From c5cbdd0c924e849f85f6b7ce71d4e4d27f60f9cb Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Fri, 15 May 2026 17:24:04 -0300 Subject: [PATCH] chore(prototypes): multitenant lfx capability boundary POC (RFC) Working POC of the multitenant capability boundary applied to lfx's existing service injection. Not for merge; lives under prototypes/. - CapabilityVariableService implements VariableServiceProtocol and is registered via @register_service(ServiceType.VARIABLE_SERVICE), replacing lfx's default at runtime. - The real Basic Prompting starter project runs through lfx in a worker subprocess whose env has been scrubbed of DB and provider credentials. - A short-lived run token gates variable resolution. Granted scope: the flow completes against real OpenAI. Empty scope: the shim returns PermissionError and lfx surfaces a ComponentBuildError. Also establishes prototypes/ as a lint-free sandbox in the root ruff config and pre-commit hooks (ruff, detect-secrets), matching the existing pattern for docs/ python examples. --- .pre-commit-config.yaml | 6 +- prototypes/mt-custom-components/README.md | 115 + .../capability_variable_service.py | 94 + .../flows/basic_prompting.json | 1272 +++++++ .../mt-custom-components/pyproject.toml | 21 + .../runtime_api/__init__.py | 0 .../mt-custom-components/runtime_api/auth.py | 72 + .../mt-custom-components/runtime_api/main.py | 171 + .../mt-custom-components/runtime_api/store.py | 51 + .../scripts/orchestrator.py | 235 ++ .../mt-custom-components/scripts/run.sh | 21 + .../mt-custom-components/scripts/worker.py | 143 + prototypes/mt-custom-components/uv.lock | 3191 +++++++++++++++++ pyproject.toml | 1 + 14 files changed, 5390 insertions(+), 3 deletions(-) create mode 100644 prototypes/mt-custom-components/README.md create mode 100644 prototypes/mt-custom-components/capability_variable_service.py create mode 100644 prototypes/mt-custom-components/flows/basic_prompting.json create mode 100644 prototypes/mt-custom-components/pyproject.toml create mode 100644 prototypes/mt-custom-components/runtime_api/__init__.py create mode 100644 prototypes/mt-custom-components/runtime_api/auth.py create mode 100644 prototypes/mt-custom-components/runtime_api/main.py create mode 100644 prototypes/mt-custom-components/runtime_api/store.py create mode 100755 prototypes/mt-custom-components/scripts/orchestrator.py create mode 100755 prototypes/mt-custom-components/scripts/run.sh create mode 100755 prototypes/mt-custom-components/scripts/worker.py create mode 100644 prototypes/mt-custom-components/uv.lock diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 899821a340..c447aef99a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -19,14 +19,14 @@ repos: language: system types_or: [python, pyi] args: [--fix] - exclude: ^docs/.*/API-Reference/python-examples/ + exclude: (^docs/.*/API-Reference/python-examples/|^prototypes/) - id: ruff-format name: ruff format entry: uv run ruff format language: system types_or: [python, pyi] args: [--config, pyproject.toml] - exclude: ^docs/.*/API-Reference/python-examples/ + exclude: (^docs/.*/API-Reference/python-examples/|^prototypes/) - id: validate-migrations name: Validate Alembic Migrations (Expand-Contract) entry: python src/backend/base/langflow/alembic/migration_validator.py @@ -47,7 +47,7 @@ repos: hooks: - id: detect-secrets args: ["--baseline", ".secrets.baseline"] - exclude: '(^docs/|^SECURITY\.md$|src/lfx/src/lfx/_assets/component_index\.json$|src/lfx/README\.md$)' + exclude: '(^docs/|^SECURITY\.md$|src/lfx/src/lfx/_assets/component_index\.json$|src/lfx/README\.md$|^prototypes/)' - repo: local hooks: - id: local-biome-check diff --git a/prototypes/mt-custom-components/README.md b/prototypes/mt-custom-components/README.md new file mode 100644 index 0000000000..8dbda7808b --- /dev/null +++ b/prototypes/mt-custom-components/README.md @@ -0,0 +1,115 @@ +# Multitenant lfx Prototype + +A working POC of the multitenant capability boundary applied to the real +lfx codebase. Loads the actual `Basic Prompting` starter project, runs it +through real lfx, and proves the worker process can complete the flow with +**no DB credentials and no provider keys in its env** — only a short-lived +run token. + +Design doc: `~/Projects/ideas/2026-05-14-langflow-multitenant-custom-components.md`. + +## What it shows + +- lfx's existing `VariableServiceProtocol` is the right injection point. A + capability-backed `CapabilityVariableService` is registered via + `@register_service(ServiceType.VARIABLE_SERVICE)` and quietly replaces + lfx's default. +- The actual `src/backend/base/langflow/initial_setup/starter_projects/ + Basic Prompting.json` loads and runs through `lfx.load.aload_flow_from_json` + → `graph.async_start`. +- The lfx process runs as a **subprocess worker** with `LANGFLOW_DATABASE_URL`, + `PG*`, `OPENAI_API_KEY` and friends scrubbed from its env. It refuses to + boot if any of them are present. +- The LanguageModel component's api_key (`load_from_db: true` → + `value: "OPENAI_API_KEY"`) is fetched through the capability shim → + Runtime API → returns the seeded value. Verified at the end by checking + the API's event log for the `variable_read`. +- With a scope-less token, the capability shim returns `PermissionError`, + lfx surfaces it as `ComponentBuildError: OpenAI API key is required`, + the flow fails closed. + +## Run + +```bash +cd prototypes/mt-custom-components + +# Happy path: scoped token, flow completes against real OpenAI +bash scripts/run.sh + +# Boundary refusal: empty-scope token, flow fails closed +bash scripts/run.sh --deny +``` + +The orchestrator inherits your shell's `OPENAI_API_KEY` and seeds it into +the Runtime API. The worker subprocess never sees the key in its env. If +you don't have a real key set, the orchestrator seeds the sentinel +`"MOCK-not-a-real-key"` and the lfx run still proceeds (it just fails at +the actual OpenAI HTTP call — the boundary verification still passes +because the variable read was logged). + +## Layout + +``` +runtime_api/ Control-plane stand-in + main.py FastAPI: variables / memory / artifacts / events + auth.py JWT mint/verify and scope check (HS256, prototype) + store.py In-memory tenant-keyed store + +capability_variable_service.py The shim: implements VariableServiceProtocol, + registered via @register_service. Every + get_variable() call goes to the Runtime API + carrying the worker's run token. + +flows/basic_prompting.json Real Langflow starter project, with the + LanguageModel api_key field set to + value="OPENAI_API_KEY", load_from_db=True so + the variable service is actually consulted. + +scripts/ + orchestrator.py Control plane: starts the Runtime API, + seeds the variable, mints the run token, + builds a scrubbed env, spawns the worker. + Never imports lfx. + worker.py Worker subprocess: refuses to boot under + forbidden env, registers the capability + shim, loads + runs the flow, emits a + single JSON outcome line. + run.sh Convenience wrapper. +``` + +## Where it differs from production + +This is a POC. The shape lines up with the design doc; the production +hardening does not: + +- **Token signing.** HS256 with a shared secret. Real version wants + asymmetric signing (control plane signs, workers only verify) and + mutual TLS on the Runtime API. +- **Runtime API store.