dir}'. \"\n f\"Please run in 'Generate' mode first to create the guard code, \"\n f\"or verify the project name is correct.\"\n )\n raise ValueError(msg)\n\n try:\n load_toolguards(code_dir)\n except FileNotFoundError as exc:\n msg = (\n f\"Policies: Required guard code files missing in '{code_dir}'. \"\n f\"Please run in 'Generate' mode to create the guard code.\"\n )\n raise ValueError(msg) from exc\n except Exception as exc:\n msg = (\n f\"Policies: Failed to load guard code from '{code_dir}'. \"\n f\"The cached code may be invalid or corrupted. \"\n f\"Try running in 'Generate' mode to rebuild the guard code. \"\n f\"Error: {exc!s}\"\n )\n raise ValueError(msg) from exc\n\n def _validate_before_using_cache(self, code_dir: Path) -> None:\n if not self.in_tools:\n msg = \"Policies: in_tools cannot be empty!\"\n raise ValueError(msg)\n\n self._verify_cached_guards(code_dir)\n\n def make_toolguard_result(self) -> ToolGuardsCodeGenerationResult:\n attrs = self.get_vertex().data[\"node\"][\"template\"]\n if not attrs:\n raise ValueError\n\n result_str = attrs[str(RESULTS_FILENAME)][\"value\"]\n result = ToolGuardsCodeGenerationResult.model_validate_json(result_str)\n\n result.domain.app_types.content = attrs.get(str(result.domain.app_types.file_name))[\"value\"]\n result.domain.app_api.content = attrs.get(str(result.domain.app_api.file_name))[\"value\"]\n result.domain.app_api_impl.content = attrs.get(str(result.domain.app_api_impl.file_name))[\"value\"]\n\n for tool in result.tools.values():\n tool.guard_file.content = attrs.get(str(tool.guard_file.file_name))[\"value\"]\n for tool_item in tool.item_guard_files:\n tool_item.content = attrs.get(str(tool_item.file_name))[\"value\"]\n\n return result\n\n async def guard_tools(self) -> list[Tool]:\n if self.enabled:\n mode = getattr(self, \"mode\", MODE_GENERATE)\n if mode == MODE_GENERATE:\n self.log(f\"Start generating guard code at {self.work_dir}\", name=\"info\")\n self.validate_before_generate()\n await self.generate()\n self.log(f\"Policies code generation saved to {self.work_dir}\", name=\"info\")\n self.log(\"Review the generated files in the details panel on the right.\", name=\"info\")\n\n else: # mode == \"guard\"\n self.log(f\"using cache from {self.work_dir}\", name=\"info\")\n code_dir = self.work_dir / STEP2\n self._validate_before_using_cache(code_dir)\n try:\n tg_result = self.make_toolguard_result()\n tg_runtime = load_toolguards_from_memory(tg_result)\n guarded_tools = [GuardedTool(tool, self.in_tools, tg_runtime) for tool in self.in_tools]\n return cast(\"list[Tool]\", guarded_tools)\n except Exception as e:\n logger.exception(e)\n raise\n\n return self.in_tools\n\n @staticmethod\n def _to_snake_case(human_name: str) -> str:\n \"\"\"Convert human-readable name to snake_case, sanitizing path traversal attempts.\"\"\"\n # Convert to lowercase\n result = human_name.lower()\n\n # Replace any non-alphanumeric character (including path traversal chars) with underscore\n result = re.sub(r\"[^a-z0-9]+\", \"_\", result)\n\n # Strip leading/trailing underscores\n result = result.strip(\"_\")\n\n # Ensure the result contains at least one alphanumeric character\n if not result or not re.search(r\"[a-z0-9]\", result):\n msg = \"Project name must contain at least one alphanumeric character\"\n raise ValueError(msg)\n\n return result\n" - }, - "enabled": { - "_input_type": "BoolInput", - "advanced": false, - "display_name": "Enabled", - "dynamic": false, - "info": "If `true` - guards tool calls. If `false`, skip policy validation.", - "list": false, - "list_add_label": "Add More", - "name": "enabled", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "bool", - "value": true - }, - "in_tools": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Tools", - "dynamic": false, - "info": "These are the tools that the agent can use to help with tasks.", - "input_types": [ - "Tool" - ], - "list": true, - "list_add_label": "Add More", - "name": "in_tools", - "override_skip": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "other", - "value": "" - }, - "mode": { - "_input_type": "TabInput", - "advanced": false, - "display_name": "Activity", - "dynamic": false, - "info": "Generate new guard code or apply existing guard. Review generated files in the details panel on the right.", - "name": "mode", - "options": [ - "🛠️ Generate", - "🛡️ Guard" - ], - "override_skip": false, - "placeholder": "", - "real_time_refresh": true, - "required": false, - "show": true, - "title_case": false, - "tool_mode": true, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "tab", - "value": "🛠️ Generate" - }, - "model": { - "_input_type": "ModelInput", - "advanced": false, - "display_name": "Language Model", - "dynamic": false, - "external_options": { - "fields": { - "data": { - "node": { - "display_name": "Connect other models", - "icon": "CornerDownLeft", - "name": "connect_other_models" - } - } - } - }, - "info": "Select LLM for Policies buildtime. We recommend using Anthropic Claude-Sonnet series for this task.", - "input_types": [ - "LanguageModel" - ], - "list": false, - "list_add_label": "Add More", - "model_type": "language", - "name": "model", - "override_skip": false, - "placeholder": "Setup Provider", - "real_time_refresh": true, - "refresh_button": true, - "required": true, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "track_in_telemetry": false, - "type": "model", - "value": "" - }, - "policies": { - "_input_type": "StrInput", - "advanced": false, - "display_name": "Policies", - "dynamic": false, - "info": "One or more clear, well-defined and self-contained business policies", - "list": true, - "list_add_label": "Add Policy", - "load_from_db": false, - "name": "policies", - "override_skip": false, - "placeholder": "Add business policy...", - "required": false, - "show": true, - "title_case": false, - "tool_mode": true, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "project": { - "_input_type": "MultilineInput", - "advanced": false, - "ai_enabled": false, - "copy_field": false, - "display_name": "Policies Project", - "dynamic": false, - "info": "Folder name of the generated code", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "multiline": true, - "name": "project", - "override_skip": false, - "password": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "my_project" - } - }, - "tool_mode": false } } ], @@ -92885,20 +92626,20 @@ "icon": "MongoDB", "legacy": false, "metadata": { - "code_hash": "3a502cb4d313", + "code_hash": "5d231ac6a0f1", "dependencies": { "dependencies": [ { "name": "certifi", - "version": "2026.4.22" + "version": "2026.5.20" }, { - "name": "langchain_community", - "version": "0.4.1" + "name": "langchain_mongodb", + "version": "0.11.0" }, { "name": "pymongo", - "version": "4.10.1" + "version": "4.17.0" }, { "name": "lfx", @@ -92906,7 +92647,7 @@ }, { "name": "bson", - "version": "4.10.1" + "version": "4.17.0" } ], "total_dependencies": 5 @@ -92964,7 +92705,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import tempfile\nimport time\n\nimport certifi\nfrom langchain_community.vectorstores import MongoDBAtlasVectorSearch\nfrom pymongo.collection import Collection\nfrom pymongo.operations import SearchIndexModel\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, SecretStrInput, StrInput\nfrom lfx.schema.data import Data\n\n\nclass MongoVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"MongoDB Atlas\"\n description = \"MongoDB Atlas Vector Store with search capabilities\"\n name = \"MongoDBAtlasVector\"\n icon = \"MongoDB\"\n INSERT_MODES = [\"append\", \"overwrite\"]\n SIMILARITY_OPTIONS = [\"cosine\", \"euclidean\", \"dotProduct\"]\n QUANTIZATION_OPTIONS = [\"scalar\", \"binary\"]\n inputs = [\n SecretStrInput(name=\"mongodb_atlas_cluster_uri\", display_name=\"MongoDB Atlas Cluster URI\", required=True),\n BoolInput(name=\"enable_mtls\", display_name=\"Enable mTLS\", value=False, advanced=True, required=True),\n SecretStrInput(\n name=\"mongodb_atlas_client_cert\",\n display_name=\"MongoDB Atlas Combined Client Certificate\",\n required=False,\n info=\"Client Certificate combined with the private key in the following format:\\n \"\n \"-----BEGIN PRIVATE KEY-----\\n...\\n -----END PRIVATE KEY-----\\n-----BEGIN CERTIFICATE-----\\n\"\n \"...\\n-----END CERTIFICATE-----\\n\",\n ),\n StrInput(name=\"db_name\", display_name=\"Database Name\", required=True),\n StrInput(name=\"collection_name\", display_name=\"Collection Name\", required=True),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n required=True,\n info=\"The name of Atlas Search index, it should be a Vector Search.\",\n ),\n *LCVectorStoreComponent.inputs,\n DropdownInput(\n name=\"insert_mode\",\n display_name=\"Insert Mode\",\n options=INSERT_MODES,\n value=INSERT_MODES[0],\n info=\"How to insert new documents into the collection.\",\n advanced=True,\n ),\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n StrInput(\n name=\"index_field\",\n display_name=\"Index Field\",\n advanced=True,\n required=True,\n info=\"The field to index.\",\n value=\"embedding\",\n ),\n StrInput(\n name=\"filter_field\", display_name=\"Filter Field\", advanced=True, info=\"The field to filter the index.\"\n ),\n IntInput(\n name=\"number_dimensions\",\n display_name=\"Number of Dimensions\",\n info=\"Embedding Context Length.\",\n value=1536,\n advanced=True,\n required=True,\n ),\n DropdownInput(\n name=\"similarity\",\n display_name=\"Similarity\",\n options=SIMILARITY_OPTIONS,\n value=SIMILARITY_OPTIONS[0],\n info=\"The method used to measure the similarity between vectors.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"quantization\",\n display_name=\"Quantization\",\n options=QUANTIZATION_OPTIONS,\n value=None,\n info=\"Quantization reduces memory costs converting 32-bit floats to smaller data types\",\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> MongoDBAtlasVectorSearch:\n try:\n from pymongo import MongoClient\n except ImportError as e:\n msg = \"Please install pymongo to use MongoDB Atlas Vector Store\"\n raise ImportError(msg) from e\n\n # Create temporary files for the client certificate\n if self.enable_mtls:\n client_cert_path = None\n try:\n client_cert = self.mongodb_atlas_client_cert.replace(\" \", \"\\n\")\n client_cert = client_cert.replace(\"-----BEGIN\\nPRIVATE\\nKEY-----\", \"-----BEGIN PRIVATE KEY-----\")\n client_cert = client_cert.replace(\n \"-----END\\nPRIVATE\\nKEY-----\\n-----BEGIN\\nCERTIFICATE-----\",\n \"-----END PRIVATE KEY-----\\n-----BEGIN CERTIFICATE-----\",\n )\n client_cert = client_cert.replace(\"-----END\\nCERTIFICATE-----\", \"-----END CERTIFICATE-----\")\n with tempfile.NamedTemporaryFile(delete=False) as client_cert_file:\n client_cert_file.write(client_cert.encode(\"utf-8\"))\n client_cert_path = client_cert_file.name\n\n except Exception as e:\n msg = f\"Failed to write certificate to temporary file: {e}\"\n raise ValueError(msg) from e\n\n try:\n mongo_client: MongoClient = (\n MongoClient(\n self.mongodb_atlas_cluster_uri,\n tls=True,\n tlsCertificateKeyFile=client_cert_path,\n tlsCAFile=certifi.where(),\n )\n if self.enable_mtls\n else MongoClient(self.mongodb_atlas_cluster_uri)\n )\n\n collection = mongo_client[self.db_name][self.collection_name]\n\n except Exception as e:\n msg = f\"Failed to connect to MongoDB Atlas: {e}\"\n raise ValueError(msg) from e\n\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents:\n self.__insert_mode(collection)\n\n return MongoDBAtlasVectorSearch.from_documents(\n documents=documents, embedding=self.embedding, collection=collection, index_name=self.index_name\n )\n return MongoDBAtlasVectorSearch(embedding=self.embedding, collection=collection, index_name=self.index_name)\n\n def search_documents(self) -> list[Data]:\n from bson.objectid import ObjectId\n\n vector_store = self.build_vector_store()\n\n self.verify_search_index(vector_store._collection)\n\n if self.search_query and isinstance(self.search_query, str):\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n for doc in docs:\n doc.metadata = {\n key: str(value) if isinstance(value, ObjectId) else value for key, value in doc.metadata.items()\n }\n\n data = docs_to_data(docs)\n self.status = data\n return data\n return []\n\n def __insert_mode(self, collection: Collection) -> None:\n if self.insert_mode == \"overwrite\":\n collection.delete_many({}) # Delete all documents while preserving collection structure\n\n def verify_search_index(self, collection: Collection) -> None:\n \"\"\"Verify if the search index exists, if not, create it.\n\n Args:\n collection (Collection): The collection to verify the search index on.\n \"\"\"\n indexes = collection.list_search_indexes()\n\n index_names_types = {idx[\"name\"]: idx[\"type\"] for idx in indexes}\n index_names = list(index_names_types.keys())\n index_type = index_names_types.get(self.index_name)\n if self.index_name not in index_names and index_type != \"vectorSearch\":\n collection.create_search_index(self.__create_index_definition())\n\n time.sleep(20) # Give some time for index to be ready\n\n def __create_index_definition(self) -> SearchIndexModel:\n fields = [\n {\n \"type\": \"vector\",\n \"path\": self.index_field,\n \"numDimensions\": self.number_dimensions,\n \"similarity\": self.similarity,\n \"quantization\": self.quantization,\n }\n ]\n if self.filter_field:\n fields.append({\"type\": \"filter\", \"path\": self.filter_field})\n return SearchIndexModel(definition={\"fields\": fields}, name=self.index_name, type=\"vectorSearch\")\n" + "value": "import tempfile\nimport time\n\nimport certifi\nfrom langchain_mongodb import MongoDBAtlasVectorSearch\nfrom pymongo.collection import Collection\nfrom pymongo.operations import SearchIndexModel\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, SecretStrInput, StrInput\nfrom lfx.schema.data import Data\n\n\nclass MongoVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"MongoDB Atlas\"\n description = \"MongoDB Atlas Vector Store with search capabilities\"\n name = \"MongoDBAtlasVector\"\n icon = \"MongoDB\"\n INSERT_MODES = [\"append\", \"overwrite\"]\n SIMILARITY_OPTIONS = [\"cosine\", \"euclidean\", \"dotProduct\"]\n QUANTIZATION_OPTIONS = [\"scalar\", \"binary\"]\n inputs = [\n SecretStrInput(name=\"mongodb_atlas_cluster_uri\", display_name=\"MongoDB Atlas Cluster URI\", required=True),\n BoolInput(name=\"enable_mtls\", display_name=\"Enable mTLS\", value=False, advanced=True, required=True),\n SecretStrInput(\n name=\"mongodb_atlas_client_cert\",\n display_name=\"MongoDB Atlas Combined Client Certificate\",\n required=False,\n info=\"Client Certificate combined with the private key in the following format:\\n \"\n \"-----BEGIN PRIVATE KEY-----\\n...\\n -----END PRIVATE KEY-----\\n-----BEGIN CERTIFICATE-----\\n\"\n \"...\\n-----END CERTIFICATE-----\\n\",\n ),\n StrInput(name=\"db_name\", display_name=\"Database Name\", required=True),\n StrInput(name=\"collection_name\", display_name=\"Collection Name\", required=True),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n required=True,\n info=\"The name of Atlas Search index, it should be a Vector Search.\",\n ),\n *LCVectorStoreComponent.inputs,\n DropdownInput(\n name=\"insert_mode\",\n display_name=\"Insert Mode\",\n options=INSERT_MODES,\n value=INSERT_MODES[0],\n info=\"How to insert new documents into the collection.\",\n advanced=True,\n ),\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n StrInput(\n name=\"index_field\",\n display_name=\"Index Field\",\n advanced=True,\n required=True,\n info=\"The field to index.\",\n value=\"embedding\",\n ),\n StrInput(\n name=\"filter_field\", display_name=\"Filter Field\", advanced=True, info=\"The field to filter the index.\"\n ),\n IntInput(\n name=\"number_dimensions\",\n display_name=\"Number of Dimensions\",\n info=\"Embedding Context Length.\",\n value=1536,\n advanced=True,\n required=True,\n ),\n DropdownInput(\n name=\"similarity\",\n display_name=\"Similarity\",\n options=SIMILARITY_OPTIONS,\n value=SIMILARITY_OPTIONS[0],\n info=\"The method used to measure the similarity between vectors.\",\n advanced=True,\n ),\n DropdownInput(\n name=\"quantization\",\n display_name=\"Quantization\",\n options=QUANTIZATION_OPTIONS,\n value=None,\n info=\"Quantization reduces memory costs converting 32-bit floats to smaller data types\",\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> MongoDBAtlasVectorSearch:\n try:\n from pymongo import MongoClient\n except ImportError as e:\n msg = \"Please install pymongo to use MongoDB Atlas Vector Store\"\n raise ImportError(msg) from e\n\n # Create temporary files for the client certificate\n if self.enable_mtls:\n client_cert_path = None\n try:\n client_cert = self.mongodb_atlas_client_cert.replace(\" \", \"\\n\")\n client_cert = client_cert.replace(\"-----BEGIN\\nPRIVATE\\nKEY-----\", \"-----BEGIN PRIVATE KEY-----\")\n client_cert = client_cert.replace(\n \"-----END\\nPRIVATE\\nKEY-----\\n-----BEGIN\\nCERTIFICATE-----\",\n \"-----END PRIVATE KEY-----\\n-----BEGIN CERTIFICATE-----\",\n )\n client_cert = client_cert.replace(\"-----END\\nCERTIFICATE-----\", \"-----END CERTIFICATE-----\")\n with tempfile.NamedTemporaryFile(delete=False) as client_cert_file:\n client_cert_file.write(client_cert.encode(\"utf-8\"))\n client_cert_path = client_cert_file.name\n\n except Exception as e:\n msg = f\"Failed to write certificate to temporary file: {e}\"\n raise ValueError(msg) from e\n\n try:\n mongo_client: MongoClient = (\n MongoClient(\n self.mongodb_atlas_cluster_uri,\n tls=True,\n tlsCertificateKeyFile=client_cert_path,\n tlsCAFile=certifi.where(),\n )\n if self.enable_mtls\n else MongoClient(self.mongodb_atlas_cluster_uri)\n )\n\n collection = mongo_client[self.db_name][self.collection_name]\n\n except Exception as e:\n msg = f\"Failed to connect to MongoDB Atlas: {e}\"\n raise ValueError(msg) from e\n\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents:\n self.__insert_mode(collection)\n\n return MongoDBAtlasVectorSearch.from_documents(\n documents=documents, embedding=self.embedding, collection=collection, index_name=self.index_name\n )\n return MongoDBAtlasVectorSearch(embedding=self.embedding, collection=collection, index_name=self.index_name)\n\n def search_documents(self) -> list[Data]:\n from bson.objectid import ObjectId\n\n vector_store = self.build_vector_store()\n\n self.verify_search_index(vector_store._collection)\n\n if self.search_query and isinstance(self.search_query, str):\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n for doc in docs:\n doc.metadata = {\n key: str(value) if isinstance(value, ObjectId) else value for key, value in doc.metadata.items()\n }\n\n data = docs_to_data(docs)\n self.status = data\n return data\n return []\n\n def __insert_mode(self, collection: Collection) -> None:\n if self.insert_mode == \"overwrite\":\n collection.delete_many({}) # Delete all documents while preserving collection structure\n\n def verify_search_index(self, collection: Collection) -> None:\n \"\"\"Verify if the search index exists, if not, create it.\n\n Args:\n collection (Collection): The collection to verify the search index on.\n \"\"\"\n indexes = collection.list_search_indexes()\n\n index_names_types = {idx[\"name\"]: idx[\"type\"] for idx in indexes}\n index_names = list(index_names_types.keys())\n index_type = index_names_types.get(self.index_name)\n if self.index_name not in index_names and index_type != \"vectorSearch\":\n collection.create_search_index(self.__create_index_definition())\n\n time.sleep(20) # Give some time for index to be ready\n\n def __create_index_definition(self) -> SearchIndexModel:\n fields = [\n {\n \"type\": \"vector\",\n \"path\": self.index_field,\n \"numDimensions\": self.number_dimensions,\n \"similarity\": self.similarity,\n \"quantization\": self.quantization,\n }\n ]\n if self.filter_field:\n fields.append({\"type\": \"filter\", \"path\": self.filter_field})\n return SearchIndexModel(definition={\"fields\": fields}, name=self.index_name, type=\"vectorSearch\")\n" }, "collection_name": { "_input_type": "StrInput", @@ -93375,7 +93116,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -93552,11 +93293,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -93829,11 +93570,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "pydantic", @@ -94787,7 +94528,7 @@ "dependencies": [ { "name": "pypdf", - "version": "6.10.2" + "version": "6.12.1" }, { "name": "lfx", @@ -96979,7 +96720,7 @@ "dependencies": [ { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "lfx", @@ -97535,7 +97276,7 @@ "dependencies": [ { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "pydantic", @@ -97547,7 +97288,7 @@ }, { "name": "openai", - "version": "2.33.0" + "version": "2.38.0" } ], "total_dependencies": 4 @@ -97974,7 +97715,7 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "pydantic", @@ -98264,335 +98005,6 @@ } } ], - [ - "perplexity", - { - "PerplexityModel": { - "base_classes": [ - "LanguageModel", - "Message" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Generate text using Perplexity LLMs.", - "display_name": "Perplexity", - "documentation": "https://python.langchain.com/v0.2/docs/integrations/chat/perplexity/", - "edited": false, - "field_order": [ - "input_value", - "system_message", - "stream", - "model_name", - "max_tokens", - "api_key", - "temperature", - "top_p", - "n" - ], - "frozen": false, - "icon": "Perplexity", - "legacy": false, - "metadata": { - "code_hash": "b970844e376e", - "dependencies": { - "dependencies": [ - { - "name": "langchain_community", - "version": "0.4.1" - }, - { - "name": "pydantic", - "version": "2.12.5" - }, - { - "name": "lfx", - "version": null - } - ], - "total_dependencies": 3 - }, - "keywords": [ - "model", - "llm", - "language model", - "large language model" - ], - "module": "lfx.components.perplexity.perplexity.PerplexityComponent" - }, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Model Response", - "group_outputs": false, - "method": "text_response", - "name": "text_output", - "selected": "Message", - "tool_mode": true, - "types": [ - "Message" - ], - "value": "__UNDEFINED__" - }, - { - "allows_loop": false, - "cache": true, - "display_name": "Language Model", - "group_outputs": false, - "method": "build_model", - "name": "model_output", - "selected": "LanguageModel", - "tool_mode": true, - "types": [ - "LanguageModel" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "api_key": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "Perplexity API Key", - "dynamic": false, - "info": "The Perplexity API Key to use for the Perplexity model.", - "input_types": [], - "load_from_db": true, - "name": "api_key", - "override_skip": false, - "password": true, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "from langchain_community.chat_models import ChatPerplexity\nfrom pydantic.v1 import SecretStr\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.io import DropdownInput, FloatInput, IntInput, SecretStrInput, SliderInput\n\n\nclass PerplexityComponent(LCModelComponent):\n display_name = \"Perplexity\"\n description = \"Generate text using Perplexity LLMs.\"\n documentation = \"https://python.langchain.com/v0.2/docs/integrations/chat/perplexity/\"\n icon = \"Perplexity\"\n name = \"PerplexityModel\"\n\n inputs = [\n *LCModelComponent.get_base_inputs(),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=[\n \"llama-3.1-sonar-small-128k-online\",\n \"llama-3.1-sonar-large-128k-online\",\n \"llama-3.1-sonar-huge-128k-online\",\n \"llama-3.1-sonar-small-128k-chat\",\n \"llama-3.1-sonar-large-128k-chat\",\n \"llama-3.1-8b-instruct\",\n \"llama-3.1-70b-instruct\",\n ],\n value=\"llama-3.1-sonar-small-128k-online\",\n ),\n IntInput(name=\"max_tokens\", display_name=\"Max Output Tokens\", info=\"The maximum number of tokens to generate.\"),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Perplexity API Key\",\n info=\"The Perplexity API Key to use for the Perplexity model.\",\n advanced=False,\n required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.75, range_spec=RangeSpec(min=0, max=2, step=0.05)\n ),\n FloatInput(\n name=\"top_p\",\n display_name=\"Top P\",\n info=\"The maximum cumulative probability of tokens to consider when sampling.\",\n advanced=True,\n ),\n IntInput(\n name=\"n\",\n display_name=\"N\",\n info=\"Number of chat completions to generate for each prompt. \"\n \"Note that the API may not return the full n completions if duplicates are generated.\",\n advanced=True,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n api_key = SecretStr(self.api_key).get_secret_value()\n temperature = self.temperature\n model = self.model_name\n max_tokens = self.max_tokens\n top_p = self.top_p\n n = self.n\n\n return ChatPerplexity(\n model=model,\n temperature=temperature or 0.75,\n pplx_api_key=api_key,\n top_p=top_p or None,\n n=n or 1,\n max_tokens=max_tokens,\n )\n" - }, - "input_value": { - "_input_type": "MessageInput", - "advanced": false, - "display_name": "Input", - "dynamic": false, - "info": "", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "input_value", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "max_tokens": { - "_input_type": "IntInput", - "advanced": false, - "display_name": "Max Output Tokens", - "dynamic": false, - "info": "The maximum number of tokens to generate.", - "list": false, - "list_add_label": "Add More", - "name": "max_tokens", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "int", - "value": 0 - }, - "model_name": { - "_input_type": "DropdownInput", - "advanced": false, - "combobox": false, - "dialog_inputs": {}, - "display_name": "Model Name", - "dynamic": false, - "external_options": {}, - "info": "", - "name": "model_name", - "options": [ - "llama-3.1-sonar-small-128k-online", - "llama-3.1-sonar-large-128k-online", - "llama-3.1-sonar-huge-128k-online", - "llama-3.1-sonar-small-128k-chat", - "llama-3.1-sonar-large-128k-chat", - "llama-3.1-8b-instruct", - "llama-3.1-70b-instruct" - ], - "options_metadata": [], - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "toggle": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "str", - "value": "llama-3.1-sonar-small-128k-online" - }, - "n": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "N", - "dynamic": false, - "info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.", - "list": false, - "list_add_label": "Add More", - "name": "n", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "int", - "value": 0 - }, - "stream": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Stream", - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "list": false, - "list_add_label": "Add More", - "name": "stream", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "bool", - "value": false - }, - "system_message": { - "_input_type": "MultilineInput", - "advanced": false, - "ai_enabled": false, - "copy_field": false, - "display_name": "System Message", - "dynamic": false, - "info": "System message to pass to the model.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "multiline": true, - "name": "system_message", - "override_skip": false, - "password": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_input": true, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "temperature": { - "_input_type": "SliderInput", - "advanced": false, - "display_name": "Temperature", - "dynamic": false, - "info": "", - "max_label": "", - "max_label_icon": "", - "min_label": "", - "min_label_icon": "", - "name": "temperature", - "override_skip": false, - "placeholder": "", - "range_spec": { - "max": 2.0, - "min": 0.0, - "step": 0.05, - "step_type": "float" - }, - "required": false, - "show": true, - "slider_buttons": false, - "slider_buttons_options": [], - "slider_input": false, - "title_case": false, - "tool_mode": false, - "track_in_telemetry": false, - "type": "slider", - "value": 0.75 - }, - "top_p": { - "_input_type": "FloatInput", - "advanced": true, - "display_name": "Top P", - "dynamic": false, - "info": "The maximum cumulative probability of tokens to consider when sampling.", - "list": false, - "list_add_label": "Add More", - "name": "top_p", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "float", - "value": "" - } - }, - "tool_mode": false - } - } - ], [ "pgvector", { @@ -98626,7 +98038,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -98882,11 +98294,11 @@ "dependencies": [ { "name": "numpy", - "version": "2.4.4" + "version": "2.4.6" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -99818,7 +99230,7 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "lfx", @@ -101882,7 +101294,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -103369,7 +102781,7 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "lfx", @@ -103954,7 +103366,7 @@ }, { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" } ], "total_dependencies": 2 @@ -104443,16 +103855,16 @@ "icon": "Qdrant", "legacy": false, "metadata": { - "code_hash": "bcfc1728e7b1", + "code_hash": "5b5845fc3abd", "dependencies": { "dependencies": [ { - "name": "langchain_community", - "version": "0.4.1" + "name": "langchain_core", + "version": "1.4.0" }, { - "name": "langchain_core", - "version": "1.3.2" + "name": "langchain_qdrant", + "version": "1.1.0" }, { "name": "lfx", @@ -104460,7 +103872,7 @@ }, { "name": "qdrant_client", - "version": "1.9.2" + "version": "1.18.0" } ], "total_dependencies": 4 @@ -104537,7 +103949,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from langchain_community.vectorstores import Qdrant\nfrom langchain_core.embeddings import Embeddings\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import (\n DropdownInput,\n HandleInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom lfx.schema.data import Data\n\n\nclass QdrantVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Qdrant\"\n description = \"Qdrant Vector Store with search capabilities\"\n icon = \"Qdrant\"\n\n inputs = [\n StrInput(name=\"collection_name\", display_name=\"Collection Name\", required=True),\n StrInput(name=\"host\", display_name=\"Host\", value=\"localhost\", advanced=True),\n IntInput(name=\"port\", display_name=\"Port\", value=6333, advanced=True),\n IntInput(name=\"grpc_port\", display_name=\"gRPC Port\", value=6334, advanced=True),\n SecretStrInput(name=\"api_key\", display_name=\"Qdrant API Key\", advanced=True),\n StrInput(name=\"prefix\", display_name=\"Prefix\", advanced=True),\n IntInput(name=\"timeout\", display_name=\"Timeout\", advanced=True),\n StrInput(name=\"path\", display_name=\"Path\", advanced=True),\n StrInput(name=\"url\", display_name=\"URL\", advanced=True),\n DropdownInput(\n name=\"distance_func\",\n display_name=\"Distance Function\",\n options=[\"Cosine\", \"Euclidean\", \"Dot Product\"],\n value=\"Cosine\",\n advanced=True,\n ),\n StrInput(name=\"content_payload_key\", display_name=\"Content Payload Key\", value=\"page_content\", advanced=True),\n StrInput(name=\"metadata_payload_key\", display_name=\"Metadata Payload Key\", value=\"metadata\", advanced=True),\n *LCVectorStoreComponent.inputs,\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> Qdrant:\n qdrant_kwargs = {\n \"collection_name\": self.collection_name,\n \"content_payload_key\": self.content_payload_key,\n \"metadata_payload_key\": self.metadata_payload_key,\n }\n\n server_kwargs = {\n \"host\": self.host or None,\n \"port\": int(self.port), # Ensure port is an integer\n \"grpc_port\": int(self.grpc_port), # Ensure grpc_port is an integer\n \"api_key\": self.api_key,\n \"prefix\": self.prefix,\n # Ensure timeout is an integer\n \"timeout\": int(self.timeout) if self.timeout else None,\n \"path\": self.path or None,\n \"url\": self.url or None,\n }\n\n server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None}\n\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if not isinstance(self.embedding, Embeddings):\n msg = \"Invalid embedding object\"\n raise TypeError(msg)\n\n if documents:\n qdrant = Qdrant.from_documents(documents, embedding=self.embedding, **qdrant_kwargs, **server_kwargs)\n else:\n from qdrant_client import QdrantClient\n\n client = QdrantClient(**server_kwargs)\n qdrant = Qdrant(embeddings=self.embedding, client=client, **qdrant_kwargs)\n\n return qdrant\n\n def search_documents(self) -> list[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n return []\n" + "value": "from langchain_core.embeddings import Embeddings\nfrom langchain_qdrant import QdrantVectorStore\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import (\n DropdownInput,\n HandleInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\nfrom lfx.schema.data import Data\n\n\nclass QdrantVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Qdrant\"\n description = \"Qdrant Vector Store with search capabilities\"\n icon = \"Qdrant\"\n\n inputs = [\n StrInput(name=\"collection_name\", display_name=\"Collection Name\", required=True),\n StrInput(name=\"host\", display_name=\"Host\", value=\"localhost\", advanced=True),\n IntInput(name=\"port\", display_name=\"Port\", value=6333, advanced=True),\n IntInput(name=\"grpc_port\", display_name=\"gRPC Port\", value=6334, advanced=True),\n SecretStrInput(name=\"api_key\", display_name=\"Qdrant API Key\", advanced=True),\n StrInput(name=\"prefix\", display_name=\"Prefix\", advanced=True),\n IntInput(name=\"timeout\", display_name=\"Timeout\", advanced=True),\n StrInput(name=\"path\", display_name=\"Path\", advanced=True),\n StrInput(name=\"url\", display_name=\"URL\", advanced=True),\n DropdownInput(\n name=\"distance_func\",\n display_name=\"Distance Function\",\n options=[\"Cosine\", \"Euclidean\", \"Dot Product\"],\n value=\"Cosine\",\n advanced=True,\n ),\n StrInput(name=\"content_payload_key\", display_name=\"Content Payload Key\", value=\"page_content\", advanced=True),\n StrInput(name=\"metadata_payload_key\", display_name=\"Metadata Payload Key\", value=\"metadata\", advanced=True),\n *LCVectorStoreComponent.inputs,\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> QdrantVectorStore:\n qdrant_kwargs = {\n \"collection_name\": self.collection_name,\n \"content_payload_key\": self.content_payload_key,\n \"metadata_payload_key\": self.metadata_payload_key,\n }\n\n server_kwargs = {\n \"host\": self.host or None,\n \"port\": int(self.port), # Ensure port is an integer\n \"grpc_port\": int(self.grpc_port), # Ensure grpc_port is an integer\n \"api_key\": self.api_key,\n \"prefix\": self.prefix,\n # Ensure timeout is an integer\n \"timeout\": int(self.timeout) if self.timeout else None,\n \"path\": self.path or None,\n \"url\": self.url or None,\n }\n\n server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None}\n\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if not isinstance(self.embedding, Embeddings):\n msg = \"Invalid embedding object\"\n raise TypeError(msg)\n\n if documents:\n qdrant = QdrantVectorStore.from_documents(\n documents, embedding=self.embedding, **qdrant_kwargs, **server_kwargs\n )\n else:\n from qdrant_client import QdrantClient\n\n client = QdrantClient(**server_kwargs)\n qdrant = QdrantVectorStore(embedding=self.embedding, client=client, **qdrant_kwargs)\n\n return qdrant\n\n def search_documents(self) -> list[Data]:\n vector_store: QdrantVectorStore = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n return []\n" }, "collection_name": { "_input_type": "StrInput", @@ -104925,7 +104337,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_text_splitters", @@ -105197,7 +104609,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -106174,7 +105586,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -106396,11 +105808,11 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -106616,7 +106028,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "supabase", @@ -107468,11 +106880,11 @@ "dependencies": [ { "name": "pytest", - "version": "8.4.2" + "version": "9.0.3" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -107598,7 +107010,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -107784,11 +107196,11 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -108007,11 +107419,11 @@ "dependencies": [ { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -108302,11 +107714,11 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "langchain_experimental", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "pydantic", @@ -108468,15 +107880,15 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_classic", - "version": "1.0.4" + "version": "1.0.7" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -108655,11 +108067,11 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -108894,11 +108306,11 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -109120,7 +108532,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -109518,7 +108930,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -109649,7 +109061,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -109859,7 +109271,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -111649,7 +111061,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -112263,7 +111675,7 @@ "dependencies": [ { "name": "langchain_experimental", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -112402,7 +111814,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -112679,7 +112091,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" } ], "total_dependencies": 2 @@ -114236,7 +113648,7 @@ "dependencies": [ { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "lfx", @@ -114574,7 +113986,7 @@ "dependencies": [ { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "pydantic", @@ -114586,7 +113998,7 @@ }, { "name": "openai", - "version": "2.33.0" + "version": "2.38.0" } ], "total_dependencies": 4 @@ -114987,7 +114399,7 @@ }, { "name": "vlmrun", - "version": "0.6.2" + "version": "0.6.3" } ], "total_dependencies": 3 @@ -115194,328 +114606,6 @@ } } ], - [ - "weaviate", - { - "Weaviate": { - "base_classes": [ - "JSON", - "Table" - ], - "beta": false, - "conditional_paths": [], - "custom_fields": {}, - "description": "Weaviate Vector Store with search capabilities", - "display_name": "Weaviate", - "documentation": "", - "edited": false, - "field_order": [ - "url", - "api_key", - "index_name", - "text_key", - "ingest_data", - "search_query", - "should_cache_vector_store", - "embedding", - "number_of_results", - "search_by_text" - ], - "frozen": false, - "icon": "Weaviate", - "legacy": false, - "metadata": { - "code_hash": "ccc9f1e0149d", - "dependencies": { - "dependencies": [ - { - "name": "weaviate", - "version": "4.16.2" - }, - { - "name": "langchain_community", - "version": "0.4.1" - }, - { - "name": "lfx", - "version": null - } - ], - "total_dependencies": 3 - }, - "module": "lfx.components.weaviate.weaviate.WeaviateVectorStoreComponent" - }, - "minimized": false, - "output_types": [], - "outputs": [ - { - "allows_loop": false, - "cache": true, - "display_name": "Search Results", - "group_outputs": false, - "method": "search_documents", - "name": "search_results", - "selected": "JSON", - "tool_mode": true, - "types": [ - "JSON" - ], - "value": "__UNDEFINED__" - }, - { - "allows_loop": false, - "cache": true, - "display_name": "Table", - "group_outputs": false, - "method": "as_dataframe", - "name": "dataframe", - "selected": "Table", - "tool_mode": true, - "types": [ - "Table" - ], - "value": "__UNDEFINED__" - } - ], - "pinned": false, - "template": { - "_type": "Component", - "api_key": { - "_input_type": "SecretStrInput", - "advanced": false, - "display_name": "API Key", - "dynamic": false, - "info": "", - "input_types": [], - "load_from_db": true, - "name": "api_key", - "override_skip": false, - "password": true, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "code": { - "advanced": true, - "dynamic": true, - "fileTypes": [], - "file_path": "", - "info": "", - "list": false, - "load_from_db": false, - "multiline": true, - "name": "code", - "password": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "type": "code", - "value": "import weaviate\nfrom langchain_community.vectorstores import Weaviate\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import BoolInput, HandleInput, IntInput, SecretStrInput, StrInput\nfrom lfx.schema.data import Data\n\n\nclass WeaviateVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Weaviate\"\n description = \"Weaviate Vector Store with search capabilities\"\n name = \"Weaviate\"\n icon = \"Weaviate\"\n\n inputs = [\n StrInput(name=\"url\", display_name=\"Weaviate URL\", value=\"http://localhost:8080\", required=True),\n SecretStrInput(name=\"api_key\", display_name=\"API Key\", required=False),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n required=True,\n info=\"Requires capitalized index name.\",\n ),\n StrInput(name=\"text_key\", display_name=\"Text Key\", value=\"text\", advanced=True),\n *LCVectorStoreComponent.inputs,\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n BoolInput(name=\"search_by_text\", display_name=\"Search By Text\", advanced=True),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> Weaviate:\n if self.api_key:\n auth_config = weaviate.AuthApiKey(api_key=self.api_key)\n client = weaviate.Client(url=self.url, auth_client_secret=auth_config)\n else:\n client = weaviate.Client(url=self.url)\n\n if self.index_name != self.index_name.capitalize():\n msg = f\"Weaviate requires the index name to be capitalized. Use: {self.index_name.capitalize()}\"\n raise ValueError(msg)\n\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents and self.embedding:\n return Weaviate.from_documents(\n client=client,\n index_name=self.index_name,\n documents=documents,\n embedding=self.embedding,\n by_text=self.search_by_text,\n )\n\n return Weaviate(\n client=client,\n index_name=self.index_name,\n text_key=self.text_key,\n embedding=self.embedding,\n by_text=self.search_by_text,\n )\n\n def search_documents(self) -> list[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n return []\n" - }, - "embedding": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Embedding", - "dynamic": false, - "info": "", - "input_types": [ - "Embeddings" - ], - "list": false, - "list_add_label": "Add More", - "name": "embedding", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "other", - "value": "" - }, - "index_name": { - "_input_type": "StrInput", - "advanced": false, - "display_name": "Index Name", - "dynamic": false, - "info": "Requires capitalized index name.", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "index_name", - "override_skip": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "" - }, - "ingest_data": { - "_input_type": "HandleInput", - "advanced": false, - "display_name": "Ingest Data", - "dynamic": false, - "info": "", - "input_types": [ - "Data", - "DataFrame", - "Table" - ], - "list": true, - "list_add_label": "Add More", - "name": "ingest_data", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "other", - "value": "" - }, - "number_of_results": { - "_input_type": "IntInput", - "advanced": true, - "display_name": "Number of Results", - "dynamic": false, - "info": "Number of results to return.", - "list": false, - "list_add_label": "Add More", - "name": "number_of_results", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "int", - "value": 4 - }, - "search_by_text": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Search By Text", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "name": "search_by_text", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "bool", - "value": false - }, - "search_query": { - "_input_type": "QueryInput", - "advanced": false, - "display_name": "Search Query", - "dynamic": false, - "info": "Enter a query to run a similarity search.", - "input_types": [ - "Message" - ], - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "search_query", - "override_skip": false, - "placeholder": "Enter a query...", - "required": false, - "show": true, - "title_case": false, - "tool_mode": true, - "trace_as_input": true, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "query", - "value": "" - }, - "should_cache_vector_store": { - "_input_type": "BoolInput", - "advanced": true, - "display_name": "Cache Vector Store", - "dynamic": false, - "info": "If True, the vector store will be cached for the current build of the component. This is useful for components that have multiple output methods and want to share the same vector store.", - "list": false, - "list_add_label": "Add More", - "name": "should_cache_vector_store", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": true, - "type": "bool", - "value": true - }, - "text_key": { - "_input_type": "StrInput", - "advanced": true, - "display_name": "Text Key", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "text_key", - "override_skip": false, - "placeholder": "", - "required": false, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "text" - }, - "url": { - "_input_type": "StrInput", - "advanced": false, - "display_name": "Weaviate URL", - "dynamic": false, - "info": "", - "list": false, - "list_add_label": "Add More", - "load_from_db": false, - "name": "url", - "override_skip": false, - "placeholder": "", - "required": true, - "show": true, - "title_case": false, - "tool_mode": false, - "trace_as_metadata": true, - "track_in_telemetry": false, - "type": "str", - "value": "http://localhost:8080" - } - }, - "tool_mode": false - } - } - ], [ "wikipedia", { @@ -115546,7 +114636,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", @@ -115655,7 +114745,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -115851,7 +114941,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -115991,11 +115081,11 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "pydantic", @@ -116011,7 +115101,7 @@ }, { "name": "openai", - "version": "2.33.0" + "version": "2.38.0" } ], "total_dependencies": 6 @@ -116375,7 +115465,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -116559,11 +115649,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "lfx", @@ -116748,11 +115838,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "lfx", @@ -117064,11 +116154,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "lfx", @@ -117262,7 +116352,7 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "youtube_transcript_api", @@ -117463,11 +116553,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "lfx", @@ -117730,11 +116820,11 @@ "dependencies": [ { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "lfx", @@ -118098,9 +117188,9 @@ ] ], "metadata": { - "num_components": 355, - "num_modules": 97 + "num_components": 352, + "num_modules": 95 }, - "sha256": "9f07471d53b13443be5f928057f47000fac40d687dbfc96ac40c655881d6f050", - "version": "0.4.2" + "sha256": "2f7ddc4dd6885e496aee96e8d5ea7055f8002564df87648fe126fdc371becd5c", + "version": "0.4.4" } From d5bdc6175f1684948bb5e6efe77bbe8865a30c62 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 15:59:10 -0400 Subject: [PATCH 14/30] chore: update test_all_modules_importable --- .../tests/unit/components/test_all_modules_importable.