** In-memory dict keyed by `(tenant_id, name)`. Real + version is whatever the control plane's source-of-truth is. +- **Subprocess vs container.** The "worker tier" here is a Python + subprocess with a scrubbed env. Real multitenant workers belong in + containers / Firecracker / a managed microVM; the shape of the + capability boundary doesn't change. +- **One worker per flow.** Per-vertex isolation (a separate worker for + each component) is the design's stronger story — useful when first-party + components are trusted but user custom components in the same flow are + not. Out of scope here. +- **Capability surface.** Only `variables:read:*` is exercised because + Basic Prompting only needs the api_key. Memory / files / artifacts / + events endpoints exist on the Runtime API and have the same shape. + +## One real finding worth flagging + +`src/lfx/src/lfx/base/models/unified_models/credentials.py:32` checks +`os.environ.get(var_name)` **before** consulting the variable service. In +this prototype that means the worker env *must* be scrubbed of provider +keys — otherwise the env value silently bypasses the capability boundary. +Real integration should either invert that order in multitenant mode, or +make the variable service the sole resolver. The orchestrator here scrubs +defensively for the same reason. + +Separately, `credentials.py:100` catches `(ValueError, Exception)` broadly +when the variable service raises. Our shim's `PermissionError` (scope +denied) gets flattened to "API key is required" — same outcome, lossier +message. Worth letting typed denials bubble. diff --git a/prototypes/mt-custom-components/capability_variable_service.py b/prototypes/mt-custom-components/capability_variable_service.py new file mode 100644 index 0000000000..2598c6a12b --- /dev/null +++ b/prototypes/mt-custom-components/capability_variable_service.py @@ -0,0 +1,94 @@ +"""Capability-backed VariableService for lfx. + +Replaces lfx's default in-memory / env-fallback variable service. Every +`get_variable(name, ...)` call goes to the prototype's Runtime API with a +run token. The token's scopes determine what is reachable. The worker (or +the lfx process registering this service) never holds the DB. + +Registration is via lfx's @register_service decorator, applied when this +module is imported. +""" + +from __future__ import annotations + +import os + +import httpx +from lfx.log.logger import logger +from lfx.services.base import Service +from lfx.services.registry import register_service +from lfx.services.schema import ServiceType + + +class _RuntimeAPIConfig: + """Read RUNTIME_API_URL + RUN_TOKEN once at instantiation time. + + The token is supplied by the orchestrator that boots lfx. In Phase A, + that orchestrator is `scripts/run_basic_prompting_lfx.py`. In a real + Stepflow-shaped deployment, the worker process would receive these via + env from the control plane. + """ + + def __init__(self) -> None: + self.base_url = os.environ["MT_RUNTIME_API_URL"].rstrip("/") + self.token = os.environ["MT_RUN_TOKEN"] + + +@register_service(ServiceType.VARIABLE_SERVICE) +class CapabilityVariableService(Service): + name = "variable_service" + + def __init__(self) -> None: + super().__init__() + try: + self._config: _RuntimeAPIConfig | None = _RuntimeAPIConfig() + except KeyError: + # Tests / non-prototype usage: fall back to None and let calls error + # if anyone actually hits the service without configuration. + self._config = None + self._client = httpx.AsyncClient(timeout=10.0) + self.set_ready() + logger.debug("CapabilityVariableService registered") + + async def get_variable(self, name: str, **kwargs) -> str | None: # noqa: ARG002 + """Fetch the variable through the Runtime API. + + Signature absorbs `user_id`, `field`, `session` kwargs from the + langflow call path. We don't use them here — capability scoping + happens through the token, not through the kwargs. + """ + if self._config is None: + msg = ( + "CapabilityVariableService called without MT_RUNTIME_API_URL " + "and MT_RUN_TOKEN set. The prototype orchestrator is " + "responsible for setting these before lfx starts." + ) + raise RuntimeError(msg) + url = f"{self._config.base_url}/runtime/variables/{name}" + r = await self._client.get(url, headers={"Authorization": f"Bearer {self._config.token}"}) + if r.status_code == 200: + logger.debug(f"variable '{name}' resolved through capability service") + return r.json()["value"] + if r.status_code in (401, 403): + # Surface as a clear, actionable error. Langflow would otherwise + # mask this as "variable not found". + msg = f"capability denied for variables:read:{name}: {r.text}" + raise PermissionError(msg) + if r.status_code == 404: + return None + r.raise_for_status() + return None + + def set_variable(self, name: str, value: str, **kwargs) -> None: + # The capability boundary doesn't expose variable writes to workers + # in this prototype. Authoring writes happen in the control plane. + msg = "set_variable is not supported in the capability-backed tier" + raise NotImplementedError(msg) + + async def get_all_decrypted_variables(self, user_id, session) -> dict[str, str]: # noqa: ARG002 + # Not implemented: enumerating all of a tenant's variables in the + # worker tier is exactly the kind of broad read the design refuses. + return {} + + async def teardown(self) -> None: + await self._client.aclose() diff --git a/prototypes/mt-custom-components/flows/basic_prompting.json b/prototypes/mt-custom-components/flows/basic_prompting.json new file mode 100644 index 0000000000..1e92f5af67 --- /dev/null +++ b/prototypes/mt-custom-components/flows/basic_prompting.json @@ -0,0 +1,1272 @@ +{ + "data": { + "edges": [ + { + "animated": false, + "className": "", + "data": { + "sourceHandle": { + "dataType": "ChatInput", + "id": "ChatInput-b6UCc", + "name": "message", + "output_types": [ + "Message" + ] + }, + "targetHandle": { + "fieldName": "input_value", + "id": "LanguageModelComponent-FLeYF", + "inputTypes": [ + "Message" + ], + "type": "str" + } + }, + "id": "reactflow__edge-ChatInput-b6UCc{\u0153dataType\u0153:\u0153ChatInput\u0153,\u0153id\u0153:\u0153ChatInput-b6UCc\u0153,\u0153name\u0153:\u0153message\u0153,\u0153output_types\u0153:[\u0153Message\u0153]}-LanguageModelComponent-FLeYF{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153LanguageModelComponent-FLeYF\u0153,\u0153inputTypes\u0153:[\u0153Message\u0153],\u0153type\u0153:\u0153str\u0153}", + "selected": false, + "source": "ChatInput-b6UCc", + "sourceHandle": "{\u0153dataType\u0153: \u0153ChatInput\u0153, \u0153id\u0153: \u0153ChatInput-b6UCc\u0153, \u0153name\u0153: \u0153message\u0153, \u0153output_types\u0153: [\u0153Message\u0153]}", + "target": "LanguageModelComponent-FLeYF", + "targetHandle": "{\u0153fieldName\u0153: \u0153input_value\u0153, \u0153id\u0153: \u0153LanguageModelComponent-FLeYF\u0153, \u0153inputTypes\u0153: [\u0153Message\u0153], \u0153type\u0153: \u0153str\u0153}" + }, + { + "animated": false, + "className": "", + "data": { + "sourceHandle": { + "dataType": "Prompt", + "id": "Prompt-t2oaK", + "name": "prompt", + "output_types": [ + "Message" + ] + }, + "targetHandle": { + "fieldName": "system_message", + "id": "LanguageModelComponent-FLeYF", + "inputTypes": [ + "Message" + ], + "type": "str" + } + }, + "id": "reactflow__edge-Prompt-t2oaK{\u0153dataType\u0153:\u0153Prompt\u0153,\u0153id\u0153:\u0153Prompt-t2oaK\u0153,\u0153name\u0153:\u0153prompt\u0153,\u0153output_types\u0153:[\u0153Message\u0153]}-LanguageModelComponent-FLeYF{\u0153fieldName\u0153:\u0153system_message\u0153,\u0153id\u0153:\u0153LanguageModelComponent-FLeYF\u0153,\u0153inputTypes\u0153:[\u0153Message\u0153],\u0153type\u0153:\u0153str\u0153}", + "selected": false, + "source": "Prompt-t2oaK", + "sourceHandle": "{\u0153dataType\u0153: \u0153Prompt\u0153, \u0153id\u0153: \u0153Prompt-t2oaK\u0153, \u0153name\u0153: \u0153prompt\u0153, \u0153output_types\u0153: [\u0153Message\u0153]}", + "target": "LanguageModelComponent-FLeYF", + "targetHandle": "{\u0153fieldName\u0153: \u0153system_message\u0153, \u0153id\u0153: \u0153LanguageModelComponent-FLeYF\u0153, \u0153inputTypes\u0153: [\u0153Message\u0153], \u0153type\u0153: \u0153str\u0153}" + }, + { + "animated": false, + "className": "", + "data": { + "sourceHandle": { + "dataType": "LanguageModelComponent", + "id": "LanguageModelComponent-FLeYF", + "name": "text_output", + "output_types": [ + "Message" + ] + }, + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-yK0AU", + "inputTypes": [ + "Data", + "JSON", + "DataFrame", + "Table", + "Message" + ], + "type": "str" + } + }, + "id": "reactflow__edge-LanguageModelComponent-FLeYF{\u0153dataType\u0153:\u0153LanguageModelComponent\u0153,\u0153id\u0153:\u0153LanguageModelComponent-FLeYF\u0153,\u0153name\u0153:\u0153text_output\u0153,\u0153output_types\u0153:[\u0153Message\u0153]}-ChatOutput-yK0AU{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153ChatOutput-yK0AU\u0153,\u0153inputTypes\u0153:[\u0153Data\u0153,\u0153JSON\u0153,\u0153DataFrame\u0153,\u0153Table\u0153,\u0153Message\u0153],\u0153type\u0153:\u0153str\u0153}", + "selected": false, + "source": "LanguageModelComponent-FLeYF", + "sourceHandle": "{\u0153dataType\u0153: \u0153LanguageModelComponent\u0153, \u0153id\u0153: \u0153LanguageModelComponent-FLeYF\u0153, \u0153name\u0153: \u0153text_output\u0153, \u0153output_types\u0153: [\u0153Message\u0153]}", + "target": "ChatOutput-yK0AU", + "targetHandle": "{\u0153fieldName\u0153: \u0153input_value\u0153, \u0153id\u0153: \u0153ChatOutput-yK0AU\u0153, \u0153inputTypes\u0153: [\u0153Data\u0153, \u0153JSON\u0153, \u0153DataFrame\u0153, \u0153Table\u0153, \u0153Message\u0153], \u0153type\u0153: \u0153str\u0153}" + } + ], + "nodes": [ + { + "data": { + "description": "Get chat inputs from the Playground.", + "display_name": "Chat Input", + "id": "ChatInput-b6UCc", + "node": { + "base_classes": [ + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Get chat inputs from the Playground.", + "display_name": "Chat Input", + "documentation": "", + "edited": false, + "field_order": [ + "input_value", + "should_store_message", + "sender", + "sender_name", + "session_id", + "context_id", + "files" + ], + "frozen": false, + "icon": "MessagesSquare", + "legacy": false, + "lf_version": "1.7.0", + "metadata": { + "code_hash": "7a26c54d89ed", + "dependencies": { + "dependencies": [ + { + "name": "lfx", + "version": null + } + ], + "total_dependencies": 1 + }, + "module": "lfx.components.input_output.chat.ChatInput" + }, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Chat Message", + "group_outputs": false, + "method": "message_response", + "name": "message", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "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 lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom lfx.schema.message import Message\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n documentation: str = \"https://docs.langflow.org/chat-input-and-output\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n temp_file=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n # Ensure files is a list and filter out empty/None values\n files = self.files if self.files else []\n if files and not isinstance(files, list):\n files = [files]\n # Filter out None/empty values\n files = [f for f in files if f is not None and f != \"\"]\n\n session_id = self.session_id or self.graph.session_id or \"\"\n message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=session_id,\n context_id=self.context_id,\n files=files,\n )\n if session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n" + }, + "context_id": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Context ID", + "dynamic": false, + "info": "The context ID of the chat. Adds an extra layer to the local memory.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "context_id", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "files": { + "advanced": true, + "display_name": "Files", + "dynamic": false, + "fileTypes": [ + "csv", + "json", + "pdf", + "txt", + "md", + "mdx", + "yaml", + "yml", + "xml", + "html", + "htm", + "docx", + "py", + "sh", + "sql", + "js", + "ts", + "tsx", + "jpg", + "jpeg", + "png", + "bmp", + "image" + ], + "file_path": "", + "info": "Files to be sent with the message.", + "list": true, + "name": "files", + "placeholder": "", + "required": false, + "show": true, + "temp_file": true, + "title_case": false, + "trace_as_metadata": true, + "type": "file", + "value": "" + }, + "input_value": { + "advanced": false, + "display_name": "Input Text", + "dynamic": false, + "info": "Message to be passed as input.", + "input_types": [], + "list": false, + "load_from_db": false, + "multiline": true, + "name": "input_value", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "Hello" + }, + "sender": { + "advanced": true, + "display_name": "Sender Type", + "dynamic": false, + "info": "Type of sender.", + "name": "sender", + "options": [ + "Machine", + "User" + ], + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "str", + "value": "User" + }, + "sender_name": { + "advanced": true, + "display_name": "Sender Name", + "dynamic": false, + "info": "Name of the sender.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "sender_name", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "User" + }, + "session_id": { + "advanced": true, + "display_name": "Session ID", + "dynamic": false, + "info": "The session ID of the chat. If empty, the current session ID parameter will be used.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "session_id", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "should_store_message": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Store Messages", + "dynamic": false, + "info": "Store the message in the history.", + "list": false, + "name": "should_store_message", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + } + } + }, + "selected_output": "message", + "type": "ChatInput" + }, + "dragging": false, + "height": 234, + "id": "ChatInput-b6UCc", + "measured": { + "height": 234, + "width": 320 + }, + "position": { + "x": 689.5720422421635, + "y": 765.155834131403 + }, + "positionAbsolute": { + "x": 689.5720422421635, + "y": 765.155834131403 + }, + "selected": false, + "type": "genericNode" + }, + { + "data": { + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt Template", + "id": "Prompt-t2oaK", + "node": { + "base_classes": [ + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": { + "template": [] + }, + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt Template", + "documentation": "", + "edited": false, + "field_order": [ + "template", + "use_double_brackets", + "tool_placeholder" + ], + "frozen": false, + "icon": "prompts", + "legacy": false, + "lf_version": "1.7.0", + "metadata": { + "code_hash": "3d3fd7b8a36f", + "dependencies": { + "dependencies": [ + { + "name": "lfx", + "version": null + } + ], + "total_dependencies": 1 + }, + "module": "lfx.components.models_and_agents.prompt.PromptComponent" + }, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Prompt", + "group_outputs": false, + "method": "build_prompt", + "name": "prompt", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "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 typing import Any\n\nfrom lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.input_mixin import FieldTypes\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import BoolInput, MessageTextInput, Output, PromptInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\nfrom lfx.utils.mustache_security import validate_mustache_template\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n BoolInput(\n name=\"use_double_brackets\",\n display_name=\"Use Double Brackets\",\n value=False,\n advanced=True,\n info=\"Use {{variable}} syntax instead of {variable}.