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/backend/tests/unit/components/test_all_modules_importable.py b/src/backend/tests/unit/components/test_all_modules_importable.py index 2b0fb0ad6d..7a6f9a4377 100644 --- a/src/backend/tests/unit/components/test_all_modules_importable.py +++ b/src/backend/tests/unit/components/test_all_modules_importable.py @@ -394,6 +394,8 @@ class TestDirectModuleImports: "langchain_cohere", "langchain_pinecone", "langchain_chroma", + "langchain_qdrant", + "langchain_mongodb", "qdrant_client", "pymongo", "cassandra", From 1bae7983217d882571e94e5c6540bf3efbea35e8 Mon Sep 17 00:00:00 2001 From: "autofix-ci[bot]" <114827586+autofix-ci[bot]@users.noreply.github.com> Date: Mon, 25 May 2026 20:02:18 +0000 Subject: [PATCH 15/30] [autofix.ci] apply automated fixes --- .../Basic Prompt Chaining.json | 4 ++-- .../starter_projects/Basic Prompting.json | 4 ++-- .../starter_projects/Blog Writer.json | 8 +++---- .../Custom Component Generator.json | 16 ++++++------- .../starter_projects/Document Q&A.json | 8 +++---- .../Financial Report Parser.json | 4 ++-- .../starter_projects/Hybrid Search RAG.json | 6 ++--- .../Image Sentiment Analysis.json | 4 ++-- .../Instagram Copywriter.json | 6 ++--- .../starter_projects/Invoice Summarizer.json | 8 +++---- .../starter_projects/Knowledge Retrieval.json | 12 +++++----- .../starter_projects/Market Research.json | 6 ++--- .../starter_projects/Meeting Summary.json | 12 +++++----- .../starter_projects/Memory Chatbot.json | 4 ++-- .../starter_projects/News Aggregator.json | 14 +++++------ .../starter_projects/Nvidia Remix.json | 10 ++++---- .../starter_projects/Pokédex Agent.json | 6 ++--- .../Portfolio Website Code Generator.json | 8 +++---- .../starter_projects/Price Deal Finder.json | 6 ++--- .../starter_projects/Research Agent.json | 6 ++--- .../Research Translation Loop.json | 4 ++-- .../SEO Keyword Generator.json | 4 ++-- .../starter_projects/SaaS Pricing.json | 6 ++--- .../starter_projects/Search agent.json | 6 ++--- .../Sequential Tasks Agents.json | 12 +++++----- .../starter_projects/Simple Agent.json | 10 ++++---- .../starter_projects/Social Media Agent.json | 18 +++++++------- .../Text Sentiment Analysis.json | 12 +++++----- .../Travel Planning Agents.json | 16 ++++++------- .../Twitter Thread Generator.json | 4 ++-- .../starter_projects/Vector Store RAG.json | 24 +++++++++---------- .../starter_projects/Youtube Analysis.json | 12 +++++----- 32 files changed, 140 insertions(+), 140 deletions(-) diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json index 8fe249ba02..b69cd37cbb 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json @@ -661,11 +661,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json index 523a61fd6e..5a9a838611 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json @@ -622,11 +622,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json index 42a6352c2f..1dbb86447b 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json @@ -538,11 +538,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1037,7 +1037,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -1045,7 +1045,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "markitdown", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json index ff485f3c96..ebf4cdc217 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json @@ -925,7 +925,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -933,7 +933,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "markitdown", @@ -1322,7 +1322,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -1330,7 +1330,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "markitdown", @@ -1725,7 +1725,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -1733,7 +1733,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "markitdown", @@ -2396,11 +2396,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json index 2503356a6c..2d60319b68 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json @@ -416,11 +416,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1319,7 +1319,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -1327,7 +1327,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 4 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json index 2e10b8dd63..8e208aa0e2 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json @@ -130,11 +130,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json index af539282b2..abaadad2ac 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json @@ -674,11 +674,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1521,7 +1521,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json index acda571ac9..c9a2ca40ed 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json @@ -456,11 +456,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json index 3d98625f5b..80a7266273 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json @@ -1124,11 +1124,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -2084,7 +2084,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json index 27258112df..47a5098208 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json @@ -342,11 +342,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -679,7 +679,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -1192,7 +1192,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json index c0b9806e42..e84961ebf3 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json @@ -259,11 +259,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -541,11 +541,11 @@ "dependencies": [ { "name": "chromadb", - "version": "1.5.8" + "version": "1.5.9" }, { "name": "cryptography", - "version": "47.0.0" + "version": "48.0.0" }, { "name": "langchain_chroma", @@ -565,7 +565,7 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "langchain_huggingface", @@ -585,7 +585,7 @@ }, { "name": "langchain_ibm", - "version": "1.0.7" + "version": "1.0.11" } ], "total_dependencies": 12 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json index 8a203ffb24..0f81f8b278 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json @@ -452,11 +452,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1204,7 +1204,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json index f6c5f5d314..c7dac54724 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json @@ -685,11 +685,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -965,11 +965,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1245,11 +1245,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json index 45852ae88d..7327661ad7 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json @@ -425,11 +425,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json b/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json index 7ae3584863..7678f5c1c0 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json @@ -886,11 +886,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1186,7 +1186,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -1757,15 +1757,15 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1781,7 +1781,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 7 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json b/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json index 948b42e1e1..e10452a85b 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json @@ -513,11 +513,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -813,7 +813,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -1750,7 +1750,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -2105,7 +2105,7 @@ "dependencies": [ { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json index 07b7363350..81104a7ae5 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json @@ -396,11 +396,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1251,7 +1251,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json index bbf8fe2f5c..9640146f15 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json @@ -328,11 +328,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -941,7 +941,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -949,7 +949,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 4 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json index b4fdd81237..eec831333c 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json @@ -420,11 +420,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1620,7 +1620,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json index 54fd28e161..914e9d8a63 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json @@ -1768,11 +1768,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -2823,7 +2823,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json index a214e7c2cb..51b070be5c 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json @@ -372,11 +372,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json index d191bcb2b6..71f5e961f3 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json @@ -628,11 +628,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json index ae15e88168..f0700733da 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json @@ -405,11 +405,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -905,7 +905,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json index dd45e9e19b..d5288ce6aa 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Search agent.json @@ -571,11 +571,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -952,7 +952,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json index 1284b8654d..056db25f7e 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json @@ -370,7 +370,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -958,7 +958,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -2403,7 +2403,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -2976,7 +2976,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -3791,11 +3791,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json index 69ca825bc8..208c59f9b4 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json @@ -652,11 +652,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -951,7 +951,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -1527,7 +1527,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -1535,7 +1535,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "markitdown", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json index 3e89aff1d2..3d8d01c001 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json @@ -152,15 +152,15 @@ "dependencies": [ { "name": "apify_client", - "version": "2.5.0" + "version": "3.0.1" }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -384,15 +384,15 @@ "dependencies": [ { "name": "apify_client", - "version": "2.5.0" + "version": "3.0.1" }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -976,11 +976,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1301,7 +1301,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json index 64b84fa493..a4ce25180d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Text Sentiment Analysis.json @@ -826,11 +826,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1108,11 +1108,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -2633,7 +2633,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -2641,7 +2641,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 4 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json index 4fbc085a25..adfbc02689 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json @@ -503,11 +503,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -824,7 +824,7 @@ "dependencies": [ { "name": "requests", - "version": "2.33.1" + "version": "2.34.2" }, { "name": "bs4", @@ -832,7 +832,7 @@ }, { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "markitdown", @@ -1434,7 +1434,7 @@ "dependencies": [ { "name": "langchain_community", - "version": "0.4.1" + "version": "0.4.2" }, { "name": "lfx", @@ -1717,7 +1717,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -2300,7 +2300,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -2883,7 +2883,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json index 6ed227754f..766b4fc14b 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json @@ -706,11 +706,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json index 7967fed23e..ca4797c4ec 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json @@ -1075,11 +1075,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1635,7 +1635,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" }, { "name": "pydantic", @@ -1643,7 +1643,7 @@ }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" } ], "total_dependencies": 4 @@ -2683,11 +2683,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "cryptography", - "version": "47.0.0" + "version": "48.0.0" }, { "name": "langchain_chroma", @@ -2703,11 +2703,11 @@ }, { "name": "numpy", - "version": "2.4.4" + "version": "2.4.6" }, { "name": "chromadb", - "version": "1.5.8" + "version": "1.5.9" } ], "total_dependencies": 7 @@ -3053,11 +3053,11 @@ "dependencies": [ { "name": "chromadb", - "version": "1.5.8" + "version": "1.5.9" }, { "name": "cryptography", - "version": "47.0.0" + "version": "48.0.0" }, { "name": "langchain_chroma", @@ -3077,7 +3077,7 @@ }, { "name": "langchain_openai", - "version": "1.2.1" + "version": "1.2.2" }, { "name": "langchain_huggingface", @@ -3097,7 +3097,7 @@ }, { "name": "langchain_ibm", - "version": "1.0.7" + "version": "1.0.11" } ], "total_dependencies": 12 diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json index 5441bdbe3e..18a33d3036 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json @@ -270,11 +270,11 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "googleapiclient", - "version": "2.195.0" + "version": "2.196.0" }, { "name": "lfx", @@ -513,7 +513,7 @@ }, { "name": "langchain_core", - "version": "1.3.2" + "version": "1.4.0" } ], "total_dependencies": 3 @@ -1293,11 +1293,11 @@ "dependencies": [ { "name": "orjson", - "version": "3.11.8" + "version": "3.11.9" }, { "name": "fastapi", - "version": "0.136.1" + "version": "0.136.3" }, { "name": "lfx", @@ -1579,7 +1579,7 @@ "dependencies": [ { "name": "pandas", - "version": "2.2.3" + "version": "2.3.3" }, { "name": "youtube_transcript_api", From cb6d09fc5d0fa996500bc1b99cec9711542cc99a Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 16:27:18 -0400 Subject: [PATCH 16/30] chore: update pkg in test_all_modules_importable --- .../tests/unit/components/test_all_modules_importable.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/backend/tests/unit/components/test_all_modules_importable.py b/src/backend/tests/unit/components/test_all_modules_importable.py index 7a6f9a4377..b2c802ab72 100644 --- a/src/backend/tests/unit/components/test_all_modules_importable.py +++ b/src/backend/tests/unit/components/test_all_modules_importable.py @@ -550,6 +550,8 @@ class TestDirectModuleImports: "langchain_chroma", "langchain_pinecone", "qdrant_client", + "langchain_qdrant", + "langchain_mongodb", "weaviate", "chromadb", "pinecone", From 9224534f70949fc621f7c73f92fe44283e5807ba Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 16:55:53 -0400 Subject: [PATCH 17/30] chore: port over mem0ai changes from 1.10.0 https://github.com/langflow-ai/langflow/pull/13292 --- src/backend/base/pyproject.toml | 4 ++-- src/backend/base/uv.lock | 7 +++++-- src/lfx/src/lfx/components/mem0/mem0_chat_memory.py | 4 ++-- uv.lock | 10 +++++----- 4 files changed, 14 insertions(+), 11 deletions(-) diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 95deb96b15..682a59323d 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -199,7 +199,7 @@ faiss = [ "faiss-cpu==1.9.0.post1; python_version<'3.14'", "faiss-cpu>=1.13.2; python_version>='3.14'", ] -qdrant = ["qdrant-client>=1.9.2"] +qdrant = ["qdrant-client>=1.12.0,<2.0.0"] qdrant-vectors = ["langchain-qdrant>=1.0.0,<2.0.0"] weaviate = ["weaviate-client>=4.10.2,<5.0.0"] chroma = [ @@ -293,7 +293,7 @@ composio = [ ragstack = ["ragstack-ai-knowledge-store==0.2.1; python_version < '3.13'"] opensearch = ["opensearch-py==2.8.0"] atlassian = ["atlassian-python-api==3.41.16"] -mem0 = ["mem0ai>=0.1.34"] +mem0 = ["mem0ai>=2.0.2,<3.0.0"] needle = ["needle-python>=0.4.0"] sseclient = ["sseclient-py==1.8.0"] openinference = ["openinference-instrumentation-langchain>=0.1.29"] diff --git a/src/backend/base/uv.lock b/src/backend/base/uv.lock index fb47106212..40fe5b9336 100644 --- a/src/backend/base/uv.lock +++ b/src/backend/base/uv.lock @@ -6753,6 +6753,7 @@ all = [ { name = "langchain-ollama" }, { name = "langchain-openai" }, { name = "langchain-pinecone", marker = "python_full_version < '3.14'" }, + { name = "langchain-qdrant" }, { name = "langchain-sambanova" }, { name = "langchain-unstructured" }, { name = "langfuse", marker = "python_full_version < '3.14'" }, @@ -6922,6 +6923,7 @@ complete = [ { name = "langchain-ollama" }, { name = "langchain-openai" }, { name = "langchain-pinecone", marker = "python_full_version < '3.14'" }, + { name = "langchain-qdrant" }, { name = "langchain-sambanova" }, { name = "langchain-unstructured" }, { name = "langfuse", marker = "python_full_version < '3.14'" }, @@ -7475,6 +7477,7 @@ requires-dist = [ { name = "langflow-base", extras = ["pypdf"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["pytube"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["qdrant"], marker = "extra == 'complete'" }, + { name = "langflow-base", extras = ["qdrant-vectors"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["qianfan"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["ragstack"], marker = "extra == 'complete'" }, { name = "langflow-base", extras = ["redis"], marker = "extra == 'complete'" }, @@ -7510,7 +7513,7 @@ requires-dist = [ { name = "markupsafe", marker = "extra == 'markup'", specifier = "==3.0.2" }, { name = "mcp", specifier = ">=1.17.0,<2.0.0" }, { name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.17.0,<2.0.0" }, - { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.34" }, + { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=2.0.2,<3.0.0" }, { name = "metal-sdk", marker = "extra == 'metal'", specifier = "==2.5.1" }, { name = "metaphor-python", marker = "extra == 'metaphor'", specifier = "==0.1.23" }, { name = "mlx", marker = "python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'mlx'", specifier = ">=0.29.0" }, @@ -7556,7 +7559,7 @@ requires-dist = [ { name = "python-docx", marker = "extra == 'docx'", specifier = ">=1.1.0" }, { name = "python-multipart", specifier = ">=0.0.12,<1.0.0" }, { name = "pytube", marker = "extra == 'pytube'", specifier = "==15.0.0" }, - { name = "qdrant-client", marker = "extra == 'qdrant'", specifier = ">=1.9.2" }, + { name = "qdrant-client", marker = "extra == 'qdrant'", specifier = ">=1.12.0,<2.0.0" }, { name = "qianfan", marker = "extra == 'qianfan'", specifier = "==0.3.5" }, { name = "ragstack-ai-knowledge-store", marker = "python_full_version < '3.13' and extra == 'ragstack'", specifier = "==0.2.1" }, { name = "redis", marker = "extra == 'redis'", specifier = ">=7.4.0,<8.0.0" }, diff --git a/src/lfx/src/lfx/components/mem0/mem0_chat_memory.py b/src/lfx/src/lfx/components/mem0/mem0_chat_memory.py index 40d31a21d3..7dcde99648 100644 --- a/src/lfx/src/lfx/components/mem0/mem0_chat_memory.py +++ b/src/lfx/src/lfx/components/mem0/mem0_chat_memory.py @@ -135,10 +135,10 @@ class Mem0MemoryComponent(LCChatMemoryComponent): try: if search_query: logger.info("Performing search with query.") - related_memories = mem0_memory.search(query=search_query, user_id=user_id) + related_memories = mem0_memory.search(query=search_query, filters={"user_id": user_id}) else: logger.info("Retrieving all memories for user_id: %s", user_id) - related_memories = mem0_memory.get_all(user_id=user_id) + related_memories = mem0_memory.get_all(filters={"user_id": user_id}) except Exception: logger.exception("Failed to retrieve related memories from Mem0.") raise diff --git a/uv.lock b/uv.lock index 857bf44846..046cd9510d 100644 --- a/uv.lock +++ b/uv.lock @@ -5298,15 +5298,15 @@ wheels = [ [[package]] name = "hypothesis" -version = "6.152.9" +version = "6.152.10" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "exceptiongroup", marker = "python_full_version < '3.11'" }, { name = "sortedcontainers" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/3b/fb/a5651eb0cd03ecee644e8f4d8cd2027b40a08bf1488f46201e794aebe9b5/hypothesis-6.152.9.tar.gz", hash = "sha256:de4711d69ce3a18009047c3b44882810fd0c0348c1558e920a4b0d2c45f59e1e", size = 472009, upload-time = "2026-05-19T17:10:19.586Z" } +sdist = { url = "https://files.pythonhosted.org/packages/de/c8/857f2a57812b22d14c4615e7c7e5b5b8808e7b0a6a330ffab0eaad15a91c/hypothesis-6.152.10.tar.gz", hash = "sha256:313245552fcdfbc9a37787293eda783da18c2baac280626c922b2c7fd2aa1e26", size = 471995, upload-time = "2026-05-25T20:31:24.492Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/79/d9/40ef7ed22f0c45c2316467edb9afb8fc172cd089cb329c0ee6da6b74416c/hypothesis-6.152.9-py3-none-any.whl", hash = "sha256:9c4fdccb1eac0b12ec740c12290d0e6a0bea3526a3f0bf812b7643bb563c2d8b", size = 538148, upload-time = "2026-05-19T17:10:16.131Z" }, + { url = "https://files.pythonhosted.org/packages/d7/8c/2ad9a273f80550b08cbe2a94e7816a09488dcf1e0e4b450a50d39f8d7497/hypothesis-6.152.10-py3-none-any.whl", hash = "sha256:7479246f2ea941d0f82fdc64fd17397f35d951a4253597cac9fc69eda93a4ea8", size = 538155, upload-time = "2026-05-25T20:31:22.423Z" }, ] [[package]] @@ -8015,7 +8015,7 @@ requires-dist = [ { name = "markupsafe", marker = "extra == 'markup'", specifier = "==3.0.2" }, { name = "mcp", specifier = ">=1.17.0,<2.0.0" }, { name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.17.0,<2.0.0" }, - { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.34" }, + { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=2.0.2,<3.0.0" }, { name = "metal-sdk", marker = "extra == 'metal'", specifier = "==2.5.1" }, { name = "metaphor-python", marker = "extra == 'metaphor'", specifier = "==0.1.23" }, { name = "mlx", marker = "python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'mlx'", specifier = ">=0.29.0" }, @@ -8061,7 +8061,7 @@ requires-dist = [ { name = "python-docx", marker = "extra == 'docx'", specifier = ">=1.1.0" }, { name = "python-multipart", specifier = ">=0.0.12,<1.0.0" }, { name = "pytube", marker = "extra == 'pytube'", specifier = "==15.0.0" }, - { name = "qdrant-client", marker = "extra == 'qdrant'", specifier = ">=1.9.2" }, + { name = "qdrant-client", marker = "extra == 'qdrant'", specifier = ">=1.12.0,<2.0.0" }, { name = "qianfan", marker = "extra == 'qianfan'", specifier = "==0.3.5" }, { name = "ragstack-ai-knowledge-store", marker = "python_full_version < '3.13' and extra == 'ragstack'", specifier = "==0.2.1" }, { name = "redis", marker = "extra == 'redis'", specifier = ">=7.4.0,<8.0.0" }, From a7ae55e5d5d7a3b8d4bd4da8e8981cf25add1e0b Mon Sep 17 00:00:00 2001 From: "autofix-ci[bot]" <114827586+autofix-ci[bot]@users.noreply.github.com> Date: Mon, 25 May 2026 20:59:17 +0000 Subject: [PATCH 18/30] [autofix.ci] apply automated fixes --- src/lfx/src/lfx/_assets/component_index.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/lfx/src/lfx/_assets/component_index.json b/src/lfx/src/lfx/_assets/component_index.json index 2e971b82fc..11332e9f5b 100644 --- a/src/lfx/src/lfx/_assets/component_index.json +++ b/src/lfx/src/lfx/_assets/component_index.json @@ -89128,7 +89128,7 @@ "icon": "Mem0", "legacy": false, "metadata": { - "code_hash": "309abc9375f4", + "code_hash": "f2832e1bc71f", "dependencies": { "dependencies": [ { @@ -89195,7 +89195,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import os\n\nfrom mem0 import Memory, MemoryClient\n\nfrom lfx.base.memory.model import LCChatMemoryComponent\nfrom lfx.inputs.inputs import DictInput, HandleInput, MessageTextInput, NestedDictInput, SecretStrInput\nfrom lfx.io import Output\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\ndisable_component_in_astra_cloud_msg = (\n \"Mem0 chat memory is not supported in Astra cloud environment. Please use local storage mode or mem0 cloud.\"\n)\n\n\nclass Mem0MemoryComponent(LCChatMemoryComponent):\n display_name = \"Mem0 Chat Memory\"\n description = \"Retrieves and stores chat messages using Mem0 memory storage.\"\n name = \"mem0_chat_memory\"\n icon: str = \"Mem0\"\n inputs = [\n NestedDictInput(\n name=\"mem0_config\",\n display_name=\"Mem0 Configuration\",\n info=\"\"\"Configuration dictionary for initializing Mem0 memory instance.\n Example:\n {\n \"graph_store\": {\n \"provider\": \"neo4j\",\n \"config\": {\n \"url\": \"neo4j+s://your-neo4j-url\",\n \"username\": \"neo4j\",\n \"password\": \"your-password\"\n }\n },\n \"version\": \"v1.1\"\n }\"\"\",\n input_types=[\"Data\", \"JSON\"],\n ),\n MessageTextInput(\n name=\"ingest_message\",\n display_name=\"Message to Ingest\",\n info=\"The message content to be ingested into Mem0 memory.\",\n ),\n HandleInput(\n name=\"existing_memory\",\n display_name=\"Existing Memory Instance\",\n input_types=[\"Memory\"],\n info=\"Optional existing Mem0 memory instance. If not provided, a new instance will be created.\",\n ),\n MessageTextInput(\n name=\"user_id\", display_name=\"User ID\", info=\"Identifier for the user associated with the messages.\"\n ),\n MessageTextInput(\n name=\"search_query\", display_name=\"Search Query\", info=\"Input text for searching related memories in Mem0.\"\n ),\n SecretStrInput(\n name=\"mem0_api_key\",\n display_name=\"Mem0 API Key\",\n info=\"API key for Mem0 platform. Leave empty to use the local version.\",\n ),\n DictInput(\n name=\"metadata\",\n display_name=\"Metadata\",\n info=\"Additional metadata to associate with the ingested message.\",\n advanced=True,\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n required=False,\n info=\"API key for OpenAI. Required if using OpenAI Embeddings without a provided configuration.\",\n ),\n ]\n\n outputs = [\n Output(name=\"memory\", display_name=\"Mem0 Memory\", method=\"ingest_data\"),\n Output(\n name=\"search_results\",\n display_name=\"Search Results\",\n method=\"build_search_results\",\n ),\n ]\n\n def build_mem0(self) -> Memory:\n \"\"\"Initializes a Mem0 memory instance based on provided configuration and API keys.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n if self.openai_api_key:\n os.environ[\"OPENAI_API_KEY\"] = self.openai_api_key\n\n try:\n if not self.mem0_api_key:\n return Memory.from_config(config_dict=dict(self.mem0_config)) if self.mem0_config else Memory()\n if self.mem0_config:\n return MemoryClient.from_config(api_key=self.mem0_api_key, config_dict=dict(self.mem0_config))\n return MemoryClient(api_key=self.mem0_api_key)\n except ImportError as e:\n msg = \"Mem0 is not properly installed. Please install it with 'pip install -U mem0ai'.\"\n raise ImportError(msg) from e\n\n def ingest_data(self) -> Memory:\n \"\"\"Ingests a new message into Mem0 memory and returns the updated memory instance.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n mem0_memory = self.existing_memory or self.build_mem0()\n\n if not self.ingest_message or not self.user_id:\n logger.warning(\"Missing 'ingest_message' or 'user_id'; cannot ingest data.\")\n return mem0_memory\n\n metadata = self.metadata or {}\n\n logger.info(\"Ingesting message for user_id: %s\", self.user_id)\n\n try:\n mem0_memory.add(self.ingest_message, user_id=self.user_id, metadata=metadata)\n except Exception:\n logger.exception(\"Failed to add message to Mem0 memory.\")\n raise\n\n return mem0_memory\n\n def build_search_results(self) -> Data:\n \"\"\"Searches the Mem0 memory for related messages based on the search query and returns the results.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n mem0_memory = self.ingest_data()\n search_query = self.search_query\n user_id = self.user_id\n\n logger.info(\"Search query: %s\", search_query)\n\n try:\n if search_query:\n logger.info(\"Performing search with query.\")\n related_memories = mem0_memory.search(query=search_query, user_id=user_id)\n else:\n logger.info(\"Retrieving all memories for user_id: %s\", user_id)\n related_memories = mem0_memory.get_all(user_id=user_id)\n except Exception:\n logger.exception(\"Failed to retrieve related memories from Mem0.\")\n raise\n\n logger.info(\"Related memories retrieved: %s\", related_memories)\n return related_memories\n" + "value": "import os\n\nfrom mem0 import Memory, MemoryClient\n\nfrom lfx.base.memory.model import LCChatMemoryComponent\nfrom lfx.inputs.inputs import DictInput, HandleInput, MessageTextInput, NestedDictInput, SecretStrInput\nfrom lfx.io import Output\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\ndisable_component_in_astra_cloud_msg = (\n \"Mem0 chat memory is not supported in Astra cloud environment. Please use local storage mode or mem0 cloud.\"\n)\n\n\nclass Mem0MemoryComponent(LCChatMemoryComponent):\n display_name = \"Mem0 Chat Memory\"\n description = \"Retrieves and stores chat messages using Mem0 memory storage.\"\n name = \"mem0_chat_memory\"\n icon: str = \"Mem0\"\n inputs = [\n NestedDictInput(\n name=\"mem0_config\",\n display_name=\"Mem0 Configuration\",\n info=\"\"\"Configuration dictionary for initializing Mem0 memory instance.\n Example:\n {\n \"graph_store\": {\n \"provider\": \"neo4j\",\n \"config\": {\n \"url\": \"neo4j+s://your-neo4j-url\",\n \"username\": \"neo4j\",\n \"password\": \"your-password\"\n }\n },\n \"version\": \"v1.1\"\n }\"\"\",\n input_types=[\"Data\", \"JSON\"],\n ),\n MessageTextInput(\n name=\"ingest_message\",\n display_name=\"Message to Ingest\",\n info=\"The message content to be ingested into Mem0 memory.\",\n ),\n HandleInput(\n name=\"existing_memory\",\n display_name=\"Existing Memory Instance\",\n input_types=[\"Memory\"],\n info=\"Optional existing Mem0 memory instance. If not provided, a new instance will be created.\",\n ),\n MessageTextInput(\n name=\"user_id\", display_name=\"User ID\", info=\"Identifier for the user associated with the messages.\"\n ),\n MessageTextInput(\n name=\"search_query\", display_name=\"Search Query\", info=\"Input text for searching related memories in Mem0.\"\n ),\n SecretStrInput(\n name=\"mem0_api_key\",\n display_name=\"Mem0 API Key\",\n info=\"API key for Mem0 platform. Leave empty to use the local version.\",\n ),\n DictInput(\n name=\"metadata\",\n display_name=\"Metadata\",\n info=\"Additional metadata to associate with the ingested message.\",\n advanced=True,\n ),\n SecretStrInput(\n name=\"openai_api_key\",\n display_name=\"OpenAI API Key\",\n required=False,\n info=\"API key for OpenAI. Required if using OpenAI Embeddings without a provided configuration.\",\n ),\n ]\n\n outputs = [\n Output(name=\"memory\", display_name=\"Mem0 Memory\", method=\"ingest_data\"),\n Output(\n name=\"search_results\",\n display_name=\"Search Results\",\n method=\"build_search_results\",\n ),\n ]\n\n def build_mem0(self) -> Memory:\n \"\"\"Initializes a Mem0 memory instance based on provided configuration and API keys.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n if self.openai_api_key:\n os.environ[\"OPENAI_API_KEY\"] = self.openai_api_key\n\n try:\n if not self.mem0_api_key:\n return Memory.from_config(config_dict=dict(self.mem0_config)) if self.mem0_config else Memory()\n if self.mem0_config:\n return MemoryClient.from_config(api_key=self.mem0_api_key, config_dict=dict(self.mem0_config))\n return MemoryClient(api_key=self.mem0_api_key)\n except ImportError as e:\n msg = \"Mem0 is not properly installed. Please install it with 'pip install -U mem0ai'.\"\n raise ImportError(msg) from e\n\n def ingest_data(self) -> Memory:\n \"\"\"Ingests a new message into Mem0 memory and returns the updated memory instance.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n mem0_memory = self.existing_memory or self.build_mem0()\n\n if not self.ingest_message or not self.user_id:\n logger.warning(\"Missing 'ingest_message' or 'user_id'; cannot ingest data.\")\n return mem0_memory\n\n metadata = self.metadata or {}\n\n logger.info(\"Ingesting message for user_id: %s\", self.user_id)\n\n try:\n mem0_memory.add(self.ingest_message, user_id=self.user_id, metadata=metadata)\n except Exception:\n logger.exception(\"Failed to add message to Mem0 memory.\")\n raise\n\n return mem0_memory\n\n def build_search_results(self) -> Data:\n \"\"\"Searches the Mem0 memory for related messages based on the search query and returns the results.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n mem0_memory = self.ingest_data()\n search_query = self.search_query\n user_id = self.user_id\n\n logger.info(\"Search query: %s\", search_query)\n\n try:\n if search_query:\n logger.info(\"Performing search with query.\")\n related_memories = mem0_memory.search(query=search_query, filters={\"user_id\": user_id})\n else:\n logger.info(\"Retrieving all memories for user_id: %s\", user_id)\n related_memories = mem0_memory.get_all(filters={\"user_id\": user_id})\n except Exception:\n logger.exception(\"Failed to retrieve related memories from Mem0.\")\n raise\n\n logger.info(\"Related memories retrieved: %s\", related_memories)\n return related_memories\n" }, "existing_memory": { "_input_type": "HandleInput", @@ -117191,6 +117191,6 @@ "num_components": 352, "num_modules": 95 }, - "sha256": "2f7ddc4dd6885e496aee96e8d5ea7055f8002564df87648fe126fdc371becd5c", + "sha256": "d60b45d05f808dc772d0cf7056ce022abec53ee15cdc5deb6c968804fb91a7a2", "version": "0.4.4" } From a752275fbd3dd423e7f063376dcfdff33d7c1c11 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 17:34:11 -0400 Subject: [PATCH 19/30] chore: add to deps and loose missing test --- src/backend/base/pyproject.toml | 2 ++ src/backend/base/uv.lock | 3 ++ .../bundles/mem0/test_mem0_component.py | 33 ++++++++++++++++++- uv.lock | 3 ++ 4 files changed, 40 insertions(+), 1 deletion(-) diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 682a59323d..d6884a7f2f 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -25,6 +25,8 @@ dependencies = [ "gunicorn>=25.3.0,<27.0.0", "langchain~=1.2.0", "langchain-community~=0.4.1", + "langchain-qdrant>=1.0.0,<2.0.0", + "langchain-qdrant>=0.11.0", "langchain-core>=1.3.3,<2.0.0", "langchainhub~=0.1.15", "loguru>=0.7.1,<1.0.0", diff --git a/src/backend/base/uv.lock b/src/backend/base/uv.lock index 40fe5b9336..78331baf3d 100644 --- a/src/backend/base/uv.lock +++ b/src/backend/base/uv.lock @@ -6627,6 +6627,7 @@ dependencies = [ { name = "langchain-core" }, { name = "langchain-experimental" }, { name = "langchain-ibm", marker = "python_full_version < '3.14'" }, + { name = "langchain-qdrant" }, { name = "langchainhub" }, { name = "langgraph-checkpoint" }, { name = "lfx" }, @@ -7394,6 +7395,8 @@ requires-dist = [ { name = "langchain-ollama", marker = "extra == 'ollama'", specifier = "~=0.3.10" }, { name = "langchain-openai", marker = "extra == 'openai'", specifier = ">=1.1.6" }, { name = "langchain-pinecone", marker = "python_full_version < '3.14' and extra == 'pinecone'", specifier = "~=0.2.13" }, + { name = "langchain-qdrant", specifier = ">=0.11.0" }, + { name = "langchain-qdrant", specifier = ">=1.0.0,<2.0.0" }, { name = "langchain-qdrant", marker = "extra == 'qdrant-vectors'", specifier = ">=1.0.0,<2.0.0" }, { name = "langchain-sambanova", marker = "extra == 'sambanova'", specifier = "~=1.0.0" }, { name = "langchain-unstructured", marker = "extra == 'langchain-unstructured'", specifier = "~=1.0.0" }, diff --git a/src/backend/tests/unit/components/bundles/mem0/test_mem0_component.py b/src/backend/tests/unit/components/bundles/mem0/test_mem0_component.py index 1242d25413..3736831eac 100644 --- a/src/backend/tests/unit/components/bundles/mem0/test_mem0_component.py +++ b/src/backend/tests/unit/components/bundles/mem0/test_mem0_component.py @@ -1,7 +1,7 @@ """Unit tests for Mem0MemoryComponent cloud validation.""" import os -from unittest.mock import patch +from unittest.mock import Mock, patch import pytest from lfx.components.mem0.mem0_chat_memory import Mem0MemoryComponent @@ -29,3 +29,34 @@ class TestMem0CloudValidation: component = Mem0MemoryComponent(openai_api_key="test-key") component.build_mem0() mock_memory.assert_called_once() + + def test_build_search_results_uses_mem0_2_filters_for_search(self): + """Test that search uses filters for Mem0 2.x entity scoping.""" + memory = Mock() + memory.search.return_value = [{"memory": "hello"}] + component = Mem0MemoryComponent(_user_id="u").set( + existing_memory=memory, + ingest_message="hello", + search_query="x", + ) + + result = component.build_search_results() + + assert result == [{"memory": "hello"}] + memory.add.assert_called_once_with("hello", user_id="u", metadata={}) + memory.search.assert_called_once_with(query="x", filters={"user_id": "u"}) + + def test_build_search_results_uses_mem0_2_filters_for_get_all(self): + """Test that get_all uses filters for Mem0 2.x entity scoping.""" + memory = Mock() + memory.get_all.return_value = [{"memory": "hello"}] + component = Mem0MemoryComponent(_user_id="u").set( + existing_memory=memory, + ingest_message="hello", + ) + + result = component.build_search_results() + + assert result == [{"memory": "hello"}] + memory.add.assert_called_once_with("hello", user_id="u", metadata={}) + memory.get_all.assert_called_once_with(filters={"user_id": "u"}) diff --git a/uv.lock b/uv.lock index 046cd9510d..70b7152639 100644 --- a/uv.lock +++ b/uv.lock @@ -7129,6 +7129,7 @@ dependencies = [ { name = "langchain-core" }, { name = "langchain-experimental" }, { name = "langchain-ibm", marker = "python_full_version < '3.14'" }, + { name = "langchain-qdrant" }, { name = "langchainhub" }, { name = "langgraph-checkpoint" }, { name = "lfx" }, @@ -7896,6 +7897,8 @@ requires-dist = [ { name = "langchain-ollama", marker = "extra == 'ollama'", specifier = "~=0.3.10" }, { name = "langchain-openai", marker = "extra == 'openai'", specifier = ">=1.1.6" }, { name = "langchain-pinecone", marker = "python_full_version < '3.14' and extra == 'pinecone'", specifier = "~=0.2.13" }, + { name = "langchain-qdrant", specifier = ">=0.11.0" }, + { name = "langchain-qdrant", specifier = ">=1.0.0,<2.0.0" }, { name = "langchain-qdrant", marker = "extra == 'qdrant-vectors'", specifier = ">=1.0.0,<2.0.0" }, { name = "langchain-sambanova", marker = "extra == 'sambanova'", specifier = "~=1.0.0" }, { name = "langchain-unstructured", marker = "extra == 'langchain-unstructured'", specifier = "~=1.0.0" }, From 00fe8c001c3b384f2f1cba65f7fe1e962fbfad74 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 19:48:52 -0400 Subject: [PATCH 20/30] chore: dup langchain-qdrant to langchain-mongodb --- src/backend/base/pyproject.toml | 2 +- src/backend/base/uv.lock | 84 ++++++++++++++++++--------------- uv.lock | 84 ++++++++++++++++++--------------- 3 files changed, 93 insertions(+), 77 deletions(-) diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index d6884a7f2f..8a304c7838 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "langchain~=1.2.0", "langchain-community~=0.4.1", "langchain-qdrant>=1.0.0,<2.0.0", - 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'qdrant-vectors'", specifier = ">=1.0.0,<2.0.0" }, { name = "langchain-sambanova", marker = "extra == 'sambanova'", specifier = "~=1.0.0" }, From 7ac198ac372b0ff5935076c8d4645fb24236a711 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 20:43:23 -0400 Subject: [PATCH 21/30] chore: update opik, perplexity , weaviate --- src/backend/base/pyproject.toml | 6 +- src/backend/base/uv.lock | 60 ++++++++++++++-- src/lfx/pyproject.toml | 2 + .../lfx/components/perplexity/perplexity.py | 2 +- .../src/lfx/components/weaviate/weaviate.py | 8 +-- uv.lock | 68 +++++++++++++++++-- 6 files changed, 128 insertions(+), 18 deletions(-) diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 8a304c7838..1f806ee5e0 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -25,8 +25,10 @@ dependencies = [ "gunicorn>=25.3.0,<27.0.0", "langchain~=1.2.0", "langchain-community~=0.4.1", - "langchain-qdrant>=1.0.0,<2.0.0", 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b/src/lfx/pyproject.toml @@ -98,7 +98,9 @@ integration = [ "langchain-openai>=1.1.12,<2.0.0", "langchain-community>=0.4.1,<1.0.0", "langchain-mongodb>=0.11.0", + "langchain-perplexity>=1.0.0,<2.0.0", "langchain-qdrant>=1.1.0", + "langchain-weaviate>=0.0.6", "beautifulsoup4>=4.12.0", "lxml>=5.0.0", ] diff --git a/src/lfx/src/lfx/components/perplexity/perplexity.py b/src/lfx/src/lfx/components/perplexity/perplexity.py index 49f39216f9..56442977e2 100644 --- a/src/lfx/src/lfx/components/perplexity/perplexity.py +++ b/src/lfx/src/lfx/components/perplexity/perplexity.py @@ -1,4 +1,4 @@ -from langchain_community.chat_models import ChatPerplexity +from langchain_perplexity import ChatPerplexity from pydantic.v1 import SecretStr from lfx.base.models.model import LCModelComponent diff --git a/src/lfx/src/lfx/components/weaviate/weaviate.py b/src/lfx/src/lfx/components/weaviate/weaviate.py index 9eb477ec66..8e735aa21c 100644 --- a/src/lfx/src/lfx/components/weaviate/weaviate.py +++ b/src/lfx/src/lfx/components/weaviate/weaviate.py @@ -1,5 +1,5 @@ import weaviate -from langchain_community.vectorstores import Weaviate +from langchain_weaviate import WeaviateVectorStore from lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store from lfx.helpers.data import docs_to_data @@ -36,7 +36,7 @@ class WeaviateVectorStoreComponent(LCVectorStoreComponent): ] @check_cached_vector_store - def build_vector_store(self) -> Weaviate: + def build_vector_store(self) -> WeaviateVectorStore: if self.api_key: auth_config = weaviate.AuthApiKey(api_key=self.api_key) client = weaviate.Client(url=self.url, auth_client_secret=auth_config) @@ -58,7 +58,7 @@ class WeaviateVectorStoreComponent(LCVectorStoreComponent): documents.append(_input) if documents and self.embedding: - return Weaviate.from_documents( + return WeaviateVectorStore.from_documents( client=client, index_name=self.index_name, documents=documents, @@ -66,7 +66,7 @@ class 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"langchain-qdrant" }, + { name = "langchain-weaviate" }, { name = "lxml" }, ] @@ -8483,7 +8518,9 @@ integration = [ { name = "langchain-community", specifier = ">=0.4.1,<1.0.0" }, { name = "langchain-mongodb", specifier = ">=0.11.0" }, { name = "langchain-openai", specifier = ">=1.1.12,<2.0.0" }, + { name = "langchain-perplexity", specifier = ">=1.0.0,<2.0.0" }, { name = "langchain-qdrant", specifier = ">=1.1.0" }, + { name = "langchain-weaviate", specifier = ">=0.0.6" }, { name = "lxml", specifier = ">=5.0.0" }, ] @@ -11202,7 +11239,7 @@ wheels = [ [[package]] name = "opik" -version = "1.11.14" +version = "2.0.47" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "boto3-stubs", extra = ["bedrock-runtime"] }, @@ -11225,9 +11262,9 @@ dependencies = [ { name = "uuid6" }, { name = "watchfiles" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/1d/65/00eb1eb10e301e4cc7498193e0d0a34fb1204f300f0a62e2a9d1a8635aa2/opik-1.11.14.tar.gz", hash = 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"https://files.pythonhosted.org/packages/ac/44/6bc67dcc5c7fa0ffaea51990bdf6158846e968da434c5eb7272f0db57c8a/perplexityai-0.34.0-py3-none-any.whl", hash = "sha256:df08280b320ff755777d5d044f74d03fed44789337ad16ed6d75f07619e64a03", size = 131934, upload-time = "2026-05-13T22:52:48.425Z" }, +] + [[package]] name = "pexpect" version = "4.9.0" From 23f3453a65cccd99bad8b46255bdf3fa6e2e7ff6 Mon Sep 17 00:00:00 2001 From: "autofix-ci[bot]" <114827586+autofix-ci[bot]@users.noreply.github.com> Date: Tue, 26 May 2026 00:47:06 +0000 Subject: [PATCH 22/30] [autofix.ci] apply automated fixes --- src/lfx/src/lfx/_assets/component_index.json | 657 ++++++++++++++++++- 1 file changed, 654 insertions(+), 3 deletions(-) diff --git a/src/lfx/src/lfx/_assets/component_index.json b/src/lfx/src/lfx/_assets/component_index.json index 11332e9f5b..862f8152ec 100644 --- a/src/lfx/src/lfx/_assets/component_index.json +++ b/src/lfx/src/lfx/_assets/component_index.json @@ -98005,6 +98005,335 @@ } } ], + [ + "perplexity", + { + "PerplexityModel": { + "base_classes": [ + "LanguageModel", + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Generate text using Perplexity LLMs.", + "display_name": "Perplexity", + "documentation": "https://python.langchain.com/v0.2/docs/integrations/chat/perplexity/", + "edited": false, + "field_order": [ + "input_value", + "system_message", + "stream", + "model_name", + "max_tokens", + "api_key", + "temperature", + "top_p", + "n" + ], + "frozen": false, + "icon": "Perplexity", + "legacy": false, + "metadata": { + "code_hash": "df738acfd582", + "dependencies": { + "dependencies": [ + { + "name": "langchain_perplexity", + "version": "1.2.0" + }, + { + "name": "pydantic", + "version": "2.12.5" + }, + { + "name": "lfx", + "version": null + } + ], + "total_dependencies": 3 + }, + "keywords": [ + "model", + "llm", + "language model", + "large language model" + ], + "module": "lfx.components.perplexity.perplexity.PerplexityComponent" + }, + "minimized": false, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Model Response", + "group_outputs": false, + "method": "text_response", + "name": "text_output", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Language Model", + "group_outputs": false, + "method": "build_model", + "name": "model_output", + "selected": "LanguageModel", + "tool_mode": true, + "types": [ + "LanguageModel" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "api_key": { + "_input_type": "SecretStrInput", + "advanced": false, + "display_name": "Perplexity API Key", + "dynamic": false, + "info": "The Perplexity API Key to use for the Perplexity model.", + "input_types": [], + "load_from_db": true, + "name": "api_key", + "override_skip": false, + "password": true, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "code": { + "advanced": true, + "dynamic": true, + "fileTypes": [], + "file_path": "", + "info": "", + "list": false, + "load_from_db": false, + "multiline": true, + "name": "code", + "password": false, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "type": "code", + "value": "from langchain_perplexity import ChatPerplexity\nfrom pydantic.v1 import SecretStr\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.io import DropdownInput, FloatInput, IntInput, SecretStrInput, SliderInput\n\n\nclass PerplexityComponent(LCModelComponent):\n display_name = \"Perplexity\"\n description = \"Generate text using Perplexity LLMs.\"\n documentation = \"https://python.langchain.com/v0.2/docs/integrations/chat/perplexity/\"\n icon = \"Perplexity\"\n name = \"PerplexityModel\"\n\n inputs = [\n *LCModelComponent.get_base_inputs(),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=[\n \"llama-3.1-sonar-small-128k-online\",\n \"llama-3.1-sonar-large-128k-online\",\n \"llama-3.1-sonar-huge-128k-online\",\n \"llama-3.1-sonar-small-128k-chat\",\n \"llama-3.1-sonar-large-128k-chat\",\n \"llama-3.1-8b-instruct\",\n \"llama-3.1-70b-instruct\",\n ],\n value=\"llama-3.1-sonar-small-128k-online\",\n ),\n IntInput(name=\"max_tokens\", display_name=\"Max Output Tokens\", info=\"The maximum number of tokens to generate.\"),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Perplexity API Key\",\n info=\"The Perplexity API Key to use for the Perplexity model.\",\n advanced=False,\n required=True,\n ),\n SliderInput(\n name=\"temperature\", display_name=\"Temperature\", value=0.75, range_spec=RangeSpec(min=0, max=2, step=0.05)\n ),\n FloatInput(\n name=\"top_p\",\n display_name=\"Top P\",\n info=\"The maximum cumulative probability of tokens to consider when sampling.\",\n advanced=True,\n ),\n IntInput(\n name=\"n\",\n display_name=\"N\",\n info=\"Number of chat completions to generate for each prompt. \"\n \"Note that the API may not return the full n completions if duplicates are generated.