\",\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n show=False,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n \"\"\"Update the template field type based on the selected mode.\"\"\"\n if field_name == \"use_double_brackets\":\n # Change the template field type based on mode\n is_mustache = field_value is True\n if is_mustache:\n build_config[\"template\"][\"type\"] = FieldTypes.MUSTACHE_PROMPT.value\n else:\n build_config[\"template\"][\"type\"] = FieldTypes.PROMPT.value\n\n # Re-process the template to update variables when mode changes\n template_value = build_config.get(\"template\", {}).get(\"value\", \"\")\n if template_value:\n # Ensure custom_fields is properly initialized\n if \"custom_fields\" not in build_config:\n build_config[\"custom_fields\"] = {}\n\n # Clean up fields from the OLD mode before processing with NEW mode\n # This ensures we don't keep fields with wrong syntax even if validation fails\n old_custom_fields = build_config[\"custom_fields\"].get(\"template\", [])\n for old_field in list(old_custom_fields):\n # Remove the field from custom_fields and template\n if old_field in old_custom_fields:\n old_custom_fields.remove(old_field)\n build_config.pop(old_field, None)\n\n # Try to process template with new mode to add new variables\n # If validation fails, at least we cleaned up old fields\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template_value)\n\n # Re-process template with new mode to add new variables\n _ = process_prompt_template(\n template=template_value,\n name=\"template\",\n custom_fields=build_config[\"custom_fields\"],\n frontend_node_template=build_config,\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, we still updated the mode and cleaned old fields\n # User will see error when they try to save\n logger.debug(f\"Template validation failed during mode switch: {e}\")\n return build_config\n\n async def build_prompt(self) -> Message:\n use_double_brackets = self.use_double_brackets if hasattr(self, \"use_double_brackets\") else False\n template_format = \"mustache\" if use_double_brackets else \"f-string\"\n prompt = await Message.from_template_and_variables(template_format=template_format, **self._attributes)\n self.status = prompt.text\n return prompt\n\n def _update_template(self, frontend_node: dict):\n prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(prompt_template)\n\n custom_fields = frontend_node[\"custom_fields\"]\n frontend_node_template = frontend_node[\"template\"]\n _ = process_prompt_template(\n template=prompt_template,\n name=\"template\",\n custom_fields=custom_fields,\n frontend_node_template=frontend_node_template,\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be created\n logger.debug(f\"Template validation failed in _update_template: {e}\")\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n use_double_brackets = frontend_node[\"template\"].get(\"use_double_brackets\", {}).get(\"value\", False)\n is_mustache = use_double_brackets is True\n\n try:\n # Validate mustache templates for security\n if is_mustache:\n validate_mustache_template(template)\n\n # Kept it duplicated for backwards compatibility\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n is_mustache=is_mustache,\n )\n except ValueError as e:\n # If validation fails, don't add variables but allow component to be updated\n logger.debug(f\"Template validation failed in update_frontend_node: {e}\")\n # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n # and update the frontend_node with those values\n update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n return frontend_node\n\n def _get_fallback_input(self, **kwargs):\n return DefaultPromptField(**kwargs)\n" + }, + "template": { + "_input_type": "PromptInput", + "advanced": false, + "display_name": "Template", + "dynamic": false, + "info": "", + "list": false, + "load_from_db": false, + "name": "template", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "type": "prompt", + "value": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh." + }, + "tool_placeholder": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Tool Placeholder", + "dynamic": false, + "info": "A placeholder input for tool mode.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "tool_placeholder", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": true, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "use_double_brackets": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Use Double Brackets", + "dynamic": false, + "info": "Use {{variable}} syntax instead of {variable}.", + "list": false, + "list_add_label": "Add More", + "name": "use_double_brackets", + "override_skip": false, + "placeholder": "", + "real_time_refresh": true, + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "bool", + "value": false + } + }, + "tool_mode": false + }, + "selected_output": "prompt", + "type": "Prompt" + }, + "dragging": false, + "height": 260, + "id": "Prompt-t2oaK", + "measured": { + "height": 260, + "width": 320 + }, + "position": { + "x": 688.9222183027662, + "y": 1044.5004597498394 + }, + "positionAbsolute": { + "x": 690.2015147036818, + "y": 1018.5443911764344 + }, + "selected": false, + "type": "genericNode" + }, + { + "data": { + "id": "undefined-Ua7t1", + "node": { + "description": "# \ud83d\udcd6 README\nThis template demonstrates a standard chat flow with additional instructions provided by a prompt. Prompts provide instructions and inputs for a Large Language Model (LLM) beyond the standard user-provided chat input. In this example, the prompt describes the LLM's role and persona.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Open the **Playground** to start the chat and run the flow.\n\n## Next steps\nChange the prompt template, model, or model settings, such as **Temperature**, and then see how the responses change with these different inputs.\n\ud83d\udca1 Some component settings are hidden by default; to view all settings click **Controls** in each component's header menu.\n\ud83d\udca1 You can use curly braces to create variables in your template, such as `{variable}`. These can be populated from other components, with Langflow global variables, or at runtime.", + "display_name": "", + "documentation": "", + "template": { + "backgroundColor": "neutral" + } + } + }, + "dragging": false, + "height": 403, + "id": "undefined-Ua7t1", + "measured": { + "height": 403, + "width": 324 + }, + "position": { + "x": 309.22132329147314, + "y": 803.5424763412283 + }, + "positionAbsolute": { + "x": 66.38770028934243, + "y": 749.744424427066 + }, + "resizing": false, + "selected": false, + "style": { + "height": 250, + "width": 324 + }, + "type": "noteNode", + "width": 324 + }, + { + "data": { + "id": "note-Px1sq", + "node": { + "description": "### Configure your Model Provider", + "display_name": "", + "documentation": "", + "template": { + "backgroundColor": "transparent" + } + }, + "type": "note" + }, + "dragging": false, + "height": 324, + "id": "note-Px1sq", + "measured": { + "height": 324, + "width": 351 + }, + "position": { + "x": 1067.5551137574816, + "y": 740.1751340155314 + }, + "positionAbsolute": { + "x": 1075.829573520873, + "y": 657.2057655038416 + }, + "resizing": false, + "selected": false, + "style": { + "height": 324, + "width": 324 + }, + "type": "noteNode", + "width": 351 + }, + { + "data": { + "id": "ChatOutput-yK0AU", + "node": { + "base_classes": [ + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Display a chat message in the Playground.", + "display_name": "Chat Output", + "documentation": "", + "edited": false, + "field_order": [ + "input_value", + "should_store_message", + "sender", + "sender_name", + "session_id", + "context_id", + "data_template", + "clean_data" + ], + "frozen": false, + "icon": "MessagesSquare", + "legacy": false, + "lf_version": "1.7.0", + "metadata": { + "code_hash": "84009527d08c", + "dependencies": { + "dependencies": [ + { + "name": "orjson", + "version": "3.11.8" + }, + { + "name": "fastapi", + "version": "0.136.1" + }, + { + "name": "lfx", + "version": null + } + ], + "total_dependencies": 3 + }, + "module": "lfx.components.input_output.chat_output.ChatOutput" + }, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Output Message", + "group_outputs": false, + "method": "message_response", + "name": "message", + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "template": { + "_type": "Component", + "clean_data": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Basic Clean Data", + "dynamic": false, + "info": "Whether to clean data before converting to string.", + "list": false, + "list_add_label": "Add More", + "name": "clean_data", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + }, + "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 collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/chat-input-and-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"JSON\", \"DataFrame\", \"Table\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"context_id\",\n display_name=\"Context ID\",\n info=\"The context ID of the chat. Adds an extra layer to the local memory.\",\n value=\"\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n advanced=True,\n info=\"Whether to clean data before converting to string.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, _, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message) and not self.is_connected_to_chat_input():\n message = self.input_value\n # Update message properties\n message.text = text\n # Preserve existing session_id from the incoming message if it exists\n existing_session_id = message.session_id\n else:\n message = Message(text=text)\n existing_session_id = None\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n # Preserve session_id from incoming message, or use component/graph session_id\n message.session_id = (\n self.session_id or existing_session_id or (self.graph.session_id if hasattr(self, \"graph\") else None) or \"\"\n )\n message.context_id = self.context_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if message.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n # Set accumulated token usage from all upstream LLM vertices.\n # This must happen AFTER send_message() because streaming captures\n # usage from chunks and would overwrite accumulated totals.\n if hasattr(self, \"_vertex\") and self._vertex is not None:\n accumulated_usage = self._vertex._accumulate_upstream_token_usage() # noqa: SLF001\n if accumulated_usage:\n message.properties.usage = accumulated_usage\n if self.should_store_message and message.get_id():\n message = await self._update_stored_message(message)\n await self._send_message_event(message, id_=message.get_id())\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n clean_data: bool = getattr(self, \"clean_data\", False)\n return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" + }, + "context_id": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Context ID", + "dynamic": false, + "info": "The context ID of the chat. Adds an extra layer to the local memory.", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "context_id", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "data_template": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Data Template", + "dynamic": false, + "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "data_template", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "{text}" + }, + "input_value": { + "_input_type": "MessageInput", + "advanced": false, + "display_name": "Inputs", + "dynamic": false, + "info": "Message to be passed as output.", + "input_types": [ + "Data", + "JSON", + "DataFrame", + "Table", + "Message" + ], + "list": false, + "load_from_db": false, + "name": "input_value", + "placeholder": "", + "required": true, + "show": true, + "title_case": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "sender": { + "_input_type": "DropdownInput", + "advanced": true, + "combobox": false, + "display_name": "Sender Type", + "dynamic": false, + "info": "Type of sender.", + "name": "sender", + "options": [ + "Machine", + "User" + ], + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "str", + "value": "Machine" + }, + "sender_name": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Sender Name", + "dynamic": false, + "info": "Name of the sender.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "sender_name", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "AI" + }, + "session_id": { + "_input_type": "MessageTextInput", + "advanced": true, + "display_name": "Session ID", + "dynamic": false, + "info": "The session ID of the chat. If empty, the current session ID parameter will be used.", + "input_types": [ + "Message" + ], + "list": false, + "load_from_db": false, + "name": "session_id", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "type": "str", + "value": "" + }, + "should_store_message": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Store Messages", + "dynamic": false, + "info": "Store the message in the history.", + "list": false, + "name": "should_store_message", + "placeholder": "", + "required": false, + "show": true, + "title_case": false, + "trace_as_metadata": true, + "type": "bool", + "value": true + } + }, + "tool_mode": false + }, + "type": "ChatOutput" + }, + "dragging": false, + "height": 234, + "id": "ChatOutput-yK0AU", + "measured": { + "height": 234, + "width": 320 + }, + "position": { + "x": 1460.070372772908, + "y": 872.7273956769025 + }, + "positionAbsolute": { + "x": 1444.936881624563, + "y": 872.7273956769025 + }, + "selected": false, + "type": "genericNode", + "width": 320 + }, + { + "data": { + "id": "LanguageModelComponent-FLeYF", + "node": { + "base_classes": [ + "LanguageModel", + "Message" + ], + "beta": false, + "conditional_paths": [], + "custom_fields": {}, + "description": "Runs