\",\n advanced=True,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n api_key = SecretStr(self.api_key).get_secret_value()\n temperature = self.temperature\n model = self.model_name\n max_tokens = self.max_tokens\n top_p = self.top_p\n n = self.n\n\n return ChatPerplexity(\n model=model,\n temperature=temperature or 0.75,\n pplx_api_key=api_key,\n top_p=top_p or None,\n n=n or 1,\n max_tokens=max_tokens,\n )\n" + }, + "input_value": { + "_input_type": "MessageInput", + "advanced": false, + "display_name": "Input", + "dynamic": false, + "info": "", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "input_value", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "max_tokens": { + "_input_type": "IntInput", + "advanced": false, + "display_name": "Max Output Tokens", + "dynamic": false, + "info": "The maximum number of tokens to generate.", + "list": false, + "list_add_label": "Add More", + "name": "max_tokens", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 0 + }, + "model_name": { + "_input_type": "DropdownInput", + "advanced": false, + "combobox": false, + "dialog_inputs": {}, + "display_name": "Model Name", + "dynamic": false, + "external_options": {}, + "info": "", + "name": "model_name", + "options": [ + "llama-3.1-sonar-small-128k-online", + "llama-3.1-sonar-large-128k-online", + "llama-3.1-sonar-huge-128k-online", + "llama-3.1-sonar-small-128k-chat", + "llama-3.1-sonar-large-128k-chat", + "llama-3.1-8b-instruct", + "llama-3.1-70b-instruct" + ], + "options_metadata": [], + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "toggle": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "str", + "value": "llama-3.1-sonar-small-128k-online" + }, + "n": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "N", + "dynamic": false, + "info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.", + "list": false, + "list_add_label": "Add More", + "name": "n", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 0 + }, + "stream": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Stream", + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "list": false, + "list_add_label": "Add More", + "name": "stream", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": false + }, + "system_message": { + "_input_type": "MultilineInput", + "advanced": false, + "ai_enabled": false, + "copy_field": false, + "display_name": "System Message", + "dynamic": false, + "info": "System message to pass to the model.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "multiline": true, + "name": "system_message", + "override_skip": false, + "password": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "temperature": { + "_input_type": "SliderInput", + "advanced": false, + "display_name": "Temperature", + "dynamic": false, + "info": "", + "max_label": "", + "max_label_icon": "", + "min_label": "", + "min_label_icon": "", + "name": "temperature", + "override_skip": false, + "placeholder": "", + "range_spec": { + "max": 2.0, + "min": 0.0, + "step": 0.05, + "step_type": "float" + }, + "required": false, + "show": true, + "slider_buttons": false, + "slider_buttons_options": [], + "slider_input": false, + "title_case": false, + "tool_mode": false, + "track_in_telemetry": false, + "type": "slider", + "value": 0.75 + }, + "top_p": { + "_input_type": "FloatInput", + "advanced": true, + "display_name": "Top P", + "dynamic": false, + "info": "The maximum cumulative probability of tokens to consider when sampling.", + "list": false, + "list_add_label": "Add More", + "name": "top_p", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "float", + "value": "" + } + }, + "tool_mode": false + } + } + ], [ "pgvector", { @@ -114606,6 +114935,328 @@ } } ], + [ + "weaviate", + { + "Weaviate": { + "base_classes": [ + "JSON", + "Table" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Weaviate Vector Store with search capabilities", + "display_name": "Weaviate", + "documentation": "", + "edited": false, + "field_order": [ + "url", + "api_key", + "index_name", + "text_key", + "ingest_data", + "search_query", + "should_cache_vector_store", + "embedding", + "number_of_results", + "search_by_text" + ], + "frozen": false, + "icon": "Weaviate", + "legacy": false, + "metadata": { + "code_hash": "14aad330ac20", + "dependencies": { + "dependencies": [ + { + "name": "weaviate", + "version": "4.16.2" + }, + { + "name": "langchain_weaviate", + "version": "0.0.6" + }, + { + "name": "lfx", + "version": null + } + ], + "total_dependencies": 3 + }, + "module": "lfx.components.weaviate.weaviate.WeaviateVectorStoreComponent" + }, + "minimized": false, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Search Results", + "group_outputs": false, + "method": "search_documents", + "name": "search_results", + "selected": "JSON", + "tool_mode": true, + "types": [ + "JSON" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Table", + "group_outputs": false, + "method": "as_dataframe", + "name": "dataframe", + "selected": "Table", + "tool_mode": true, + "types": [ + "Table" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "api_key": { + "_input_type": "SecretStrInput", + "advanced": false, + "display_name": "API Key", + "dynamic": false, + "info": "", + "input_types": [], + "load_from_db": true, + "name": "api_key", + "override_skip": false, + "password": true, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "code": { + "advanced": true, + "dynamic": true, + "fileTypes": [], + "file_path": "", + "info": "", + "list": false, + "load_from_db": false, + "multiline": true, + "name": "code", + "password": false, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "type": "code", + "value": "import weaviate\nfrom langchain_weaviate import WeaviateVectorStore\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.helpers.data import docs_to_data\nfrom lfx.io import BoolInput, HandleInput, IntInput, SecretStrInput, StrInput\nfrom lfx.schema.data import Data\n\n\nclass WeaviateVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Weaviate\"\n description = \"Weaviate Vector Store with search capabilities\"\n name = \"Weaviate\"\n icon = \"Weaviate\"\n\n inputs = [\n StrInput(name=\"url\", display_name=\"Weaviate URL\", value=\"http://localhost:8080\", required=True),\n SecretStrInput(name=\"api_key\", display_name=\"API Key\", required=False),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n required=True,\n info=\"Requires capitalized index name.\",\n ),\n StrInput(name=\"text_key\", display_name=\"Text Key\", value=\"text\", advanced=True),\n *LCVectorStoreComponent.inputs,\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n BoolInput(name=\"search_by_text\", display_name=\"Search By Text\", advanced=True),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> WeaviateVectorStore:\n if self.api_key:\n auth_config = weaviate.AuthApiKey(api_key=self.api_key)\n client = weaviate.Client(url=self.url, auth_client_secret=auth_config)\n else:\n client = weaviate.Client(url=self.url)\n\n if self.index_name != self.index_name.capitalize():\n msg = f\"Weaviate requires the index name to be capitalized. Use: {self.index_name.capitalize()}\"\n raise ValueError(msg)\n\n # Convert DataFrame to Data if needed using parent's method\n self.ingest_data = self._prepare_ingest_data()\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents and self.embedding:\n return WeaviateVectorStore.from_documents(\n client=client,\n index_name=self.index_name,\n documents=documents,\n embedding=self.embedding,\n by_text=self.search_by_text,\n )\n\n return WeaviateVectorStore(\n client=client,\n index_name=self.index_name,\n text_key=self.text_key,\n embedding=self.embedding,\n by_text=self.search_by_text,\n )\n\n def search_documents(self) -> list[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n return []\n" + }, + "embedding": { + "_input_type": "HandleInput", + "advanced": false, + "display_name": "Embedding", + "dynamic": false, + "info": "", + "input_types": [ + "Embeddings" + ], + "list": false, + "list_add_label": "Add More", + "name": "embedding", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "other", + "value": "" + }, + "index_name": { + "_input_type": "StrInput", + "advanced": false, + "display_name": "Index Name", + "dynamic": false, + "info": "Requires capitalized index name.", + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "index_name", + "override_skip": false, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "ingest_data": { + "_input_type": "HandleInput", + "advanced": false, + "display_name": "Ingest Data", + "dynamic": false, + "info": "", + "input_types": [ + "Data", + "DataFrame", + "Table" + ], + "list": true, + "list_add_label": "Add More", + "name": "ingest_data", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "other", + "value": "" + }, + "number_of_results": { + "_input_type": "IntInput", + "advanced": true, + "display_name": "Number of Results", + "dynamic": false, + "info": "Number of results to return.", + "list": false, + "list_add_label": "Add More", + "name": "number_of_results", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "int", + "value": 4 + }, + "search_by_text": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Search By Text", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "name": "search_by_text", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": false + }, + "search_query": { + "_input_type": "QueryInput", + "advanced": false, + "display_name": "Search Query", + "dynamic": false, + "info": "Enter a query to run a similarity search.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "search_query", + "override_skip": false, + "placeholder": "Enter a query...", + "required": false, + "show": true, + "title_case": false, + "tool_mode": true, + "trace_as_input": true, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "query", + "value": "" + }, + "should_cache_vector_store": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Cache Vector Store", + "dynamic": false, + "info": "If True, the vector store will be cached for the current build of the component. This is useful for components that have multiple output methods and want to share the same vector store.", + "list": false, + "list_add_label": "Add More", + "name": "should_cache_vector_store", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": true + }, + "text_key": { + "_input_type": "StrInput", + "advanced": true, + "display_name": "Text Key", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "text_key", + "override_skip": false, + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "text" + }, + "url": { + "_input_type": "StrInput", + "advanced": false, + "display_name": "Weaviate URL", + "dynamic": false, + "info": "", + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "url", + "override_skip": false, + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "http://localhost:8080" + } + }, + "tool_mode": false + } + } + ], [ "wikipedia", { @@ -117188,9 +117839,9 @@ ] ], "metadata": { - "num_components": 352, - "num_modules": 95 + "num_components": 354, + "num_modules": 97 }, - "sha256": "d60b45d05f808dc772d0cf7056ce022abec53ee15cdc5deb6c968804fb91a7a2", + "sha256": "790bbe7625c78d5d6e6f43ed8bf86504397b5cc8e702b6b4773f5b4e0b2968c5", "version": "0.4.4" } From e192f91eeb950697963a841dbd21548e204374ca Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Mon, 25 May 2026 21:49:52 -0400 Subject: [PATCH 23/30] fix: ChatBedrock playwright failures --- src/frontend/tests/assets/flow_group_test.json | 2 +- src/frontend/tests/core/unit/dropdownComponent.spec.ts | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/frontend/tests/assets/flow_group_test.json b/src/frontend/tests/assets/flow_group_test.json index f2fcd2e946..108b0f7e98 100644 --- a/src/frontend/tests/assets/flow_group_test.json +++ b/src/frontend/tests/assets/flow_group_test.json @@ -289,7 +289,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langchain.llms import BaseLLM\nfrom langchain_community.chat_models.openai import ChatOpenAI\nfrom lfx.custom import CustomComponent\nfrom lfx.field_typing import BaseLanguageModel, NestedDict\n\n\nclass ChatOpenAIComponent(CustomComponent):\n display_name = \"ChatOpenAI\"\n description = \"`OpenAI` Chat large language models API.\"\n icon = \"OpenAI\"\n\n def build_config(self):\n return {\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": False,\n \"required\": False,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n \"required\": False,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"required\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": False,\n \"required\": False,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"advanced\": False,\n \"required\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"required\": False,\n \"value\": 0.7,\n },\n }\n\n def build(\n self,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n model_name: str = \"gpt-4-1106-preview\",\n openai_api_base: Optional[str] = None,\n openai_api_key: Optional[str] = None,\n temperature: float = 0.7,\n ) -> Union[BaseLanguageModel, BaseLLM]:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n return ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n", + "value": "from typing import Optional, Union\n\nfrom langchain.llms import BaseLLM\nfrom langchain_openai import ChatOpenAI\nfrom lfx.custom import CustomComponent\nfrom lfx.field_typing import BaseLanguageModel, NestedDict\n\n\nclass ChatOpenAIComponent(CustomComponent):\n display_name = \"ChatOpenAI\"\n description = \"`OpenAI` Chat large language models API.\"\n icon = \"OpenAI\"\n\n def build_config(self):\n return {\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": False,\n \"required\": False,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n \"required\": False,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"required\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": False,\n \"required\": False,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"advanced\": False,\n \"required\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"required\": False,\n \"value\": 0.7,\n },\n }\n\n def build(\n self,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n model_name: str = \"gpt-4-1106-preview\",\n openai_api_base: Optional[str] = None,\n openai_api_key: Optional[str] = None,\n temperature: float = 0.7,\n ) -> Union[BaseLanguageModel, BaseLLM]:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n return ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/frontend/tests/core/unit/dropdownComponent.spec.ts b/src/frontend/tests/core/unit/dropdownComponent.spec.ts index 8a7509c81d..d7b6be2bb7 100644 --- a/src/frontend/tests/core/unit/dropdownComponent.spec.ts +++ b/src/frontend/tests/core/unit/dropdownComponent.spec.ts @@ -143,7 +143,7 @@ test( await page.getByTestId("code-button-modal").last().click(); await page.locator("textarea").press("Control+a"); - const emptyOptionsCode = `from langchain_community.chat_models.bedrock import BedrockChat + const emptyOptionsCode = `from langchain_aws import ChatBedrock from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent @@ -234,7 +234,7 @@ class AmazonBedrockComponent(LCModelComponent): cache = self.cache stream = self.stream try: - output = BedrockChat( + output = ChatBedrock( credentials_profile_name=credentials_profile_name, model_id=model_id, region_name=region_name, From 0a5f25f32b3aba5e378e8ea3c10da80f4510a565 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Tue, 26 May 2026 00:00:12 -0400 Subject: [PATCH 24/30] chore: port Remove the diskcache dependency port of https://github.com/langflow-ai/langflow/pull/12953 --- docs/docs/Develop/memory.mdx | 105 ++++++++------- pyproject.toml | 1 - .../base/langflow/services/cache/disk.py | 95 ------------- .../base/langflow/services/cache/factory.py | 6 - .../langflow/services/flow_events/factory.py | 11 +- .../langflow/services/flow_events/service.py | 114 ++++++++++++---- src/backend/base/pyproject.toml | 5 - src/backend/base/uv.lock | 123 ++--------------- .../tests/unit/test_flow_events_service.py | 34 ++++- src/lfx/src/lfx/services/settings/base.py | 6 +- uv.lock | 125 ++---------------- 11 files changed, 202 insertions(+), 423 deletions(-) delete mode 100644 src/backend/base/langflow/services/cache/disk.py diff --git a/docs/docs/Develop/memory.mdx b/docs/docs/Develop/memory.mdx index 49743931f1..65e92b5153 100644 --- a/docs/docs/Develop/memory.mdx +++ b/docs/docs/Develop/memory.mdx @@ -14,26 +14,26 @@ Langflow's default storage option is a [SQLite](https://www.sqlite.org/) databas The default storage path depends on your operating system and installation method: - **Langflow Desktop**: - - **macOS**: `/Users//.langflow/data/database.db` - - **Windows**: `C:\Users\\AppData\Roaming\com.LangflowDesktop\data\database.db` + - **macOS**: `/Users//.langflow/data/database.db` + - **Windows**: `C:\Users\\AppData\Roaming\com.LangflowDesktop\data\database.db` - **Langflow OSS** - - **macOS/Windows/Linux/WSL with `uv pip install`**: `/lib/python3.12/site-packages/langflow/langflow.db` (Python version can vary. Database isn't shared between virtual environments because it is tied to the venv path.) - - **macOS/Windows/Linux/WSL with `git clone`**: `/src/backend/base/langflow/langflow.db` + - **macOS/Windows/Linux/WSL with `uv pip install`**: `/lib/python3.12/site-packages/langflow/langflow.db` (Python version can vary. Database isn't shared between virtual environments because it is tied to the venv path.) + - **macOS/Windows/Linux/WSL with `git clone`**: `/src/backend/base/langflow/langflow.db` Langflow offers a few alternatives to the default database path: -* **Config directory**: Set `LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True` to store the database in your Langflow config directory as set in [`LANGFLOW_CONFIG_DIR`](/logging). +- **Config directory**: Set `LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True` to store the database in your Langflow config directory as set in [`LANGFLOW_CONFIG_DIR`](/logging). -* **External PostgreSQL database**: You can use an external PostgreSQL database for all of your Langflow storage. -For more information, see [Configure external memory](#configure-external-memory) +- **External PostgreSQL database**: You can use an external PostgreSQL database for all of your Langflow storage. + For more information, see [Configure external memory](#configure-external-memory) - External storage can be useful if you want to preserve the data after uninstalling Langflow or to share the same database between multiple virtual environments. + External storage can be useful if you want to preserve the data after uninstalling Langflow or to share the same database between multiple virtual environments. -* **Separate chat memory**: You can selectively use external storage for chat memory only, separate from other Langflow storage. -For more information, see [Store chat memory](#store-chat-memory). +- **Separate chat memory**: You can selectively use external storage for chat memory only, separate from other Langflow storage. + For more information, see [Store chat memory](#store-chat-memory). -* **No database**: To disable all database operations and run a no-op session, set `LANGFLOW_USE_NOOP_DATABASE=True` in your [Langflow environment variables](/environment-variables). -This is useful for testing when you don't want to persist any data. +- **No database**: To disable all database operations and run a no-op session, set `LANGFLOW_USE_NOOP_DATABASE=True` in your [Langflow environment variables](/environment-variables). + This is useful for testing when you don't want to persist any data. ## Langflow database tables @@ -78,31 +78,30 @@ LANGFLOW_DATABASE_URL=postgresql://user:password@localhost:5432/langflow To fine-tune your database connection pool and timeout settings, you can set the following additional environment variables: -* `LANGFLOW_DATABASE_CONNECTION_RETRY`: Whether to retry lost connections to your Langflow database. If `true`, Langflow tries to connect to the database again if the connection fails. Default: `false`. +- `LANGFLOW_DATABASE_CONNECTION_RETRY`: Whether to retry lost connections to your Langflow database. If `true`, Langflow tries to connect to the database again if the connection fails. Default: `false`. -* `LANGFLOW_DB_CONNECT_TIMEOUT`: The number of seconds to wait before giving up on a lock to be released or establishing a connection to the database. This may be separate from the `pool_timeout` in `LANGFLOW_DB_CONNECTION_SETTINGS`. Default: 30. +- `LANGFLOW_DB_CONNECT_TIMEOUT`: The number of seconds to wait before giving up on a lock to be released or establishing a connection to the database. This may be separate from the `pool_timeout` in `LANGFLOW_DB_CONNECTION_SETTINGS`. Default: 30. -* `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace identifier for PostgreSQL advisory lock during migrations. If not provided, a hash of the database URL will be used. Useful when multiple Langflow instances share the same database and need coordinated migration locking +- `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace identifier for PostgreSQL advisory lock during migrations. If not provided, a hash of the database URL will be used. Useful when multiple Langflow instances share the same database and need coordinated migration locking -* `LANGFLOW_DB_CONNECTION_SETTINGS`: A JSON dictionary containing the following database connection pool settings: +- `LANGFLOW_DB_CONNECTION_SETTINGS`: A JSON dictionary containing the following database connection pool settings: + - `pool_size`: The base number of connections to keep open in the connection pool. Default: 20. + - `max_overflow`: Maximum number of connections that can be created in excess of `pool_size` if needed. Default: 30. + - `pool_timeout`: Number of seconds to wait for a connection from the pool before timing out. Default: 30. + - `pool_pre_ping`: If `true`, the pool tests connections for liveness upon each checkout. Default: `true`. + - `pool_recycle`: Number of seconds after which a connection is automatically recycled. Default: 1800 (30 minutes). + - `echo`: If `true`, SQL queries are logged for debugging purposes. Default: `false`. - - `pool_size`: The base number of connections to keep open in the connection pool. Default: 20. - - `max_overflow`: Maximum number of connections that can be created in excess of `pool_size` if needed. Default: 30. - - `pool_timeout`: Number of seconds to wait for a connection from the pool before timing out. Default: 30. - - `pool_pre_ping`: If `true`, the pool tests connections for liveness upon each checkout. Default: `true`. - - `pool_recycle`: Number of seconds after which a connection is automatically recycled. Default: 1800 (30 minutes). - - `echo`: If `true`, SQL queries are logged for debugging purposes. Default: `false`. + For example: - For example: + ```text + LANGFLOW_DB_CONNECTION_SETTINGS='{"pool_size": 20, "max_overflow": 30, "pool_timeout": 30, "pool_pre_ping": true, "pool_recycle": 1800, "echo": false}' + ``` - ```text - LANGFLOW_DB_CONNECTION_SETTINGS='{"pool_size": 20, "max_overflow": 30, "pool_timeout": 30, "pool_pre_ping": true, "pool_recycle": 1800, "echo": false}' - ``` + Don't use the deprecated environment variables `LANGFLOW_DB_POOL_SIZE` or `LANGFLOW_DB_MAX_OVERFLOW`. + Instead, use `pool_size` and `max_overflow` in `LANGFLOW_DB_CONNECTION_SETTINGS`. - Don't use the deprecated environment variables `LANGFLOW_DB_POOL_SIZE` or `LANGFLOW_DB_MAX_OVERFLOW`. - Instead, use `pool_size` and `max_overflow` in `LANGFLOW_DB_CONNECTION_SETTINGS`. - -* `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace for PostgreSQL advisory locks used during database migrations. This is useful when running multiple Langflow instances that share the same PostgreSQL database. Each instance should use a unique namespace to avoid conflicts. If not set, Langflow uses a default namespace. This setting only applies when using PostgreSQL as your database backend. +- `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace for PostgreSQL advisory locks used during database migrations. This is useful when running multiple Langflow instances that share the same PostgreSQL database. Each instance should use a unique namespace to avoid conflicts. If not set, Langflow uses a default namespace. This setting only applies when using PostgreSQL as your database backend. ## Configure cache memory @@ -117,15 +116,15 @@ Langflow officially supports only the default asynchronous, in-memory cache, whi Other cache options, such as Redis, are experimental and can change without notice. If you want to use a non-default cache setting, you can use the following environment variables: -| Variable | Type | Default | Description | -|----------|------|---------|-------------| -| `LANGFLOW_CACHE_TYPE` | String | `async` | Set the cache type for Langflow's internal caching system. Possible values: `async`, `redis`, `memory`, `disk`. If you set the type to `redis`, then you must also set the `LANGFLOW_REDIS_*` environment variables. | -| `LANGFLOW_LANGCHAIN_CACHE` | String | `InMemoryCache` | Set the cache storage type for the LangChain caching system (a Langflow dependency), either `InMemoryCache` or `SQLiteCache`. | -| `LANGFLOW_REDIS_HOST` | String | `localhost` | Redis server hostname if `LANGFLOW_CACHE_TYPE=redis`. | -| `LANGFLOW_REDIS_PORT` | Integer | `6379` | Redis server port if `LANGFLOW_CACHE_TYPE=redis`. | -| `LANGFLOW_REDIS_DB` | Integer | `0` | Redis database number if `LANGFLOW_CACHE_TYPE=redis`. | -| `LANGFLOW_REDIS_CACHE_EXPIRE` | Integer | `3600` | Cache expiration time in seconds if `LANGFLOW_CACHE_TYPE=redis`. | -| `LANGFLOW_REDIS_PASSWORD` | String | Not set | Optional password for Redis authentication if `LANGFLOW_CACHE_TYPE=redis`. | +| Variable | Type | Default | Description | +| ----------------------------- | ------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `LANGFLOW_CACHE_TYPE` | String | `async` | Set the cache type for Langflow's internal caching system. Possible values: `async`, `redis`, `memory`. If you set the type to `redis`, then you must also set the `LANGFLOW_REDIS_*` environment variables. The `disk` backend was removed in 1.9.4 — switch to `async` for in-memory or `redis` for cross-worker. | +| `LANGFLOW_LANGCHAIN_CACHE` | String | `InMemoryCache` | Set the cache storage type for the LangChain caching system (a Langflow dependency), either `InMemoryCache` or `SQLiteCache`. | +| `LANGFLOW_REDIS_HOST` | String | `localhost` | Redis server hostname if `LANGFLOW_CACHE_TYPE=redis`. | +| `LANGFLOW_REDIS_PORT` | Integer | `6379` | Redis server port if `LANGFLOW_CACHE_TYPE=redis`. | +| `LANGFLOW_REDIS_DB` | Integer | `0` | Redis database number if `LANGFLOW_CACHE_TYPE=redis`. | +| `LANGFLOW_REDIS_CACHE_EXPIRE` | Integer | `3600` | Cache expiration time in seconds if `LANGFLOW_CACHE_TYPE=redis`. | +| `LANGFLOW_REDIS_PASSWORD` | String | Not set | Optional password for Redis authentication if `LANGFLOW_CACHE_TYPE=redis`. | ## Store chat memory @@ -161,27 +160,27 @@ For example, if you use user IDs as session IDs, then each user's chat history i Where and how chat memory is stored depends on the components used in your flow: -* **Agent component**: This component has built-in chat memory that is enabled by default. -This memory allows the agent to retrieve and reference messages from previous conversations associated with the same session ID. -All messages are stored in [Langflow storage](#storage-options-and-paths), and the component provides minimal memory configuration options, such as the number of messages to retrieve. +- **Agent component**: This component has built-in chat memory that is enabled by default. + This memory allows the agent to retrieve and reference messages from previous conversations associated with the same session ID. + All messages are stored in [Langflow storage](#storage-options-and-paths), and the component provides minimal memory configuration options, such as the number of messages to retrieve. - The **Agent** component's built-in chat memory is sufficient for most use cases. + The **Agent** component's built-in chat memory is sufficient for most use cases. - If you want to use external chat memory storage, retrieve memories outside the context of a chat, or use chat memory with a language model component (not an agent), you must use the **Message History** component (with or without a third-party chat memory component). + If you want to use external chat memory storage, retrieve memories outside the context of a chat, or use chat memory with a language model component (not an agent), you must use the **Message History** component (with or without a third-party chat memory component). -* **Message History component**: By default, this component stores and retrieves memories from Langflow storage, unless you attach a third-party chat memory component. It provides a few more options for sorting and filtering memories, although most of these options are also built-in to the **Agent** component as configurable or fixed parameters. +- **Message History component**: By default, this component stores and retrieves memories from Langflow storage, unless you attach a third-party chat memory component. It provides a few more options for sorting and filtering memories, although most of these options are also built-in to the **Agent** component as configurable or fixed parameters. - You can use the **Message History** component with or without a language model or agent. - For example, if you need to retrieve data from memories outside of chat, you can use the **Message History** component to fetch that data directly from your chat memory database without feeding it into a chat. + You can use the **Message History** component with or without a language model or agent. + For example, if you need to retrieve data from memories outside of chat, you can use the **Message History** component to fetch that data directly from your chat memory database without feeding it into a chat. -* **Third-party chat memory components**: Use one of these components only if you need to store or retrieve chat memories from a dedicated external chat memory database. -Typically, this is necessary only if you have specific storage needs that aren't met by Langflow storage. -For example, if you want to manage chat memory data by directly working with the database, or if you want to use a different database than the default Langflow storage. +- **Third-party chat memory components**: Use one of these components only if you need to store or retrieve chat memories from a dedicated external chat memory database. + Typically, this is necessary only if you have specific storage needs that aren't met by Langflow storage. + For example, if you want to manage chat memory data by directly working with the database, or if you want to use a different database than the default Langflow storage. For more information and examples, see [**Message History** component](/message-history) and [Agent memory](/agents#agent-memory). ## See also -* [Langflow file management](/concepts-file-management) -* [Langflow logs](/logging) -* [Langflow environment variables](/environment-variables) \ No newline at end of file +- [Langflow file management](/concepts-file-management) +- [Langflow logs](/logging) +- [Langflow environment variables](/environment-variables) diff --git a/pyproject.toml b/pyproject.toml index ae8a2d8fa8..b7631342db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -115,7 +115,6 @@ cassio = [ "cassio>=0.1.7" ] local = [ - "llama-cpp-python~=0.2.0", "sentence-transformers>=2.3.1", "ctransformers>=0.2.10" ] diff --git a/src/backend/base/langflow/services/cache/disk.py b/src/backend/base/langflow/services/cache/disk.py deleted file mode 100644 index 04f9bc9c2c..0000000000 --- a/src/backend/base/langflow/services/cache/disk.py +++ /dev/null @@ -1,95 +0,0 @@ -import asyncio -import pickle -import time -from typing import Generic - -from diskcache import Cache -from lfx.log.logger import logger -from lfx.services.cache.utils import CACHE_MISS - -from langflow.services.cache.base import AsyncBaseCacheService, AsyncLockType - - -class AsyncDiskCache(AsyncBaseCacheService, Generic[AsyncLockType]): - def __init__(self, cache_dir, max_size=None, expiration_time=3600) -> None: - self.cache = Cache(cache_dir) - # Let's clear the cache for now to maintain a similar - # behavior as the in-memory cache - # Later we should implement endpoints for the frontend to grab - # output logs from the cache - if len(self.cache) > 0: - self.cache.clear() - self.lock = asyncio.Lock() - self.max_size = max_size - self.expiration_time = expiration_time - - async def get(self, key, lock: asyncio.Lock | None = None): - if not lock: - async with self.lock: - return await asyncio.to_thread(self._get, key) - else: - return await asyncio.to_thread(self._get, key) - - def _get(self, key): - item = self.cache.get(key, default=None) - if item: - if time.time() - item["time"] < self.expiration_time: - self.cache.touch(key) # Refresh the expiry time - return pickle.loads(item["value"]) if isinstance(item["value"], bytes) else item["value"] - logger.info(f"Cache item for key '{key}' has expired and will be deleted.") - self.cache.delete(key) # Log before deleting the expired item - return CACHE_MISS - - async def set(self, key, value, lock: asyncio.Lock | None = None) -> None: - if not lock: - async with self.lock: - await self._set(key, value) - else: - await self._set(key, value) - - async def _set(self, key, value) -> None: - if self.max_size and len(self.cache) >= self.max_size: - await asyncio.to_thread(self.cache.cull) - item = {"value": pickle.dumps(value) if not isinstance(value, str | bytes) else value, "time": time.time()} - await asyncio.to_thread(self.cache.set, key, item) - - async def delete(self, key, lock: asyncio.Lock | None = None) -> None: - if not lock: - async with self.lock: - await self._delete(key) - else: - await self._delete(key) - - async def _delete(self, key) -> None: - await asyncio.to_thread(self.cache.delete, key) - - async def clear(self, lock: asyncio.Lock | None = None) -> None: - if not lock: - async with self.lock: - await self._clear() - else: - await self._clear() - - async def _clear(self) -> None: - await asyncio.to_thread(self.cache.clear) - - async def upsert(self, key, value, lock: asyncio.Lock | None = None) -> None: - if not lock: - async with self.lock: - await self._upsert(key, value) - else: - await self._upsert(key, value) - - async def _upsert(self, key, value) -> None: - existing_value = await asyncio.to_thread(self._get, key) - if existing_value is not CACHE_MISS and isinstance(existing_value, dict) and isinstance(value, dict): - existing_value.update(value) - value = existing_value - await self.set(key, value) - - async def contains(self, key) -> bool: - return await asyncio.to_thread(self.cache.__contains__, key) - - async def teardown(self) -> None: - # Clean up the cache directory - self.cache.clear(retry=True) diff --git a/src/backend/base/langflow/services/cache/factory.py b/src/backend/base/langflow/services/cache/factory.py index b0f08c15e6..8ce6eaba74 100644 --- a/src/backend/base/langflow/services/cache/factory.py +++ b/src/backend/base/langflow/services/cache/factory.py @@ -5,7 +5,6 @@ from typing import TYPE_CHECKING from lfx.log.logger import logger from typing_extensions import override -from langflow.services.cache.disk import AsyncDiskCache from langflow.services.cache.service import AsyncInMemoryCache, CacheService, RedisCache, ThreadingInMemoryCache from langflow.services.factory import ServiceFactory @@ -36,9 +35,4 @@ class CacheServiceFactory(ServiceFactory): return ThreadingInMemoryCache(expiration_time=settings_service.settings.cache_expire) if settings_service.settings.cache_type == "async": return AsyncInMemoryCache(expiration_time=settings_service.settings.cache_expire) - if settings_service.settings.cache_type == "disk": - return AsyncDiskCache( - cache_dir=settings_service.settings.config_dir, - expiration_time=settings_service.settings.cache_expire, - ) return None diff --git a/src/backend/base/langflow/services/flow_events/factory.py b/src/backend/base/langflow/services/flow_events/factory.py index af6db6fda8..1bdc6916da 100644 --- a/src/backend/base/langflow/services/flow_events/factory.py +++ b/src/backend/base/langflow/services/flow_events/factory.py @@ -1,13 +1,20 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + from typing_extensions import override from langflow.services.factory import ServiceFactory from langflow.services.flow_events.service import FlowEventsService +if TYPE_CHECKING: + from lfx.services.settings.service import SettingsService + class FlowEventsServiceFactory(ServiceFactory): def __init__(self) -> None: super().__init__(FlowEventsService) @override - def create(self): - return FlowEventsService() + def create(self, settings_service: SettingsService): + return FlowEventsService(cache_dir=settings_service.settings.cache_dir) diff --git a/src/backend/base/langflow/services/flow_events/service.py b/src/backend/base/langflow/services/flow_events/service.py index ec4498d363..60594d5bea 100644 --- a/src/backend/base/langflow/services/flow_events/service.py +++ b/src/backend/base/langflow/services/flow_events/service.py @@ -1,14 +1,13 @@ from __future__ import annotations -import json +import sqlite3 import tempfile +import threading import time -from dataclasses import asdict, dataclass +from dataclasses import dataclass from pathlib import Path from typing import Literal, get_args -from diskcache import Cache - from langflow.services.base import Service FLOW_EVENT_TYPES = Literal[ @@ -29,12 +28,25 @@ class FlowEvent: summary: str = "" -class FlowEventsService(Service): - """Disk-backed event queue keyed by flow_id. +_SCHEMA = """ +CREATE TABLE IF NOT EXISTS flow_events ( + flow_id TEXT NOT NULL, + ts REAL NOT NULL, + type TEXT NOT NULL, + summary TEXT NOT NULL DEFAULT '', + expires_at REAL NOT NULL +); +CREATE INDEX IF NOT EXISTS idx_flow_events_flow_ts ON flow_events(flow_id, ts); +CREATE INDEX IF NOT EXISTS idx_flow_events_expires ON flow_events(expires_at); +""" - Uses diskcache for cross-worker visibility (multiple uvicorn/gunicorn workers - share the same SQLite-backed cache directory). TTL-based cleanup is handled - by diskcache's built-in expiry. + +class FlowEventsService(Service): + """SQLite-backed event queue keyed by flow_id. + + Uses Python's stdlib sqlite3 in WAL mode for cross-worker visibility (multiple + uvicorn/gunicorn workers share the same on-disk database file). TTL-based + cleanup is performed lazily on each write and read. Limitations: - Events are ephemeral: lost on disk cleanup or container restart. @@ -49,28 +61,67 @@ class FlowEventsService(Service): SETTLE_TIMEOUT: float = 10.0 MAX_EVENTS_PER_FLOW: int = 1000 + _VALID_EVENT_TYPES: frozenset[str] = frozenset(get_args(FLOW_EVENT_TYPES)) + def __init__(self, cache_dir: str | Path | None = None) -> None: if cache_dir is None: cache_dir = Path(tempfile.gettempdir()) / "langflow_flow_events" - self._cache = Cache(str(cache_dir)) - - _VALID_EVENT_TYPES: frozenset[str] = frozenset(get_args(FLOW_EVENT_TYPES)) + cache_dir = Path(cache_dir) + cache_dir.mkdir(parents=True, exist_ok=True) + self._db_path = cache_dir / "flow_events.sqlite" + # isolation_level=None puts pysqlite in autocommit mode so we manage + # transactions explicitly via BEGIN/COMMIT. + self._conn = sqlite3.connect( + str(self._db_path), + isolation_level=None, + check_same_thread=False, + timeout=5.0, + ) + # Serialize use of the single connection across asyncio tasks / FastAPI threads + # in this worker. WAL handles cross-worker concurrency. + self._lock = threading.Lock() + with self._lock: + self._conn.execute("PRAGMA journal_mode=WAL") + self._conn.execute("PRAGMA synchronous=NORMAL") + self._conn.execute("PRAGMA busy_timeout=5000") + self._conn.executescript(_SCHEMA) def append(self, flow_id: str, event_type: str, summary: str = "") -> FlowEvent: if event_type not in self._VALID_EVENT_TYPES: msg = f"Invalid event type: {event_type!r}. Must be one of {sorted(self._VALID_EVENT_TYPES)}" raise ValueError(msg) - event = FlowEvent(type=event_type, timestamp=time.time(), summary=summary) - key = f"flow_events:{flow_id}" + now = time.time() + event = FlowEvent(type=event_type, timestamp=now, summary=summary) + expires_at = now + self.TTL_SECONDS - with self._cache.transact(): - raw = self._cache.get(key, default=None) - events: list[dict] = json.loads(raw) if raw else [] - events.append(asdict(event)) - # Trim to max size - if len(events) > self.MAX_EVENTS_PER_FLOW: - events = events[-self.MAX_EVENTS_PER_FLOW :] - self._cache.set(key, json.dumps(events), expire=self.TTL_SECONDS) + with self._lock: + self._conn.execute("BEGIN IMMEDIATE") + try: + # Opportunistic TTL cleanup across all flows. + self._conn.execute("DELETE FROM flow_events WHERE expires_at < ?", (now,)) + self._conn.execute( + "INSERT INTO flow_events (flow_id, ts, type, summary, expires_at) VALUES (?, ?, ?, ?, ?)", + (flow_id, now, event_type, summary, expires_at), + ) + # Bound per-flow size: keep only the most recent MAX_EVENTS_PER_FLOW rows. + # Order by (ts, rowid) so events appended in the same microsecond have a + # stable, insertion-aware ordering when picking which rows to drop. + self._conn.execute( + """ + DELETE FROM flow_events + WHERE rowid IN ( + SELECT rowid FROM flow_events + WHERE flow_id = ? + ORDER BY ts DESC, rowid DESC + LIMIT -1 OFFSET ? + ) + """, + (flow_id, self.MAX_EVENTS_PER_FLOW), + ) + self._conn.execute("COMMIT") + except Exception: + self._conn.execute("ROLLBACK") + raise return event @@ -82,9 +133,19 @@ class FlowEventsService(Service): - A flow_settled event exists after `since`, OR - The most recent event is older than SETTLE_TIMEOUT seconds. """ - key = f"flow_events:{flow_id}" - raw = self._cache.get(key, default=None) - all_events = [FlowEvent(**e) for e in json.loads(raw)] if raw else [] + now = time.time() + with self._lock: + rows = self._conn.execute( + """ + SELECT type, ts, summary + FROM flow_events + WHERE flow_id = ? AND expires_at >= ? + ORDER BY ts ASC, rowid ASC + """, + (flow_id, now), + ).fetchall() + + all_events = [FlowEvent(type=r[0], timestamp=r[1], summary=r[2]) for r in rows] after = [e for e in all_events if e.timestamp > since] @@ -100,4 +161,5 @@ class FlowEventsService(Service): return after, settled async def teardown(self) -> None: - self._cache.close() + with self._lock: + self._conn.close() diff --git a/src/backend/base/pyproject.toml b/src/backend/base/pyproject.toml index 1f806ee5e0..4221e07afb 100644 --- a/src/backend/base/pyproject.toml +++ b/src/backend/base/pyproject.toml @@ -77,7 +77,6 @@ dependencies = [ "filelock>=3.20.1,<4.0.0", "grandalf>=0.8.0,<1.0.0", "spider-client>=0.0.27,<1.0.0", - "diskcache>=5.6.3,<6.0.0", "clickhouse-connect==0.7.19", "assemblyai>=0.33.0,<1.0.0", "fastapi-pagination>=0.13.1,<1.0.0", @@ -182,7 +181,6 @@ postgresql = [ ] local = [ - "llama-cpp-python>=0.2.0", "sentence-transformers>=2.0.0", "ctransformers>=0.2" ] @@ -234,7 +232,6 @@ sambanova = ["langchain-sambanova~=1.0.0"] groq = ["langchain-groq~=1.1.1"] # Individual local LLM providers -llama-cpp = ["llama-cpp-python~=0.2.0"] sentence-transformers = ["sentence-transformers>=2.3.1"] ctransformers = ["ctransformers>=0.2.10"] @@ -252,7 +249,6 @@ pytube = ["pytube==15.0.0"] # Individual agent frameworks # crewai is commented out due to httpx version conflict # crewai = ["crewai>=0.126.0"] -dspy = ["dspy-ai==2.5.41"] smolagents = ["smolagents>=1.8.0"] # Individual tools @@ -410,7 +406,6 @@ complete = [ "langflow-base[docx]", "langflow-base[pytube]", # "langflow-base[crewai]", # commented out due to httpx version conflict - 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{ name = "dspy-ai" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, @@ -7038,9 +6977,6 @@ docling = [ docx = [ { name = "python-docx" }, ] -dspy = [ - { name = "dspy-ai" }, -] duckduckgo = [ { name = "ddgs" }, ] @@ -7132,12 +7068,8 @@ lark = [ litellm = [ { name = "litellm" }, ] -llama-cpp = [ - { name = "llama-cpp-python" }, -] local = [ { name = "ctransformers" }, - { name = "llama-cpp-python" }, { name = "sentence-transformers" }, ] markdown = [ @@ -7361,11 +7293,9 @@ requires-dist = [ { name = "datasets", marker = "extra == 'datasets'", specifier = ">2.14.7,<5.0.0" }, { name = "ddgs", marker = "extra == 'duckduckgo'", specifier = ">=9.0.0" }, { name = "defusedxml", specifier = ">=0.7.1,<1.0.0" }, - { name = "diskcache", specifier = ">=5.6.3,<6.0.0" }, { name = "docling", marker = "(platform_machine != 'x86_64' and extra == 'docling') or (sys_platform != 'darwin' and extra == 'docling')", specifier = ">=2.36.1,<3.0.0" }, { name = "docling-core", marker = "extra == 'docling'", specifier = ">=2.77.0,<3.0.0" }, { name = "docstring-parser", specifier = ">=0.16,<1.0.0" }, - 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# Force re-append to reset TTL for the key, then read - # Actually just read -- the key should have expired via diskcache expire + # The row should be filtered out by the expires_at >= now() predicate in get_since. events, _ = svc.get_since("flow-1", 0.0) assert len(events) == 0 @@ -202,3 +208,27 @@ def test_cross_worker_ttl_expiry(tmp_path): worker_b = FlowEventsService(cache_dir=shared_dir) events, _ = worker_b.get_since("flow-1", 0.0) assert len(events) == 0 + + +def test_multi_process_visibility(tmp_path): + """A separate OS process can write events that this process reads. + + Proves the WAL-mode sqlite backend gives the same cross-worker contract that + the prior diskcache-based implementation did under multi-uvicorn/gunicorn. + """ + shared_dir = tmp_path / "shared_cache" + + # Use spawn so the child does not inherit the parent's sqlite handle. + ctx = multiprocessing.get_context("spawn") + proc = ctx.Process( + target=_worker_append, + args=(str(shared_dir), "flow-mp", "component_added", "from-other-process"), + ) + proc.start() + proc.join(timeout=10) + assert proc.exitcode == 0, "Child process failed to append event" + + reader = FlowEventsService(cache_dir=shared_dir) + events, _ = reader.get_since("flow-mp", 0.0) + assert len(events) == 1 + assert events[0].summary == "from-other-process" diff --git a/src/lfx/src/lfx/services/settings/base.py b/src/lfx/src/lfx/services/settings/base.py index ede8aa4b8d..a6e4afa0b7 100644 --- a/src/lfx/src/lfx/services/settings/base.py +++ b/src/lfx/src/lfx/services/settings/base.py @@ -180,10 +180,10 @@ class Settings(BaseSettings): Controlled by LANGFLOW_USE_NOOP_DATABASE env variable.""" # cache configuration - cache_type: Literal["async", "redis", "memory", "disk"] = "async" - """The cache type can be 'async' or 'redis'.""" + cache_type: Literal["async", "redis", "memory"] = "async" + """The cache backend: 'async' (default in-memory), 'memory' (sync in-memory), or 'redis'.""" cache_expire: int = 3600 - """The cache expire in seconds.""" + """Directory used by FlowEventsService for cross-worker event storage. 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{ name = "diskcache" }, { name = "docstring-parser" }, { name = "duckdb" }, { name = "dynaconf" }, @@ -7252,7 +7191,6 @@ all = [ { name = "ddgs" }, { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "docling-core" }, - { name = "dspy-ai" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, @@ -7422,7 +7360,6 @@ complete = [ { name = "ddgs" }, { name = "docling", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "docling-core" }, - { name = "dspy-ai" }, { name = "easyocr", marker = "platform_machine != 'x86_64' or sys_platform != 'darwin'" }, { name = "elasticsearch" }, { name = "faiss-cpu", version = "1.9.0.post1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" }, @@ -7540,9 +7477,6 @@ docling = [ docx = [ { name = "python-docx" }, ] -dspy = [ - 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{ name = "typing-extensions" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/1f/19/89836022affc1bf470e2485e28872b489254a66fe587155edba731a07112/llama_cpp_python-0.2.90.tar.gz", hash = "sha256:419b041c62dbdb9f7e67883a6ef2f247d583d08417058776be0bff05b4ec9e3d", size = 63762953, upload-time = "2024-08-29T07:00:35.267Z" } - [[package]] name = "llm-sandbox" version = "0.3.39" @@ -11267,25 +11179,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/3b/72/58dcedaca964398d64824b0d85311e89031cf13ec100ee0440c65d87e5cc/opik-2.0.47-py3-none-any.whl", hash = "sha256:aa1a1aadbff5c804355a6645e55b78739fe44d6d055d74676495a42d756c4451", size = 1623856, upload-time = "2026-05-25T04:44:03.994Z" }, ] -[[package]] -name = "optuna" -version = "4.8.