a language model given a specified provider.", + "display_name": "Language Model", + "documentation": "https://docs.langflow.org/components-models", + "edited": false, + "field_order": [ + "model", + "api_key", + "base_url_ibm_watsonx", + "project_id", + "ollama_base_url", + "input_value", + "system_message", + "stream", + "temperature", + "max_tokens" + ], + "frozen": false, + "icon": "brain-circuit", + "last_updated": "2026-02-12T21:08:04.082Z", + "legacy": false, + "lf_version": "1.7.0", + "metadata": { + "code_hash": "7fe3257f2169", + "dependencies": { + "dependencies": [ + { + "name": "lfx", + "version": null + } + ], + "total_dependencies": 1 + }, + "keywords": [ + "model", + "llm", + "language model", + "large language model" + ], + "module": "lfx.components.models_and_agents.language_model.LanguageModelComponent" + }, + "minimized": false, + "output_types": [], + "outputs": [ + { + "allows_loop": false, + "cache": true, + "display_name": "Model Response", + "group_outputs": false, + "hidden": null, + "loop_types": null, + "method": "text_response", + "name": "text_output", + "options": null, + "required_inputs": null, + "selected": "Message", + "tool_mode": true, + "types": [ + "Message" + ], + "value": "__UNDEFINED__" + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Language Model", + "group_outputs": false, + "hidden": null, + "loop_types": null, + "method": "build_model", + "name": "model_output", + "options": null, + "required_inputs": null, + "selected": "LanguageModel", + "tool_mode": true, + "types": [ + "LanguageModel" + ], + "value": "__UNDEFINED__" + } + ], + "pinned": false, + "priority": 0, + "template": { + "_frontend_node_flow_id": { + "value": "c1cb7b3c-2589-4754-ac61-8cf88df4a07e" + }, + "_frontend_node_folder_id": { + "value": "ce670bdf-fcca-4006-9f28-b58452b08ea2" + }, + "_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": "", + "real_time_refresh": true, + "required": false, + "show": true, + "title_case": false, + "track_in_telemetry": false, + "type": "str", + "value": "OPENAI_API_KEY" + }, + "base_url_ibm_watsonx": { + "_input_type": "DropdownInput", + "advanced": false, + "combobox": false, + "dialog_inputs": {}, + "display_name": "watsonx API Endpoint", + "dynamic": false, + "external_options": {}, + "info": "The base URL of the API (IBM watsonx.ai only)", + "name": "base_url_ibm_watsonx", + "options": [ + "https://us-south.ml.cloud.ibm.com", + "https://eu-de.ml.cloud.ibm.com", + "https://eu-gb.ml.cloud.ibm.com", + "https://au-syd.ml.cloud.ibm.com", + "https://jp-tok.ml.cloud.ibm.com", + "https://ca-tor.ml.cloud.ibm.com" + ], + "options_metadata": [], + "override_skip": false, + "placeholder": "", + "real_time_refresh": true, + "required": false, + "show": false, + "title_case": false, + "toggle": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": true, + "type": "str", + "value": "https://us-south.ml.cloud.ibm.com" + }, + "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 lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.unified_models import (\n get_llm,\n handle_model_input_update,\n)\nfrom lfx.base.models.watsonx_constants import IBM_WATSONX_URLS\nfrom lfx.field_typing.constants import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, StrInput\nfrom lfx.io import IntInput, MessageInput, ModelInput, MultilineInput, SecretStrInput, SliderInput\n\nDEFAULT_OLLAMA_URL = \"http://localhost:11434\"\n\n\nclass LanguageModelComponent(LCModelComponent):\n display_name = \"Language Model\"\n description = \"Runs a language model given a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-models\"\n icon = \"brain-circuit\"\n category = \"models\"\n\n inputs = [\n ModelInput(\n name=\"model\",\n display_name=\"Language Model\",\n info=\"Select your model provider\",\n real_time_refresh=True,\n required=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"API Key\",\n info=\"Overrides global provider settings. Leave blank to use your pre-configured API Key.\",\n required=False,\n show=True,\n real_time_refresh=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"base_url_ibm_watsonx\",\n display_name=\"watsonx API Endpoint\",\n info=\"The base URL of the API (IBM watsonx.ai only)\",\n options=IBM_WATSONX_URLS,\n value=IBM_WATSONX_URLS[0],\n combobox=True,\n show=False,\n real_time_refresh=True,\n ),\n StrInput(\n name=\"project_id\",\n display_name=\"watsonx Project ID\",\n info=\"The project ID associated with the foundation model (IBM watsonx.ai only)\",\n show=False,\n required=False,\n ),\n StrInput(\n name=\"ollama_base_url\",\n display_name=\"Ollama API URL\",\n info=f\"Endpoint of the Ollama API (Ollama only). Defaults to {DEFAULT_OLLAMA_URL}\",\n value=DEFAULT_OLLAMA_URL,\n show=False,\n real_time_refresh=True,\n ),\n MessageInput(\n name=\"input_value\",\n display_name=\"Input\",\n info=\"The input text to send to the model\",\n ),\n MultilineInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"A system message that helps set the behavior of the assistant\",\n advanced=False,\n ),\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=\"Whether to stream the response\",\n value=False,\n advanced=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Controls randomness in responses\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n info=\"Maximum number of tokens to generate. Field name varies by provider.\",\n advanced=True,\n range_spec=RangeSpec(min=1, max=128000, step=1, step_type=\"int\"),\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n return get_llm(\n model=self.model,\n user_id=self.user_id,\n api_key=self.api_key,\n temperature=self.temperature,\n stream=self.stream,\n max_tokens=getattr(self, \"max_tokens\", None),\n watsonx_url=getattr(self, \"base_url_ibm_watsonx\", None),\n watsonx_project_id=getattr(self, \"project_id\", None),\n ollama_base_url=getattr(self, \"ollama_base_url\", None),\n )\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 return handle_model_input_update(self, build_config, field_value, field_name)\n" + }, + "input_value": { + "_input_type": "MessageInput", + "advanced": false, + "display_name": "Input", + "dynamic": false, + "info": "The input text to send to the model", + "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": "" + }, + "is_refresh": false, + "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 your model provider", + "input_types": [ + "LanguageModel" + ], + "list": false, + "list_add_label": "Add More", + "model_type": "language", + "name": "model", + "options": [], + "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": [ + { + "name": "gpt-4o-mini", + "provider": "OpenAI" + } + ] + }, + "ollama_base_url": { + "_input_type": "MessageInput", + "advanced": false, + "display_name": "Ollama API URL", + "dynamic": false, + "info": "Endpoint of the Ollama API (Ollama only). Defaults to http://localhost:11434", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "ollama_base_url", + "override_skip": false, + "placeholder": "", + "real_time_refresh": true, + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_input": true, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "project_id": { + "_input_type": "StrInput", + "advanced": false, + "display_name": "watsonx Project ID", + "dynamic": false, + "info": "The project ID associated with the foundation model (IBM watsonx.ai