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "alembic" }, - { name = "colorlog" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, - { name = "packaging" }, - { name = "pyyaml" }, - { name = "sqlalchemy" }, - { name = "tqdm" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/bf/9b/62f120fb2ecbc4338bee70c5a3671c8e561714f3aa1a046b897ff142050e/optuna-4.8.0.tar.gz", hash = "sha256:6f7043e9f8ecb5e607af86a7eb00fb5ec2be26c3b08c201209a73d36aff37a38", size = 482603, upload-time = "2026-03-16T04:59:58.659Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ac/24/7c731839566d30dc70556d9824ef17692d896c15e3df627bce8c16f753e1/optuna-4.8.0-py3-none-any.whl", hash = "sha256:c57a7682679c36bfc9bca0da430698179e513874074b71bebedb0334964ab930", size = 419456, upload-time = "2026-03-16T04:59:56.977Z" }, -] - [[package]] name = "orjson" version = "3.11.9" From 26736d6d7d47e3ecb688524b6dcae0dfa5e2448f Mon Sep 17 00:00:00 2001 From: Eric Hare Date: Tue, 26 May 2026 07:09:27 -0700 Subject: [PATCH 25/30] fix: align diskcache removal port --- .../langflow/services/flow_events/service.py | 2 +- .../tests/unit/services/test_cache_factory.py | 21 +++++++++++++++++++ src/lfx/src/lfx/services/settings/base.py | 21 +++++++++++++++++++ 3 files changed, 43 insertions(+), 1 deletion(-) create mode 100644 src/backend/tests/unit/services/test_cache_factory.py diff --git a/src/backend/base/langflow/services/flow_events/service.py b/src/backend/base/langflow/services/flow_events/service.py index 60594d5bea..44af32769c 100644 --- a/src/backend/base/langflow/services/flow_events/service.py +++ b/src/backend/base/langflow/services/flow_events/service.py @@ -90,6 +90,7 @@ class FlowEventsService(Service): if event_type not in self._VALID_EVENT_TYPES: msg = f"Invalid event type: {event_type!r}. Must be one of {sorted(self._VALID_EVENT_TYPES)}" raise ValueError(msg) + now = time.time() event = FlowEvent(type=event_type, timestamp=now, summary=summary) expires_at = now + self.TTL_SECONDS @@ -146,7 +147,6 @@ class FlowEventsService(Service): ).fetchall() all_events = [FlowEvent(type=r[0], timestamp=r[1], summary=r[2]) for r in rows] - after = [e for e in all_events if e.timestamp > since] if not after and not all_events: diff --git a/src/backend/tests/unit/services/test_cache_factory.py b/src/backend/tests/unit/services/test_cache_factory.py new file mode 100644 index 0000000000..2ad52bf6b2 --- /dev/null +++ b/src/backend/tests/unit/services/test_cache_factory.py @@ -0,0 +1,21 @@ +"""Regression tests for the cache factory after diskcache removal.""" + +from __future__ import annotations + +import pytest +from lfx.services.settings.base import Settings +from pydantic import ValidationError + + +def test_cache_type_disk_is_rejected(monkeypatch): + """LANGFLOW_CACHE_TYPE=disk must fail validation now that the disk backend is gone.""" + monkeypatch.setenv("LANGFLOW_CACHE_TYPE", "disk") + with pytest.raises(ValidationError): + Settings() + + +@pytest.mark.parametrize("cache_type", ["async", "memory", "redis"]) +def test_supported_cache_types_validate(monkeypatch, cache_type): + monkeypatch.setenv("LANGFLOW_CACHE_TYPE", cache_type) + settings = Settings() + assert settings.cache_type == cache_type diff --git a/src/lfx/src/lfx/services/settings/base.py b/src/lfx/src/lfx/services/settings/base.py index a6e4afa0b7..ce0bd9ed4a 100644 --- a/src/lfx/src/lfx/services/settings/base.py +++ b/src/lfx/src/lfx/services/settings/base.py @@ -183,6 +183,8 @@ class Settings(BaseSettings): cache_type: Literal["async", "redis", "memory"] = "async" """The cache backend: 'async' (default in-memory), 'memory' (sync in-memory), or 'redis'.""" cache_expire: int = 3600 + """The cache expire in seconds.""" + cache_dir: str | None = None """Directory used by FlowEventsService for cross-worker event storage. Defaults to a temp dir if not set.""" variable_store: str = "db" """The store can be 'db' or 'kubernetes'.""" @@ -568,6 +570,25 @@ class Settings(BaseSettings): return str(value) + @field_validator("cache_dir", mode="before") + @classmethod + def validate_cache_dir(cls, value): + """Validate and normalize cache_dir path. + + If not set, returns None and FlowEventsService will use its default temp directory. + If set, resolves to an absolute path and creates the directory if needed. + """ + if not value: + return None + + if isinstance(value, str): + value = Path(value) + value = value.resolve() + if not value.exists(): + value.mkdir(parents=True, exist_ok=True) + + return str(value) + @field_validator("database_url", mode="before") @classmethod def set_database_url(cls, value, info): From 01cd7dc88f692f9721bf9ef84aad04481cd69462 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Tue, 26 May 2026 10:26:52 -0400 Subject: [PATCH 26/30] chore: remove dup doc line --- docs/docs/Develop/memory.mdx | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/docs/docs/Develop/memory.mdx b/docs/docs/Develop/memory.mdx index 65e92b5153..2e99b7f581 100644 --- a/docs/docs/Develop/memory.mdx +++ b/docs/docs/Develop/memory.mdx @@ -25,10 +25,9 @@ Langflow offers a few alternatives to the default database path: - **Config directory**: Set `LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True` to store the database in your Langflow config directory as set in [`LANGFLOW_CONFIG_DIR`](/logging). - **External PostgreSQL database**: You can use an external PostgreSQL database for all of your Langflow storage. + External storage can be useful if you want to preserve the data after uninstalling Langflow or to share the same database between multiple virtual environments. For more information, see [Configure external memory](#configure-external-memory) - External storage can be useful if you want to preserve the data after uninstalling Langflow or to share the same database between multiple virtual environments. - - **Separate chat memory**: You can selectively use external storage for chat memory only, separate from other Langflow storage. For more information, see [Store chat memory](#store-chat-memory). @@ -82,7 +81,7 @@ To fine-tune your database connection pool and timeout settings, you can set the - `LANGFLOW_DB_CONNECT_TIMEOUT`: The number of seconds to wait before giving up on a lock to be released or establishing a connection to the database. This may be separate from the `pool_timeout` in `LANGFLOW_DB_CONNECTION_SETTINGS`. Default: 30. -- `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace identifier for PostgreSQL advisory lock during migrations. If not provided, a hash of the database URL will be used. Useful when multiple Langflow instances share the same database and need coordinated migration locking +- `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace for PostgreSQL advisory locks used during database migrations. This is useful when running multiple Langflow instances that share the same PostgreSQL database. Each instance should use a unique namespace to avoid conflicts. If not set, Langflow uses a default namespace. This setting only applies when using PostgreSQL as your database backend. - `LANGFLOW_DB_CONNECTION_SETTINGS`: A JSON dictionary containing the following database connection pool settings: - `pool_size`: The base number of connections to keep open in the connection pool. Default: 20. @@ -101,8 +100,6 @@ To fine-tune your database connection pool and timeout settings, you can set the Don't use the deprecated environment variables `LANGFLOW_DB_POOL_SIZE` or `LANGFLOW_DB_MAX_OVERFLOW`. Instead, use `pool_size` and `max_overflow` in `LANGFLOW_DB_CONNECTION_SETTINGS`. -- `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace for PostgreSQL advisory locks used during database migrations. This is useful when running multiple Langflow instances that share the same PostgreSQL database. Each instance should use a unique namespace to avoid conflicts. If not set, Langflow uses a default namespace. This setting only applies when using PostgreSQL as your database backend. - ## Configure cache memory The default Langflow caching behavior is an asynchronous, in-memory cache: From 87039b46d3c9005563f58368b2b30b54c3ba8e4b Mon Sep 17 00:00:00 2001 From: Eric Hare Date: Tue, 26 May 2026 08:08:19 -0700 Subject: [PATCH 27/30] fix: port chroma collection hardening (#13339) * fix: port chroma collection hardening * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> --- .secrets.baseline | 25 ++------ .../base/langflow/api/utils/kb_helpers.py | 7 ++- .../base/langflow/api/v1/knowledge_bases.py | 7 ++- .../starter_projects/Knowledge Retrieval.json | 4 +- .../starter_projects/Vector Store RAG.json | 8 +-- .../test_chroma_vector_store_component.py | 57 +++++++++++++++++++ .../tests/unit/test_knowledge_bases_api.py | 5 ++ src/lfx/src/lfx/_assets/component_index.json | 18 +++--- .../lfx/base/vectorstores/chroma_security.py | 28 +++++++++ src/lfx/src/lfx/components/chroma/chroma.py | 2 + .../files_and_knowledge/ingestion.py | 3 + .../files_and_knowledge/retrieval.py | 2 + .../lfx/components/vectorstores/local_db.py | 2 + 13 files changed, 130 insertions(+), 38 deletions(-) create mode 100644 src/lfx/src/lfx/base/vectorstores/chroma_security.py diff --git a/.secrets.baseline b/.secrets.baseline index 5f03c909df..790328a78e 100644 --- a/.secrets.baseline +++ b/.secrets.baseline @@ -2051,14 +2051,6 @@ "hashed_secret": "d6e6d7b4b115cd3b9d172623199f8c403055fecc", "is_verified": false, "line_number": 257 - }, - { - "type": "Hex High Entropy String", - "filename": "src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json", - "hashed_secret": "1f01a7c11bde62eaf153d74394c282aa11574f2a", - "is_verified": false, - "line_number": 539, - "is_secret": false } ], "src/backend/base/langflow/initial_setup/starter_projects/Market Research.json": [ @@ -2509,7 +2501,7 @@ "filename": "src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json", "hashed_secret": "54ed260e3bc31bc77ee06754dff850981d39a66c", "is_verified": false, - "line_number": 275, + "line_number": 277, "is_secret": false }, { @@ -2517,7 +2509,7 @@ "filename": "src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json", "hashed_secret": "76d1ef48b3fa8990b7a1aff6a177e5a5b2d018f8", "is_verified": false, - "line_number": 750, + "line_number": 752, "is_secret": false }, { @@ -2525,22 +2517,15 @@ "filename": "src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json", "hashed_secret": "d6e6d7b4b115cd3b9d172623199f8c403055fecc", "is_verified": false, - "line_number": 1071 + "line_number": 1073 }, { "type": "Hex High Entropy String", "filename": "src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json", "hashed_secret": "2317af15ade380e78be36f9ffdc6415d596a8715", "is_verified": false, - "line_number": 2301, + "line_number": 2306, "is_secret": false - }, - { - "type": "Hex High Entropy String", - "filename": "src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json", - "hashed_secret": "d2d44958e9ddf0f3c8f9019ce3f56548e801c023", - "is_verified": false, - "line_number": 2696 } ], "src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json": [ @@ -8287,5 +8272,5 @@ } ] }, - "generated_at": "2026-05-15T00:15:30Z" + "generated_at": "2026-05-26T14:34:58Z" } diff --git a/src/backend/base/langflow/api/utils/kb_helpers.py b/src/backend/base/langflow/api/utils/kb_helpers.py index b195777f19..fb80cf667d 100644 --- a/src/backend/base/langflow/api/utils/kb_helpers.py +++ b/src/backend/base/langflow/api/utils/kb_helpers.py @@ -19,6 +19,7 @@ from langchain_core.documents import Document from langchain_text_splitters import RecursiveCharacterTextSplitter from lfx.base.data.utils import extract_text_from_bytes from lfx.base.models.unified_models import get_embedding_model_options +from lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs from lfx.components.models_and_agents.embedding_model import EmbeddingModelComponent from lfx.log import logger @@ -114,7 +115,7 @@ class KBStorageHelper: has_data = any((kb_path / m).exists() for m in ["chroma", "chroma.sqlite3", "index"]) if has_data: client = KBStorageHelper.get_fresh_chroma_client(kb_path) - chroma = Chroma(client=client, collection_name=kb_name) + chroma = Chroma(client=client, collection_name=kb_name, **chroma_langchain_collection_kwargs()) with contextlib.suppress(Exception): chroma.delete_collection() chroma = None @@ -261,7 +262,7 @@ class KBAnalysisHelper: try: if created_locally: client = KBStorageHelper.get_fresh_chroma_client(kb_path) - chroma = Chroma(client=client, collection_name=kb_path.name) + chroma = Chroma(client=client, collection_name=kb_path.name, **chroma_langchain_collection_kwargs()) if chroma is None: return @@ -452,6 +453,7 @@ class KBIngestionHelper: client=client, embedding_function=embeddings, collection_name=kb_name, + **chroma_langchain_collection_kwargs(), ) job_id_str = str(task_job_id) @@ -544,6 +546,7 @@ class KBIngestionHelper: chroma = Chroma( client=client, collection_name=kb_name, + **chroma_langchain_collection_kwargs(), ) await chroma.adelete(where={"job_id": str(job_id)}) await logger.ainfo(f"Cleaned up chunks for job {job_id} in knowledge base '{kb_name}'") diff --git a/src/backend/base/langflow/api/v1/knowledge_bases.py b/src/backend/base/langflow/api/v1/knowledge_bases.py index 62ddbf41a1..01bb679b9f 100644 --- a/src/backend/base/langflow/api/v1/knowledge_bases.py +++ b/src/backend/base/langflow/api/v1/knowledge_bases.py @@ -11,6 +11,10 @@ from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, Upload from langchain_chroma import Chroma from langchain_text_splitters import RecursiveCharacterTextSplitter from lfx.base.data.utils import extract_text_from_bytes +from lfx.base.vectorstores.chroma_security import ( + chroma_client_create_collection_kwargs, + chroma_langchain_collection_kwargs, +) from lfx.log import logger from langflow.api.utils import CurrentActiveUser @@ -108,7 +112,7 @@ async def create_knowledge_base( # This ensures files exist for read operations and avoids 'readonly' errors later try: client = KBStorageHelper.get_fresh_chroma_client(kb_path) - client.create_collection(name=kb_name) + client.create_collection(name=kb_name, **chroma_client_create_collection_kwargs()) except (OSError, ValueError, chromadb.errors.ChromaError) as e: logger.warning("Initial Chroma setup for %s failed: %s", kb_name, e) finally: @@ -573,6 +577,7 @@ async def get_knowledge_base_chunks( chroma = Chroma( client=client, collection_name=kb_name, + **chroma_langchain_collection_kwargs(), ) # Access the raw collection diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json index e84961ebf3..bf7a42937e 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json @@ -536,7 +536,7 @@ "last_updated": "2025-08-26T16:19:16.681Z", "legacy": false, "metadata": { - "code_hash": "8b5ca1f38f6e", + "code_hash": "673e3087b073", "dependencies": { "dependencies": [ { @@ -646,7 +646,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\nimport os\nimport uuid\nfrom pathlib import Path\nfrom typing import Any\n\nimport chromadb\nimport chromadb.api.client\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom pydantic import SecretStr\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import (\n get_model_provider_variable_mapping,\n get_provider_all_variables,\n)\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.services.deps import get_settings_service, get_variable_service, session_scope\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge retrieval is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeBaseComponent(Component):\n display_name = \"Knowledge Base\"\n description = \"Search and retrieve data from knowledge.\"\n icon = \"download\"\n name = \"KnowledgeBase\"\n\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"API key for the embedding provider to generate embeddings.\",\n advanced=True,\n required=False,\n ),\n MessageTextInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n info=\"Optional search query to filter knowledge base data.\",\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of top results to return from the knowledge base.\",\n value=5,\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"include_metadata\",\n display_name=\"Include Metadata\",\n info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n value=True,\n advanced=False,\n ),\n BoolInput(\n name=\"include_embeddings\",\n display_name=\"Include Embeddings\",\n info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"retrieve_data\",\n display_name=\"Results\",\n method=\"retrieve_data\",\n info=\"Returns the data from the selected knowledge base.\",\n ),\n ]\n\n async def update_build_config(self, build_config, field_value, field_name=None): # noqa: ARG002\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n if field_name == \"knowledge_base\":\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id, # Use the user_id from the component context\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n\n @property\n def _user_uuid(self) -> uuid.UUID | None:\n \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n if not self.user_id:\n return None\n return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n def _get_kb_metadata(self, kb_path: Path) -> dict:\n \"\"\"Load and process knowledge base metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n metadata: dict[str, Any] = {}\n metadata_file = kb_path / \"embedding_metadata.json\"\n if not metadata_file.exists():\n logger.warning(f\"Embedding metadata file not found at {metadata_file}\")\n return metadata\n\n try:\n with metadata_file.open(\"r\", encoding=\"utf-8\") as f:\n metadata = json.load(f)\n except json.JSONDecodeError:\n logger.error(f\"Error decoding JSON from {metadata_file}\")\n return {}\n\n # Decrypt API key if it exists\n if \"api_key\" in metadata and metadata.get(\"api_key\"):\n settings_service = get_settings_service()\n try:\n decrypted_key = decrypt_api_key(metadata[\"api_key\"], settings_service)\n metadata[\"api_key\"] = decrypted_key\n except (InvalidToken, TypeError, ValueError) as e:\n logger.error(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n metadata[\"api_key\"] = None\n return metadata\n\n async def _resolve_provider_variables(self, provider: str) -> dict[str, str]:\n \"\"\"Resolve all global variables for a provider using the async session.\n\n This avoids the run_until_complete thread dance by doing the lookup\n directly in the already-running async context.\n \"\"\"\n result: dict[str, str] = {}\n provider_vars = get_provider_all_variables(provider)\n user_id = self._user_uuid\n if not provider_vars or not user_id:\n return result\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return result\n\n for var_info in provider_vars:\n var_key = var_info.get(\"variable_key\")\n if not var_key:\n continue\n try:\n value = await variable_service.get_variable(\n user_id=user_id,\n name=var_key,\n field=\"\",\n session=session,\n )\n if value and str(value).strip():\n result[var_key] = str(value)\n except (ValueError, KeyError, AttributeError) as e:\n logger.debug(f\"Variable service lookup failed for '{var_key}', falling back to environment: {e}\")\n env_value = os.environ.get(var_key)\n if env_value and env_value.strip():\n result[var_key] = env_value\n return result\n\n async def _resolve_api_key(self, provider: str) -> str | None:\n \"\"\"Resolve the API key for the given provider.\n\n Priority: user override > metadata (decrypted) > global variable.\n \"\"\"\n provider_variable_map = get_model_provider_variable_mapping()\n variable_name = provider_variable_map.get(provider)\n user_id = self._user_uuid\n if not variable_name or not user_id:\n return None\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return None\n try:\n return await variable_service.get_variable(\n user_id=user_id,\n name=variable_name,\n field=\"\",\n session=session,\n )\n except (ValueError, KeyError, AttributeError):\n return None\n\n def _build_embeddings(self, metadata: dict, *, api_key: str | None = None, provider_vars: dict | None = None):\n \"\"\"Build embedding model from metadata.\n\n Args:\n metadata: The knowledge base embedding metadata.\n api_key: Pre-resolved API key (user override > metadata > global).\n provider_vars: Pre-resolved provider variables (for Ollama/WatsonX).\n \"\"\"\n provider = metadata.get(\"embedding_provider\")\n model = metadata.get(\"embedding_model\")\n chunk_size = metadata.get(\"chunk_size\")\n\n # Handle various providers\n if provider == \"OpenAI\":\n from langchain_openai import OpenAIEmbeddings\n\n if not api_key:\n msg = (\n \"OpenAI API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n openai_kwargs: dict = {\"model\": model, \"api_key\": api_key}\n if chunk_size is not None:\n openai_kwargs[\"chunk_size\"] = chunk_size\n return OpenAIEmbeddings(**openai_kwargs)\n if provider == \"HuggingFace\":\n from langchain_huggingface import HuggingFaceEmbeddings\n\n return HuggingFaceEmbeddings(\n model=model,\n )\n if provider == \"Cohere\":\n from langchain_cohere import CohereEmbeddings\n\n if not api_key:\n msg = \"Cohere API key is required when using Cohere provider\"\n raise ValueError(msg)\n return CohereEmbeddings(\n model=model,\n cohere_api_key=api_key,\n )\n if provider == \"Google Generative AI\":\n from langchain_google_genai import GoogleGenerativeAIEmbeddings\n\n if not api_key:\n msg = (\n \"Google API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n return GoogleGenerativeAIEmbeddings(\n model=model,\n google_api_key=api_key,\n )\n if provider == \"Ollama\":\n from langchain_ollama import OllamaEmbeddings\n\n all_vars = provider_vars or {}\n base_url = all_vars.get(\"OLLAMA_BASE_URL\")\n kwargs: dict = {\"model\": model}\n if base_url:\n kwargs[\"base_url\"] = base_url\n return OllamaEmbeddings(**kwargs)\n if provider == \"IBM WatsonX\":\n from langchain_ibm import WatsonxEmbeddings\n\n all_vars = provider_vars or {}\n watsonx_apikey = api_key or all_vars.get(\"WATSONX_APIKEY\")\n watsonx_project_id = all_vars.get(\"WATSONX_PROJECT_ID\")\n watsonx_url = all_vars.get(\"WATSONX_URL\")\n if not watsonx_apikey:\n msg = (\n \"IBM WatsonX API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n kwargs = {\"model_id\": model, \"apikey\": watsonx_apikey}\n if watsonx_project_id:\n kwargs[\"project_id\"] = watsonx_project_id\n if watsonx_url:\n kwargs[\"url\"] = watsonx_url\n return WatsonxEmbeddings(**kwargs)\n if provider == \"Custom\":\n # For custom embedding models, we would need additional configuration\n msg = \"Custom embedding models not yet supported\"\n raise NotImplementedError(msg)\n msg = f\"Embedding provider '{provider}' is not supported for retrieval.\"\n raise NotImplementedError(msg)\n\n async def retrieve_data(self) -> DataFrame:\n \"\"\"Retrieve data from the selected knowledge base by reading the Chroma collection.\n\n Returns:\n A DataFrame containing the data rows from the knowledge base.\n \"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n # Get the current user\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching Knowledge Base data.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n kb_path = _get_knowledge_bases_root_path() / kb_user / self.knowledge_base\n\n metadata = self._get_kb_metadata(kb_path)\n if not metadata:\n msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n raise ValueError(msg)\n\n # Resolve API key: user override > metadata (decrypted) > global variable\n provider = metadata.get(\"embedding_provider\")\n runtime_api_key = self.api_key.get_secret_value() if isinstance(self.api_key, SecretStr) else self.api_key\n api_key = runtime_api_key or metadata.get(\"api_key\")\n if not api_key and provider:\n api_key = await self._resolve_api_key(provider)\n\n # Resolve provider-specific variables (e.g. base_url for Ollama, project_id for WatsonX)\n provider_vars: dict[str, str] = {}\n if provider in {\"Ollama\", \"IBM WatsonX\"}:\n provider_vars = await self._resolve_provider_variables(provider)\n\n # Build the embedder for the knowledge base\n embedding_function = self._build_embeddings(metadata, api_key=api_key, provider_vars=provider_vars)\n\n # Clear Chroma's singleton client cache to avoid \"different settings\"\n # conflicts when ingestion and retrieval run in the same process.\n chromadb.api.client.SharedSystemClient.clear_system_cache()\n chroma = Chroma(\n persist_directory=str(kb_path),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n )\n\n # If a search query is provided, perform a similarity search\n if self.search_query:\n # Use the search query to perform a similarity search\n logger.info(\"Performing similarity search\")\n results = chroma.similarity_search_with_score(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n else:\n results = chroma.similarity_search(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n\n # For each result, make it a tuple to match the expected output format\n results = [(doc, 0) for doc in results] # Assign a dummy score of 0\n\n # If include_embeddings is enabled, get embeddings for the results\n id_to_embedding = {}\n if self.include_embeddings and results:\n doc_ids = [doc[0].metadata.get(\"_id\") for doc in results if doc[0].metadata.get(\"_id\")]\n\n # Only proceed if we have valid document IDs\n if doc_ids:\n # Access underlying collection to get embeddings\n collection = chroma._collection # noqa: SLF001\n embeddings_result = collection.get(where={\"_id\": {\"$in\": doc_ids}}, include=[\"metadatas\", \"embeddings\"])\n\n # Create a mapping from document ID to embedding\n for i, metadata in enumerate(embeddings_result.get(\"metadatas\", [])):\n if metadata and \"_id\" in metadata:\n id_to_embedding[metadata[\"_id\"]] = embeddings_result[\"embeddings\"][i]\n\n # Build output data based on include_metadata setting\n data_list = []\n for doc in results:\n kwargs = {\n \"content\": doc[0].page_content,\n }\n if self.search_query:\n kwargs[\"_score\"] = -1 * doc[1]\n if self.include_metadata:\n # Include all metadata, embeddings, and content\n kwargs.update(doc[0].metadata)\n if self.include_embeddings:\n kwargs[\"_embeddings\"] = id_to_embedding.get(doc[0].metadata.get(\"_id\"))\n\n data_list.append(Data(**kwargs))\n\n # Return the DataFrame containing the data\n return DataFrame(data=data_list)\n" + "value": "import json\nimport os\nimport uuid\nfrom pathlib import Path\nfrom typing import Any\n\nimport chromadb\nimport chromadb.api.client\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom pydantic import SecretStr\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import (\n get_model_provider_variable_mapping,\n get_provider_all_variables,\n)\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.services.deps import get_settings_service, get_variable_service, session_scope\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge retrieval is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeBaseComponent(Component):\n display_name = \"Knowledge Base\"\n description = \"Search and retrieve data from knowledge.\"\n icon = \"download\"\n name = \"KnowledgeBase\"\n\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"API key for the embedding provider to generate embeddings.\",\n advanced=True,\n required=False,\n ),\n MessageTextInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n info=\"Optional search query to filter knowledge base data.\",\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of top results to return from the knowledge base.\",\n value=5,\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"include_metadata\",\n display_name=\"Include Metadata\",\n info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n value=True,\n advanced=False,\n ),\n BoolInput(\n name=\"include_embeddings\",\n display_name=\"Include Embeddings\",\n info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"retrieve_data\",\n display_name=\"Results\",\n method=\"retrieve_data\",\n info=\"Returns the data from the selected knowledge base.\",\n ),\n ]\n\n async def update_build_config(self, build_config, field_value, field_name=None): # noqa: ARG002\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n if field_name == \"knowledge_base\":\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id, # Use the user_id from the component context\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n\n @property\n def _user_uuid(self) -> uuid.UUID | None:\n \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n if not self.user_id:\n return None\n return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n def _get_kb_metadata(self, kb_path: Path) -> dict:\n \"\"\"Load and process knowledge base metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n metadata: dict[str, Any] = {}\n metadata_file = kb_path / \"embedding_metadata.json\"\n if not metadata_file.exists():\n logger.warning(f\"Embedding metadata file not found at {metadata_file}\")\n return metadata\n\n try:\n with metadata_file.open(\"r\", encoding=\"utf-8\") as f:\n metadata = json.load(f)\n except json.JSONDecodeError:\n logger.error(f\"Error decoding JSON from {metadata_file}\")\n return {}\n\n # Decrypt API key if it exists\n if \"api_key\" in metadata and metadata.get(\"api_key\"):\n settings_service = get_settings_service()\n try:\n decrypted_key = decrypt_api_key(metadata[\"api_key\"], settings_service)\n metadata[\"api_key\"] = decrypted_key\n except (InvalidToken, TypeError, ValueError) as e:\n logger.error(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n metadata[\"api_key\"] = None\n return metadata\n\n async def _resolve_provider_variables(self, provider: str) -> dict[str, str]:\n \"\"\"Resolve all global variables for a provider using the async session.\n\n This avoids the run_until_complete thread dance by doing the lookup\n directly in the already-running async context.\n \"\"\"\n result: dict[str, str] = {}\n provider_vars = get_provider_all_variables(provider)\n user_id = self._user_uuid\n if not provider_vars or not user_id:\n return result\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return result\n\n for var_info in provider_vars:\n var_key = var_info.get(\"variable_key\")\n if not var_key:\n continue\n try:\n value = await variable_service.get_variable(\n user_id=user_id,\n name=var_key,\n field=\"\",\n session=session,\n )\n if value and str(value).strip():\n result[var_key] = str(value)\n except (ValueError, KeyError, AttributeError) as e:\n logger.debug(f\"Variable service lookup failed for '{var_key}', falling back to environment: {e}\")\n env_value = os.environ.get(var_key)\n if env_value and env_value.strip():\n result[var_key] = env_value\n return result\n\n async def _resolve_api_key(self, provider: str) -> str | None:\n \"\"\"Resolve the API key for the given provider.\n\n Priority: user override > metadata (decrypted) > global variable.\n \"\"\"\n provider_variable_map = get_model_provider_variable_mapping()\n variable_name = provider_variable_map.get(provider)\n user_id = self._user_uuid\n if not variable_name or not user_id:\n return None\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return None\n try:\n return await variable_service.get_variable(\n user_id=user_id,\n name=variable_name,\n field=\"\",\n session=session,\n )\n except (ValueError, KeyError, AttributeError):\n return None\n\n def _build_embeddings(self, metadata: dict, *, api_key: str | None = None, provider_vars: dict | None = None):\n \"\"\"Build embedding model from metadata.\n\n Args:\n metadata: The knowledge base embedding metadata.\n api_key: Pre-resolved API key (user override > metadata > global).\n provider_vars: Pre-resolved provider variables (for Ollama/WatsonX).\n \"\"\"\n provider = metadata.get(\"embedding_provider\")\n model = metadata.get(\"embedding_model\")\n chunk_size = metadata.get(\"chunk_size\")\n\n # Handle various providers\n if provider == \"OpenAI\":\n from langchain_openai import OpenAIEmbeddings\n\n if not api_key:\n msg = (\n \"OpenAI API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n openai_kwargs: dict = {\"model\": model, \"api_key\": api_key}\n if chunk_size is not None:\n openai_kwargs[\"chunk_size\"] = chunk_size\n return OpenAIEmbeddings(**openai_kwargs)\n if provider == \"HuggingFace\":\n from langchain_huggingface import HuggingFaceEmbeddings\n\n return HuggingFaceEmbeddings(\n model=model,\n )\n if provider == \"Cohere\":\n from langchain_cohere import CohereEmbeddings\n\n if not api_key:\n msg = \"Cohere API key is required when using Cohere provider\"\n raise ValueError(msg)\n return CohereEmbeddings(\n model=model,\n cohere_api_key=api_key,\n )\n if provider == \"Google Generative AI\":\n from langchain_google_genai import GoogleGenerativeAIEmbeddings\n\n if not api_key:\n msg = (\n \"Google API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n return GoogleGenerativeAIEmbeddings(\n model=model,\n google_api_key=api_key,\n )\n if provider == \"Ollama\":\n from langchain_ollama import OllamaEmbeddings\n\n all_vars = provider_vars or {}\n base_url = all_vars.get(\"OLLAMA_BASE_URL\")\n kwargs: dict = {\"model\": model}\n if base_url:\n kwargs[\"base_url\"] = base_url\n return OllamaEmbeddings(**kwargs)\n if provider == \"IBM WatsonX\":\n from langchain_ibm import WatsonxEmbeddings\n\n all_vars = provider_vars or {}\n watsonx_apikey = api_key or all_vars.get(\"WATSONX_APIKEY\")\n watsonx_project_id = all_vars.get(\"WATSONX_PROJECT_ID\")\n watsonx_url = all_vars.get(\"WATSONX_URL\")\n if not watsonx_apikey:\n msg = (\n \"IBM WatsonX API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n kwargs = {\"model_id\": model, \"apikey\": watsonx_apikey}\n if watsonx_project_id:\n kwargs[\"project_id\"] = watsonx_project_id\n if watsonx_url:\n kwargs[\"url\"] = watsonx_url\n return WatsonxEmbeddings(**kwargs)\n if provider == \"Custom\":\n # For custom embedding models, we would need additional configuration\n msg = \"Custom embedding models not yet supported\"\n raise NotImplementedError(msg)\n msg = f\"Embedding provider '{provider}' is not supported for retrieval.\"\n raise NotImplementedError(msg)\n\n async def retrieve_data(self) -> DataFrame:\n \"\"\"Retrieve data from the selected knowledge base by reading the Chroma collection.\n\n Returns:\n A DataFrame containing the data rows from the knowledge base.\n \"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n # Get the current user\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching Knowledge Base data.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n kb_path = _get_knowledge_bases_root_path() / kb_user / self.knowledge_base\n\n metadata = self._get_kb_metadata(kb_path)\n if not metadata:\n msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n raise ValueError(msg)\n\n # Resolve API key: user override > metadata (decrypted) > global variable\n provider = metadata.get(\"embedding_provider\")\n runtime_api_key = self.api_key.get_secret_value() if isinstance(self.api_key, SecretStr) else self.api_key\n api_key = runtime_api_key or metadata.get(\"api_key\")\n if not api_key and provider:\n api_key = await self._resolve_api_key(provider)\n\n # Resolve provider-specific variables (e.g. base_url for Ollama, project_id for WatsonX)\n provider_vars: dict[str, str] = {}\n if provider in {\"Ollama\", \"IBM WatsonX\"}:\n provider_vars = await self._resolve_provider_variables(provider)\n\n # Build the embedder for the knowledge base\n embedding_function = self._build_embeddings(metadata, api_key=api_key, provider_vars=provider_vars)\n\n # Clear Chroma's singleton client cache to avoid \"different settings\"\n # conflicts when ingestion and retrieval run in the same process.\n chromadb.api.client.SharedSystemClient.clear_system_cache()\n chroma = Chroma(\n persist_directory=str(kb_path),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # If a search query is provided, perform a similarity search\n if self.search_query:\n # Use the search query to perform a similarity search\n logger.info(\"Performing similarity search\")\n results = chroma.similarity_search_with_score(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n else:\n results = chroma.similarity_search(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n\n # For each result, make it a tuple to match the expected output format\n results = [(doc, 0) for doc in results] # Assign a dummy score of 0\n\n # If include_embeddings is enabled, get embeddings for the results\n id_to_embedding = {}\n if self.include_embeddings and results:\n doc_ids = [doc[0].metadata.get(\"_id\") for doc in results if doc[0].metadata.get(\"_id\")]\n\n # Only proceed if we have valid document IDs\n if doc_ids:\n # Access underlying collection to get embeddings\n collection = chroma._collection # noqa: SLF001\n embeddings_result = collection.get(where={\"_id\": {\"$in\": doc_ids}}, include=[\"metadatas\", \"embeddings\"])\n\n # Create a mapping from document ID to embedding\n for i, metadata in enumerate(embeddings_result.get(\"metadatas\", [])):\n if metadata and \"_id\" in metadata:\n id_to_embedding[metadata[\"_id\"]] = embeddings_result[\"embeddings\"][i]\n\n # Build output data based on include_metadata setting\n data_list = []\n for doc in results:\n kwargs = {\n \"content\": doc[0].page_content,\n }\n if self.search_query:\n kwargs[\"_score\"] = -1 * doc[1]\n if self.include_metadata:\n # Include all metadata, embeddings, and content\n kwargs.update(doc[0].metadata)\n if self.include_embeddings:\n kwargs[\"_embeddings\"] = id_to_embedding.get(doc[0].metadata.get(\"_id\"))\n\n data_list.append(Data(**kwargs))\n\n # Return the DataFrame containing the data\n return DataFrame(data=data_list)\n" }, "include_embeddings": { "_input_type": "BoolInput", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json index ca4797c4ec..264f419c60 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json @@ -2678,7 +2678,7 @@ "legacy": false, "lf_version": "1.9.0", "metadata": { - "code_hash": "1b571470646c", + "code_hash": "d6904604cfc8", "dependencies": { "dependencies": [ { @@ -2810,7 +2810,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport re\nimport uuid\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any\n\nimport pandas as pd\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key, encrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.io import (\n BoolInput,\n DropdownInput,\n HandleInput,\n IntInput,\n ModelInput,\n Output,\n SecretStrInput,\n StrInput,\n TableInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n get_settings_service,\n session_scope,\n)\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\nif TYPE_CHECKING:\n from lfx.schema.dataframe import DataFrame\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge ingestion is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeIngestionComponent(Component):\n \"\"\"Create or append to Langflow Knowledge from a DataFrame.\"\"\"\n\n # ------ UI metadata ---------------------------------------------------\n display_name = \"Knowledge Ingestion\"\n description = \"Create or update knowledge in Langflow.\"\n icon = \"upload\"\n name = \"KnowledgeIngestion\"\n\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self._cached_kb_path: Path | None = None\n\n @dataclass\n class NewKnowledgeBaseInput:\n functionality: str = \"create\"\n fields: dict[str, dict] = field(\n default_factory=lambda: {\n \"data\": {\n \"node\": {\n \"name\": \"create_knowledge_base\",\n \"description\": \"Create new knowledge in Langflow.\",\n \"display_name\": \"Create new knowledge\",\n \"field_order\": [\n \"01_new_kb_name\",\n \"02_embedding_model\",\n ],\n \"template\": {\n \"01_new_kb_name\": StrInput(\n name=\"new_kb_name\",\n display_name=\"Knowledge Name\",\n info=\"Name of the new knowledge to create.\",\n required=True,\n ),\n \"02_embedding_model\": ModelInput(\n name=\"embedding_model\",\n display_name=\"Choose Embedding Model\",\n info=(\n \"Select the embedding model to use for this knowledge base. \"\n \"Langflow uses the configured credentials for that model provider.\"\n ),\n required=True,\n model_type=\"embedding\",\n ),\n },\n },\n }\n }\n )\n\n # ------ Inputs --------------------------------------------------------\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n dialog_inputs=asdict(NewKnowledgeBaseInput()),\n ),\n HandleInput(\n name=\"input_df\",\n display_name=\"Input\",\n info=(\n \"Table with all original columns (already chunked / processed). \"\n \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n \"it is converted to a DataFrame automatically.\"\n ),\n input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n required=True,\n ),\n TableInput(\n name=\"column_config\",\n display_name=\"Column Configuration\",\n info=\"Configure column behavior for the knowledge base.\",\n required=True,\n table_schema=[\n {\n \"name\": \"column_name\",\n \"display_name\": \"Column Name\",\n \"type\": \"str\",\n \"description\": \"Name of the column in the source DataFrame\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"vectorize\",\n \"display_name\": \"Vectorize\",\n \"type\": \"boolean\",\n \"description\": \"Create embeddings for this column\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"identifier\",\n \"display_name\": \"Identifier\",\n \"type\": \"boolean\",\n \"description\": \"Use this column as unique identifier\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n value=[\n {\n \"column_name\": \"text\",\n \"vectorize\": True,\n \"identifier\": True,\n },\n ],\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"Batch size for processing embeddings\",\n advanced=True,\n value=1000,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n info=\"Allow duplicate rows in the knowledge base\",\n advanced=True,\n value=False,\n ),\n ]\n\n # ------ Outputs -------------------------------------------------------\n outputs = [Output(display_name=\"Results\", name=\"dataframe_output\", method=\"build_kb_info\")]\n\n # ------ Internal helpers ---------------------------------------------\n def _get_kb_root(self) -> Path:\n \"\"\"Return the root directory for knowledge bases.\"\"\"\n return _get_knowledge_bases_root_path()\n\n @staticmethod\n def _scalar_notna(value) -> bool:\n \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n ``pd.notna`` returns an array when given an array-like input, which\n cannot be used directly in a boolean context. This helper collapses the\n result to a single scalar ``bool``.\n \"\"\"\n result = pd.notna(value)\n # If result is array-like (numpy array, list, etc.), treat non-empty arrays as \"present\"\n if hasattr(result, \"__iter__\") and not isinstance(result, str):\n import numpy as np\n\n arr = np.asarray(result)\n return arr.size > 0 and arr.all()\n return bool(result)\n\n def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n if not self.column_config:\n msg = \"Column configuration cannot be empty\"\n raise ValueError(msg)\n\n # Convert table input to list of dicts (similar to Structured Output)\n config_list = self.column_config if isinstance(self.column_config, list) else []\n\n # Validate column names exist in DataFrame\n df_columns = set(df_source.columns)\n for config in config_list:\n col_name = config.get(\"column_name\")\n if col_name not in df_columns:\n msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n raise ValueError(msg)\n\n return config_list\n\n def _build_embedding_metadata(\n self,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> dict[str, Any]:\n \"\"\"Build embedding model metadata from a model selection dict.\n\n Args:\n model_selection: Model selection list from ModelInput\n (e.g. [{'name': ..., 'provider': ..., 'metadata': ...}])\n api_key: Optional runtime API key override.\n \"\"\"\n model_dict = model_selection[0] if isinstance(model_selection, list) else model_selection\n embedding_model = model_dict.get(\"name\", \"\")\n embedding_provider = model_dict.get(\"provider\", \"Unknown\")\n\n api_key_to_save = None\n if api_key and hasattr(api_key, \"get_secret_value\"):\n api_key_to_save = api_key.get_secret_value()\n elif isinstance(api_key, str):\n api_key_to_save = api_key\n\n encrypted_api_key = None\n if api_key_to_save:\n settings_service = get_settings_service()\n try:\n encrypted_api_key = encrypt_api_key(api_key_to_save, settings_service=settings_service)\n except (TypeError, ValueError) as e:\n self.log(f\"Could not encrypt API key: {e}\")\n\n return {\n \"embedding_provider\": embedding_provider,\n \"embedding_model\": embedding_model,\n \"model_selection\": model_dict, # Store full selection for get_embeddings() reconstruction\n \"api_key\": encrypted_api_key,\n \"api_key_used\": bool(api_key),\n \"chunk_size\": self.chunk_size,\n \"created_at\": datetime.now(timezone.utc).isoformat(),\n }\n\n def _save_embedding_metadata(\n self,\n kb_path: Path,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> None:\n \"\"\"Save embedding model metadata.\"\"\"\n embedding_metadata = self._build_embedding_metadata(model_selection, api_key)\n metadata_path = kb_path / \"embedding_metadata.json\"\n metadata_path.write_text(json.dumps(embedding_metadata, indent=2))\n\n def _update_metadata_metrics(self, kb_path: Path, chroma: Chroma) -> None:\n \"\"\"Update embedding_metadata.json with accurate chunk/word/character counts.\n\n This ensures the Knowledge Base modal displays correct stats after\n component-based ingestion, matching the behavior of API-based ingestion.\n Delegates to KBAnalysisHelper.update_text_metrics to avoid duplicating\n the batched metrics counting logic.\n \"\"\"\n import chromadb.errors\n from langflow.api.utils.kb_helpers import KBAnalysisHelper, KBStorageHelper\n\n metadata_path = kb_path / \"embedding_metadata.json\"\n if not metadata_path.exists():\n return\n\n try:\n metadata = json.loads(metadata_path.read_text())\n KBAnalysisHelper.update_text_metrics(kb_path, metadata, chroma)\n metadata[\"size\"] = KBStorageHelper.get_directory_size(kb_path)\n metadata_path.write_text(json.dumps(metadata, indent=2))\n except (OSError, ValueError, TypeError, json.JSONDecodeError, chromadb.errors.ChromaError) as e:\n self.log(f\"Warning: Could not update metadata metrics: {e}\")\n\n def _save_kb_files(\n self,\n kb_path: Path,\n config_list: list[dict[str, Any]],\n ) -> None:\n \"\"\"Save KB files using File Component storage patterns.\"\"\"\n try:\n # Create directory (following File Component patterns)\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save column configuration\n # Only do this if the file doesn't exist already\n cfg_path = kb_path / \"schema.json\"\n if not cfg_path.exists():\n cfg_path.write_text(json.dumps(config_list, indent=2))\n\n except (OSError, TypeError, ValueError) as e:\n self.log(f\"Error saving KB files: {e}\")\n\n def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n \"\"\"Build detailed column metadata.\"\"\"\n metadata: dict[str, Any] = {\n \"total_columns\": len(df_source.columns),\n \"mapped_columns\": len(config_list),\n \"unmapped_columns\": len(df_source.columns) - len(config_list),\n \"columns\": [],\n \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n }\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n # Add to columns list\n metadata[\"columns\"].append(\n {\n \"name\": col_name,\n \"vectorize\": vectorize,\n \"identifier\": identifier,\n }\n )\n\n # Update summary\n if vectorize:\n metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n if identifier:\n metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n return metadata\n\n async def _create_vector_store(\n self,\n df_source: pd.DataFrame,\n config_list: list[dict[str, Any]],\n embedding_function,\n ) -> Chroma:\n \"\"\"Create vector store following Local DB component pattern.\n\n Returns the Chroma instance so callers can use it for metrics updates.\n \"\"\"\n # Set up vector store directory\n vector_store_dir = await self._kb_path()\n if not vector_store_dir:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n vector_store_dir.mkdir(parents=True, exist_ok=True)\n\n # Convert DataFrame to Data objects (following Local DB pattern)\n data_objects = await self._convert_df_to_data_objects(df_source, config_list)\n\n # Create vector store\n chroma = Chroma(\n persist_directory=str(vector_store_dir),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n )\n\n # Convert Data objects to LangChain Documents\n documents = []\n for data_obj in data_objects:\n doc = data_obj.to_lc_document()\n documents.append(doc)\n\n # Add documents to vector store\n if documents:\n chroma.add_documents(documents)\n self.log(f\"Added {len(documents)} documents to vector store '{self.knowledge_base}'\")\n\n return chroma\n\n async def _convert_df_to_data_objects(\n self, df_source: pd.DataFrame, config_list: list[dict[str, Any]]\n ) -> list[Data]:\n \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n data_objects: list[Data] = []\n\n # Set up vector store directory\n kb_path = await self._kb_path()\n\n # If we don't allow duplicates, we need to get the existing hashes\n chroma = Chroma(\n persist_directory=str(kb_path),\n collection_name=self.knowledge_base,\n )\n\n # Get all documents and their metadata\n all_docs = chroma.get()\n\n # Extract all _id values from metadata\n id_list = [metadata.get(\"_id\") for metadata in all_docs[\"metadatas\"] if metadata.get(\"_id\")]\n\n # Get column roles\n content_cols = []\n identifier_cols = []\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n if vectorize:\n content_cols.append(col_name)\n elif identifier:\n identifier_cols.append(col_name)\n\n # Convert each row to a Data object\n for _, row in df_source.iterrows():\n # Build content text from identifier columns using list comprehension\n identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n # Join all parts into a single string\n page_content = \" \".join(identifier_parts)\n\n # Build metadata from NON-vectorized columns only (simple key-value pairs)\n data_dict = {\n \"text\": page_content, # Main content for vectorization\n }\n\n # Add identifier columns if they exist\n if identifier_cols:\n identifier_parts = [\n str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n ]\n page_content = \" \".join(identifier_parts)\n\n # Add metadata columns as simple key-value pairs\n for col in df_source.columns:\n if col not in content_cols and col in row and self._scalar_notna(row[col]):\n # Convert to simple types for Chroma metadata\n value = row[col]\n data_dict[col] = str(value) # Convert complex types to string\n\n # Hash the page_content for unique ID\n page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n data_dict[\"_id\"] = page_content_hash\n\n # If duplicates are disallowed, and hash exists, prevent adding this row\n if not self.allow_duplicates and page_content_hash in id_list:\n self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n continue\n\n # Create Data object - everything except \"text\" becomes metadata\n data_obj = Data(data=data_dict)\n data_objects.append(data_obj)\n\n return data_objects\n\n def is_valid_collection_name(self, name, min_length: int = 3, max_length: int = 63) -> bool:\n \"\"\"Validates collection name against conditions 1-3.\n\n 1. Contains 3-63 characters\n 2. Starts and ends with alphanumeric character\n 3. Contains only alphanumeric characters, underscores, or hyphens.\n\n Args:\n name (str): Collection name to validate\n min_length (int): Minimum length of the name\n max_length (int): Maximum length of the name\n\n Returns:\n bool: True if valid, False otherwise\n \"\"\"\n # Check length (condition 1)\n if not (min_length <= len(name) <= max_length):\n return False\n\n # Check start/end with alphanumeric (condition 2)\n if not (name[0].isalnum() and name[-1].isalnum()):\n return False\n\n # Check allowed characters (condition 3)\n return re.match(r\"^[a-zA-Z0-9_-]+$\", name) is not None\n\n async def _kb_path(self) -> Path | None:\n # Check if we already have the path cached\n cached_path = getattr(self, \"_cached_kb_path\", None)\n if cached_path is not None:\n return cached_path\n\n # If not cached, compute it\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base path.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n\n kb_root = self._get_kb_root()\n\n # Cache the result\n self._cached_kb_path = kb_root / kb_user / self.knowledge_base\n\n return self._cached_kb_path\n\n # ---------------------------------------------------------------------\n # OUTPUT METHODS\n # ---------------------------------------------------------------------\n async def build_kb_info(self) -> Data:\n \"\"\"Main ingestion routine → returns a dict with KB metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n try:\n input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n # Validate column configuration (using Structured Output patterns)\n config_list = self._validate_column_config(df_source)\n column_metadata = self._build_column_metadata(config_list, df_source)\n\n # Read the embedding info from the knowledge base folder\n kb_path = await self._kb_path()\n if not kb_path:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n metadata_path = kb_path / \"embedding_metadata.json\"\n api_key = None\n model_selection = None\n\n # Read stored metadata\n if metadata_path.exists():\n settings_service = get_settings_service()\n stored_metadata = json.loads(metadata_path.read_text())\n\n # Prefer stored model_selection dict (new format)\n model_selection = stored_metadata.get(\"model_selection\")\n if model_selection:\n model_selection = [model_selection] if isinstance(model_selection, dict) else model_selection\n else:\n # Backward compat: reconstruct from old string-based metadata\n embedding_model_name = stored_metadata.get(\"embedding_model\")\n embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n if embedding_model_name:\n # Look up full model info from available options\n try:\n all_options = get_embedding_model_options(user_id=self.user_id)\n match = next(\n (o for o in all_options if o.get(\"name\") == embedding_model_name),\n None,\n )\n if match:\n model_selection = [match]\n else:\n self.log(\n f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n \"from stored metadata is no longer available in the model registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n \"The knowledge base was created with an older format and the model \"\n \"is not available in the current registry. \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg)\n except ValueError:\n raise\n except Exception: # noqa: BLE001\n self.log(\n f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Could not look up embedding model '{embedding_model_name}' \"\n f\"(provider: {embedding_provider}). \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg) # noqa: B904\n\n # Decrypt stored API key\n encrypted_key = stored_metadata.get(\"api_key\")\n if encrypted_key:\n try:\n api_key = decrypt_api_key(encrypted_key, settings_service)\n except (InvalidToken, TypeError, ValueError) as e:\n self.log(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n\n # Check if a custom API key was provided\n if self.api_key:\n api_key = self.api_key\n if model_selection:\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n api_key=api_key,\n )\n\n if not model_selection:\n msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n raise ValueError(msg)\n\n # Build the embedding function via the shared utility\n embedding_function = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n api_key=api_key,\n chunk_size=self.chunk_size,\n )\n\n # Create vector store following Local DB component pattern\n chroma = await self._create_vector_store(df_source, config_list, embedding_function=embedding_function)\n\n # Save KB files (using File Component storage patterns)\n self._save_kb_files(kb_path, config_list)\n\n # Update embedding_metadata.json with accurate text metrics\n # so the KB modal and API show correct chunks/words/characters\n self._update_metadata_metrics(kb_path, chroma)\n\n # Build metadata response\n meta: dict[str, Any] = {\n \"kb_id\": str(uuid.uuid4()),\n \"kb_name\": self.knowledge_base,\n \"rows\": len(df_source),\n \"column_metadata\": column_metadata,\n \"path\": str(kb_path),\n \"config_columns\": len(config_list),\n \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n }\n\n # Set status message\n self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n return Data(data=meta)\n\n except (OSError, ValueError, RuntimeError, KeyError) as e:\n msg = f\"Error during KB ingestion: {e}\"\n raise RuntimeError(msg) from e\n\n async def update_build_config(\n self,\n build_config,\n field_value: Any,\n field_name: str | None = None,\n ):\n \"\"\"Update build configuration based on provider selection.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n # Populate the dialog's embedding model options so the ModelInput renders correctly\n try:\n dialog_template = (\n build_config[\"knowledge_base\"]\n .get(\"dialog_inputs\", {})\n .get(\"fields\", {})\n .get(\"data\", {})\n .get(\"node\", {})\n .get(\"template\", {})\n )\n if \"02_embedding_model\" in dialog_template:\n embedding_options = get_embedding_model_options(user_id=self.user_id)\n dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n except Exception: # noqa: BLE001\n self.log(\"Failed to populate embedding model options in dialog\")\n\n # Create a new knowledge base\n if field_name == \"knowledge_base\":\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base list.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n # Validate the knowledge base name - Make sure it follows these rules:\n if not self.is_valid_collection_name(field_value[\"01_new_kb_name\"]):\n msg = f\"Invalid knowledge base name: {field_value['01_new_kb_name']}\"\n raise ValueError(msg)\n\n # The model selection comes from ModelInput as a list of dicts\n model_selection = field_value[\"02_embedding_model\"]\n if isinstance(model_selection, dict):\n model_selection = [model_selection]\n\n # Build and validate the embedding model via the shared utility\n embed_model = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n )\n\n # Try to generate a dummy embedding to validate without blocking the event loop\n try:\n await asyncio.wait_for(\n asyncio.to_thread(embed_model.embed_query, \"test\"),\n timeout=10,\n )\n except TimeoutError as e:\n msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n raise ValueError(msg) from e\n except Exception as e:\n msg = f\"Embedding validation failed: {e!s}\"\n raise ValueError(msg) from e\n\n # Create the new knowledge base directory\n kb_path = _get_knowledge_bases_root_path() / kb_user / field_value[\"01_new_kb_name\"]\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save the embedding metadata\n build_config[\"knowledge_base\"][\"value\"] = field_value[\"01_new_kb_name\"]\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n )\n\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id,\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n" + "value": "from __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport re\nimport uuid\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any\n\nimport pandas as pd\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key, encrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.io import (\n BoolInput,\n DropdownInput,\n HandleInput,\n IntInput,\n ModelInput,\n Output,\n SecretStrInput,\n StrInput,\n TableInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n get_settings_service,\n session_scope,\n)\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\nif TYPE_CHECKING:\n from lfx.schema.dataframe import DataFrame\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge ingestion is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeIngestionComponent(Component):\n \"\"\"Create or append to Langflow Knowledge from a DataFrame.\"\"\"\n\n # ------ UI metadata ---------------------------------------------------\n display_name = \"Knowledge Ingestion\"\n description = \"Create or update knowledge in Langflow.\"\n icon = \"upload\"\n name = \"KnowledgeIngestion\"\n\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self._cached_kb_path: Path | None = None\n\n @dataclass\n class NewKnowledgeBaseInput:\n functionality: str = \"create\"\n fields: dict[str, dict] = field(\n default_factory=lambda: {\n \"data\": {\n \"node\": {\n \"name\": \"create_knowledge_base\",\n \"description\": \"Create new knowledge in Langflow.\",\n \"display_name\": \"Create new knowledge\",\n \"field_order\": [\n \"01_new_kb_name\",\n \"02_embedding_model\",\n ],\n \"template\": {\n \"01_new_kb_name\": StrInput(\n name=\"new_kb_name\",\n display_name=\"Knowledge Name\",\n info=\"Name of the new knowledge to create.\",\n required=True,\n ),\n \"02_embedding_model\": ModelInput(\n name=\"embedding_model\",\n display_name=\"Choose Embedding Model\",\n info=(\n \"Select the embedding model to use for this knowledge base. \"\n \"Langflow uses the configured credentials for that model provider.\"\n ),\n required=True,\n model_type=\"embedding\",\n ),\n },\n },\n }\n }\n )\n\n # ------ Inputs --------------------------------------------------------\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n dialog_inputs=asdict(NewKnowledgeBaseInput()),\n ),\n HandleInput(\n name=\"input_df\",\n display_name=\"Input\",\n info=(\n \"Table with all original columns (already chunked / processed). \"\n \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n \"it is converted to a DataFrame automatically.\"\n ),\n input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n required=True,\n ),\n TableInput(\n name=\"column_config\",\n display_name=\"Column Configuration\",\n info=\"Configure column behavior for the knowledge base.\",\n required=True,\n table_schema=[\n {\n \"name\": \"column_name\",\n \"display_name\": \"Column Name\",\n \"type\": \"str\",\n \"description\": \"Name of the column in the source DataFrame\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"vectorize\",\n \"display_name\": \"Vectorize\",\n \"type\": \"boolean\",\n \"description\": \"Create embeddings for this column\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"identifier\",\n \"display_name\": \"Identifier\",\n \"type\": \"boolean\",\n \"description\": \"Use this column as unique identifier\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n value=[\n {\n \"column_name\": \"text\",\n \"vectorize\": True,\n \"identifier\": True,\n },\n ],\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"Batch size for processing embeddings\",\n advanced=True,\n value=1000,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n info=\"Allow duplicate rows in the knowledge base\",\n advanced=True,\n value=False,\n ),\n ]\n\n # ------ Outputs -------------------------------------------------------\n outputs = [Output(display_name=\"Results\", name=\"dataframe_output\", method=\"build_kb_info\")]\n\n # ------ Internal helpers ---------------------------------------------\n def _get_kb_root(self) -> Path:\n \"\"\"Return the root directory for knowledge bases.\"\"\"\n return _get_knowledge_bases_root_path()\n\n @staticmethod\n def _scalar_notna(value) -> bool:\n \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n ``pd.notna`` returns an array when given an array-like input, which\n cannot be used directly in a boolean context. This helper collapses the\n result to a single scalar ``bool``.\n \"\"\"\n result = pd.notna(value)\n # If result is array-like (numpy array, list, etc.), treat non-empty arrays as \"present\"\n if hasattr(result, \"__iter__\") and not isinstance(result, str):\n import numpy as np\n\n arr = np.asarray(result)\n return arr.size > 0 and arr.all()\n return bool(result)\n\n def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n if not self.column_config:\n msg = \"Column configuration cannot be empty\"\n raise ValueError(msg)\n\n # Convert table input to list of dicts (similar to Structured Output)\n config_list = self.column_config if isinstance(self.column_config, list) else []\n\n # Validate column names exist in DataFrame\n df_columns = set(df_source.columns)\n for config in config_list:\n col_name = config.get(\"column_name\")\n if col_name not in df_columns:\n msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n raise ValueError(msg)\n\n return config_list\n\n def _build_embedding_metadata(\n self,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> dict[str, Any]:\n \"\"\"Build embedding model metadata from a model selection dict.\n\n Args:\n model_selection: Model selection list from ModelInput\n (e.g. [{'name': ..., 'provider': ..., 'metadata': ...}])\n api_key: Optional runtime API key override.\n \"\"\"\n model_dict = model_selection[0] if isinstance(model_selection, list) else model_selection\n embedding_model = model_dict.get(\"name\", \"\")\n embedding_provider = model_dict.get(\"provider\", \"Unknown\")\n\n api_key_to_save = None\n if api_key and hasattr(api_key, \"get_secret_value\"):\n api_key_to_save = api_key.get_secret_value()\n elif isinstance(api_key, str):\n api_key_to_save = api_key\n\n encrypted_api_key = None\n if api_key_to_save:\n settings_service = get_settings_service()\n try:\n encrypted_api_key = encrypt_api_key(api_key_to_save, settings_service=settings_service)\n except (TypeError, ValueError) as e:\n self.log(f\"Could not encrypt API key: {e}\")\n\n return {\n \"embedding_provider\": embedding_provider,\n \"embedding_model\": embedding_model,\n \"model_selection\": model_dict, # Store full selection for get_embeddings() reconstruction\n \"api_key\": encrypted_api_key,\n \"api_key_used\": bool(api_key),\n \"chunk_size\": self.chunk_size,\n \"created_at\": datetime.now(timezone.utc).isoformat(),\n }\n\n def _save_embedding_metadata(\n self,\n kb_path: Path,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> None:\n \"\"\"Save embedding model metadata.\"\"\"\n embedding_metadata = self._build_embedding_metadata(model_selection, api_key)\n metadata_path = kb_path / \"embedding_metadata.json\"\n metadata_path.write_text(json.dumps(embedding_metadata, indent=2))\n\n def _update_metadata_metrics(self, kb_path: Path, chroma: Chroma) -> None:\n \"\"\"Update embedding_metadata.json with accurate chunk/word/character counts.\n\n This ensures the Knowledge Base modal displays correct stats after\n component-based ingestion, matching the behavior of API-based ingestion.\n Delegates to KBAnalysisHelper.update_text_metrics to avoid duplicating\n the batched metrics counting logic.\n \"\"\"\n import chromadb.errors\n from langflow.api.utils.kb_helpers import KBAnalysisHelper, KBStorageHelper\n\n metadata_path = kb_path / \"embedding_metadata.json\"\n if not metadata_path.exists():\n return\n\n try:\n metadata = json.loads(metadata_path.read_text())\n KBAnalysisHelper.update_text_metrics(kb_path, metadata, chroma)\n metadata[\"size\"] = KBStorageHelper.get_directory_size(kb_path)\n metadata_path.write_text(json.dumps(metadata, indent=2))\n except (OSError, ValueError, TypeError, json.JSONDecodeError, chromadb.errors.ChromaError) as e:\n self.log(f\"Warning: Could not update metadata metrics: {e}\")\n\n def _save_kb_files(\n self,\n kb_path: Path,\n config_list: list[dict[str, Any]],\n ) -> None:\n \"\"\"Save KB files using File Component storage patterns.\"\"\"\n try:\n # Create directory (following File Component patterns)\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save column configuration\n # Only do this if the file doesn't exist already\n cfg_path = kb_path / \"schema.json\"\n if not cfg_path.exists():\n cfg_path.write_text(json.dumps(config_list, indent=2))\n\n except (OSError, TypeError, ValueError) as e:\n self.log(f\"Error saving KB files: {e}\")\n\n def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n \"\"\"Build detailed column metadata.\"\"\"\n metadata: dict[str, Any] = {\n \"total_columns\": len(df_source.columns),\n \"mapped_columns\": len(config_list),\n \"unmapped_columns\": len(df_source.columns) - len(config_list),\n \"columns\": [],\n \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n }\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n # Add to columns list\n metadata[\"columns\"].append(\n {\n \"name\": col_name,\n \"vectorize\": vectorize,\n \"identifier\": identifier,\n }\n )\n\n # Update summary\n if vectorize:\n metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n if identifier:\n metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n return metadata\n\n async def _create_vector_store(\n self,\n df_source: pd.DataFrame,\n config_list: list[dict[str, Any]],\n embedding_function,\n ) -> Chroma:\n \"\"\"Create vector store following Local DB component pattern.\n\n Returns the Chroma instance so callers can use it for metrics updates.\n \"\"\"\n # Set up vector store directory\n vector_store_dir = await self._kb_path()\n if not vector_store_dir:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n vector_store_dir.mkdir(parents=True, exist_ok=True)\n\n # Convert DataFrame to Data objects (following Local DB pattern)\n data_objects = await self._convert_df_to_data_objects(df_source, config_list)\n\n # Create vector store\n chroma = Chroma(\n persist_directory=str(vector_store_dir),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # Convert Data objects to LangChain Documents\n documents = []\n for data_obj in data_objects:\n doc = data_obj.to_lc_document()\n documents.append(doc)\n\n # Add documents to vector store\n if documents:\n chroma.add_documents(documents)\n self.log(f\"Added {len(documents)} documents to vector store '{self.knowledge_base}'\")\n\n return chroma\n\n async def _convert_df_to_data_objects(\n self, df_source: pd.DataFrame, config_list: list[dict[str, Any]]\n ) -> list[Data]:\n \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n data_objects: list[Data] = []\n\n # Set up vector store directory\n kb_path = await self._kb_path()\n\n # If we don't allow duplicates, we need to get the existing hashes\n chroma = Chroma(\n persist_directory=str(kb_path),\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # Get all documents and their metadata\n all_docs = chroma.get()\n\n # Extract all _id values from metadata\n id_list = [metadata.get(\"_id\") for metadata in all_docs[\"metadatas\"] if metadata.get(\"_id\")]\n\n # Get column roles\n content_cols = []\n identifier_cols = []\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n if vectorize:\n content_cols.append(col_name)\n elif identifier:\n identifier_cols.append(col_name)\n\n # Convert each row to a Data object\n for _, row in df_source.iterrows():\n # Build content text from identifier columns using list comprehension\n identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n # Join all parts into a single string\n page_content = \" \".join(identifier_parts)\n\n # Build metadata from NON-vectorized columns only (simple key-value pairs)\n data_dict = {\n \"text\": page_content, # Main content for vectorization\n }\n\n # Add identifier columns if they exist\n if identifier_cols:\n identifier_parts = [\n str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n ]\n page_content = \" \".join(identifier_parts)\n\n # Add metadata columns as simple key-value pairs\n for col in df_source.columns:\n if col not in content_cols and col in row and self._scalar_notna(row[col]):\n # Convert to simple types for Chroma metadata\n value = row[col]\n data_dict[col] = str(value) # Convert complex types to string\n\n # Hash the page_content for unique ID\n page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n data_dict[\"_id\"] = page_content_hash\n\n # If duplicates are disallowed, and hash exists, prevent adding this row\n if not self.allow_duplicates and page_content_hash in id_list:\n self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n continue\n\n # Create Data object - everything except \"text\" becomes metadata\n data_obj = Data(data=data_dict)\n data_objects.append(data_obj)\n\n return data_objects\n\n def is_valid_collection_name(self, name, min_length: int = 3, max_length: int = 63) -> bool:\n \"\"\"Validates collection name against conditions 1-3.\n\n 1. Contains 3-63 characters\n 2. Starts and ends with alphanumeric character\n 3. Contains only alphanumeric characters, underscores, or hyphens.\n\n Args:\n name (str): Collection name to validate\n min_length (int): Minimum length of the name\n max_length (int): Maximum length of the name\n\n Returns:\n bool: True if valid, False otherwise\n \"\"\"\n # Check length (condition 1)\n if not (min_length <= len(name) <= max_length):\n return False\n\n # Check start/end with alphanumeric (condition 2)\n if not (name[0].isalnum() and name[-1].isalnum()):\n return False\n\n # Check allowed characters (condition 3)\n return re.match(r\"^[a-zA-Z0-9_-]+$\", name) is not None\n\n async def _kb_path(self) -> Path | None:\n # Check if we already have the path cached\n cached_path = getattr(self, \"_cached_kb_path\", None)\n if cached_path is not None:\n return cached_path\n\n # If not cached, compute it\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base path.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n\n kb_root = self._get_kb_root()\n\n # Cache the result\n self._cached_kb_path = kb_root / kb_user / self.knowledge_base\n\n return self._cached_kb_path\n\n # ---------------------------------------------------------------------\n # OUTPUT METHODS\n # ---------------------------------------------------------------------\n async def build_kb_info(self) -> Data:\n \"\"\"Main ingestion routine → returns a dict with KB metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n try:\n input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n # Validate column configuration (using Structured Output patterns)\n config_list = self._validate_column_config(df_source)\n column_metadata = self._build_column_metadata(config_list, df_source)\n\n # Read the embedding info from the knowledge base folder\n kb_path = await self._kb_path()\n if not kb_path:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n metadata_path = kb_path / \"embedding_metadata.json\"\n api_key = None\n model_selection = None\n\n # Read stored metadata\n if metadata_path.exists():\n settings_service = get_settings_service()\n stored_metadata = json.loads(metadata_path.read_text())\n\n # Prefer stored model_selection dict (new format)\n model_selection = stored_metadata.get(\"model_selection\")\n if model_selection:\n model_selection = [model_selection] if isinstance(model_selection, dict) else model_selection\n else:\n # Backward compat: reconstruct from old string-based metadata\n embedding_model_name = stored_metadata.get(\"embedding_model\")\n embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n if embedding_model_name:\n # Look up full model info from available options\n try:\n all_options = get_embedding_model_options(user_id=self.user_id)\n match = next(\n (o for o in all_options if o.get(\"name\") == embedding_model_name),\n None,\n )\n if match:\n model_selection = [match]\n else:\n self.log(\n f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n \"from stored metadata is no longer available in the model registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n \"The knowledge base was created with an older format and the model \"\n \"is not available in the current registry. \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg)\n except ValueError:\n raise\n except Exception: # noqa: BLE001\n self.log(\n f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Could not look up embedding model '{embedding_model_name}' \"\n f\"(provider: {embedding_provider}). \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg) # noqa: B904\n\n # Decrypt stored API key\n encrypted_key = stored_metadata.get(\"api_key\")\n if encrypted_key:\n try:\n api_key = decrypt_api_key(encrypted_key, settings_service)\n except (InvalidToken, TypeError, ValueError) as e:\n self.log(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n\n # Check if a custom API key was provided\n if self.api_key:\n api_key = self.api_key\n if model_selection:\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n api_key=api_key,\n )\n\n if not model_selection:\n msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n raise ValueError(msg)\n\n # Build the embedding function via the shared utility\n embedding_function = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n api_key=api_key,\n chunk_size=self.chunk_size,\n )\n\n # Create vector store following Local DB component pattern\n chroma = await self._create_vector_store(df_source, config_list, embedding_function=embedding_function)\n\n # Save KB files (using File Component storage patterns)\n self._save_kb_files(kb_path, config_list)\n\n # Update embedding_metadata.json with accurate text metrics\n # so the KB modal and API show correct chunks/words/characters\n self._update_metadata_metrics(kb_path, chroma)\n\n # Build metadata response\n meta: dict[str, Any] = {\n \"kb_id\": str(uuid.uuid4()),\n \"kb_name\": self.knowledge_base,\n \"rows\": len(df_source),\n \"column_metadata\": column_metadata,\n \"path\": str(kb_path),\n \"config_columns\": len(config_list),\n \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n }\n\n # Set status message\n self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n return Data(data=meta)\n\n except (OSError, ValueError, RuntimeError, KeyError) as e:\n msg = f\"Error during KB ingestion: {e}\"\n raise RuntimeError(msg) from e\n\n async def update_build_config(\n self,\n build_config,\n field_value: Any,\n field_name: str | None = None,\n ):\n \"\"\"Update build configuration based on provider selection.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n # Populate the dialog's embedding model options so the ModelInput renders correctly\n try:\n dialog_template = (\n build_config[\"knowledge_base\"]\n .get(\"dialog_inputs\", {})\n .get(\"fields\", {})\n .get(\"data\", {})\n .get(\"node\", {})\n .get(\"template\", {})\n )\n if \"02_embedding_model\" in dialog_template:\n embedding_options = get_embedding_model_options(user_id=self.user_id)\n dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n except Exception: # noqa: BLE001\n self.log(\"Failed to populate embedding model options in dialog\")\n\n # Create a new knowledge base\n if field_name == \"knowledge_base\":\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base list.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n # Validate the knowledge base name - Make sure it follows these rules:\n if not self.is_valid_collection_name(field_value[\"01_new_kb_name\"]):\n msg = f\"Invalid knowledge base name: {field_value['01_new_kb_name']}\"\n raise ValueError(msg)\n\n # The model selection comes from ModelInput as a list of dicts\n model_selection = field_value[\"02_embedding_model\"]\n if isinstance(model_selection, dict):\n model_selection = [model_selection]\n\n # Build and validate the embedding model via the shared utility\n embed_model = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n )\n\n # Try to generate a dummy embedding to validate without blocking the event loop\n try:\n await asyncio.wait_for(\n asyncio.to_thread(embed_model.embed_query, \"test\"),\n timeout=10,\n )\n except TimeoutError as e:\n msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n raise ValueError(msg) from e\n except Exception as e:\n msg = f\"Embedding validation failed: {e!s}\"\n raise ValueError(msg) from e\n\n # Create the new knowledge base directory\n kb_path = _get_knowledge_bases_root_path() / kb_user / field_value[\"01_new_kb_name\"]\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save the embedding metadata\n build_config[\"knowledge_base\"][\"value\"] = field_value[\"01_new_kb_name\"]\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n )\n\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id,\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n" }, "column_config": { "_input_type": "TableInput", @@ -3048,7 +3048,7 @@ "legacy": false, "lf_version": "1.9.0", "metadata": { - "code_hash": "8b5ca1f38f6e", + "code_hash": "673e3087b073", "dependencies": { "dependencies": [ { @@ -3160,7 +3160,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\nimport os\nimport uuid\nfrom pathlib import Path\nfrom typing import Any\n\nimport chromadb\nimport chromadb.api.client\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom pydantic import SecretStr\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import (\n get_model_provider_variable_mapping,\n get_provider_all_variables,\n)\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.services.deps import get_settings_service, get_variable_service, session_scope\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge retrieval is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeBaseComponent(Component):\n display_name = \"Knowledge Base\"\n description = \"Search and retrieve data from knowledge.\"\n icon = \"download\"\n name = \"KnowledgeBase\"\n\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"API key for the embedding provider to generate embeddings.\",\n advanced=True,\n required=False,\n ),\n MessageTextInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n info=\"Optional search query to filter knowledge base data.\",\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of top results to return from the knowledge base.\",\n value=5,\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"include_metadata\",\n display_name=\"Include Metadata\",\n info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n value=True,\n advanced=False,\n ),\n BoolInput(\n name=\"include_embeddings\",\n display_name=\"Include Embeddings\",\n info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"retrieve_data\",\n display_name=\"Results\",\n method=\"retrieve_data\",\n info=\"Returns the data from the selected knowledge base.\",\n ),\n ]\n\n async def update_build_config(self, build_config, field_value, field_name=None): # noqa: ARG002\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n if field_name == \"knowledge_base\":\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id, # Use the user_id from the component context\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n\n @property\n def _user_uuid(self) -> uuid.UUID | None:\n \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n if not self.user_id:\n return None\n return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n def _get_kb_metadata(self, kb_path: Path) -> dict:\n \"\"\"Load and process knowledge base metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n metadata: dict[str, Any] = {}\n metadata_file = kb_path / \"embedding_metadata.json\"\n if not metadata_file.exists():\n logger.warning(f\"Embedding metadata file not found at {metadata_file}\")\n return metadata\n\n try:\n with metadata_file.open(\"r\", encoding=\"utf-8\") as f:\n metadata = json.load(f)\n except json.JSONDecodeError:\n logger.error(f\"Error decoding JSON from {metadata_file}\")\n return {}\n\n # Decrypt API key if it exists\n if \"api_key\" in metadata and metadata.get(\"api_key\"):\n settings_service = get_settings_service()\n try:\n decrypted_key = decrypt_api_key(metadata[\"api_key\"], settings_service)\n metadata[\"api_key\"] = decrypted_key\n except (InvalidToken, TypeError, ValueError) as e:\n logger.error(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n metadata[\"api_key\"] = None\n return metadata\n\n async def _resolve_provider_variables(self, provider: str) -> dict[str, str]:\n \"\"\"Resolve all global variables for a provider using the async session.\n\n This avoids the run_until_complete thread dance by doing the lookup\n directly in the already-running async context.\n \"\"\"\n result: dict[str, str] = {}\n provider_vars = get_provider_all_variables(provider)\n user_id = self._user_uuid\n if not provider_vars or not user_id:\n return result\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return result\n\n for var_info in provider_vars:\n var_key = var_info.get(\"variable_key\")\n if not var_key:\n continue\n try:\n value = await variable_service.get_variable(\n user_id=user_id,\n name=var_key,\n field=\"\",\n session=session,\n )\n if value and str(value).strip():\n result[var_key] = str(value)\n except (ValueError, KeyError, AttributeError) as e:\n logger.debug(f\"Variable service lookup failed for '{var_key}', falling back to environment: {e}\")\n env_value = os.environ.get(var_key)\n if env_value and env_value.strip():\n result[var_key] = env_value\n return result\n\n async def _resolve_api_key(self, provider: str) -> str | None:\n \"\"\"Resolve the API key for the given provider.\n\n Priority: user override > metadata (decrypted) > global variable.\n \"\"\"\n provider_variable_map = get_model_provider_variable_mapping()\n variable_name = provider_variable_map.get(provider)\n user_id = self._user_uuid\n if not variable_name or not user_id:\n return None\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return None\n try:\n return await variable_service.get_variable(\n user_id=user_id,\n name=variable_name,\n field=\"\",\n session=session,\n )\n except (ValueError, KeyError, AttributeError):\n return None\n\n def _build_embeddings(self, metadata: dict, *, api_key: str | None = None, provider_vars: dict | None = None):\n \"\"\"Build embedding model from metadata.\n\n Args:\n metadata: The knowledge base embedding metadata.\n api_key: Pre-resolved API key (user override > metadata > global).\n provider_vars: Pre-resolved provider variables (for Ollama/WatsonX).\n \"\"\"\n provider = metadata.get(\"embedding_provider\")\n model = metadata.get(\"embedding_model\")\n chunk_size = metadata.get(\"chunk_size\")\n\n # Handle various providers\n if provider == \"OpenAI\":\n from langchain_openai import OpenAIEmbeddings\n\n if not api_key:\n msg = (\n \"OpenAI API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n openai_kwargs: dict = {\"model\": model, \"api_key\": api_key}\n if chunk_size is not None:\n openai_kwargs[\"chunk_size\"] = chunk_size\n return OpenAIEmbeddings(**openai_kwargs)\n if provider == \"HuggingFace\":\n from langchain_huggingface import HuggingFaceEmbeddings\n\n return HuggingFaceEmbeddings(\n model=model,\n )\n if provider == \"Cohere\":\n from langchain_cohere import CohereEmbeddings\n\n if not api_key:\n msg = \"Cohere API key is required when using Cohere provider\"\n raise ValueError(msg)\n return CohereEmbeddings(\n model=model,\n cohere_api_key=api_key,\n )\n if provider == \"Google Generative AI\":\n from langchain_google_genai import GoogleGenerativeAIEmbeddings\n\n if not api_key:\n msg = (\n \"Google API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n return GoogleGenerativeAIEmbeddings(\n model=model,\n google_api_key=api_key,\n )\n if provider == \"Ollama\":\n from langchain_ollama import OllamaEmbeddings\n\n all_vars = provider_vars or {}\n base_url = all_vars.get(\"OLLAMA_BASE_URL\")\n kwargs: dict = {\"model\": model}\n if base_url:\n kwargs[\"base_url\"] = base_url\n return OllamaEmbeddings(**kwargs)\n if provider == \"IBM WatsonX\":\n from langchain_ibm import WatsonxEmbeddings\n\n all_vars = provider_vars or {}\n watsonx_apikey = api_key or all_vars.get(\"WATSONX_APIKEY\")\n watsonx_project_id = all_vars.get(\"WATSONX_PROJECT_ID\")\n watsonx_url = all_vars.get(\"WATSONX_URL\")\n if not watsonx_apikey:\n msg = (\n \"IBM WatsonX API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n kwargs = {\"model_id\": model, \"apikey\": watsonx_apikey}\n if watsonx_project_id:\n kwargs[\"project_id\"] = watsonx_project_id\n if watsonx_url:\n kwargs[\"url\"] = watsonx_url\n return WatsonxEmbeddings(**kwargs)\n if provider == \"Custom\":\n # For custom embedding models, we would need additional configuration\n msg = \"Custom embedding models not yet supported\"\n raise NotImplementedError(msg)\n msg = f\"Embedding provider '{provider}' is not supported for retrieval.\"\n raise NotImplementedError(msg)\n\n async def retrieve_data(self) -> DataFrame:\n \"\"\"Retrieve data from the selected knowledge base by reading the Chroma collection.\n\n Returns:\n A DataFrame containing the data rows from the knowledge base.\n \"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n # Get the current user\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching Knowledge Base data.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n kb_path = _get_knowledge_bases_root_path() / kb_user / self.knowledge_base\n\n metadata = self._get_kb_metadata(kb_path)\n if not metadata:\n msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n raise ValueError(msg)\n\n # Resolve API key: user override > metadata (decrypted) > global variable\n provider = metadata.get(\"embedding_provider\")\n runtime_api_key = self.api_key.get_secret_value() if isinstance(self.api_key, SecretStr) else self.api_key\n api_key = runtime_api_key or metadata.get(\"api_key\")\n if not api_key and provider:\n api_key = await self._resolve_api_key(provider)\n\n # Resolve provider-specific variables (e.g. base_url for Ollama, project_id for WatsonX)\n provider_vars: dict[str, str] = {}\n if provider in {\"Ollama\", \"IBM WatsonX\"}:\n provider_vars = await self._resolve_provider_variables(provider)\n\n # Build the embedder for the knowledge base\n embedding_function = self._build_embeddings(metadata, api_key=api_key, provider_vars=provider_vars)\n\n # Clear Chroma's singleton client cache to avoid \"different settings\"\n # conflicts when ingestion and retrieval run in the same process.\n chromadb.api.client.SharedSystemClient.clear_system_cache()\n chroma = Chroma(\n persist_directory=str(kb_path),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n )\n\n # If a search query is provided, perform a similarity search\n if self.search_query:\n # Use the search query to perform a similarity search\n logger.info(\"Performing similarity search\")\n results = chroma.similarity_search_with_score(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n else:\n results = chroma.similarity_search(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n\n # For each result, make it a tuple to match the expected output format\n results = [(doc, 0) for doc in results] # Assign a dummy score of 0\n\n # If include_embeddings is enabled, get embeddings for the results\n id_to_embedding = {}\n if self.include_embeddings and results:\n doc_ids = [doc[0].metadata.get(\"_id\") for doc in results if doc[0].metadata.get(\"_id\")]\n\n # Only proceed if we have valid document IDs\n if doc_ids:\n # Access underlying collection to get embeddings\n collection = chroma._collection # noqa: SLF001\n embeddings_result = collection.get(where={\"_id\": {\"$in\": doc_ids}}, include=[\"metadatas\", \"embeddings\"])\n\n # Create a mapping from document ID to embedding\n for i, metadata in enumerate(embeddings_result.get(\"metadatas\", [])):\n if metadata and \"_id\" in metadata:\n id_to_embedding[metadata[\"_id\"]] = embeddings_result[\"embeddings\"][i]\n\n # Build output data based on include_metadata setting\n data_list = []\n for doc in results:\n kwargs = {\n \"content\": doc[0].page_content,\n }\n if self.search_query:\n kwargs[\"_score\"] = -1 * doc[1]\n if self.include_metadata:\n # Include all metadata, embeddings, and content\n kwargs.update(doc[0].metadata)\n if self.include_embeddings:\n kwargs[\"_embeddings\"] = id_to_embedding.get(doc[0].metadata.get(\"_id\"))\n\n data_list.append(Data(**kwargs))\n\n # Return the DataFrame containing the data\n return DataFrame(data=data_list)\n" + "value": "import json\nimport os\nimport uuid\nfrom pathlib import Path\nfrom typing import Any\n\nimport chromadb\nimport chromadb.api.client\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom pydantic import SecretStr\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import (\n get_model_provider_variable_mapping,\n get_provider_all_variables,\n)\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.services.deps import get_settings_service, get_variable_service, session_scope\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge retrieval is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeBaseComponent(Component):\n display_name = \"Knowledge Base\"\n description = \"Search and retrieve data from knowledge.\"\n icon = \"download\"\n name = \"KnowledgeBase\"\n\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"API key for the embedding provider to generate embeddings.\",\n advanced=True,\n required=False,\n ),\n MessageTextInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n info=\"Optional search query to filter knowledge base data.\",\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of top results to return from the knowledge base.\",\n value=5,\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"include_metadata\",\n display_name=\"Include Metadata\",\n info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n value=True,\n advanced=False,\n ),\n BoolInput(\n name=\"include_embeddings\",\n display_name=\"Include Embeddings\",\n info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"retrieve_data\",\n display_name=\"Results\",\n method=\"retrieve_data\",\n info=\"Returns the data from the selected knowledge base.\",\n ),\n ]\n\n async def update_build_config(self, build_config, field_value, field_name=None): # noqa: ARG002\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n if field_name == \"knowledge_base\":\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id, # Use the user_id from the component context\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n\n @property\n def _user_uuid(self) -> uuid.UUID | None:\n \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n if not self.user_id:\n return None\n return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n def _get_kb_metadata(self, kb_path: Path) -> dict:\n \"\"\"Load and process knowledge base metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n metadata: dict[str, Any] = {}\n metadata_file = kb_path / \"embedding_metadata.json\"\n if not metadata_file.exists():\n logger.warning(f\"Embedding metadata file not found at {metadata_file}\")\n return metadata\n\n try:\n with metadata_file.open(\"r\", encoding=\"utf-8\") as f:\n metadata = json.load(f)\n except json.JSONDecodeError:\n logger.error(f\"Error decoding JSON from {metadata_file}\")\n return {}\n\n # Decrypt API key if it exists\n if \"api_key\" in metadata and metadata.get(\"api_key\"):\n settings_service = get_settings_service()\n try:\n decrypted_key = decrypt_api_key(metadata[\"api_key\"], settings_service)\n metadata[\"api_key\"] = decrypted_key\n except (InvalidToken, TypeError, ValueError) as e:\n logger.error(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n metadata[\"api_key\"] = None\n return metadata\n\n async def _resolve_provider_variables(self, provider: str) -> dict[str, str]:\n \"\"\"Resolve all global variables for a provider using the async session.