only)", + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "name": "project_id", + "override_skip": false, + "placeholder": "", + "required": false, + "show": false, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "track_in_telemetry": false, + "type": "str", + "value": "" + }, + "stream": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Stream", + "dynamic": false, + "info": "Whether to stream the response", + "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": "A system message that helps set the behavior of the assistant", + "input_types": [ + "Message" + ], + "list": false, + "list_add_label": "Add More", + "load_from_db": false, + "multiline": true, + "name": "system_message", + "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": "" + }, + "temperature": { + "_input_type": "SliderInput", + "advanced": true, + "display_name": "Temperature", + "dynamic": false, + "info": "Controls randomness in responses", + "max_label": "", + "max_label_icon": "", + "min_label": "", + "min_label_icon": "", + "name": "temperature", + "override_skip": false, + "placeholder": "", + "range_spec": { + "max": 1, + "min": 0, + "step": 0.01, + "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": 0.1 + } + }, + "tool_mode": false + }, + "selected_output": "text_output", + "showNode": true, + "type": "LanguageModelComponent" + }, + "dragging": false, + "id": "LanguageModelComponent-FLeYF", + "measured": { + "height": 341, + "width": 320 + }, + "position": { + "x": 1081.9499905915025, + "y": 793.6091126848561 + }, + "selected": false, + "type": "genericNode" + } + ], + "viewport": { + "x": -211.08737632991608, + "y": -465.0975538356132, + "zoom": 0.8960810767526934 + } + }, + "description": "Perform basic prompting with an OpenAI model.", + "endpoint_name": null, + "id": "c1cb7b3c-2589-4754-ac61-8cf88df4a07e", + "is_component": false, + "last_tested_version": "1.8.0", + "name": "Basic Prompting", + "tags": [ + "chatbots" + ] +} \ No newline at end of file diff --git a/prototypes/mt-custom-components/pyproject.toml b/prototypes/mt-custom-components/pyproject.toml new file mode 100644 index 0000000000..8041cc7a00 --- /dev/null +++ b/prototypes/mt-custom-components/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "mt-lfx-prototype" +version = "0.0.1" +description = "Multitenant lfx prototype: capability-backed services + isolated worker subprocess" +requires-python = ">=3.11" +dependencies = [ + "fastapi>=0.115", + "uvicorn[standard]>=0.30", + "pyjwt>=2.9", + "httpx>=0.27", + "pydantic>=2.8", + "lfx", + # Used by the Basic Prompting LanguageModel component. + "langchain-openai>=0.2", +] + +[tool.uv.sources] +lfx = { path = "../../src/lfx", editable = true } + +[tool.uv] +package = false diff --git a/prototypes/mt-custom-components/runtime_api/__init__.py b/prototypes/mt-custom-components/runtime_api/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/prototypes/mt-custom-components/runtime_api/auth.py b/prototypes/mt-custom-components/runtime_api/auth.py new file mode 100644 index 0000000000..56e1478625 --- /dev/null +++ b/prototypes/mt-custom-components/runtime_api/auth.py @@ -0,0 +1,72 @@ +"""Run-token mint/verify and scope matching. + +Prototype simplification: HS256 with a shared secret. Production design calls +for asymmetric signing plus mutual TLS between worker and Runtime API. +""" + +from __future__ import annotations + +import os +import time +from dataclasses import dataclass + +import jwt + +ALGO = "HS256" +SECRET = os.environ.get("RUNTIME_API_SECRET", "dev-secret-change-me") + + +@dataclass(frozen=True) +class RunClaims: + tenant_id: str + user_id: str + flow_id: str + run_id: str + component_id: str + scopes: tuple[str, ...] + exp: int + + +def mint( + *, + tenant_id: str, + user_id: str, + flow_id: str, + run_id: str, + component_id: str, + scopes: list[str], + ttl_seconds: int = 300, +) -> str: + payload = { + "tenant_id": tenant_id, + "user_id": user_id, + "flow_id": flow_id, + "run_id": run_id, + "component_id": component_id, + "scopes": scopes, + "exp": int(time.time()) + ttl_seconds, + } + return jwt.encode(payload, SECRET, algorithm=ALGO) + + +def verify(token: str) -> RunClaims: + payload = jwt.decode(token, SECRET, algorithms=[ALGO]) + return RunClaims( + tenant_id=payload["tenant_id"], + user_id=payload["user_id"], + flow_id=payload["flow_id"], + run_id=payload["run_id"], + component_id=payload["component_id"], + scopes=tuple(payload["scopes"]), + exp=payload["exp"], + ) + + +def has_scope(claims: RunClaims, required: str) -> bool: + """Exact-match scope check. + + Scopes look like 'variables:read:openai_api_key' or + 'memory:write:session:abc'. Prototype keeps matching literal; a real + implementation would support hierarchical or category scopes. + """ + return required in claims.scopes diff --git a/prototypes/mt-custom-components/runtime_api/main.py b/prototypes/mt-custom-components/runtime_api/main.py new file mode 100644 index 0000000000..9e5f4b6f04 --- /dev/null +++ b/prototypes/mt-custom-components/runtime_api/main.py @@ -0,0 +1,171 @@ +"""Runtime API. + +What this proves: +- The worker never receives DB credentials. It receives a short-lived run + token. All DB-backed behavior goes through these endpoints. +- Scope claims on the token determine what is reachable. No claim, no access. +- Tenant isolation is enforced by the control plane, not by user code. +""" + +from __future__ import annotations + +from typing import Any + +from fastapi import Depends, FastAPI, Header, HTTPException +from pydantic import BaseModel + +from . import auth +from .store import store + +app = FastAPI(title="Langflow Runtime API (prototype)") + + +def get_claims(authorization: str = Header(...)) -> auth.RunClaims: + if not authorization.lower().startswith("bearer "): + raise HTTPException(401, "missing bearer token") + token = authorization.split(" ", 1)[1].strip() + try: + return auth.verify(token) + except Exception as exc: + raise HTTPException(401, f"invalid token: {exc}") from exc + + +def require_scope(claims: auth.RunClaims, scope: str) -> None: + if not auth.has_scope(claims, scope): + raise HTTPException(403, f"missing scope: {scope}") + + +# Admin helpers. Real control plane mints tokens internally; we expose this for +# the demo so a shell script can drive end-to-end flows. + + +class MintRequest(BaseModel): + tenant_id: str + user_id: str + flow_id: str + run_id: str + component_id: str + scopes: list[str] + ttl_seconds: int = 300 + + +class MintResponse(BaseModel): + token: str + + +@app.post("/admin/mint-token", response_model=MintResponse) +def mint_token(req: MintRequest) -> MintResponse: + token = auth.mint( + tenant_id=req.tenant_id, + user_id=req.user_id, + flow_id=req.flow_id, + run_id=req.run_id, + component_id=req.component_id, + scopes=req.scopes, + ttl_seconds=req.ttl_seconds, + ) + return MintResponse(token=token) + + +class SeedVariableRequest(BaseModel): + tenant_id: str + name: str + value: str + + +@app.post("/admin/seed-variable") +def seed_variable(req: SeedVariableRequest) -> dict[str, str]: + store.seed_variable(req.tenant_id, req.name, req.value) + return {"status": "ok"} + + +@app.get("/admin/artifacts/{tenant_id}/{run_id}") +def list_artifacts(tenant_id: str, run_id: str) -> list[dict[str, Any]]: + return store.read_artifacts(tenant_id, run_id) + + +@app.get("/admin/events") +def list_events() -> list[dict[str, Any]]: + return store.read_events() + + +# Runtime