\n\n This avoids the run_until_complete thread dance by doing the lookup\n directly in the already-running async context.\n \"\"\"\n result: dict[str, str] = {}\n provider_vars = get_provider_all_variables(provider)\n user_id = self._user_uuid\n if not provider_vars or not user_id:\n return result\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return result\n\n for var_info in provider_vars:\n var_key = var_info.get(\"variable_key\")\n if not var_key:\n continue\n try:\n value = await variable_service.get_variable(\n user_id=user_id,\n name=var_key,\n field=\"\",\n session=session,\n )\n if value and str(value).strip():\n result[var_key] = str(value)\n except (ValueError, KeyError, AttributeError) as e:\n logger.debug(f\"Variable service lookup failed for '{var_key}', falling back to environment: {e}\")\n env_value = os.environ.get(var_key)\n if env_value and env_value.strip():\n result[var_key] = env_value\n return result\n\n async def _resolve_api_key(self, provider: str) -> str | None:\n \"\"\"Resolve the API key for the given provider.\n\n Priority: user override > metadata (decrypted) > global variable.\n \"\"\"\n provider_variable_map = get_model_provider_variable_mapping()\n variable_name = provider_variable_map.get(provider)\n user_id = self._user_uuid\n if not variable_name or not user_id:\n return None\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return None\n try:\n return await variable_service.get_variable(\n user_id=user_id,\n name=variable_name,\n field=\"\",\n session=session,\n )\n except (ValueError, KeyError, AttributeError):\n return None\n\n def _build_embeddings(self, metadata: dict, *, api_key: str | None = None, provider_vars: dict | None = None):\n \"\"\"Build embedding model from metadata.\n\n Args:\n metadata: The knowledge base embedding metadata.\n api_key: Pre-resolved API key (user override > metadata > global).\n provider_vars: Pre-resolved provider variables (for Ollama/WatsonX).\n \"\"\"\n provider = metadata.get(\"embedding_provider\")\n model = metadata.get(\"embedding_model\")\n chunk_size = metadata.get(\"chunk_size\")\n\n # Handle various providers\n if provider == \"OpenAI\":\n from langchain_openai import OpenAIEmbeddings\n\n if not api_key:\n msg = (\n \"OpenAI API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n openai_kwargs: dict = {\"model\": model, \"api_key\": api_key}\n if chunk_size is not None:\n openai_kwargs[\"chunk_size\"] = chunk_size\n return OpenAIEmbeddings(**openai_kwargs)\n if provider == \"HuggingFace\":\n from langchain_huggingface import HuggingFaceEmbeddings\n\n return HuggingFaceEmbeddings(\n model=model,\n )\n if provider == \"Cohere\":\n from langchain_cohere import CohereEmbeddings\n\n if not api_key:\n msg = \"Cohere API key is required when using Cohere provider\"\n raise ValueError(msg)\n return CohereEmbeddings(\n model=model,\n cohere_api_key=api_key,\n )\n if provider == \"Google Generative AI\":\n from langchain_google_genai import GoogleGenerativeAIEmbeddings\n\n if not api_key:\n msg = (\n \"Google API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n return GoogleGenerativeAIEmbeddings(\n model=model,\n google_api_key=api_key,\n )\n if provider == \"Ollama\":\n from langchain_ollama import OllamaEmbeddings\n\n all_vars = provider_vars or {}\n base_url = all_vars.get(\"OLLAMA_BASE_URL\")\n kwargs: dict = {\"model\": model}\n if base_url:\n kwargs[\"base_url\"] = base_url\n return OllamaEmbeddings(**kwargs)\n if provider == \"IBM WatsonX\":\n from langchain_ibm import WatsonxEmbeddings\n\n all_vars = provider_vars or {}\n watsonx_apikey = api_key or all_vars.get(\"WATSONX_APIKEY\")\n watsonx_project_id = all_vars.get(\"WATSONX_PROJECT_ID\")\n watsonx_url = all_vars.get(\"WATSONX_URL\")\n if not watsonx_apikey:\n msg = (\n \"IBM WatsonX API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n kwargs = {\"model_id\": model, \"apikey\": watsonx_apikey}\n if watsonx_project_id:\n kwargs[\"project_id\"] = watsonx_project_id\n if watsonx_url:\n kwargs[\"url\"] = watsonx_url\n return WatsonxEmbeddings(**kwargs)\n if provider == \"Custom\":\n # For custom embedding models, we would need additional configuration\n msg = \"Custom embedding models not yet supported\"\n raise NotImplementedError(msg)\n msg = f\"Embedding provider '{provider}' is not supported for retrieval.\"\n raise NotImplementedError(msg)\n\n async def retrieve_data(self) -> DataFrame:\n \"\"\"Retrieve data from the selected knowledge base by reading the Chroma collection.\n\n Returns:\n A DataFrame containing the data rows from the knowledge base.\n \"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n # Get the current user\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching Knowledge Base data.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n kb_path = _get_knowledge_bases_root_path() / kb_user / self.knowledge_base\n\n metadata = self._get_kb_metadata(kb_path)\n if not metadata:\n msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n raise ValueError(msg)\n\n # Resolve API key: user override > metadata (decrypted) > global variable\n provider = metadata.get(\"embedding_provider\")\n runtime_api_key = self.api_key.get_secret_value() if isinstance(self.api_key, SecretStr) else self.api_key\n api_key = runtime_api_key or metadata.get(\"api_key\")\n if not api_key and provider:\n api_key = await self._resolve_api_key(provider)\n\n # Resolve provider-specific variables (e.g. base_url for Ollama, project_id for WatsonX)\n provider_vars: dict[str, str] = {}\n if provider in {\"Ollama\", \"IBM WatsonX\"}:\n provider_vars = await self._resolve_provider_variables(provider)\n\n # Build the embedder for the knowledge base\n embedding_function = self._build_embeddings(metadata, api_key=api_key, provider_vars=provider_vars)\n\n # Clear Chroma's singleton client cache to avoid \"different settings\"\n # conflicts when ingestion and retrieval run in the same process.\n chromadb.api.client.SharedSystemClient.clear_system_cache()\n chroma = Chroma(\n persist_directory=str(kb_path),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # If a search query is provided, perform a similarity search\n if self.search_query:\n # Use the search query to perform a similarity search\n logger.info(\"Performing similarity search\")\n results = chroma.similarity_search_with_score(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n else:\n results = chroma.similarity_search(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n\n # For each result, make it a tuple to match the expected output format\n results = [(doc, 0) for doc in results] # Assign a dummy score of 0\n\n # If include_embeddings is enabled, get embeddings for the results\n id_to_embedding = {}\n if self.include_embeddings and results:\n doc_ids = [doc[0].metadata.get(\"_id\") for doc in results if doc[0].metadata.get(\"_id\")]\n\n # Only proceed if we have valid document IDs\n if doc_ids:\n # Access underlying collection to get embeddings\n collection = chroma._collection # noqa: SLF001\n embeddings_result = collection.get(where={\"_id\": {\"$in\": doc_ids}}, include=[\"metadatas\", \"embeddings\"])\n\n # Create a mapping from document ID to embedding\n for i, metadata in enumerate(embeddings_result.get(\"metadatas\", [])):\n if metadata and \"_id\" in metadata:\n id_to_embedding[metadata[\"_id\"]] = embeddings_result[\"embeddings\"][i]\n\n # Build output data based on include_metadata setting\n data_list = []\n for doc in results:\n kwargs = {\n \"content\": doc[0].page_content,\n }\n if self.search_query:\n kwargs[\"_score\"] = -1 * doc[1]\n if self.include_metadata:\n # Include all metadata, embeddings, and content\n kwargs.update(doc[0].metadata)\n if self.include_embeddings:\n kwargs[\"_embeddings\"] = id_to_embedding.get(doc[0].metadata.get(\"_id\"))\n\n data_list.append(Data(**kwargs))\n\n # Return the DataFrame containing the data\n return DataFrame(data=data_list)\n" }, "include_embeddings": { "_input_type": "BoolInput", diff --git a/src/backend/tests/unit/components/vectorstores/test_chroma_vector_store_component.py b/src/backend/tests/unit/components/vectorstores/test_chroma_vector_store_component.py index 2ebbb0662c..765de164ef 100644 --- a/src/backend/tests/unit/components/vectorstores/test_chroma_vector_store_component.py +++ b/src/backend/tests/unit/components/vectorstores/test_chroma_vector_store_component.py @@ -1,13 +1,70 @@ from pathlib import Path from typing import Any +from unittest.mock import MagicMock, patch import pytest +from lfx.base.vectorstores.chroma_security import ( + chroma_client_create_collection_kwargs, + chroma_langchain_collection_kwargs, +) from lfx.components.chroma import ChromaVectorStoreComponent from lfx.schema.data import Data from tests.base import ComponentTestBaseWithoutClient, VersionComponentMapping +def test_remote_chroma_server_uses_http_client() -> None: + mock_client = MagicMock() + mock_chroma = MagicMock() + mock_chroma.get.return_value = {"ids": [], "documents": [], "metadatas": []} + + with ( + patch("chromadb.Client", return_value=mock_client), + patch("langchain_chroma.Chroma", return_value=mock_chroma) as mock_chroma_class, + ): + component = ChromaVectorStoreComponent().set( + collection_name="remote_collection", + persist_directory=None, + embedding=None, + ingest_data=[], + chroma_server_cors_allow_origins=[], + chroma_server_host="localhost", + chroma_server_http_port=8000, + chroma_server_grpc_port=None, + chroma_server_ssl_enabled=False, + ) + component._cached_vector_store = None + + component.build_vector_store() + + mock_chroma_class.assert_called_once_with( + persist_directory=None, + client=mock_client, + embedding_function=None, + collection_name="remote_collection", + collection_configuration={"embedding_function": None}, + ) + + +def test_chroma_collection_security_kwargs_disable_server_side_embedding_functions() -> None: + assert chroma_langchain_collection_kwargs() == { + "collection_configuration": {"embedding_function": None}, + } + assert chroma_client_create_collection_kwargs() == { + "configuration": {"embedding_function": None}, + "embedding_function": None, + } + + +def test_chroma_collection_security_kwargs_are_fresh_dicts() -> None: + first = chroma_langchain_collection_kwargs() + first["collection_configuration"]["embedding_function"] = "unsafe" + + assert chroma_langchain_collection_kwargs() == { + "collection_configuration": {"embedding_function": None}, + } + + @pytest.mark.api_key_required class TestChromaVectorStoreComponent(ComponentTestBaseWithoutClient): @pytest.fixture diff --git a/src/backend/tests/unit/test_knowledge_bases_api.py b/src/backend/tests/unit/test_knowledge_bases_api.py index 548eb55351..384ed4af68 100644 --- a/src/backend/tests/unit/test_knowledge_bases_api.py +++ b/src/backend/tests/unit/test_knowledge_bases_api.py @@ -211,6 +211,11 @@ class TestKnowledgeBaseAPI: assert response.status_code == 201 data = response.json() assert data["name"] == "New KB" + mock_fresh_client.return_value.create_collection.assert_called_once_with( + name=kb_name, + configuration={"embedding_function": None}, + embedding_function=None, + ) @patch("langflow.api.v1.knowledge_bases.KBStorageHelper.get_root_path") async def test_create_kb_path_traversal_single_level( diff --git a/src/lfx/src/lfx/_assets/component_index.json b/src/lfx/src/lfx/_assets/component_index.json index 862f8152ec..c9330ec42e 100644 --- a/src/lfx/src/lfx/_assets/component_index.json +++ b/src/lfx/src/lfx/_assets/component_index.json @@ -10027,7 +10027,7 @@ "icon": "Chroma", "legacy": false, "metadata": { - "code_hash": "82d38624f19a", + "code_hash": "fb95c1b84d71", "dependencies": { "dependencies": [ { @@ -10228,7 +10228,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from copy import deepcopy\nfrom typing import TYPE_CHECKING\n\nfrom chromadb.config import Settings\nfrom langchain_chroma import Chroma\nfrom typing_extensions import override\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.utils import chroma_collection_to_data\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, IntInput, StrInput\nfrom lfx.schema.data import Data\n\nif TYPE_CHECKING:\n from lfx.schema.dataframe import DataFrame\n\n\nclass ChromaVectorStoreComponent(LCVectorStoreComponent):\n \"\"\"Chroma Vector Store with search capabilities.\"\"\"\n\n display_name: str = \"Chroma DB\"\n description: str = \"Chroma Vector Store with search capabilities\"\n name = \"Chroma\"\n icon = \"Chroma\"\n\n inputs = [\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n value=\"langflow\",\n ),\n StrInput(\n name=\"persist_directory\",\n display_name=\"Persist Directory\",\n ),\n *LCVectorStoreComponent.inputs,\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n StrInput(\n name=\"chroma_server_cors_allow_origins\",\n display_name=\"Server CORS Allow Origins\",\n advanced=True,\n ),\n StrInput(\n name=\"chroma_server_host\",\n display_name=\"Server Host\",\n advanced=True,\n ),\n IntInput(\n name=\"chroma_server_http_port\",\n display_name=\"Server HTTP Port\",\n advanced=True,\n ),\n IntInput(\n name=\"chroma_server_grpc_port\",\n display_name=\"Server gRPC Port\",\n advanced=True,\n ),\n BoolInput(\n name=\"chroma_server_ssl_enabled\",\n display_name=\"Server SSL Enabled\",\n advanced=True,\n ),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n advanced=True,\n info=\"If false, will not add documents that are already in the Vector Store.\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n advanced=True,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=10,\n ),\n IntInput(\n name=\"limit\",\n display_name=\"Limit\",\n advanced=True,\n info=\"Limit the number of records to compare when Allow Duplicates is False.\",\n ),\n ]\n\n @override\n @check_cached_vector_store\n def build_vector_store(self) -> Chroma:\n \"\"\"Builds the Chroma object.\"\"\"\n try:\n from chromadb import Client\n from langchain_chroma import Chroma\n except ImportError as e:\n msg = \"Could not import Chroma integration package. Please install it with `pip install langchain-chroma`.\"\n raise ImportError(msg) from e\n # Chroma settings\n chroma_settings = None\n client = None\n if self.chroma_server_host:\n chroma_settings = Settings(\n chroma_server_cors_allow_origins=self.chroma_server_cors_allow_origins or [],\n chroma_server_host=self.chroma_server_host,\n chroma_server_http_port=self.chroma_server_http_port or None,\n chroma_server_grpc_port=self.chroma_server_grpc_port or None,\n chroma_server_ssl_enabled=self.chroma_server_ssl_enabled,\n )\n client = Client(settings=chroma_settings)\n\n # Check persist_directory and expand it if it is a relative path\n persist_directory = self.resolve_path(self.persist_directory) if self.persist_directory is not None else None\n\n chroma = Chroma(\n persist_directory=persist_directory,\n client=client,\n embedding_function=self.embedding,\n collection_name=self.collection_name,\n )\n\n self._add_documents_to_vector_store(chroma)\n limit = int(self.limit) if self.limit is not None and str(self.limit).strip() else None\n self.status = chroma_collection_to_data(chroma.get(limit=limit))\n return chroma\n\n def _add_documents_to_vector_store(self, vector_store: \"Chroma\") -> None:\n \"\"\"Adds documents to the Vector Store.\"\"\"\n ingest_data: list | Data | DataFrame = self.ingest_data\n if not ingest_data:\n self.status = \"\"\n return\n\n # Convert DataFrame to Data if needed using parent's method\n ingest_data = self._prepare_ingest_data()\n\n stored_documents_without_id = []\n if self.allow_duplicates:\n stored_data = []\n else:\n limit = int(self.limit) if self.limit is not None and str(self.limit).strip() else None\n stored_data = chroma_collection_to_data(vector_store.get(limit=limit))\n for value in deepcopy(stored_data):\n del value.id\n stored_documents_without_id.append(value)\n\n documents = []\n for _input in ingest_data or []:\n if isinstance(_input, Data):\n if _input not in stored_documents_without_id:\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise TypeError(msg)\n\n if documents and self.embedding is not None:\n self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n # Filter complex metadata to prevent ChromaDB errors\n try:\n from langchain_community.vectorstores.utils import filter_complex_metadata\n\n filtered_documents = filter_complex_metadata(documents)\n vector_store.add_documents(filtered_documents)\n except ImportError:\n self.log(\"Warning: Could not import filter_complex_metadata. Adding documents without filtering.\")\n vector_store.add_documents(documents)\n else:\n self.log(\"No documents to add to the Vector Store.\")\n" + "value": "from copy import deepcopy\nfrom typing import TYPE_CHECKING\n\nfrom chromadb.config import Settings\nfrom langchain_chroma import Chroma\nfrom typing_extensions import override\n\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.utils import chroma_collection_to_data\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, IntInput, StrInput\nfrom lfx.schema.data import Data\n\nif TYPE_CHECKING:\n from lfx.schema.dataframe import DataFrame\n\n\nclass ChromaVectorStoreComponent(LCVectorStoreComponent):\n \"\"\"Chroma Vector Store with search capabilities.\"\"\"\n\n display_name: str = \"Chroma DB\"\n description: str = \"Chroma Vector Store with search capabilities\"\n name = \"Chroma\"\n icon = \"Chroma\"\n\n inputs = [\n StrInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n value=\"langflow\",\n ),\n StrInput(\n name=\"persist_directory\",\n display_name=\"Persist Directory\",\n ),\n *LCVectorStoreComponent.inputs,\n HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n StrInput(\n name=\"chroma_server_cors_allow_origins\",\n display_name=\"Server CORS Allow Origins\",\n advanced=True,\n ),\n StrInput(\n name=\"chroma_server_host\",\n display_name=\"Server Host\",\n advanced=True,\n ),\n IntInput(\n name=\"chroma_server_http_port\",\n display_name=\"Server HTTP Port\",\n advanced=True,\n ),\n IntInput(\n name=\"chroma_server_grpc_port\",\n display_name=\"Server gRPC Port\",\n advanced=True,\n ),\n BoolInput(\n name=\"chroma_server_ssl_enabled\",\n display_name=\"Server SSL Enabled\",\n advanced=True,\n ),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n advanced=True,\n info=\"If false, will not add documents that are already in the Vector Store.\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n advanced=True,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=10,\n ),\n IntInput(\n name=\"limit\",\n display_name=\"Limit\",\n advanced=True,\n info=\"Limit the number of records to compare when Allow Duplicates is False.\",\n ),\n ]\n\n @override\n @check_cached_vector_store\n def build_vector_store(self) -> Chroma:\n \"\"\"Builds the Chroma object.\"\"\"\n try:\n from chromadb import Client\n from langchain_chroma import Chroma\n except ImportError as e:\n msg = \"Could not import Chroma integration package. Please install it with `pip install langchain-chroma`.\"\n raise ImportError(msg) from e\n # Chroma settings\n chroma_settings = None\n client = None\n if self.chroma_server_host:\n chroma_settings = Settings(\n chroma_server_cors_allow_origins=self.chroma_server_cors_allow_origins or [],\n chroma_server_host=self.chroma_server_host,\n chroma_server_http_port=self.chroma_server_http_port or None,\n chroma_server_grpc_port=self.chroma_server_grpc_port or None,\n chroma_server_ssl_enabled=self.chroma_server_ssl_enabled,\n )\n client = Client(settings=chroma_settings)\n\n # Check persist_directory and expand it if it is a relative path\n persist_directory = self.resolve_path(self.persist_directory) if self.persist_directory is not None else None\n\n chroma = Chroma(\n persist_directory=persist_directory,\n client=client,\n embedding_function=self.embedding,\n collection_name=self.collection_name,\n **chroma_langchain_collection_kwargs(),\n )\n\n self._add_documents_to_vector_store(chroma)\n limit = int(self.limit) if self.limit is not None and str(self.limit).strip() else None\n self.status = chroma_collection_to_data(chroma.get(limit=limit))\n return chroma\n\n def _add_documents_to_vector_store(self, vector_store: \"Chroma\") -> None:\n \"\"\"Adds documents to the Vector Store.\"\"\"\n ingest_data: list | Data | DataFrame = self.ingest_data\n if not ingest_data:\n self.status = \"\"\n return\n\n # Convert DataFrame to Data if needed using parent's method\n ingest_data = self._prepare_ingest_data()\n\n stored_documents_without_id = []\n if self.allow_duplicates:\n stored_data = []\n else:\n limit = int(self.limit) if self.limit is not None and str(self.limit).strip() else None\n stored_data = chroma_collection_to_data(vector_store.get(limit=limit))\n for value in deepcopy(stored_data):\n del value.id\n stored_documents_without_id.append(value)\n\n documents = []\n for _input in ingest_data or []:\n if isinstance(_input, Data):\n if _input not in stored_documents_without_id:\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise TypeError(msg)\n\n if documents and self.embedding is not None:\n self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n # Filter complex metadata to prevent ChromaDB errors\n try:\n from langchain_community.vectorstores.utils import filter_complex_metadata\n\n filtered_documents = filter_complex_metadata(documents)\n vector_store.add_documents(filtered_documents)\n except ImportError:\n self.log(\"Warning: Could not import filter_complex_metadata. Adding documents without filtering.\")\n vector_store.add_documents(documents)\n else:\n self.log(\"No documents to add to the Vector Store.\")\n" }, "collection_name": { "_input_type": "StrInput", @@ -68885,7 +68885,7 @@ "icon": "download", "legacy": false, "metadata": { - "code_hash": "8b5ca1f38f6e", + "code_hash": "673e3087b073", "dependencies": { "dependencies": [ { @@ -68997,7 +68997,7 @@ "show": true, "title_case": false, "type": "code", - "value": "import json\nimport os\nimport uuid\nfrom pathlib import Path\nfrom typing import Any\n\nimport chromadb\nimport chromadb.api.client\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom pydantic import SecretStr\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import (\n get_model_provider_variable_mapping,\n get_provider_all_variables,\n)\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.services.deps import get_settings_service, get_variable_service, session_scope\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge retrieval is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeBaseComponent(Component):\n display_name = \"Knowledge Base\"\n description = \"Search and retrieve data from knowledge.\"\n icon = \"download\"\n name = \"KnowledgeBase\"\n\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"API key for the embedding provider to generate embeddings.\",\n advanced=True,\n required=False,\n ),\n MessageTextInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n info=\"Optional search query to filter knowledge base data.\",\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of top results to return from the knowledge base.\",\n value=5,\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"include_metadata\",\n display_name=\"Include Metadata\",\n info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n value=True,\n advanced=False,\n ),\n BoolInput(\n name=\"include_embeddings\",\n display_name=\"Include Embeddings\",\n info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"retrieve_data\",\n display_name=\"Results\",\n method=\"retrieve_data\",\n info=\"Returns the data from the selected knowledge base.\",\n ),\n ]\n\n async def update_build_config(self, build_config, field_value, field_name=None): # noqa: ARG002\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n if field_name == \"knowledge_base\":\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id, # Use the user_id from the component context\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n\n @property\n def _user_uuid(self) -> uuid.UUID | None:\n \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n if not self.user_id:\n return None\n return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n def _get_kb_metadata(self, kb_path: Path) -> dict:\n \"\"\"Load and process knowledge base metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n metadata: dict[str, Any] = {}\n metadata_file = kb_path / \"embedding_metadata.json\"\n if not metadata_file.exists():\n logger.warning(f\"Embedding metadata file not found at {metadata_file}\")\n return metadata\n\n try:\n with metadata_file.open(\"r\", encoding=\"utf-8\") as f:\n metadata = json.load(f)\n except json.JSONDecodeError:\n logger.error(f\"Error decoding JSON from {metadata_file}\")\n return {}\n\n # Decrypt API key if it exists\n if \"api_key\" in metadata and metadata.get(\"api_key\"):\n settings_service = get_settings_service()\n try:\n decrypted_key = decrypt_api_key(metadata[\"api_key\"], settings_service)\n metadata[\"api_key\"] = decrypted_key\n except (InvalidToken, TypeError, ValueError) as e:\n logger.error(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n metadata[\"api_key\"] = None\n return metadata\n\n async def _resolve_provider_variables(self, provider: str) -> dict[str, str]:\n \"\"\"Resolve all global variables for a provider using the async session.\n\n This avoids the run_until_complete thread dance by doing the lookup\n directly in the already-running async context.\n \"\"\"\n result: dict[str, str] = {}\n provider_vars = get_provider_all_variables(provider)\n user_id = self._user_uuid\n if not provider_vars or not user_id:\n return result\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return result\n\n for var_info in provider_vars:\n var_key = var_info.get(\"variable_key\")\n if not var_key:\n continue\n try:\n value = await variable_service.get_variable(\n user_id=user_id,\n name=var_key,\n field=\"\",\n session=session,\n )\n if value and str(value).strip():\n result[var_key] = str(value)\n except (ValueError, KeyError, AttributeError) as e:\n logger.debug(f\"Variable service lookup failed for '{var_key}', falling back to environment: {e}\")\n env_value = os.environ.get(var_key)\n if env_value and env_value.strip():\n result[var_key] = env_value\n return result\n\n async def _resolve_api_key(self, provider: str) -> str | None:\n \"\"\"Resolve the API key for the given provider.\n\n Priority: user override > metadata (decrypted) > global variable.\n \"\"\"\n provider_variable_map = get_model_provider_variable_mapping()\n variable_name = provider_variable_map.get(provider)\n user_id = self._user_uuid\n if not variable_name or not user_id:\n return None\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return None\n try:\n return await variable_service.get_variable(\n user_id=user_id,\n name=variable_name,\n field=\"\",\n session=session,\n )\n except (ValueError, KeyError, AttributeError):\n return None\n\n def _build_embeddings(self, metadata: dict, *, api_key: str | None = None, provider_vars: dict | None = None):\n \"\"\"Build embedding model from metadata.\n\n Args:\n metadata: The knowledge base embedding metadata.\n api_key: Pre-resolved API key (user override > metadata > global).\n provider_vars: Pre-resolved provider variables (for Ollama/WatsonX).\n \"\"\"\n provider = metadata.get(\"embedding_provider\")\n model = metadata.get(\"embedding_model\")\n chunk_size = metadata.get(\"chunk_size\")\n\n # Handle various providers\n if provider == \"OpenAI\":\n from langchain_openai import OpenAIEmbeddings\n\n if not api_key:\n msg = (\n \"OpenAI API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n openai_kwargs: dict = {\"model\": model, \"api_key\": api_key}\n if chunk_size is not None:\n openai_kwargs[\"chunk_size\"] = chunk_size\n return OpenAIEmbeddings(**openai_kwargs)\n if provider == \"HuggingFace\":\n from langchain_huggingface import HuggingFaceEmbeddings\n\n return HuggingFaceEmbeddings(\n model=model,\n )\n if provider == \"Cohere\":\n from langchain_cohere import CohereEmbeddings\n\n if not api_key:\n msg = \"Cohere API key is required when using Cohere provider\"\n raise ValueError(msg)\n return CohereEmbeddings(\n model=model,\n cohere_api_key=api_key,\n )\n if provider == \"Google Generative AI\":\n from langchain_google_genai import GoogleGenerativeAIEmbeddings\n\n if not api_key:\n msg = (\n \"Google API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n return GoogleGenerativeAIEmbeddings(\n model=model,\n google_api_key=api_key,\n )\n if provider == \"Ollama\":\n from langchain_ollama import OllamaEmbeddings\n\n all_vars = provider_vars or {}\n base_url = all_vars.get(\"OLLAMA_BASE_URL\")\n kwargs: dict = {\"model\": model}\n if base_url:\n kwargs[\"base_url\"] = base_url\n return OllamaEmbeddings(**kwargs)\n if provider == \"IBM WatsonX\":\n from langchain_ibm import WatsonxEmbeddings\n\n all_vars = provider_vars or {}\n watsonx_apikey = api_key or all_vars.get(\"WATSONX_APIKEY\")\n watsonx_project_id = all_vars.get(\"WATSONX_PROJECT_ID\")\n watsonx_url = all_vars.get(\"WATSONX_URL\")\n if not watsonx_apikey:\n msg = (\n \"IBM WatsonX API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n kwargs = {\"model_id\": model, \"apikey\": watsonx_apikey}\n if watsonx_project_id:\n kwargs[\"project_id\"] = watsonx_project_id\n if watsonx_url:\n kwargs[\"url\"] = watsonx_url\n return WatsonxEmbeddings(**kwargs)\n if provider == \"Custom\":\n # For custom embedding models, we would need additional configuration\n msg = \"Custom embedding models not yet supported\"\n raise NotImplementedError(msg)\n msg = f\"Embedding provider '{provider}' is not supported for retrieval.\"\n raise NotImplementedError(msg)\n\n async def retrieve_data(self) -> DataFrame:\n \"\"\"Retrieve data from the selected knowledge base by reading the Chroma collection.\n\n Returns:\n A DataFrame containing the data rows from the knowledge base.\n \"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n # Get the current user\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching Knowledge Base data.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n kb_path = _get_knowledge_bases_root_path() / kb_user / self.knowledge_base\n\n metadata = self._get_kb_metadata(kb_path)\n if not metadata:\n msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n raise ValueError(msg)\n\n # Resolve API key: user override > metadata (decrypted) > global variable\n provider = metadata.get(\"embedding_provider\")\n runtime_api_key = self.api_key.get_secret_value() if isinstance(self.api_key, SecretStr) else self.api_key\n api_key = runtime_api_key or metadata.get(\"api_key\")\n if not api_key and provider:\n api_key = await self._resolve_api_key(provider)\n\n # Resolve provider-specific variables (e.g. base_url for Ollama, project_id for WatsonX)\n provider_vars: dict[str, str] = {}\n if provider in {\"Ollama\", \"IBM WatsonX\"}:\n provider_vars = await self._resolve_provider_variables(provider)\n\n # Build the embedder for the knowledge base\n embedding_function = self._build_embeddings(metadata, api_key=api_key, provider_vars=provider_vars)\n\n # Clear Chroma's singleton client cache to avoid \"different settings\"\n # conflicts when ingestion and retrieval run in the same process.\n chromadb.api.client.SharedSystemClient.clear_system_cache()\n chroma = Chroma(\n persist_directory=str(kb_path),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n )\n\n # If a search query is provided, perform a similarity search\n if self.search_query:\n # Use the search query to perform a similarity search\n logger.info(\"Performing similarity search\")\n results = chroma.similarity_search_with_score(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n else:\n results = chroma.similarity_search(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n\n # For each result, make it a tuple to match the expected output format\n results = [(doc, 0) for doc in results] # Assign a dummy score of 0\n\n # If include_embeddings is enabled, get embeddings for the results\n id_to_embedding = {}\n if self.include_embeddings and results:\n doc_ids = [doc[0].metadata.get(\"_id\") for doc in results if doc[0].metadata.get(\"_id\")]\n\n # Only proceed if we have valid document IDs\n if doc_ids:\n # Access underlying collection to get embeddings\n collection = chroma._collection # noqa: SLF001\n embeddings_result = collection.get(where={\"_id\": {\"$in\": doc_ids}}, include=[\"metadatas\", \"embeddings\"])\n\n # Create a mapping from document ID to embedding\n for i, metadata in enumerate(embeddings_result.get(\"metadatas\", [])):\n if metadata and \"_id\" in metadata:\n id_to_embedding[metadata[\"_id\"]] = embeddings_result[\"embeddings\"][i]\n\n # Build output data based on include_metadata setting\n data_list = []\n for doc in results:\n kwargs = {\n \"content\": doc[0].page_content,\n }\n if self.search_query:\n kwargs[\"_score\"] = -1 * doc[1]\n if self.include_metadata:\n # Include all metadata, embeddings, and content\n kwargs.update(doc[0].metadata)\n if self.include_embeddings:\n kwargs[\"_embeddings\"] = id_to_embedding.get(doc[0].metadata.get(\"_id\"))\n\n data_list.append(Data(**kwargs))\n\n # Return the DataFrame containing the data\n return DataFrame(data=data_list)\n" + "value": "import json\nimport os\nimport uuid\nfrom pathlib import Path\nfrom typing import Any\n\nimport chromadb\nimport chromadb.api.client\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom pydantic import SecretStr\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import (\n get_model_provider_variable_mapping,\n get_provider_all_variables,\n)\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.custom import Component\nfrom lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.services.deps import get_settings_service, get_variable_service, session_scope\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge retrieval is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeBaseComponent(Component):\n display_name = \"Knowledge Base\"\n description = \"Search and retrieve data from knowledge.\"\n icon = \"download\"\n name = \"KnowledgeBase\"\n\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"API key for the embedding provider to generate embeddings.\",\n advanced=True,\n required=False,\n ),\n MessageTextInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n info=\"Optional search query to filter knowledge base data.\",\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of top results to return from the knowledge base.\",\n value=5,\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"include_metadata\",\n display_name=\"Include Metadata\",\n info=\"Whether to include all metadata in the output. If false, only content is returned.\",\n value=True,\n advanced=False,\n ),\n BoolInput(\n name=\"include_embeddings\",\n display_name=\"Include Embeddings\",\n info=\"Whether to include embeddings in the output. Only applicable if 'Include Metadata' is enabled.\",\n value=False,\n advanced=True,\n ),\n ]\n\n outputs = [\n Output(\n name=\"retrieve_data\",\n display_name=\"Results\",\n method=\"retrieve_data\",\n info=\"Returns the data from the selected knowledge base.\",\n ),\n ]\n\n async def update_build_config(self, build_config, field_value, field_name=None): # noqa: ARG002\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n if field_name == \"knowledge_base\":\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id, # Use the user_id from the component context\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n\n @property\n def _user_uuid(self) -> uuid.UUID | None:\n \"\"\"Return self.user_id as a UUID, converting from str if necessary.\"\"\"\n if not self.user_id:\n return None\n return self.user_id if isinstance(self.user_id, uuid.UUID) else uuid.UUID(self.user_id)\n\n def _get_kb_metadata(self, kb_path: Path) -> dict:\n \"\"\"Load and process knowledge base metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n metadata: dict[str, Any] = {}\n metadata_file = kb_path / \"embedding_metadata.json\"\n if not metadata_file.exists():\n logger.warning(f\"Embedding metadata file not found at {metadata_file}\")\n return metadata\n\n try:\n with metadata_file.open(\"r\", encoding=\"utf-8\") as f:\n metadata = json.load(f)\n except json.JSONDecodeError:\n logger.error(f\"Error decoding JSON from {metadata_file}\")\n return {}\n\n # Decrypt API key if it exists\n if \"api_key\" in metadata and metadata.get(\"api_key\"):\n settings_service = get_settings_service()\n try:\n decrypted_key = decrypt_api_key(metadata[\"api_key\"], settings_service)\n metadata[\"api_key\"] = decrypted_key\n except (InvalidToken, TypeError, ValueError) as e:\n logger.error(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n metadata[\"api_key\"] = None\n return metadata\n\n async def _resolve_provider_variables(self, provider: str) -> dict[str, str]:\n \"\"\"Resolve all global variables for a provider using the async session.\n\n This avoids the run_until_complete thread dance by doing the lookup\n directly in the already-running async context.\n \"\"\"\n result: dict[str, str] = {}\n provider_vars = get_provider_all_variables(provider)\n user_id = self._user_uuid\n if not provider_vars or not user_id:\n return result\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return result\n\n for var_info in provider_vars:\n var_key = var_info.get(\"variable_key\")\n if not var_key:\n continue\n try:\n value = await variable_service.get_variable(\n user_id=user_id,\n name=var_key,\n field=\"\",\n session=session,\n )\n if value and str(value).strip():\n result[var_key] = str(value)\n except (ValueError, KeyError, AttributeError) as e:\n logger.debug(f\"Variable service lookup failed for '{var_key}', falling back to environment: {e}\")\n env_value = os.environ.get(var_key)\n if env_value and env_value.strip():\n result[var_key] = env_value\n return result\n\n async def _resolve_api_key(self, provider: str) -> str | None:\n \"\"\"Resolve the API key for the given provider.\n\n Priority: user override > metadata (decrypted) > global variable.\n \"\"\"\n provider_variable_map = get_model_provider_variable_mapping()\n variable_name = provider_variable_map.get(provider)\n user_id = self._user_uuid\n if not variable_name or not user_id:\n return None\n\n async with session_scope() as session:\n variable_service = get_variable_service()\n if variable_service is None:\n return None\n try:\n return await variable_service.get_variable(\n user_id=user_id,\n name=variable_name,\n field=\"\",\n session=session,\n )\n except (ValueError, KeyError, AttributeError):\n return None\n\n def _build_embeddings(self, metadata: dict, *, api_key: str | None = None, provider_vars: dict | None = None):\n \"\"\"Build embedding model from metadata.\n\n Args:\n metadata: The knowledge base embedding metadata.\n api_key: Pre-resolved API key (user override > metadata > global).\n provider_vars: Pre-resolved provider variables (for Ollama/WatsonX).\n \"\"\"\n provider = metadata.get(\"embedding_provider\")\n model = metadata.get(\"embedding_model\")\n chunk_size = metadata.get(\"chunk_size\")\n\n # Handle various providers\n if provider == \"OpenAI\":\n from langchain_openai import OpenAIEmbeddings\n\n if not api_key:\n msg = (\n \"OpenAI API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n openai_kwargs: dict = {\"model\": model, \"api_key\": api_key}\n if chunk_size is not None:\n openai_kwargs[\"chunk_size\"] = chunk_size\n return OpenAIEmbeddings(**openai_kwargs)\n if provider == \"HuggingFace\":\n from langchain_huggingface import HuggingFaceEmbeddings\n\n return HuggingFaceEmbeddings(\n model=model,\n )\n if provider == \"Cohere\":\n from langchain_cohere import CohereEmbeddings\n\n if not api_key:\n msg = \"Cohere API key is required when using Cohere provider\"\n raise ValueError(msg)\n return CohereEmbeddings(\n model=model,\n cohere_api_key=api_key,\n )\n if provider == \"Google Generative AI\":\n from langchain_google_genai import GoogleGenerativeAIEmbeddings\n\n if not api_key:\n msg = (\n \"Google API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n return GoogleGenerativeAIEmbeddings(\n model=model,\n google_api_key=api_key,\n )\n if provider == \"Ollama\":\n from langchain_ollama import OllamaEmbeddings\n\n all_vars = provider_vars or {}\n base_url = all_vars.get(\"OLLAMA_BASE_URL\")\n kwargs: dict = {\"model\": model}\n if base_url:\n kwargs[\"base_url\"] = base_url\n return OllamaEmbeddings(**kwargs)\n if provider == \"IBM WatsonX\":\n from langchain_ibm import WatsonxEmbeddings\n\n all_vars = provider_vars or {}\n watsonx_apikey = api_key or all_vars.get(\"WATSONX_APIKEY\")\n watsonx_project_id = all_vars.get(\"WATSONX_PROJECT_ID\")\n watsonx_url = all_vars.get(\"WATSONX_URL\")\n if not watsonx_apikey:\n msg = (\n \"IBM WatsonX API key is required. Provide it in the component's advanced settings\"\n \" or configure it globally.\"\n )\n raise ValueError(msg)\n kwargs = {\"model_id\": model, \"apikey\": watsonx_apikey}\n if watsonx_project_id:\n kwargs[\"project_id\"] = watsonx_project_id\n if watsonx_url:\n kwargs[\"url\"] = watsonx_url\n return WatsonxEmbeddings(**kwargs)\n if provider == \"Custom\":\n # For custom embedding models, we would need additional configuration\n msg = \"Custom embedding models not yet supported\"\n raise NotImplementedError(msg)\n msg = f\"Embedding provider '{provider}' is not supported for retrieval.\"\n raise NotImplementedError(msg)\n\n async def retrieve_data(self) -> DataFrame:\n \"\"\"Retrieve data from the selected knowledge base by reading the Chroma collection.\n\n Returns:\n A DataFrame containing the data rows from the knowledge base.\n \"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n # Get the current user\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching Knowledge Base data.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n kb_path = _get_knowledge_bases_root_path() / kb_user / self.knowledge_base\n\n metadata = self._get_kb_metadata(kb_path)\n if not metadata:\n msg = f\"Metadata not found for knowledge base: {self.knowledge_base}. Ensure it has been indexed.\"\n raise ValueError(msg)\n\n # Resolve API key: user override > metadata (decrypted) > global variable\n provider = metadata.get(\"embedding_provider\")\n runtime_api_key = self.api_key.get_secret_value() if isinstance(self.api_key, SecretStr) else self.api_key\n api_key = runtime_api_key or metadata.get(\"api_key\")\n if not api_key and provider:\n api_key = await self._resolve_api_key(provider)\n\n # Resolve provider-specific variables (e.g. base_url for Ollama, project_id for WatsonX)\n provider_vars: dict[str, str] = {}\n if provider in {\"Ollama\", \"IBM WatsonX\"}:\n provider_vars = await self._resolve_provider_variables(provider)\n\n # Build the embedder for the knowledge base\n embedding_function = self._build_embeddings(metadata, api_key=api_key, provider_vars=provider_vars)\n\n # Clear Chroma's singleton client cache to avoid \"different settings\"\n # conflicts when ingestion and retrieval run in the same process.\n chromadb.api.client.SharedSystemClient.clear_system_cache()\n chroma = Chroma(\n persist_directory=str(kb_path),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # If a search query is provided, perform a similarity search\n if self.search_query:\n # Use the search query to perform a similarity search\n logger.info(\"Performing similarity search\")\n results = chroma.similarity_search_with_score(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n else:\n results = chroma.similarity_search(\n query=self.search_query or \"\",\n k=self.top_k,\n )\n\n # For each result, make it a tuple to match the expected output format\n results = [(doc, 0) for doc in results] # Assign a dummy score of 0\n\n # If include_embeddings is enabled, get embeddings for the results\n id_to_embedding = {}\n if self.include_embeddings and results:\n doc_ids = [doc[0].metadata.get(\"_id\") for doc in results if doc[0].metadata.get(\"_id\")]\n\n # Only proceed if we have valid document IDs\n if doc_ids:\n # Access underlying collection to get embeddings\n collection = chroma._collection # noqa: SLF001\n embeddings_result = collection.get(where={\"_id\": {\"$in\": doc_ids}}, include=[\"metadatas\", \"embeddings\"])\n\n # Create a mapping from document ID to embedding\n for i, metadata in enumerate(embeddings_result.get(\"metadatas\", [])):\n if metadata and \"_id\" in metadata:\n id_to_embedding[metadata[\"_id\"]] = embeddings_result[\"embeddings\"][i]\n\n # Build output data based on include_metadata setting\n data_list = []\n for doc in results:\n kwargs = {\n \"content\": doc[0].page_content,\n }\n if self.search_query:\n kwargs[\"_score\"] = -1 * doc[1]\n if self.include_metadata:\n # Include all metadata, embeddings, and content\n kwargs.update(doc[0].metadata)\n if self.include_embeddings:\n kwargs[\"_embeddings\"] = id_to_embedding.get(doc[0].metadata.get(\"_id\"))\n\n data_list.append(Data(**kwargs))\n\n # Return the DataFrame containing the data\n return DataFrame(data=data_list)\n" }, "include_embeddings": { "_input_type": "BoolInput", @@ -69136,7 +69136,7 @@ "icon": "upload", "legacy": false, "metadata": { - "code_hash": "1b571470646c", + "code_hash": "d6904604cfc8", "dependencies": { "dependencies": [ { @@ -69268,7 +69268,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport re\nimport uuid\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any\n\nimport pandas as pd\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key, encrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.io import (\n BoolInput,\n DropdownInput,\n HandleInput,\n IntInput,\n ModelInput,\n Output,\n SecretStrInput,\n StrInput,\n TableInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n get_settings_service,\n session_scope,\n)\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\nif TYPE_CHECKING:\n from lfx.schema.dataframe import DataFrame\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge ingestion is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeIngestionComponent(Component):\n \"\"\"Create or append to Langflow Knowledge from a DataFrame.\"\"\"\n\n # ------ UI metadata ---------------------------------------------------\n display_name = \"Knowledge Ingestion\"\n description = \"Create or update knowledge in Langflow.\"\n icon = \"upload\"\n name = \"KnowledgeIngestion\"\n\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self._cached_kb_path: Path | None = None\n\n @dataclass\n class NewKnowledgeBaseInput:\n functionality: str = \"create\"\n fields: dict[str, dict] = field(\n default_factory=lambda: {\n \"data\": {\n \"node\": {\n \"name\": \"create_knowledge_base\",\n \"description\": \"Create new knowledge in Langflow.\",\n \"display_name\": \"Create new knowledge\",\n \"field_order\": [\n \"01_new_kb_name\",\n \"02_embedding_model\",\n ],\n \"template\": {\n \"01_new_kb_name\": StrInput(\n name=\"new_kb_name\",\n display_name=\"Knowledge Name\",\n info=\"Name of the new knowledge to create.\",\n required=True,\n ),\n \"02_embedding_model\": ModelInput(\n name=\"embedding_model\",\n display_name=\"Choose Embedding Model\",\n info=(\n \"Select the embedding model to use for this knowledge base. \"\n \"Langflow uses the configured credentials for that model provider.\"\n ),\n required=True,\n model_type=\"embedding\",\n ),\n },\n },\n }\n }\n )\n\n # ------ Inputs --------------------------------------------------------\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n dialog_inputs=asdict(NewKnowledgeBaseInput()),\n ),\n HandleInput(\n name=\"input_df\",\n display_name=\"Input\",\n info=(\n \"Table with all original columns (already chunked / processed). \"\n \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n \"it is converted to a DataFrame automatically.\"\n ),\n input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n required=True,\n ),\n TableInput(\n name=\"column_config\",\n display_name=\"Column Configuration\",\n info=\"Configure column behavior for the knowledge base.\",\n required=True,\n table_schema=[\n {\n \"name\": \"column_name\",\n \"display_name\": \"Column Name\",\n \"type\": \"str\",\n \"description\": \"Name of the column in the source DataFrame\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"vectorize\",\n \"display_name\": \"Vectorize\",\n \"type\": \"boolean\",\n \"description\": \"Create embeddings for this column\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"identifier\",\n \"display_name\": \"Identifier\",\n \"type\": \"boolean\",\n \"description\": \"Use this column as unique identifier\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n value=[\n {\n \"column_name\": \"text\",\n \"vectorize\": True,\n \"identifier\": True,\n },\n ],\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"Batch size for processing embeddings\",\n advanced=True,\n value=1000,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n info=\"Allow duplicate rows in the knowledge base\",\n advanced=True,\n value=False,\n ),\n ]\n\n # ------ Outputs -------------------------------------------------------\n outputs = [Output(display_name=\"Results\", name=\"dataframe_output\", method=\"build_kb_info\")]\n\n # ------ Internal helpers ---------------------------------------------\n def _get_kb_root(self) -> Path:\n \"\"\"Return the root directory for knowledge bases.\"\"\"\n return _get_knowledge_bases_root_path()\n\n @staticmethod\n def _scalar_notna(value) -> bool:\n \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n ``pd.notna`` returns an array when given an array-like input, which\n cannot be used directly in a boolean context. This helper collapses the\n result to a single scalar ``bool``.\n \"\"\"\n result = pd.notna(value)\n # If result is array-like (numpy array, list, etc.), treat non-empty arrays as \"present\"\n if hasattr(result, \"__iter__\") and not isinstance(result, str):\n import numpy as np\n\n arr = np.asarray(result)\n return arr.size > 0 and arr.all()\n return bool(result)\n\n def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n if not self.column_config:\n msg = \"Column configuration cannot be empty\"\n raise ValueError(msg)\n\n # Convert table input to list of dicts (similar to Structured Output)\n config_list = self.column_config if isinstance(self.column_config, list) else []\n\n # Validate column names exist in DataFrame\n df_columns = set(df_source.columns)\n for config in config_list:\n col_name = config.get(\"column_name\")\n if col_name not in df_columns:\n msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n raise ValueError(msg)\n\n return config_list\n\n def _build_embedding_metadata(\n self,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> dict[str, Any]:\n \"\"\"Build embedding model metadata from a model selection dict.\n\n Args:\n model_selection: Model selection list from ModelInput\n (e.g. [{'name': ..., 'provider': ..., 'metadata': ...}])\n api_key: Optional runtime API key override.\n \"\"\"\n model_dict = model_selection[0] if isinstance(model_selection, list) else model_selection\n embedding_model = model_dict.get(\"name\", \"\")\n embedding_provider = model_dict.get(\"provider\", \"Unknown\")\n\n api_key_to_save = None\n if api_key and hasattr(api_key, \"get_secret_value\"):\n api_key_to_save = api_key.get_secret_value()\n elif isinstance(api_key, str):\n api_key_to_save = api_key\n\n encrypted_api_key = None\n if api_key_to_save:\n settings_service = get_settings_service()\n try:\n encrypted_api_key = encrypt_api_key(api_key_to_save, settings_service=settings_service)\n except (TypeError, ValueError) as e:\n self.log(f\"Could not encrypt API key: {e}\")\n\n return {\n \"embedding_provider\": embedding_provider,\n \"embedding_model\": embedding_model,\n \"model_selection\": model_dict, # Store full selection for get_embeddings() reconstruction\n \"api_key\": encrypted_api_key,\n \"api_key_used\": bool(api_key),\n \"chunk_size\": self.chunk_size,\n \"created_at\": datetime.now(timezone.utc).isoformat(),\n }\n\n def _save_embedding_metadata(\n self,\n kb_path: Path,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> None:\n \"\"\"Save embedding model metadata.\"\"\"\n embedding_metadata = self._build_embedding_metadata(model_selection, api_key)\n metadata_path = kb_path / \"embedding_metadata.json\"\n metadata_path.write_text(json.dumps(embedding_metadata, indent=2))\n\n def _update_metadata_metrics(self, kb_path: Path, chroma: Chroma) -> None:\n \"\"\"Update embedding_metadata.json with accurate chunk/word/character counts.\n\n This ensures the Knowledge Base modal displays correct stats after\n component-based ingestion, matching the behavior of API-based ingestion.\n Delegates to KBAnalysisHelper.update_text_metrics to avoid duplicating\n the batched metrics counting logic.\n \"\"\"\n import chromadb.errors\n from langflow.api.utils.kb_helpers import KBAnalysisHelper, KBStorageHelper\n\n metadata_path = kb_path / \"embedding_metadata.json\"\n if not metadata_path.exists():\n return\n\n try:\n metadata = json.loads(metadata_path.read_text())\n KBAnalysisHelper.update_text_metrics(kb_path, metadata, chroma)\n metadata[\"size\"] = KBStorageHelper.get_directory_size(kb_path)\n metadata_path.write_text(json.dumps(metadata, indent=2))\n except (OSError, ValueError, TypeError, json.JSONDecodeError, chromadb.errors.ChromaError) as e:\n self.log(f\"Warning: Could not update metadata metrics: {e}\")\n\n def _save_kb_files(\n self,\n kb_path: Path,\n config_list: list[dict[str, Any]],\n ) -> None:\n \"\"\"Save KB files using File Component storage patterns.\"\"\"\n try:\n # Create directory (following File Component patterns)\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save column configuration\n # Only do this if the file doesn't exist already\n cfg_path = kb_path / \"schema.json\"\n if not cfg_path.exists():\n cfg_path.write_text(json.dumps(config_list, indent=2))\n\n except (OSError, TypeError, ValueError) as e:\n self.log(f\"Error saving KB files: {e}\")\n\n def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n \"\"\"Build detailed column metadata.\"\"\"\n metadata: dict[str, Any] = {\n \"total_columns\": len(df_source.columns),\n \"mapped_columns\": len(config_list),\n \"unmapped_columns\": len(df_source.columns) - len(config_list),\n \"columns\": [],\n \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n }\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n # Add to columns list\n metadata[\"columns\"].append(\n {\n \"name\": col_name,\n \"vectorize\": vectorize,\n \"identifier\": identifier,\n }\n )\n\n # Update summary\n if vectorize:\n metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n if identifier:\n metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n return metadata\n\n async def _create_vector_store(\n self,\n df_source: pd.DataFrame,\n config_list: list[dict[str, Any]],\n embedding_function,\n ) -> Chroma:\n \"\"\"Create vector store following Local DB component pattern.\n\n Returns the Chroma instance so callers can use it for metrics updates.\n \"\"\"\n # Set up vector store directory\n vector_store_dir = await self._kb_path()\n if not vector_store_dir:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n vector_store_dir.mkdir(parents=True, exist_ok=True)\n\n # Convert DataFrame to Data objects (following Local DB pattern)\n data_objects = await self._convert_df_to_data_objects(df_source, config_list)\n\n # Create vector store\n chroma = Chroma(\n persist_directory=str(vector_store_dir),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n )\n\n # Convert Data objects to LangChain Documents\n documents = []\n for data_obj in data_objects:\n doc = data_obj.to_lc_document()\n documents.append(doc)\n\n # Add documents to vector store\n if documents:\n chroma.add_documents(documents)\n self.log(f\"Added {len(documents)} documents to vector store '{self.knowledge_base}'\")\n\n return chroma\n\n async def _convert_df_to_data_objects(\n self, df_source: pd.DataFrame, config_list: list[dict[str, Any]]\n ) -> list[Data]:\n \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n data_objects: list[Data] = []\n\n # Set up vector store directory\n kb_path = await self._kb_path()\n\n # If we don't allow duplicates, we need to get the existing hashes\n chroma = Chroma(\n persist_directory=str(kb_path),\n collection_name=self.knowledge_base,\n )\n\n # Get all documents and their metadata\n all_docs = chroma.get()\n\n # Extract all _id values from metadata\n id_list = [metadata.get(\"_id\") for metadata in all_docs[\"metadatas\"] if metadata.get(\"_id\")]\n\n # Get column roles\n content_cols = []\n identifier_cols = []\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n if vectorize:\n content_cols.append(col_name)\n elif identifier:\n identifier_cols.append(col_name)\n\n # Convert each row to a Data object\n for _, row in df_source.iterrows():\n # Build content text from identifier columns using list comprehension\n identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n # Join all parts into a single string\n page_content = \" \".join(identifier_parts)\n\n # Build metadata from NON-vectorized columns only (simple key-value pairs)\n data_dict = {\n \"text\": page_content, # Main content for vectorization\n }\n\n # Add identifier columns if they exist\n if identifier_cols:\n identifier_parts = [\n str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n ]\n page_content = \" \".join(identifier_parts)\n\n # Add metadata columns as simple key-value pairs\n for col in df_source.columns:\n if col not in content_cols and col in row and self._scalar_notna(row[col]):\n # Convert to simple types for Chroma metadata\n value = row[col]\n data_dict[col] = str(value) # Convert complex types to string\n\n # Hash the page_content for unique ID\n page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n data_dict[\"_id\"] = page_content_hash\n\n # If duplicates are disallowed, and hash exists, prevent adding this row\n if not self.allow_duplicates and page_content_hash in id_list:\n self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n continue\n\n # Create Data object - everything except \"text\" becomes metadata\n data_obj = Data(data=data_dict)\n data_objects.append(data_obj)\n\n return data_objects\n\n def is_valid_collection_name(self, name, min_length: int = 3, max_length: int = 63) -> bool:\n \"\"\"Validates collection name against conditions 1-3.\n\n 1. Contains 3-63 characters\n 2. Starts and ends with alphanumeric character\n 3. Contains only alphanumeric characters, underscores, or hyphens.\n\n Args:\n name (str): Collection name to validate\n min_length (int): Minimum length of the name\n max_length (int): Maximum length of the name\n\n Returns:\n bool: True if valid, False otherwise\n \"\"\"\n # Check length (condition 1)\n if not (min_length <= len(name) <= max_length):\n return False\n\n # Check start/end with alphanumeric (condition 2)\n if not (name[0].isalnum() and name[-1].isalnum()):\n return False\n\n # Check allowed characters (condition 3)\n return re.match(r\"^[a-zA-Z0-9_-]+$\", name) is not None\n\n async def _kb_path(self) -> Path | None:\n # Check if we already have the path cached\n cached_path = getattr(self, \"_cached_kb_path\", None)\n if cached_path is not None:\n return cached_path\n\n # If not cached, compute it\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base path.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n\n kb_root = self._get_kb_root()\n\n # Cache the result\n self._cached_kb_path = kb_root / kb_user / self.knowledge_base\n\n return self._cached_kb_path\n\n # ---------------------------------------------------------------------\n # OUTPUT METHODS\n # ---------------------------------------------------------------------\n async def build_kb_info(self) -> Data:\n \"\"\"Main ingestion routine → returns a dict with KB metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n try:\n input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n # Validate column configuration (using Structured Output patterns)\n config_list = self._validate_column_config(df_source)\n column_metadata = self._build_column_metadata(config_list, df_source)\n\n # Read the embedding info from the knowledge base folder\n kb_path = await self._kb_path()\n if not kb_path:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n metadata_path = kb_path / \"embedding_metadata.json\"\n api_key = None\n model_selection = None\n\n # Read stored metadata\n if metadata_path.exists():\n settings_service = get_settings_service()\n stored_metadata = json.loads(metadata_path.read_text())\n\n # Prefer stored model_selection dict (new format)\n model_selection = stored_metadata.get(\"model_selection\")\n if model_selection:\n model_selection = [model_selection] if isinstance(model_selection, dict) else model_selection\n else:\n # Backward compat: reconstruct from old string-based metadata\n embedding_model_name = stored_metadata.get(\"embedding_model\")\n embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n if embedding_model_name:\n # Look up full model info from available options\n try:\n all_options = get_embedding_model_options(user_id=self.user_id)\n match = next(\n (o for o in all_options if o.get(\"name\") == embedding_model_name),\n None,\n )\n if match:\n model_selection = [match]\n else:\n self.log(\n f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n \"from stored metadata is no longer available in the model registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n \"The knowledge base was created with an older format and the model \"\n \"is not available in the current registry. \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg)\n except ValueError:\n raise\n except Exception: # noqa: BLE001\n self.log(\n f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Could not look up embedding model '{embedding_model_name}' \"\n f\"(provider: {embedding_provider}). \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg) # noqa: B904\n\n # Decrypt stored API key\n encrypted_key = stored_metadata.get(\"api_key\")\n if encrypted_key:\n try:\n api_key = decrypt_api_key(encrypted_key, settings_service)\n except (InvalidToken, TypeError, ValueError) as e:\n self.log(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n\n # Check if a custom API key was provided\n if self.api_key:\n api_key = self.api_key\n if model_selection:\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n api_key=api_key,\n )\n\n if not model_selection:\n msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n raise ValueError(msg)\n\n # Build the embedding function via the shared utility\n embedding_function = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n api_key=api_key,\n chunk_size=self.chunk_size,\n )\n\n # Create vector store following Local DB component pattern\n chroma = await self._create_vector_store(df_source, config_list, embedding_function=embedding_function)\n\n # Save KB files (using File Component storage patterns)\n self._save_kb_files(kb_path, config_list)\n\n # Update embedding_metadata.json with accurate text metrics\n # so the KB modal and API show correct chunks/words/characters\n self._update_metadata_metrics(kb_path, chroma)\n\n # Build metadata response\n meta: dict[str, Any] = {\n \"kb_id\": str(uuid.uuid4()),\n \"kb_name\": self.knowledge_base,\n \"rows\": len(df_source),\n \"column_metadata\": column_metadata,\n \"path\": str(kb_path),\n \"config_columns\": len(config_list),\n \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n }\n\n # Set status message\n self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n return Data(data=meta)\n\n except (OSError, ValueError, RuntimeError, KeyError) as e:\n msg = f\"Error during KB ingestion: {e}\"\n raise RuntimeError(msg) from e\n\n async def update_build_config(\n self,\n build_config,\n field_value: Any,\n field_name: str | None = None,\n ):\n \"\"\"Update build configuration based on provider selection.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n # Populate the dialog's embedding model options so the ModelInput renders correctly\n try:\n dialog_template = (\n build_config[\"knowledge_base\"]\n .get(\"dialog_inputs\", {})\n .get(\"fields\", {})\n .get(\"data\", {})\n .get(\"node\", {})\n .get(\"template\", {})\n )\n if \"02_embedding_model\" in dialog_template:\n embedding_options = get_embedding_model_options(user_id=self.user_id)\n dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n except Exception: # noqa: BLE001\n self.log(\"Failed to populate embedding model options in dialog\")\n\n # Create a new knowledge base\n if field_name == \"knowledge_base\":\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base list.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n # Validate the knowledge base name - Make sure it follows these rules:\n if not self.is_valid_collection_name(field_value[\"01_new_kb_name\"]):\n msg = f\"Invalid knowledge base name: {field_value['01_new_kb_name']}\"\n raise ValueError(msg)\n\n # The model selection comes from ModelInput as a list of dicts\n model_selection = field_value[\"02_embedding_model\"]\n if isinstance(model_selection, dict):\n model_selection = [model_selection]\n\n # Build and validate the embedding model via the shared utility\n embed_model = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n )\n\n # Try to generate a dummy embedding to validate without blocking the event loop\n try:\n await asyncio.wait_for(\n asyncio.to_thread(embed_model.embed_query, \"test\"),\n timeout=10,\n )\n except TimeoutError as e:\n msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n raise ValueError(msg) from e\n except Exception as e:\n msg = f\"Embedding validation failed: {e!s}\"\n raise ValueError(msg) from e\n\n # Create the new knowledge base directory\n kb_path = _get_knowledge_bases_root_path() / kb_user / field_value[\"01_new_kb_name\"]\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save the embedding metadata\n build_config[\"knowledge_base\"][\"value\"] = field_value[\"01_new_kb_name\"]\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n )\n\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id,\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n" + "value": "from __future__ import annotations\n\nimport asyncio\nimport hashlib\nimport json\nimport re\nimport uuid\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any\n\nimport pandas as pd\nfrom cryptography.fernet import InvalidToken\nfrom langchain_chroma import Chroma\nfrom langflow.services.auth.utils import decrypt_api_key, encrypt_api_key\nfrom langflow.services.database.models.user.crud import get_user_by_id\n\nfrom lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases\nfrom lfx.base.models.unified_models import get_embedding_model_options, get_embeddings\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.components.processing.converter import convert_to_dataframe\nfrom lfx.custom import Component\nfrom lfx.io import (\n BoolInput,\n DropdownInput,\n HandleInput,\n IntInput,\n ModelInput,\n Output,\n SecretStrInput,\n StrInput,\n TableInput,\n)\nfrom lfx.schema.data import Data\nfrom lfx.schema.table import EditMode\nfrom lfx.services.deps import (\n get_settings_service,\n session_scope,\n)\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\nif TYPE_CHECKING:\n from lfx.schema.dataframe import DataFrame\n\n_KNOWLEDGE_BASES_ROOT_PATH: Path | None = None\n\n# Error message to raise if we're in Astra cloud environment and the component is not supported.\nastra_error_msg = \"Knowledge ingestion is not supported in Astra cloud environment.\"\n\n\ndef _get_knowledge_bases_root_path() -> Path:\n \"\"\"Lazy load the knowledge bases root path from settings.\"\"\"\n global _KNOWLEDGE_BASES_ROOT_PATH # noqa: PLW0603\n if _KNOWLEDGE_BASES_ROOT_PATH is None:\n settings = get_settings_service().settings\n knowledge_directory = settings.knowledge_bases_dir\n if not knowledge_directory:\n msg = \"Knowledge bases directory is not set in the settings.\"\n raise ValueError(msg)\n _KNOWLEDGE_BASES_ROOT_PATH = Path(knowledge_directory).expanduser()\n return _KNOWLEDGE_BASES_ROOT_PATH\n\n\nclass KnowledgeIngestionComponent(Component):\n \"\"\"Create or append to Langflow Knowledge from a DataFrame.\"\"\"\n\n # ------ UI metadata ---------------------------------------------------\n display_name = \"Knowledge Ingestion\"\n description = \"Create or update knowledge in Langflow.\"\n icon = \"upload\"\n name = \"KnowledgeIngestion\"\n\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self._cached_kb_path: Path | None = None\n\n @dataclass\n class NewKnowledgeBaseInput:\n functionality: str = \"create\"\n fields: dict[str, dict] = field(\n default_factory=lambda: {\n \"data\": {\n \"node\": {\n \"name\": \"create_knowledge_base\",\n \"description\": \"Create new knowledge in Langflow.\",\n \"display_name\": \"Create new knowledge\",\n \"field_order\": [\n \"01_new_kb_name\",\n \"02_embedding_model\",\n ],\n \"template\": {\n \"01_new_kb_name\": StrInput(\n name=\"new_kb_name\",\n display_name=\"Knowledge Name\",\n info=\"Name of the new knowledge to create.\",\n required=True,\n ),\n \"02_embedding_model\": ModelInput(\n name=\"embedding_model\",\n display_name=\"Choose Embedding Model\",\n info=(\n \"Select the embedding model to use for this knowledge base. \"\n \"Langflow uses the configured credentials for that model provider.\"\n ),\n required=True,\n model_type=\"embedding\",\n ),\n },\n },\n }\n }\n )\n\n # ------ Inputs --------------------------------------------------------\n inputs = [\n DropdownInput(\n name=\"knowledge_base\",\n display_name=\"Knowledge\",\n info=\"Select the knowledge to load data from.\",\n required=True,\n options=[],\n refresh_button=True,\n real_time_refresh=True,\n dialog_inputs=asdict(NewKnowledgeBaseInput()),\n ),\n HandleInput(\n name=\"input_df\",\n display_name=\"Input\",\n info=(\n \"Table with all original columns (already chunked / processed). \"\n \"Accepts Message, Data, or DataFrame. If Message or Data is provided, \"\n \"it is converted to a DataFrame automatically.\"\n ),\n input_types=[\"Message\", \"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n required=True,\n ),\n TableInput(\n name=\"column_config\",\n display_name=\"Column Configuration\",\n info=\"Configure column behavior for the knowledge base.\",\n required=True,\n table_schema=[\n {\n \"name\": \"column_name\",\n \"display_name\": \"Column Name\",\n \"type\": \"str\",\n \"description\": \"Name of the column in the source DataFrame\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"vectorize\",\n \"display_name\": \"Vectorize\",\n \"type\": \"boolean\",\n \"description\": \"Create embeddings for this column\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"identifier\",\n \"display_name\": \"Identifier\",\n \"type\": \"boolean\",\n \"description\": \"Use this column as unique identifier\",\n \"default\": False,\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n value=[\n {\n \"column_name\": \"text\",\n \"vectorize\": True,\n \"identifier\": True,\n },\n ],\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"Batch size for processing embeddings\",\n advanced=True,\n value=1000,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"Embedding Provider API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n advanced=True,\n required=False,\n ),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n info=\"Allow duplicate rows in the knowledge base\",\n advanced=True,\n value=False,\n ),\n ]\n\n # ------ Outputs -------------------------------------------------------\n outputs = [Output(display_name=\"Results\", name=\"dataframe_output\", method=\"build_kb_info\")]\n\n # ------ Internal helpers ---------------------------------------------\n def _get_kb_root(self) -> Path:\n \"\"\"Return the root directory for knowledge bases.\"\"\"\n return _get_knowledge_bases_root_path()\n\n @staticmethod\n def _scalar_notna(value) -> bool:\n \"\"\"Check if a value is not NA, safely handling arrays and sequences.\n\n ``pd.notna`` returns an array when given an array-like input, which\n cannot be used directly in a boolean context. This helper collapses the\n result to a single scalar ``bool``.\n \"\"\"\n result = pd.notna(value)\n # If result is array-like (numpy array, list, etc.), treat non-empty arrays as \"present\"\n if hasattr(result, \"__iter__\") and not isinstance(result, str):\n import numpy as np\n\n arr = np.asarray(result)\n return arr.size > 0 and arr.all()\n return bool(result)\n\n def _validate_column_config(self, df_source: pd.DataFrame) -> list[dict[str, Any]]:\n \"\"\"Validate column configuration using Structured Output patterns.\"\"\"\n if not self.column_config:\n msg = \"Column configuration cannot be empty\"\n raise ValueError(msg)\n\n # Convert table input to list of dicts (similar to Structured Output)\n config_list = self.column_config if isinstance(self.column_config, list) else []\n\n # Validate column names exist in DataFrame\n df_columns = set(df_source.columns)\n for config in config_list:\n col_name = config.get(\"column_name\")\n if col_name not in df_columns:\n msg = f\"Column '{col_name}' not found in DataFrame. Available columns: {sorted(df_columns)}\"\n raise ValueError(msg)\n\n return config_list\n\n def _build_embedding_metadata(\n self,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> dict[str, Any]:\n \"\"\"Build embedding model metadata from a model selection dict.\n\n Args:\n model_selection: Model selection list from ModelInput\n (e.g. [{'name': ..., 'provider': ..., 'metadata': ...}])\n api_key: Optional runtime API key override.\n \"\"\"\n model_dict = model_selection[0] if isinstance(model_selection, list) else model_selection\n embedding_model = model_dict.get(\"name\", \"\")\n embedding_provider = model_dict.get(\"provider\", \"Unknown\")\n\n api_key_to_save = None\n if api_key and hasattr(api_key, \"get_secret_value\"):\n api_key_to_save = api_key.get_secret_value()\n elif isinstance(api_key, str):\n api_key_to_save = api_key\n\n encrypted_api_key = None\n if api_key_to_save:\n settings_service = get_settings_service()\n try:\n encrypted_api_key = encrypt_api_key(api_key_to_save, settings_service=settings_service)\n except (TypeError, ValueError) as e:\n self.log(f\"Could not encrypt API key: {e}\")\n\n return {\n \"embedding_provider\": embedding_provider,\n \"embedding_model\": embedding_model,\n \"model_selection\": model_dict, # Store full selection for get_embeddings() reconstruction\n \"api_key\": encrypted_api_key,\n \"api_key_used\": bool(api_key),\n \"chunk_size\": self.chunk_size,\n \"created_at\": datetime.now(timezone.utc).isoformat(),\n }\n\n def _save_embedding_metadata(\n self,\n kb_path: Path,\n model_selection: list[dict[str, Any]],\n api_key: str | None = None,\n ) -> None:\n \"\"\"Save embedding model metadata.\"\"\"\n embedding_metadata = self._build_embedding_metadata(model_selection, api_key)\n metadata_path = kb_path / \"embedding_metadata.json\"\n metadata_path.write_text(json.dumps(embedding_metadata, indent=2))\n\n def _update_metadata_metrics(self, kb_path: Path, chroma: Chroma) -> None:\n \"\"\"Update embedding_metadata.json with accurate chunk/word/character counts.\n\n This ensures the Knowledge Base modal displays correct stats after\n component-based ingestion, matching the behavior of API-based ingestion.\n Delegates to KBAnalysisHelper.update_text_metrics to avoid duplicating\n the batched metrics counting logic.\n \"\"\"\n import chromadb.errors\n from langflow.api.utils.kb_helpers import KBAnalysisHelper, KBStorageHelper\n\n metadata_path = kb_path / \"embedding_metadata.json\"\n if not metadata_path.exists():\n return\n\n try:\n metadata = json.loads(metadata_path.read_text())\n KBAnalysisHelper.update_text_metrics(kb_path, metadata, chroma)\n metadata[\"size\"] = KBStorageHelper.get_directory_size(kb_path)\n metadata_path.write_text(json.dumps(metadata, indent=2))\n except (OSError, ValueError, TypeError, json.JSONDecodeError, chromadb.errors.ChromaError) as e:\n self.log(f\"Warning: Could not update metadata metrics: {e}\")\n\n def _save_kb_files(\n self,\n kb_path: Path,\n config_list: list[dict[str, Any]],\n ) -> None:\n \"\"\"Save KB files using File Component storage patterns.\"\"\"\n try:\n # Create directory (following File Component patterns)\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save column configuration\n # Only do this if the file doesn't exist already\n cfg_path = kb_path / \"schema.json\"\n if not cfg_path.exists():\n cfg_path.write_text(json.dumps(config_list, indent=2))\n\n except (OSError, TypeError, ValueError) as e:\n self.log(f\"Error saving KB files: {e}\")\n\n def _build_column_metadata(self, config_list: list[dict[str, Any]], df_source: pd.DataFrame) -> dict[str, Any]:\n \"\"\"Build detailed column metadata.\"\"\"\n metadata: dict[str, Any] = {\n \"total_columns\": len(df_source.columns),\n \"mapped_columns\": len(config_list),\n \"unmapped_columns\": len(df_source.columns) - len(config_list),\n \"columns\": [],\n \"summary\": {\"vectorized_columns\": [], \"identifier_columns\": []},\n }\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n # Add to columns list\n metadata[\"columns\"].append(\n {\n \"name\": col_name,\n \"vectorize\": vectorize,\n \"identifier\": identifier,\n }\n )\n\n # Update summary\n if vectorize:\n metadata[\"summary\"][\"vectorized_columns\"].append(col_name)\n if identifier:\n metadata[\"summary\"][\"identifier_columns\"].append(col_name)\n\n return metadata\n\n async def _create_vector_store(\n self,\n df_source: pd.DataFrame,\n config_list: list[dict[str, Any]],\n embedding_function,\n ) -> Chroma:\n \"\"\"Create vector store following Local DB component pattern.\n\n Returns the Chroma instance so callers can use it for metrics updates.\n \"\"\"\n # Set up vector store directory\n vector_store_dir = await self._kb_path()\n if not vector_store_dir:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n vector_store_dir.mkdir(parents=True, exist_ok=True)\n\n # Convert DataFrame to Data objects (following Local DB pattern)\n data_objects = await self._convert_df_to_data_objects(df_source, config_list)\n\n # Create vector store\n chroma = Chroma(\n persist_directory=str(vector_store_dir),\n embedding_function=embedding_function,\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # Convert Data objects to LangChain Documents\n documents = []\n for data_obj in data_objects:\n doc = data_obj.to_lc_document()\n documents.append(doc)\n\n # Add documents to vector store\n if documents:\n chroma.add_documents(documents)\n self.log(f\"Added {len(documents)} documents to vector store '{self.knowledge_base}'\")\n\n return chroma\n\n async def _convert_df_to_data_objects(\n self, df_source: pd.DataFrame, config_list: list[dict[str, Any]]\n ) -> list[Data]:\n \"\"\"Convert DataFrame to Data objects for vector store.\"\"\"\n data_objects: list[Data] = []\n\n # Set up vector store directory\n kb_path = await self._kb_path()\n\n # If we don't allow duplicates, we need to get the existing hashes\n chroma = Chroma(\n persist_directory=str(kb_path),\n collection_name=self.knowledge_base,\n **chroma_langchain_collection_kwargs(),\n )\n\n # Get all documents and their metadata\n all_docs = chroma.get()\n\n # Extract all _id values from metadata\n id_list = [metadata.get(\"_id\") for metadata in all_docs[\"metadatas\"] if metadata.get(\"_id\")]\n\n # Get column roles\n content_cols = []\n identifier_cols = []\n\n for config in config_list:\n col_name = config.get(\"column_name\")\n vectorize = config.get(\"vectorize\") == \"True\" or config.get(\"vectorize\") is True\n identifier = config.get(\"identifier\") == \"True\" or config.get(\"identifier\") is True\n\n if vectorize:\n content_cols.append(col_name)\n elif identifier:\n identifier_cols.append(col_name)\n\n # Convert each row to a Data object\n for _, row in df_source.iterrows():\n # Build content text from identifier columns using list comprehension\n identifier_parts = [str(row[col]) for col in content_cols if col in row and self._scalar_notna(row[col])]\n\n # Join all parts into a single string\n page_content = \" \".join(identifier_parts)\n\n # Build metadata from NON-vectorized columns only (simple key-value pairs)\n data_dict = {\n \"text\": page_content, # Main content for vectorization\n }\n\n # Add identifier columns if they exist\n if identifier_cols:\n identifier_parts = [\n str(row[col]) for col in identifier_cols if col in row and self._scalar_notna(row[col])\n ]\n page_content = \" \".join(identifier_parts)\n\n # Add metadata columns as simple key-value pairs\n for col in df_source.columns:\n if col not in content_cols and col in row and self._scalar_notna(row[col]):\n # Convert to simple types for Chroma metadata\n value = row[col]\n data_dict[col] = str(value) # Convert complex types to string\n\n # Hash the page_content for unique ID\n page_content_hash = hashlib.sha256(page_content.encode()).hexdigest()\n data_dict[\"_id\"] = page_content_hash\n\n # If duplicates are disallowed, and hash exists, prevent adding this row\n if not self.allow_duplicates and page_content_hash in id_list:\n self.log(f\"Skipping duplicate row with hash {page_content_hash}\")\n continue\n\n # Create Data object - everything except \"text\" becomes metadata\n data_obj = Data(data=data_dict)\n data_objects.append(data_obj)\n\n return data_objects\n\n def is_valid_collection_name(self, name, min_length: int = 3, max_length: int = 63) -> bool:\n \"\"\"Validates collection name against conditions 1-3.\n\n 1. Contains 3-63 characters\n 2. Starts and ends with alphanumeric character\n 3. Contains only alphanumeric characters, underscores, or hyphens.\n\n Args:\n name (str): Collection name to validate\n min_length (int): Minimum length of the name\n max_length (int): Maximum length of the name\n\n Returns:\n bool: True if valid, False otherwise\n \"\"\"\n # Check length (condition 1)\n if not (min_length <= len(name) <= max_length):\n return False\n\n # Check start/end with alphanumeric (condition 2)\n if not (name[0].isalnum() and name[-1].isalnum()):\n return False\n\n # Check allowed characters (condition 3)\n return re.match(r\"^[a-zA-Z0-9_-]+$\", name) is not None\n\n async def _kb_path(self) -> Path | None:\n # Check if we already have the path cached\n cached_path = getattr(self, \"_cached_kb_path\", None)\n if cached_path is not None:\n return cached_path\n\n # If not cached, compute it\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base path.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n\n kb_root = self._get_kb_root()\n\n # Cache the result\n self._cached_kb_path = kb_root / kb_user / self.knowledge_base\n\n return self._cached_kb_path\n\n # ---------------------------------------------------------------------\n # OUTPUT METHODS\n # ---------------------------------------------------------------------\n async def build_kb_info(self) -> Data:\n \"\"\"Main ingestion routine → returns a dict with KB metadata.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n try:\n input_value = self.input_df[0] if isinstance(self.input_df, list) else self.input_df\n df_source: DataFrame = convert_to_dataframe(input_value, auto_parse=False)\n\n # Validate column configuration (using Structured Output patterns)\n config_list = self._validate_column_config(df_source)\n column_metadata = self._build_column_metadata(config_list, df_source)\n\n # Read the embedding info from the knowledge base folder\n kb_path = await self._kb_path()\n if not kb_path:\n msg = \"Knowledge base path is not set. Please create a new knowledge base first.\"\n raise ValueError(msg)\n metadata_path = kb_path / \"embedding_metadata.json\"\n api_key = None\n model_selection = None\n\n # Read stored metadata\n if metadata_path.exists():\n settings_service = get_settings_service()\n stored_metadata = json.loads(metadata_path.read_text())\n\n # Prefer stored model_selection dict (new format)\n model_selection = stored_metadata.get(\"model_selection\")\n if model_selection:\n model_selection = [model_selection] if isinstance(model_selection, dict) else model_selection\n else:\n # Backward compat: reconstruct from old string-based metadata\n embedding_model_name = stored_metadata.get(\"embedding_model\")\n embedding_provider = stored_metadata.get(\"embedding_provider\", \"Unknown\")\n if embedding_model_name:\n # Look up full model info from available options\n try:\n all_options = get_embedding_model_options(user_id=self.user_id)\n match = next(\n (o for o in all_options if o.get(\"name\") == embedding_model_name),\n None,\n )\n if match:\n model_selection = [match]\n else:\n self.log(\n f\"Embedding model '{embedding_model_name}' (provider: {embedding_provider}) \"\n \"from stored metadata is no longer available in the model registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Embedding model '{embedding_model_name}' is no longer recognized. \"\n \"The knowledge base was created with an older format and the model \"\n \"is not available in the current registry. \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg)\n except ValueError:\n raise\n except Exception: # noqa: BLE001\n self.log(\n f\"Failed to look up embedding model '{embedding_model_name}' in registry. \"\n \"Please re-create this knowledge base with a supported embedding model.\"\n )\n msg = (\n f\"Could not look up embedding model '{embedding_model_name}' \"\n f\"(provider: {embedding_provider}). \"\n \"Please re-create the knowledge base with a supported embedding model.\"\n )\n raise ValueError(msg) # noqa: B904\n\n # Decrypt stored API key\n encrypted_key = stored_metadata.get(\"api_key\")\n if encrypted_key:\n try:\n api_key = decrypt_api_key(encrypted_key, settings_service)\n except (InvalidToken, TypeError, ValueError) as e:\n self.log(f\"Could not decrypt API key. Please provide it manually. Error: {e}\")\n\n # Check if a custom API key was provided\n if self.api_key:\n api_key = self.api_key\n if model_selection:\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n api_key=api_key,\n )\n\n if not model_selection:\n msg = \"No embedding model configuration found. Please create the knowledge base first.\"\n raise ValueError(msg)\n\n # Build the embedding function via the shared utility\n embedding_function = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n api_key=api_key,\n chunk_size=self.chunk_size,\n )\n\n # Create vector store following Local DB component pattern\n chroma = await self._create_vector_store(df_source, config_list, embedding_function=embedding_function)\n\n # Save KB files (using File Component storage patterns)\n self._save_kb_files(kb_path, config_list)\n\n # Update embedding_metadata.json with accurate text metrics\n # so the KB modal and API show correct chunks/words/characters\n self._update_metadata_metrics(kb_path, chroma)\n\n # Build metadata response\n meta: dict[str, Any] = {\n \"kb_id\": str(uuid.uuid4()),\n \"kb_name\": self.knowledge_base,\n \"rows\": len(df_source),\n \"column_metadata\": column_metadata,\n \"path\": str(kb_path),\n \"config_columns\": len(config_list),\n \"timestamp\": datetime.now(tz=timezone.utc).isoformat(),\n }\n\n # Set status message\n self.status = f\"✅ KB **{self.knowledge_base}** saved · {len(df_source)} chunks.\"\n\n return Data(data=meta)\n\n except (OSError, ValueError, RuntimeError, KeyError) as e:\n msg = f\"Error during KB ingestion: {e}\"\n raise RuntimeError(msg) from e\n\n async def update_build_config(\n self,\n build_config,\n field_value: Any,\n field_name: str | None = None,\n ):\n \"\"\"Update build configuration based on provider selection.\"\"\"\n # Check if we're in Astra cloud environment and raise an error if we are.\n raise_error_if_astra_cloud_disable_component(astra_error_msg)\n\n # Populate the dialog's embedding model options so the ModelInput renders correctly\n try:\n dialog_template = (\n build_config[\"knowledge_base\"]\n .get(\"dialog_inputs\", {})\n .get(\"fields\", {})\n .get(\"data\", {})\n .get(\"node\", {})\n .get(\"template\", {})\n )\n if \"02_embedding_model\" in dialog_template:\n embedding_options = get_embedding_model_options(user_id=self.user_id)\n dialog_template[\"02_embedding_model\"][\"options\"] = embedding_options\n except Exception: # noqa: BLE001\n self.log(\"Failed to populate embedding model options in dialog\")\n\n # Create a new knowledge base\n if field_name == \"knowledge_base\":\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching knowledge base list.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n if not current_user:\n msg = f\"User with ID {self.user_id} not found.\"\n raise ValueError(msg)\n kb_user = current_user.username\n if isinstance(field_value, dict) and \"01_new_kb_name\" in field_value:\n # Validate the knowledge base name - Make sure it follows these rules:\n if not self.is_valid_collection_name(field_value[\"01_new_kb_name\"]):\n msg = f\"Invalid knowledge base name: {field_value['01_new_kb_name']}\"\n raise ValueError(msg)\n\n # The model selection comes from ModelInput as a list of dicts\n model_selection = field_value[\"02_embedding_model\"]\n if isinstance(model_selection, dict):\n model_selection = [model_selection]\n\n # Build and validate the embedding model via the shared utility\n embed_model = get_embeddings(\n model=model_selection,\n user_id=self.user_id,\n )\n\n # Try to generate a dummy embedding to validate without blocking the event loop\n try:\n await asyncio.wait_for(\n asyncio.to_thread(embed_model.embed_query, \"test\"),\n timeout=10,\n )\n except TimeoutError as e:\n msg = \"Embedding validation timed out. Please verify network connectivity and key.\"\n raise ValueError(msg) from e\n except Exception as e:\n msg = f\"Embedding validation failed: {e!s}\"\n raise ValueError(msg) from e\n\n # Create the new knowledge base directory\n kb_path = _get_knowledge_bases_root_path() / kb_user / field_value[\"01_new_kb_name\"]\n kb_path.mkdir(parents=True, exist_ok=True)\n\n # Save the embedding metadata\n build_config[\"knowledge_base\"][\"value\"] = field_value[\"01_new_kb_name\"]\n self._save_embedding_metadata(\n kb_path=kb_path,\n model_selection=model_selection,\n )\n\n # Update the knowledge base options dynamically\n build_config[\"knowledge_base\"][\"options\"] = await get_knowledge_bases(\n _get_knowledge_bases_root_path(),\n user_id=self.user_id,\n )\n\n # If the selected knowledge base is not available, reset it\n if build_config[\"knowledge_base\"][\"value\"] not in build_config[\"knowledge_base\"][\"options\"]:\n build_config[\"knowledge_base\"][\"value\"] = None\n\n return build_config\n" }, "column_config": { "_input_type": "TableInput", @@ -112829,7 +112829,7 @@ "icon": "database", "legacy": true, "metadata": { - "code_hash": "457b336fd756", + "code_hash": "fc7b39784f6a", "dependencies": { "dependencies": [ { @@ -112906,7 +112906,7 @@ "show": true, "title_case": false, "type": "code", - "value": "from copy import deepcopy\nfrom pathlib import Path\n\nfrom langchain_chroma import Chroma\nfrom typing_extensions import override\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.utils import chroma_collection_to_data\nfrom lfx.inputs.inputs import MultilineInput\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MessageTextInput, TabInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.template.field.base import Output\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\ndisable_component_in_astra_cloud_msg = (\n \"Local vector stores are not supported in S3/cloud mode. \"\n \"Local vector stores require local file system access for persistence. \"\n \"Please use cloud-based vector stores (Pinecone, Weaviate, etc.) or local storage mode.\"\n)\n\n\nclass LocalDBComponent(LCVectorStoreComponent):\n \"\"\"Chroma Vector Store with search capabilities.\"\"\"\n\n display_name: str = \"Local DB\"\n description: str = \"Local Vector Store with search capabilities\"\n name = \"LocalDB\"\n icon = \"database\"\n legacy = True\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Ingest\", \"Retrieve\"],\n info=\"Select the operation mode\",\n value=\"Ingest\",\n real_time_refresh=True,\n show=True,\n ),\n MessageTextInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n value=\"langflow\",\n required=True,\n ),\n MessageTextInput(\n name=\"persist_directory\",\n display_name=\"Persist Directory\",\n info=(\n \"Custom base directory to save the vector store. \"\n \"Collections will be stored under '{directory}/vector_stores/{collection_name}'. \"\n \"If not specified, it will use your system's cache folder.\"\n ),\n advanced=True,\n ),\n DropdownInput(\n name=\"existing_collections\",\n display_name=\"Existing Collections\",\n options=[], # Will be populated dynamically\n info=\"Select a previously created collection to search through its stored data.\",\n show=False,\n combobox=True,\n ),\n HandleInput(name=\"embedding\", display_name=\"Embedding\", required=True, input_types=[\"Embeddings\"]),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n advanced=True,\n info=\"If false, will not add documents that are already in the Vector Store.\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n advanced=True,\n ),\n HandleInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n is_list=True,\n info=\"Data to store. It will be embedded and indexed for semantic search.\",\n show=True,\n ),\n MultilineInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n tool_mode=True,\n info=\"Enter text to search for similar content in the selected collection.\",\n show=False,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=10,\n ),\n IntInput(\n name=\"limit\",\n display_name=\"Limit\",\n advanced=True,\n info=\"Limit the number of records to compare when Allow Duplicates is False.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Table\", name=\"dataframe\", method=\"perform_search\"),\n ]\n\n def get_vector_store_directory(self, base_dir: str | Path) -> Path:\n \"\"\"Get the full directory path for a collection.\"\"\"\n # Ensure base_dir is a Path object\n base_dir = Path(base_dir)\n # Create the full path: base_dir/vector_stores/collection_name\n full_path = base_dir / \"vector_stores\" / self.collection_name\n # Create the directory if it doesn't exist\n full_path.mkdir(parents=True, exist_ok=True)\n return full_path\n\n def get_default_persist_dir(self) -> str:\n \"\"\"Get the default persist directory from cache.\"\"\"\n from lfx.services.cache.utils import CACHE_DIR\n\n return str(self.get_vector_store_directory(CACHE_DIR))\n\n def list_existing_collections(self) -> list[str]:\n \"\"\"List existing vector store collections from the persist directory.\"\"\"\n from lfx.services.cache.utils import CACHE_DIR\n\n # Get the base directory (either custom or cache)\n base_dir = Path(self.persist_directory) if self.persist_directory else Path(CACHE_DIR)\n # Get the vector_stores subdirectory\n vector_stores_dir = base_dir / \"vector_stores\"\n if not vector_stores_dir.exists():\n return []\n\n return [d.name for d in vector_stores_dir.iterdir() if d.is_dir()]\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Update the build configuration when the mode changes.\"\"\"\n if field_name == \"mode\":\n # Hide all dynamic fields by default\n dynamic_fields = [\n \"ingest_data\",\n \"search_query\",\n \"search_type\",\n \"number_of_results\",\n \"existing_collections\",\n \"collection_name\",\n \"embedding\",\n \"allow_duplicates\",\n \"limit\",\n ]\n for field in dynamic_fields:\n if field in build_config:\n build_config[field][\"show\"] = False\n\n # Show/hide fields based on selected mode\n if field_value == \"Ingest\":\n if \"ingest_data\" in build_config:\n build_config[\"ingest_data\"][\"show\"] = True\n if \"collection_name\" in build_config:\n build_config[\"collection_name\"][\"show\"] = True\n build_config[\"collection_name\"][\"display_name\"] = \"Name Your Collection\"\n if \"persist\" in build_config:\n build_config[\"persist\"][\"show\"] = True\n if \"persist_directory\" in build_config:\n build_config[\"persist_directory\"][\"show\"] = True\n if \"embedding\" in build_config:\n build_config[\"embedding\"][\"show\"] = True\n if \"allow_duplicates\" in build_config:\n build_config[\"allow_duplicates\"][\"show\"] = True\n if \"limit\" in build_config:\n build_config[\"limit\"][\"show\"] = True\n elif field_value == \"Retrieve\":\n if \"persist\" in build_config:\n build_config[\"persist\"][\"show\"] = False\n build_config[\"search_query\"][\"show\"] = True\n build_config[\"search_type\"][\"show\"] = True\n build_config[\"number_of_results\"][\"show\"] = True\n build_config[\"embedding\"][\"show\"] = True\n build_config[\"collection_name\"][\"show\"] = False\n # Show existing collections dropdown and update its options\n if \"existing_collections\" in build_config:\n build_config[\"existing_collections\"][\"show\"] = True\n build_config[\"existing_collections\"][\"options\"] = self.list_existing_collections()\n # Hide collection_name in Retrieve mode since we use existing_collections\n elif field_name == \"existing_collections\":\n # Update collection_name when an existing collection is selected\n if \"collection_name\" in build_config:\n build_config[\"collection_name\"][\"value\"] = field_value\n\n return build_config\n\n @override\n @check_cached_vector_store\n def build_vector_store(self) -> Chroma:\n \"\"\"Builds the Chroma object.\"\"\"\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n\n try:\n from langchain_chroma import Chroma\n except ImportError as e:\n msg = \"Could not import Chroma integration package. Please install it with `pip install langchain-chroma`.\"\n raise ImportError(msg) from e\n # Chroma settings\n # chroma_settings = None\n if self.existing_collections:\n self.collection_name = self.existing_collections\n\n # Use user-provided directory or default cache directory\n if self.persist_directory:\n base_dir = self.resolve_path(self.persist_directory)\n persist_directory = str(self.get_vector_store_directory(base_dir))\n logger.debug(f\"Using custom persist directory: {persist_directory}\")\n else:\n persist_directory = self.get_default_persist_dir()\n logger.debug(f\"Using default persist directory: {persist_directory}\")\n\n chroma = Chroma(\n persist_directory=persist_directory,\n client=None,\n embedding_function=self.embedding,\n collection_name=self.collection_name,\n )\n\n self._add_documents_to_vector_store(chroma)\n self.status = chroma_collection_to_data(chroma.get(limit=self.limit))\n return chroma\n\n def _add_documents_to_vector_store(self, vector_store: \"Chroma\") -> None:\n \"\"\"Adds documents to the Vector Store.\"\"\"\n ingest_data: list | Data | DataFrame = self.ingest_data\n if not ingest_data:\n self.status = \"\"\n return\n\n # Convert DataFrame to Data if needed using parent's method\n ingest_data = self._prepare_ingest_data()\n\n stored_documents_without_id = []\n if self.allow_duplicates:\n stored_data = []\n else:\n stored_data = chroma_collection_to_data(vector_store.get(limit=self.limit))\n for value in deepcopy(stored_data):\n del value.id\n stored_documents_without_id.append(value)\n\n documents = []\n for _input in ingest_data or []:\n if isinstance(_input, Data):\n if _input not in stored_documents_without_id:\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise TypeError(msg)\n\n if documents and self.embedding is not None:\n self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n vector_store.add_documents(documents)\n else:\n self.log(\"No documents to add to the Vector Store.\")\n\n def perform_search(self) -> DataFrame:\n return DataFrame(self.search_documents())\n" + "value": "from copy import deepcopy\nfrom pathlib import Path\n\nfrom langchain_chroma import Chroma\nfrom typing_extensions import override\n\nfrom lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.utils import chroma_collection_to_data\nfrom lfx.inputs.inputs import MultilineInput\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MessageTextInput, TabInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.template.field.base import Output\nfrom lfx.utils.validate_cloud import raise_error_if_astra_cloud_disable_component\n\ndisable_component_in_astra_cloud_msg = (\n \"Local vector stores are not supported in S3/cloud mode. \"\n \"Local vector stores require local file system access for persistence. \"\n \"Please use cloud-based vector stores (Pinecone, Weaviate, etc.) or local storage mode.\"\n)\n\n\nclass LocalDBComponent(LCVectorStoreComponent):\n \"\"\"Chroma Vector Store with search capabilities.\"\"\"\n\n display_name: str = \"Local DB\"\n description: str = \"Local Vector Store with search capabilities\"\n name = \"LocalDB\"\n icon = \"database\"\n legacy = True\n\n inputs = [\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Ingest\", \"Retrieve\"],\n info=\"Select the operation mode\",\n value=\"Ingest\",\n real_time_refresh=True,\n show=True,\n ),\n MessageTextInput(\n name=\"collection_name\",\n display_name=\"Collection Name\",\n value=\"langflow\",\n required=True,\n ),\n MessageTextInput(\n name=\"persist_directory\",\n display_name=\"Persist Directory\",\n info=(\n \"Custom base directory to save the vector store. \"\n \"Collections will be stored under '{directory}/vector_stores/{collection_name}'. \"\n \"If not specified, it will use your system's cache folder.\"\n ),\n advanced=True,\n ),\n DropdownInput(\n name=\"existing_collections\",\n display_name=\"Existing Collections\",\n options=[], # Will be populated dynamically\n info=\"Select a previously created collection to search through its stored data.\",\n show=False,\n combobox=True,\n ),\n HandleInput(name=\"embedding\", display_name=\"Embedding\", required=True, input_types=[\"Embeddings\"]),\n BoolInput(\n name=\"allow_duplicates\",\n display_name=\"Allow Duplicates\",\n advanced=True,\n info=\"If false, will not add documents that are already in the Vector Store.\",\n ),\n DropdownInput(\n name=\"search_type\",\n display_name=\"Search Type\",\n options=[\"Similarity\", \"MMR\"],\n value=\"Similarity\",\n advanced=True,\n ),\n HandleInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\"],\n is_list=True,\n info=\"Data to store. It will be embedded and indexed for semantic search.\",\n show=True,\n ),\n MultilineInput(\n name=\"search_query\",\n display_name=\"Search Query\",\n tool_mode=True,\n info=\"Enter text to search for similar content in the selected collection.\",\n show=False,\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n advanced=True,\n value=10,\n ),\n IntInput(\n name=\"limit\",\n display_name=\"Limit\",\n advanced=True,\n info=\"Limit the number of records to compare when Allow Duplicates is False.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Table\", name=\"dataframe\", method=\"perform_search\"),\n ]\n\n def get_vector_store_directory(self, base_dir: str | Path) -> Path:\n \"\"\"Get the full directory path for a collection.\"\"\"\n # Ensure base_dir is a Path object\n base_dir = Path(base_dir)\n # Create the full path: base_dir/vector_stores/collection_name\n full_path = base_dir / \"vector_stores\" / self.collection_name\n # Create the directory if it doesn't exist\n full_path.mkdir(parents=True, exist_ok=True)\n return full_path\n\n def get_default_persist_dir(self) -> str:\n \"\"\"Get the default persist directory from cache.\"\"\"\n from lfx.services.cache.utils import CACHE_DIR\n\n return str(self.get_vector_store_directory(CACHE_DIR))\n\n def list_existing_collections(self) -> list[str]:\n \"\"\"List existing vector store collections from the persist directory.\"\"\"\n from lfx.services.cache.utils import CACHE_DIR\n\n # Get the base directory (either custom or cache)\n base_dir = Path(self.persist_directory) if self.persist_directory else Path(CACHE_DIR)\n # Get the vector_stores subdirectory\n vector_stores_dir = base_dir / \"vector_stores\"\n if not vector_stores_dir.exists():\n return []\n\n return [d.name for d in vector_stores_dir.iterdir() if d.is_dir()]\n\n def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Update the build configuration when the mode changes.\"\"\"\n if field_name == \"mode\":\n # Hide all dynamic fields by default\n dynamic_fields = [\n \"ingest_data\",\n \"search_query\",\n \"search_type\",\n \"number_of_results\",\n \"existing_collections\",\n \"collection_name\",\n \"embedding\",\n \"allow_duplicates\",\n \"limit\",\n ]\n for field in dynamic_fields:\n if field in build_config:\n build_config[field][\"show\"] = False\n\n # Show/hide fields based on selected mode\n if field_value == \"Ingest\":\n if \"ingest_data\" in build_config:\n build_config[\"ingest_data\"][\"show\"] = True\n if \"collection_name\" in build_config:\n build_config[\"collection_name\"][\"show\"] = True\n build_config[\"collection_name\"][\"display_name\"] = \"Name Your Collection\"\n if \"persist\" in build_config:\n build_config[\"persist\"][\"show\"] = True\n if \"persist_directory\" in build_config:\n build_config[\"persist_directory\"][\"show\"] = True\n if \"embedding\" in build_config:\n build_config[\"embedding\"][\"show\"] = True\n if \"allow_duplicates\" in build_config:\n build_config[\"allow_duplicates\"][\"show\"] = True\n if \"limit\" in build_config:\n build_config[\"limit\"][\"show\"] = True\n elif field_value == \"Retrieve\":\n if \"persist\" in build_config:\n build_config[\"persist\"][\"show\"] = False\n build_config[\"search_query\"][\"show\"] = True\n build_config[\"search_type\"][\"show\"] = True\n build_config[\"number_of_results\"][\"show\"] = True\n build_config[\"embedding\"][\"show\"] = True\n build_config[\"collection_name\"][\"show\"] = False\n # Show existing collections dropdown and update its options\n if \"existing_collections\" in build_config:\n build_config[\"existing_collections\"][\"show\"] = True\n build_config[\"existing_collections\"][\"options\"] = self.list_existing_collections()\n # Hide collection_name in Retrieve mode since we use existing_collections\n elif field_name == \"existing_collections\":\n # Update collection_name when an existing collection is selected\n if \"collection_name\" in build_config:\n build_config[\"collection_name\"][\"value\"] = field_value\n\n return build_config\n\n @override\n @check_cached_vector_store\n def build_vector_store(self) -> Chroma:\n \"\"\"Builds the Chroma object.\"\"\"\n raise_error_if_astra_cloud_disable_component(disable_component_in_astra_cloud_msg)\n\n try:\n from langchain_chroma import Chroma\n except ImportError as e:\n msg = \"Could not import Chroma integration package. Please install it with `pip install langchain-chroma`.\"\n raise ImportError(msg) from e\n # Chroma settings\n # chroma_settings = None\n if self.existing_collections:\n self.collection_name = self.existing_collections\n\n # Use user-provided directory or default cache directory\n if self.persist_directory:\n base_dir = self.resolve_path(self.persist_directory)\n persist_directory = str(self.get_vector_store_directory(base_dir))\n logger.debug(f\"Using custom persist directory: {persist_directory}\")\n else:\n persist_directory = self.get_default_persist_dir()\n logger.debug(f\"Using default persist directory: {persist_directory}\")\n\n chroma = Chroma(\n persist_directory=persist_directory,\n client=None,\n embedding_function=self.embedding,\n collection_name=self.collection_name,\n **chroma_langchain_collection_kwargs(),\n )\n\n self._add_documents_to_vector_store(chroma)\n self.status = chroma_collection_to_data(chroma.get(limit=self.limit))\n return chroma\n\n def _add_documents_to_vector_store(self, vector_store: \"Chroma\") -> None:\n \"\"\"Adds documents to the Vector Store.\"\"\"\n ingest_data: list | Data | DataFrame = self.ingest_data\n if not ingest_data:\n self.status = \"\"\n return\n\n # Convert DataFrame to Data if needed using parent's method\n ingest_data = self._prepare_ingest_data()\n\n stored_documents_without_id = []\n if self.allow_duplicates:\n stored_data = []\n else:\n stored_data = chroma_collection_to_data(vector_store.get(limit=self.limit))\n for value in deepcopy(stored_data):\n del value.id\n stored_documents_without_id.append(value)\n\n documents = []\n for _input in ingest_data or []:\n if isinstance(_input, Data):\n if _input not in stored_documents_without_id:\n documents.append(_input.to_lc_document())\n else:\n msg = \"Vector Store Inputs must be Data objects.\"\n raise TypeError(msg)\n\n if documents and self.embedding is not None:\n self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n vector_store.add_documents(documents)\n else:\n self.log(\"No documents to add to the Vector Store.\")\n\n def perform_search(self) -> DataFrame:\n return DataFrame(self.search_documents())\n" }, "collection_name": { "_input_type": "MessageTextInput", @@ -117842,6 +117842,6 @@ "num_components": 354, "num_modules": 97 }, - "sha256": "790bbe7625c78d5d6e6f43ed8bf86504397b5cc8e702b6b4773f5b4e0b2968c5", + "sha256": "c39eef05d2cf77d0fca6d21d6821b3d878a8c6127b53991d91ddf04852a9dadc", "version": "0.4.4" } diff --git a/src/lfx/src/lfx/base/vectorstores/chroma_security.py b/src/lfx/src/lfx/base/vectorstores/chroma_security.py new file mode 100644 index 0000000000..a1bba796f6 --- /dev/null +++ b/src/lfx/src/lfx/base/vectorstores/chroma_security.py @@ -0,0 +1,28 @@ +"""Safe defaults for Langflow-created Chroma collections. + +Chroma collection configuration can persist server-side embedding functions. +Some embedding functions accept model-loading kwargs such as trust_remote_code. +Langflow embeds client-side, so built-in collection creation should not register +server-side embedding code. +""" + +from __future__ import annotations + +from typing import Any + + +def chroma_langchain_collection_kwargs() -> dict[str, Any]: + """Return LangChain Chroma kwargs that disable Chroma server-side embedding code.""" + return {"collection_configuration": _chroma_collection_configuration_without_embedding_function()} + + +def chroma_client_create_collection_kwargs() -> dict[str, Any]: + """Return chromadb client kwargs that create collections without server-side embedding code.""" + return { + "configuration": _chroma_collection_configuration_without_embedding_function(), + "embedding_function": None, + } + + +def _chroma_collection_configuration_without_embedding_function() -> dict[str, Any]: + return {"embedding_function": None} diff --git a/src/lfx/src/lfx/components/chroma/chroma.py b/src/lfx/src/lfx/components/chroma/chroma.py index 244f5175f4..8d45920e3f 100644 --- a/src/lfx/src/lfx/components/chroma/chroma.py +++ b/src/lfx/src/lfx/components/chroma/chroma.py @@ -5,6 +5,7 @@ from chromadb.config import Settings from langchain_chroma import Chroma from typing_extensions import override +from lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs from lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store from lfx.base.vectorstores.utils import chroma_collection_to_data from lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, IntInput, StrInput @@ -118,6 +119,7 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent): client=client, embedding_function=self.embedding, collection_name=self.collection_name, + **chroma_langchain_collection_kwargs(), ) self._add_documents_to_vector_store(chroma) diff --git a/src/lfx/src/lfx/components/files_and_knowledge/ingestion.py b/src/lfx/src/lfx/components/files_and_knowledge/ingestion.py index 02f10438f8..05420eadf9 100644 --- a/src/lfx/src/lfx/components/files_and_knowledge/ingestion.py +++ b/src/lfx/src/lfx/components/files_and_knowledge/ingestion.py @@ -18,6 +18,7 @@ from langflow.services.database.models.user.crud import get_user_by_id from lfx.base.knowledge_bases.knowledge_base_utils import get_knowledge_bases from lfx.base.models.unified_models import get_embedding_model_options, get_embeddings +from lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs from lfx.components.processing.converter import convert_to_dataframe from lfx.custom import Component from lfx.io import ( @@ -389,6 +390,7 @@ class KnowledgeIngestionComponent(Component): persist_directory=str(vector_store_dir), embedding_function=embedding_function, collection_name=self.knowledge_base, + **chroma_langchain_collection_kwargs(), ) # Convert Data objects to LangChain Documents @@ -417,6 +419,7 @@ class KnowledgeIngestionComponent(Component): chroma = Chroma( persist_directory=str(kb_path), collection_name=self.knowledge_base, + **chroma_langchain_collection_kwargs(), ) # Get all documents and their metadata diff --git a/src/lfx/src/lfx/components/files_and_knowledge/retrieval.py b/src/lfx/src/lfx/components/files_and_knowledge/retrieval.py index f77bf129c9..8676e12f22 100644 --- a/src/lfx/src/lfx/components/files_and_knowledge/retrieval.py +++ b/src/lfx/src/lfx/components/files_and_knowledge/retrieval.py @@ -17,6 +17,7 @@ from lfx.base.models.unified_models import ( get_model_provider_variable_mapping, get_provider_all_variables, ) +from lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs from lfx.custom import Component from lfx.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput from lfx.log.logger import logger @@ -356,6 +357,7 @@ class KnowledgeBaseComponent(Component): persist_directory=str(kb_path), embedding_function=embedding_function, collection_name=self.knowledge_base, + **chroma_langchain_collection_kwargs(), ) # If a search query is provided, perform a similarity search diff --git a/src/lfx/src/lfx/components/vectorstores/local_db.py b/src/lfx/src/lfx/components/vectorstores/local_db.py index 0fa8dc9f80..7255a54cb6 100644 --- a/src/lfx/src/lfx/components/vectorstores/local_db.py +++ b/src/lfx/src/lfx/components/vectorstores/local_db.py @@ -4,6 +4,7 @@ from pathlib import Path from langchain_chroma import Chroma from typing_extensions import override +from lfx.base.vectorstores.chroma_security import chroma_langchain_collection_kwargs from lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store from lfx.base.vectorstores.utils import chroma_collection_to_data from lfx.inputs.inputs import MultilineInput @@ -226,6 +227,7 @@ class LocalDBComponent(LCVectorStoreComponent): client=None, embedding_function=self.embedding, collection_name=self.collection_name, + **chroma_langchain_collection_kwargs(), ) self._add_documents_to_vector_store(chroma) From b531f559d6f12247dcbc88478535b57c05e78fa2 Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Tue, 26 May 2026 11:46:36 -0400 Subject: [PATCH 28/30] chore: port pre-create LANGFLOW_CONFIG_DIR #13212 backport of https://github.com/langflow-ai/langflow/pull/13212 --- docker/build_and_push.Dockerfile | 9 +++++++++ docker/build_and_push_backend.Dockerfile | 13 +++++++++++-- docker/build_and_push_base.Dockerfile | 9 +++++++++ docker/build_and_push_ep.Dockerfile | 9 +++++++++ docker/build_and_push_with_extras.Dockerfile | 9 +++++++++ 5 files changed, 47 insertions(+), 2 deletions(-) diff --git a/docker/build_and_push.Dockerfile b/docker/build_and_push.Dockerfile index 18ee99d4d0..2d5e7350e8 100644 --- a/docker/build_and_push.Dockerfile +++ b/docker/build_and_push.Dockerfile @@ -105,6 +105,15 @@ RUN useradd user -u 1000 -g 0 --no-create-home --home-dir /app/data COPY --from=builder --chown=1000 /app/.venv /app/.venv ENV PATH="/app/.venv/bin:$PATH" +# Pre-create LANGFLOW_CONFIG_DIR (the default location used by the docker_example +# compose file) with the non-root user as owner. When the official compose mounts +# a fresh named volume at /app/langflow, Docker copies this directory's ownership +# and permissions into the new volume, so the in-container uid=1000 user can +# write secret_key, profile_pictures, etc. Without this, the volume is created +# as root:root and Langflow crashes during startup with PermissionError on +# /app/langflow/secret_key. See https://github.com/langflow-ai/langflow/issues/10437 +RUN mkdir -p /app/langflow && chown -R 1000:0 /app/langflow && chmod -R g+rwX /app/langflow + LABEL org.opencontainers.image.title=langflow LABEL org.opencontainers.image.authors=['Langflow'] LABEL org.opencontainers.image.licenses=MIT diff --git a/docker/build_and_push_backend.Dockerfile b/docker/build_and_push_backend.Dockerfile index 0fe2a39ec5..23aee12b8a 100644 --- a/docker/build_and_push_backend.Dockerfile +++ b/docker/build_and_push_backend.Dockerfile @@ -79,9 +79,18 @@ COPY --from=builder --chown=1000:0 /app/.venv /app/.venv ENV PATH="/app/.venv/bin:$PATH" # Create home directory and ensure proper ownership -# The user needs write access to /app/data (home) and /app (workdir) +# The user needs write access to /app/data (home) and /app (workdir). +# Also pre-create /app/langflow (LANGFLOW_CONFIG_DIR used by the docker_example +# compose file) with the non-root user as owner, so a fresh named volume mounted +# at /app/langflow inherits the correct ownership/permissions and the in-container +# uid=1000 user can write secret_key, profile_pictures, etc. Without this, the +# volume would be initialized as root:root and Langflow would crash with +# PermissionError on /app/langflow/secret_key (issue #10437). # Note: .venv is already owned by 1000:0 via COPY --chown above, so no recursive chown needed -RUN mkdir -p /app/data && chown -R 1000:0 /app/data && chown 1000:0 /app +RUN mkdir -p /app/data /app/langflow \ + && chown -R 1000:0 /app/data /app/langflow \ + && chmod -R g+rwX /app/langflow \ + && chown 1000:0 /app LABEL org.opencontainers.image.title=langflow-backend LABEL org.opencontainers.image.authors=['Langflow'] diff --git a/docker/build_and_push_base.Dockerfile b/docker/build_and_push_base.Dockerfile index 4ab6369b2b..67cef040e8 100644 --- a/docker/build_and_push_base.Dockerfile +++ b/docker/build_and_push_base.Dockerfile @@ -102,6 +102,15 @@ RUN useradd user -u 1000 -g 0 --no-create-home --home-dir /app/data COPY --from=builder --chown=1000 /app/.venv /app/.venv ENV PATH="/app/.venv/bin:$PATH" +# Pre-create LANGFLOW_CONFIG_DIR (the default location used by the docker_example +# compose file) with the non-root user as owner. When the official compose mounts +# a fresh named volume at /app/langflow, Docker copies this directory's ownership +# and permissions into the new volume, so the in-container uid=1000 user can +# write secret_key, profile_pictures, etc. Without this, the volume is created +# as root:root and Langflow crashes during startup with PermissionError on +# /app/langflow/secret_key. See https://github.com/langflow-ai/langflow/issues/10437 +RUN mkdir -p /app/langflow && chown -R 1000:0 /app/langflow && chmod -R g+rwX /app/langflow + LABEL org.opencontainers.image.title=langflow LABEL org.opencontainers.image.authors=['Langflow'] LABEL org.opencontainers.image.licenses=MIT diff --git a/docker/build_and_push_ep.Dockerfile b/docker/build_and_push_ep.Dockerfile index b88a14fae5..1ff1cc4b28 100644 --- a/docker/build_and_push_ep.Dockerfile +++ b/docker/build_and_push_ep.Dockerfile @@ -96,6 +96,15 @@ RUN useradd user -u 1000 -g 0 --no-create-home --home-dir /app/data COPY --from=builder --chown=1000 /app/.venv /app/.venv ENV PATH="/app/.venv/bin:$PATH" +# Pre-create LANGFLOW_CONFIG_DIR (the default location used by the docker_example +# compose file) with the non-root user as owner. When the official compose mounts +# a fresh named volume at /app/langflow, Docker copies this directory's ownership +# and permissions into the new volume, so the in-container uid=1000 user can +# write secret_key, profile_pictures, etc. Without this, the volume is created +# as root:root and Langflow crashes during startup with PermissionError on +# /app/langflow/secret_key. See https://github.com/langflow-ai/langflow/issues/10437 +RUN mkdir -p /app/langflow && chown -R 1000:0 /app/langflow && chmod -R g+rwX /app/langflow + LABEL org.opencontainers.image.title=langflow LABEL org.opencontainers.image.authors=['Langflow'] LABEL org.opencontainers.image.licenses=MIT diff --git a/docker/build_and_push_with_extras.Dockerfile b/docker/build_and_push_with_extras.Dockerfile index 48e016b796..88a7ea5fc0 100644 --- a/docker/build_and_push_with_extras.Dockerfile +++ b/docker/build_and_push_with_extras.Dockerfile @@ -97,6 +97,15 @@ RUN useradd user -u 1000 -g 0 --no-create-home --home-dir /app/data COPY --from=builder --chown=1000 /app/.venv /app/.venv ENV PATH="/app/.venv/bin:$PATH" +# Pre-create LANGFLOW_CONFIG_DIR (the default location used by the docker_example +# compose file) with the non-root user as owner. When the official compose mounts +# a fresh named volume at /app/langflow, Docker copies this directory's ownership +# and permissions into the new volume, so the in-container uid=1000 user can +# write secret_key, profile_pictures, etc. Without this, the volume is created +# as root:root and Langflow crashes during startup with PermissionError on +# /app/langflow/secret_key. See https://github.com/langflow-ai/langflow/issues/10437 +RUN mkdir -p /app/langflow && chown -R 1000:0 /app/langflow && chmod -R g+rwX /app/langflow + LABEL org.opencontainers.image.title=langflow LABEL org.opencontainers.image.authors=['Langflow'] LABEL org.opencontainers.image.licenses=MIT From 75b3cc9f6d9348d7502cce8c92a462266d5d772f Mon Sep 17 00:00:00 2001 From: Adam Aghili Date: Tue, 26 May 2026 11:52:52 -0400 Subject: [PATCH 29/30] chore: upgrade uv.lock --- src/backend/base/uv.lock | 36 ++++++------ uv.lock | 120 ++++++++++++++++++++------------------- 2 files changed, 81 insertions(+), 75 deletions(-) diff --git a/src/backend/base/uv.lock b/src/backend/base/uv.lock index 4400162f5f..7124178acd 100644 --- a/src/backend/base/uv.lock +++ b/src/backend/base/uv.lock @@ -540,7 +540,7 @@ wheels = [ [[package]] name = "apify-client" -version = "3.0.1" +version = "3.0.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", @@ -564,9 +564,9 @@ dependencies = [ { name = "more-itertools", marker = "python_full_version >= '3.11'" }, { name = "pydantic", extra = ["email"], marker = "python_full_version >= '3.11'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/2f/58/f394a457e8a5abad1d3f81cfaf54dd5fea1fbebba9796434c5c7a6923618/apify_client-3.0.1.tar.gz", hash = "sha256:f15627d61c3687b5b31128212146be9318a0cfb8c7d5767e758a22d51ced7f68", size = 119964, upload-time = "2026-05-22T15:23:36.759Z" } +sdist = { url = "https://files.pythonhosted.org/packages/64/08/5d6c2fa64fd93b837325368aedaa23f57897030eff31a150873f3aa697e3/apify_client-3.0.2.tar.gz", hash = "sha256:e34dc9c8ac951154a0382adfce785d9658639c373425b43ac54df510d076ac0d", size = 122200, upload-time = "2026-05-26T07:23:43.691Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/58/08/37659aea62fb3b55ba2e32d2fc4ce37baef54cf645dd2ad38064b8889d76/apify_client-3.0.1-py3-none-any.whl", hash = "sha256:41ff8c7c61d41284d4a29834ef9e40bd3963c7133b2647dd161953373f5cd444", size = 139098, upload-time = "2026-05-22T15:23:34.884Z" }, + { url = "https://files.pythonhosted.org/packages/d2/32/ac2ab06f8713f9a1313912815f57431d495cf04861a9f9a2436310dcf38e/apify_client-3.0.2-py3-none-any.whl", hash = "sha256:d78e188eb98e8357a21b57d9b31b139410b517eecc2d125c80f054cdc604296f", size = 141197, upload-time = "2026-05-26T07:23:42.039Z" }, ] [[package]] @@ -2818,7 +2818,7 @@ wheels = [ [[package]] name = "docling-core" -version = "2.77.0" +version = "2.77.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "defusedxml" }, @@ -2835,9 +2835,9 @@ dependencies = [ { name = "typer" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/8e/62/9d5abcb7b32370408be572f5fe738de521d7992d09cce817f87133fcbe0f/docling_core-2.77.0.tar.gz", hash = "sha256:d6655c7b351706cad3b91d166aed99cea9a38f45fd8c9575f8454bc3c36ff160", size = 327171, upload-time 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Integrated into nightly_build.yml workflow - Includes Slack notifications for migration test results This addresses the critical blocker identified by QA team for ensuring safe database migrations in production deployments. * docs: Add DB migration validation documentation - Comprehensive guide for LE-1259 implementation - Detailed test scenarios and execution details - Environment configuration and success criteria - Monitoring and troubleshooting guidelines - Placed in docs/docs/Deployment/ for easy access * fix: address PR review comments for DB migration validation - Use actual nightly tag from create-nightly-tag output instead of hardcoded :latest - Wire POSTGRES_VERSION env var into postgres service image tag - Remove unnecessary checkout steps from both migration jobs - Update step name from 'Create witness flow and credentials' to 'Create witness flow' - Add -f flag to curl commands for fail-fast behavior - Add flow creation verification with error handling - Fix version extraction logic to properly test nightly build instead of PyPI latest - Remove deprecated docker-compose version field - Remove duplicate Slack notification job (consolidated in nightly_build.yml) Addresses all 9 issues identified by @ogabrielluiz in PR review * docs: remove implementation summary from user-facing docs Per @ogabrielluiz review feedback, this file reads as an implementation summary (Jira ticket, branch name, 'Next Steps', 'Files Changed: 2') rather than user-facing documentation. The Deployment section is for end-user docs, and this content is better suited for the PR description. Also not added to sidebars.js, so would be an orphan page. * fix(workflows): address 6 issues from Gabriel's second review of DB migration validation Fixes all remaining issues identified in PR #13249 review: 1. Remove schedule trigger - only works on default branch, would cause duplicate runs 2. Fix postgres service image - hardcode to postgres:16 (env context not available in services) 3. Add curl fail-fast flags - use -fsSL for immediate failure on errors 4. Add flow ID verification - check witness data creation succeeded before proceeding 5. Remove orphaned Slack JSON - cleanup leftover from removed notify-results job 6. Fix version extraction - strip 'v' prefix for pip install (${VERSION#v}) 7. Fix Docker image reference in nightly_build.yml - pass full image path with tag 8. Fix success notification - check migration validation didn't fail All changes validated locally: - YAML syntax validation passed - Docker Compose config validated - Tag manipulation logic tested (v prefix handling) - Curl command structure verified - Test credentials marked with pragma allowlist secret comments Related: LE-1259, PR #13249 Depends on: PR #13212 (Docker volume permissions fix) --- .github/workflows/db-migration-validation.yml | 397 ++++++++++++++++++ .github/workflows/nightly_build.yml | 22 +- 2 files changed, 415 insertions(+), 4 deletions(-) create mode 100644 .github/workflows/db-migration-validation.yml diff --git a/.github/workflows/db-migration-validation.yml b/.github/workflows/db-migration-validation.yml new file mode 100644 index 0000000000..a69bdc3399 --- /dev/null +++ b/.github/workflows/db-migration-validation.yml @@ -0,0 +1,397 @@ +name: DB Migration Validation + +on: + workflow_call: + inputs: + nightly_tag: + description: "Nightly tag to test migration to" + required: true + type: string + workflow_dispatch: + inputs: + nightly_tag: + description: "Nightly tag to test migration to (e.g., langflowai/langflow-nightly:latest)" + required: false + type: string + default: "langflowai/langflow-nightly:latest" + # Note: This workflow is called by nightly_build.yml after Docker images are built + +env: + PYTHON_VERSION: "3.13" + POSTGRES_DB: langflow_test + POSTGRES_USER: langflow + POSTGRES_PASSWORD: langflow_test_pass # pragma: allowlist secret + +jobs: + migration-pip-venv: + name: "Migration Test: pip/venv (stable → nightly)" + runs-on: ubuntu-latest + timeout-minutes: 30 + + services: + postgres: + image: postgres:16 + env: + POSTGRES_DB: ${{ env.POSTGRES_DB }} + POSTGRES_USER: ${{ env.POSTGRES_USER }} + POSTGRES_PASSWORD: ${{ env.POSTGRES_PASSWORD }} + options: >- + --health-cmd pg_isready + --health-interval 10s + --health-timeout 5s + --health-retries 5 + ports: + - 5432:5432 + + steps: + - name: Setup Python + uses: actions/setup-python@v6 + with: + python-version: ${{ env.PYTHON_VERSION }} + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: false + + - name: Create test directory + run: | + mkdir -p migration-test + cd migration-test + + - name: Install latest stable Langflow with PostgreSQL + working-directory: migration-test + run: | + echo "Installing latest stable langflow[postgresql]..." + uv venv + source .venv/bin/activate + uv pip install "langflow[postgresql]" + + # Verify installation + python -c "import langflow; print(f'Installed Langflow version: {langflow.__version__}')" + + - name: Initialize database with stable version + working-directory: migration-test + env: + LANGFLOW_DATABASE_URL: postgresql://${{ env.POSTGRES_USER }}:${{ env.POSTGRES_PASSWORD }}@localhost:5432/${{ env.POSTGRES_DB }} # pragma: allowlist secret + LANGFLOW_SUPERUSER: admin + LANGFLOW_SUPERUSER_PASSWORD: admin123 # pragma: allowlist secret + run: | + source .venv/bin/activate + + echo "Starting Langflow to initialize database..." + timeout 120 bash -c ' + python -m langflow run --host 127.0.0.1 --port 7860 --backend-only & + LANGFLOW_PID=$! + until curl -f http://127.0.0.1:7860/health_check 2>/dev/null; do + sleep 2 + done + kill $LANGFLOW_PID + wait $LANGFLOW_PID 2>/dev/null || true + ' || { + echo "Failed to start Langflow stable version" + exit 1 + } + + echo "Database initialized successfully with stable version" + + - name: Create witness flow + working-directory: migration-test + env: + LANGFLOW_DATABASE_URL: postgresql://${{ env.POSTGRES_USER }}:${{ env.POSTGRES_PASSWORD }}@localhost:5432/${{ env.POSTGRES_DB }} + run: | + source .venv/bin/activate + + # Start Langflow briefly to create test data + python -m langflow run --host 127.0.0.1 --port 7860 --backend-only & + LANGFLOW_PID=$! + + # Wait for startup + timeout 60 bash -c 'until curl -f http://127.0.0.1:7860/health_check 2>/dev/null; do sleep 2; done' + + # Create a simple flow via API + curl -fsSL -X POST http://127.0.0.1:7860/api/v1/flows/ \ + -H "Content-Type: application/json" \ + -d '{ + "name": "Migration Witness Flow", + "description": "Test flow to verify data persistence across migration", + "data": {"nodes": [], "edges": []} + }' > flow_response.json + + # Verify flow was created + if ! grep -q '"id"' flow_response.json; then + echo "❌ Flow creation failed - no id in response" + cat flow_response.json + exit 1 + fi + echo "✅ Witness flow created successfully" + + # Stop Langflow + kill $LANGFLOW_PID + wait $LANGFLOW_PID 2>/dev/null || true + + echo "Witness data created" + + - name: Upgrade to nightly version + working-directory: migration-test + run: | + source .venv/bin/activate + + NIGHTLY_TAG="${{ inputs.nightly_tag || 'langflowai/langflow-nightly:latest' }}" + echo "Upgrading to nightly version: $NIGHTLY_TAG" + + # Extract version from Docker tag (format: langflowai/langflow-nightly:v1.10.0.dev20260522) + if [[ "$NIGHTLY_TAG" == *":"* ]]; then + VERSION="${NIGHTLY_TAG##*:}" + echo "Extracted version from tag: $VERSION" + + # Strip 'v' prefix if present (PyPI doesn't use 'v' prefix) + VERSION="${VERSION#v}" + echo "Version for PyPI: $VERSION" + + if [[ "$VERSION" == "latest" ]]; then + # Install latest nightly from PyPI + uv pip install --upgrade langflow-nightly[postgresql] + else + # Install specific version + uv pip install --upgrade "langflow-nightly[postgresql]==$VERSION" + fi + else + # Direct version string (strip 'v' prefix if present) + VERSION="${NIGHTLY_TAG#v}" + uv pip install --upgrade "langflow-nightly[postgresql]==$VERSION" + fi + + # Verify upgrade + python -c "import langflow; print(f'Upgraded to Langflow version: {langflow.__version__}')" + + - name: Run migration and verify startup + working-directory: migration-test + env: + LANGFLOW_DATABASE_URL: postgresql://${{ env.POSTGRES_USER }}:${{ env.POSTGRES_PASSWORD }}@localhost:5432/${{ env.POSTGRES_DB }} # pragma: allowlist secret + LANGFLOW_SUPERUSER: admin + LANGFLOW_SUPERUSER_PASSWORD: admin123 # pragma: allowlist secret + run: | + source .venv/bin/activate + + echo "Starting Langflow nightly to run migrations..." + timeout 180 bash -c ' + python -m langflow run --host 127.0.0.1 --port 7860 --backend-only > langflow_nightly.log 2>&1 & + LANGFLOW_PID=$! + + until curl -f http://127.0.0.1:7860/health_check 2>/dev/null; do + if ! kill -0 $LANGFLOW_PID 2>/dev/null; then + echo "Langflow process died during startup" + cat langflow_nightly.log + exit 1 + fi + sleep 2 + done + + echo "Langflow nightly started successfully" + kill $LANGFLOW_PID + wait $LANGFLOW_PID 2>/dev/null || true + ' || { + echo "Failed to start Langflow nightly version" + cat langflow_nightly.log || true + exit 1 + } + + - name: Verify witness data persisted + working-directory: migration-test + env: + LANGFLOW_DATABASE_URL: postgresql://${{ env.POSTGRES_USER }}:${{ env.POSTGRES_PASSWORD }}@localhost:5432/${{ env.POSTGRES_DB }} + run: | + source .venv/bin/activate + + # Start Langflow to query data + python -m langflow run --host 127.0.0.1 --port 7860 --backend-only & + LANGFLOW_PID=$! + + timeout 60 bash -c 'until curl -f http://127.0.0.1:7860/health_check 2>/dev/null; do sleep 2; done' + + # Verify witness flow exists + curl -f http://127.0.0.1:7860/api/v1/flows/ > flows_after_migration.json + + if grep -q "Migration Witness Flow" flows_after_migration.json; then + echo "✅ Witness flow found after migration" + else + echo "❌ Witness flow NOT found after migration" + cat flows_after_migration.json + kill $LANGFLOW_PID + exit 1 + fi + + kill $LANGFLOW_PID + wait $LANGFLOW_PID 2>/dev/null || true + + - name: Upload logs on failure + if: failure() + uses: actions/upload-artifact@v6 + with: + name: migration-pip-venv-logs + path: | + migration-test/*.log + migration-test/*.json + retention-days: 7 + + migration-docker-compose: + name: "Migration Test: Docker Compose (stable → nightly)" + runs-on: ubuntu-latest + timeout-minutes: 30 + + steps: + - name: Checkout code + uses: actions/checkout@v6 + + - name: Create Docker Compose test directory + run: | + mkdir -p docker-migration-test + cd docker-migration-test + + - name: Create Docker Compose file for stable + working-directory: docker-migration-test + run: | + cat > docker-compose.yml <<'EOF' + version: "3.8" + + services: + langflow: + image: langflowai/langflow:latest + ports: + - "7860:7860" + environment: # pragma: allowlist secret + - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow # pragma: allowlist secret + - LANGFLOW_SUPERUSER=admin + - LANGFLOW_SUPERUSER_PASSWORD=admin123 # pragma: allowlist secret + depends_on: + postgres: + condition: service_healthy + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:7860/health_check"] + interval: 10s + timeout: 5s + retries: 10 + + postgres: + image: postgres:16 + environment: # pragma: allowlist secret + - POSTGRES_USER=langflow + - POSTGRES_PASSWORD=langflow # pragma: allowlist secret + - POSTGRES_DB=langflow + volumes: + - langflow_postgres_data:/var/lib/postgresql/data + healthcheck: + test: ["CMD-SHELL", "pg_isready -U langflow"] + interval: 10s + timeout: 5s + retries: 5 + + volumes: + langflow_postgres_data: + EOF + + - name: Start stable Langflow with Docker Compose + working-directory: docker-migration-test + run: | + echo "Starting Langflow stable version..." + docker compose up -d + + echo "Waiting for Langflow to be healthy..." + timeout 180 bash -c 'until docker compose exec -T langflow curl -f http://localhost:7860/health_check 2>/dev/null; do sleep 5; done' || { + echo "Langflow stable failed to start" + docker compose logs + exit 1 + } + + echo "Langflow stable is running" + + - name: Create witness flow via API + working-directory: docker-migration-test + run: | + echo "Creating witness flow..." + curl -fsSL -X POST http://localhost:7860/api/v1/flows/ \ + -H "Content-Type: application/json" \ + -d '{ + "name": "Docker Migration Witness Flow", + "description": "Test flow for Docker Compose migration", + "data": {"nodes": [], "edges": []} + }' > flow_response.json + + # Verify flow was created + if ! grep -q '"id"' flow_response.json; then + echo "❌ Flow creation failed - no id in response" + cat flow_response.json + exit 1 + fi + echo "✅ Witness flow created successfully" + + - name: Stop stable Langflow (keep PostgreSQL volume) + working-directory: docker-migration-test + run: | + echo "Stopping Langflow stable..." + docker compose stop langflow + docker compose rm -f langflow + + - name: Update to nightly image + working-directory: docker-migration-test + run: | + NIGHTLY_TAG="${{ inputs.nightly_tag || 'langflowai/langflow-nightly:latest' }}" + echo "Updating to nightly: $NIGHTLY_TAG" + + # Update docker-compose.yml to use nightly image + sed -i "s|image: langflowai/langflow:latest|image: $NIGHTLY_TAG|" docker-compose.yml + + cat docker-compose.yml + + - name: Start nightly Langflow with same PostgreSQL volume + working-directory: docker-migration-test + run: | + echo "Starting Langflow nightly version..." + docker compose up -d langflow + + echo "Waiting for Langflow nightly to be healthy..." + timeout 180 bash -c 'until docker compose exec -T langflow curl -f http://localhost:7860/health_check 2>/dev/null; do sleep 5; done' || { + echo "Langflow nightly failed to start" + docker compose logs langflow + exit 1 + } + + echo "Langflow nightly started successfully" + + - name: Verify witness data persisted + working-directory: docker-migration-test + run: | + echo "Verifying witness flow..." + curl -f http://localhost:7860/api/v1/flows/ > flows_after_migration.json + + if grep -q "Docker Migration Witness Flow" flows_after_migration.json; then + echo "✅ Witness flow found after Docker migration" + else + echo "❌ Witness flow NOT found after Docker migration" + cat flows_after_migration.json + exit 1 + fi + + - name: Collect logs on failure + if: failure() + working-directory: docker-migration-test + run: | + docker compose logs > docker-compose-logs.txt + + - name: Upload logs on failure + if: failure() + uses: actions/upload-artifact@v6 + with: + name: migration-docker-compose-logs + path: | + docker-migration-test/*.log + docker-migration-test/*.json + docker-migration-test/*.txt + retention-days: 7 + + - name: Cleanup + if: always() + working-directory: docker-migration-test + run: | + docker compose down -v diff --git a/.github/workflows/nightly_build.yml b/.github/workflows/nightly_build.yml index e85ec6d40b..115f59aa29 100644 --- a/.github/workflows/nightly_build.yml +++ b/.github/workflows/nightly_build.yml @@ -295,15 +295,27 @@ jobs: # When triggered manually, use the provided value (default is also true) push_to_registry: ${{ github.event_name == 'schedule' || inputs.push_to_registry != false }} secrets: inherit + db-migration-validation: + name: Run DB Migration Validation + needs: [create-nightly-tag, release-nightly-build] + if: ${{ github.repository == 'langflow-ai/langflow' && always() && needs.release-nightly-build.result == 'success' }} + uses: ./.github/workflows/db-migration-validation.yml + with: + # Pass Docker image reference for docker-compose and version without 'v' prefix for pip + # release_tag format: v1.10.0.dev20260522 + # We need: langflowai/langflow-nightly:1.10.0.dev20260522 for Docker + nightly_tag: langflowai/langflow-nightly:${{ needs.create-nightly-tag.outputs.release_tag }} + secrets: inherit + slack-notification: name: Send Slack Notification - needs: [frontend-tests-linux, frontend-tests-windows, backend-unit-tests, release-nightly-build] - if: ${{ github.repository == 'langflow-ai/langflow' && !inputs.skip_slack && always() && (needs.release-nightly-build.result == 'failure' || needs.frontend-tests-linux.result == 'failure' || needs.frontend-tests-windows.result == 'failure' || needs.backend-unit-tests.result == 'failure' || needs.release-nightly-build.result == 'success') }} + needs: [frontend-tests-linux, frontend-tests-windows, backend-unit-tests, release-nightly-build, db-migration-validation] + if: ${{ github.repository == 'langflow-ai/langflow' && !inputs.skip_slack && always() && (needs.release-nightly-build.result == 'failure' || needs.frontend-tests-linux.result == 'failure' || needs.frontend-tests-windows.result == 'failure' || needs.backend-unit-tests.result == 'failure' || needs.db-migration-validation.result == 'failure' || needs.release-nightly-build.result == 'success') }} runs-on: ubuntu-latest steps: - name: Send failure notification to Slack - if: ${{ needs.release-nightly-build.result == 'failure' || needs.frontend-tests-linux.result == 'failure' || needs.frontend-tests-windows.result == 'failure' || needs.backend-unit-tests.result == 'failure' }} + if: ${{ needs.release-nightly-build.result == 'failure' || needs.frontend-tests-linux.result == 'failure' || needs.frontend-tests-windows.result == 'failure' || needs.backend-unit-tests.result == 'failure' || needs.db-migration-validation.result == 'failure' }} run: | # Determine which job failed FAILED_JOB="unknown" @@ -315,6 +327,8 @@ jobs: FAILED_JOB="frontend-tests-windows (non-blocking)" elif [ "${{ needs.backend-unit-tests.result }}" == "failure" ]; then FAILED_JOB="backend-unit-tests" + elif [ "${{ needs.db-migration-validation.result }}" == "failure" ]; then + FAILED_JOB="db-migration-validation" fi curl -X POST -H 'Content-type: application/json' \ @@ -360,7 +374,7 @@ jobs: }" ${{ secrets.LANGFLOW_ENG_SLACK_WEBHOOK_URL }} - name: Send success notification to Slack - if: ${{ !inputs.skip_slack && needs.release-nightly-build.result == 'success' }} + if: ${{ !inputs.skip_slack && needs.release-nightly-build.result == 'success' && needs.db-migration-validation.result != 'failure' }} run: | curl -X POST -H 'Content-type: application/json' \ --data '{