capability endpoints. These are the only DB-backed surface the +# worker can reach. + + +@app.get("/runtime/variables/{name}") +def get_variable(name: str, claims: auth.RunClaims = Depends(get_claims)) -> dict[str, str]: + require_scope(claims, f"variables:read:{name}") + value = store.get_variable(claims.tenant_id, name) + if value is None: + raise HTTPException(404, f"variable not found: {name}") + store.write_event( + { + "kind": "variable_read", + "tenant_id": claims.tenant_id, + "user_id": claims.user_id, + "run_id": claims.run_id, + "component_id": claims.component_id, + "name": name, + } + ) + return {"name": name, "value": value} + + +@app.get("/runtime/memory") +def get_memory(session_id: str, claims: auth.RunClaims = Depends(get_claims)) -> list[dict[str, Any]]: + require_scope(claims, f"memory:read:session:{session_id}") + return store.read_memory(claims.tenant_id, session_id) + + +class MemoryWrite(BaseModel): + session_id: str + role: str + content: str + + +@app.post("/runtime/memory") +def post_memory(body: MemoryWrite, claims: auth.RunClaims = Depends(get_claims)) -> dict[str, str]: + require_scope(claims, f"memory:write:session:{body.session_id}") + store.write_memory( + claims.tenant_id, + body.session_id, + {"role": body.role, "content": body.content, "component_id": claims.component_id}, + ) + return {"status": "ok"} + + +class ArtifactWrite(BaseModel): + kind: str + data: dict[str, Any] + + +@app.post("/runtime/artifacts") +def post_artifact(body: ArtifactWrite, claims: auth.RunClaims = Depends(get_claims)) -> dict[str, str]: + require_scope(claims, f"artifacts:write:run:{claims.run_id}") + store.write_artifact( + claims.tenant_id, + claims.run_id, + {"kind": body.kind, "data": body.data, "component_id": claims.component_id}, + ) + return {"status": "ok"} + + +class EventWrite(BaseModel): + kind: str + data: dict[str, Any] + + +@app.post("/runtime/events") +def post_event(body: EventWrite, claims: auth.RunClaims = Depends(get_claims)) -> dict[str, str]: + require_scope(claims, f"events:write:run:{claims.run_id}") + store.write_event( + { + "kind": body.kind, + "tenant_id": claims.tenant_id, + "run_id": claims.run_id, + "component_id": claims.component_id, + "data": body.data, + } + ) + return {"status": "ok"} diff --git a/prototypes/mt-custom-components/runtime_api/store.py b/prototypes/mt-custom-components/runtime_api/store.py new file mode 100644 index 0000000000..c0263f4992 --- /dev/null +++ b/prototypes/mt-custom-components/runtime_api/store.py @@ -0,0 +1,51 @@ +"""In-memory tenant-scoped store. Replaces what Langflow services hit in the DB.""" + +from __future__ import annotations + +from collections import defaultdict +from threading import Lock +from typing import Any + + +class Store: + def __init__(self) -> None: + self._lock = Lock() + self._variables: dict[tuple[str, str], str] = {} + self._memory: dict[tuple[str, str], list[dict[str, Any]]] = defaultdict(list) + self._artifacts: dict[tuple[str, str], list[dict[str, Any]]] = defaultdict(list) + self._events: list[dict[str, Any]] = [] + + def seed_variable(self, tenant_id: str, name: str, value: str) -> None: + with self._lock: + self._variables[(tenant_id, name)] = value + + def get_variable(self, tenant_id: str, name: str) -> str | None: + with self._lock: + return self._variables.get((tenant_id, name)) + + def read_memory(self, tenant_id: str, session_id: str) -> list[dict[str, Any]]: + with self._lock: + return list(self._memory[(tenant_id, session_id)]) + + def write_memory(self, tenant_id: str, session_id: str, entry: dict[str, Any]) -> None: + with self._lock: + self._memory[(tenant_id, session_id)].append(entry) + + def write_artifact(self, tenant_id: str, run_id: str, artifact: dict[str, Any]) -> None: + with self._lock: + self._artifacts[(tenant_id, run_id)].append(artifact) + + def read_artifacts(self, tenant_id: str, run_id: str) -> list[dict[str, Any]]: + with self._lock: + return list(self._artifacts[(tenant_id, run_id)]) + + def write_event(self, event: dict[str, Any]) -> None: + with self._lock: + self._events.append(event) + + def read_events(self) -> list[dict[str, Any]]: + with self._lock: + return list(self._events) + + +store = Store() diff --git a/prototypes/mt-custom-components/scripts/orchestrator.py b/prototypes/mt-custom-components/scripts/orchestrator.py new file mode 100755 index 0000000000..96304afc72 --- /dev/null +++ b/prototypes/mt-custom-components/scripts/orchestrator.py @@ -0,0 +1,235 @@ +"""Phase B orchestrator. + +Control-plane responsibilities: +- start the Runtime API +- seed the variables the flow will need +- mint a per-run token with the right scopes +- build a scrubbed env for the worker subprocess (no DB creds, no provider keys) +- spawn the worker subprocess +- capture its outcome and verify the boundary held + +The lfx process runs as the worker subprocess. The orchestrator never imports lfx. +""" + +from __future__ import annotations + +import json +import os +import socket +import subprocess +import sys +import time +from pathlib import Path + +import httpx + +PROTOTYPE_ROOT = Path(__file__).resolve().parents[1] +FLOW_PATH = PROTOTYPE_ROOT / "flows" / "basic_prompting.json" +WORKER_SCRIPT = PROTOTYPE_ROOT / "scripts" / "worker.py" + +API_HOST = "127.0.0.1" +API_PORT = 8767 +API_URL = f"http://{API_HOST}:{API_PORT}" +SECRET = "phase-b-prototype-secret-must-be-at-least-32-bytes" + + +def _wait_for_port(host: str, port: int, *, timeout: float = 10.0) -> None: + deadline = time.time() + timeout + while time.time() < deadline: + try: + with socket.create_connection((host, port), timeout=0.3): + return + except OSError: + time.sleep(0.1) + msg = f"port {host}:{port} did not come up within {timeout}s" + raise RuntimeError(msg) + + +def _start_runtime_api() -> subprocess.Popen: + proc = subprocess.Popen( + [ + sys.executable, + "-m", + "uvicorn", + "runtime_api.main:app", + "--host", + API_HOST, + "--port", + str(API_PORT), + "--log-level", + "warning", + ], + cwd=str(PROTOTYPE_ROOT), + env={**os.environ, "RUNTIME_API_SECRET": SECRET}, + ) + _wait_for_port(API_HOST, API_PORT) + return proc + + +def _seed_and_mint(client: httpx.Client, *, scopes: list[str]) -> tuple[str, str]: + """Seed OPENAI_API_KEY and mint a run token with the given scopes. + + The caller controls scopes so we can also exercise the negative path + (empty scopes -> worker should be denied at variable lookup time). + """ + openai_key = os.environ.get("OPENAI_API_KEY", "MOCK-not-a-real-key") + client.post( + "/admin/seed-variable", + json={"tenant_id": "t1", "name": "OPENAI_API_KEY", "value": openai_key}, + ).raise_for_status() + + r = client.post( + "/admin/mint-token", + json={ + "tenant_id": "t1", + "user_id": "u1", + "flow_id": "basic_prompting", + "run_id": "run-1", + "component_id": "lfx-worker", + "scopes": scopes, + "ttl_seconds": 300, + }, + ) + r.raise_for_status() + provider = "openai" if not openai_key.startswith("MOCK") else "mock" + return r.json()["token"], provider + + +def _build_worker_env(token: str) -> dict[str, str]: + """Scrub everything the worker shouldn't see. Keep PATH + Python pieces.""" + keep_prefixes = ("PATH", "PYTHONPATH", "PYTHON", "HOME", "USER", "LANG", "LC_", "TZ") + keep_exact = {"VIRTUAL_ENV"} + env: dict[str, str] = {} + for k, v in os.environ.items(): + if k in keep_exact or any(k.startswith(p) for p in keep_prefixes): + env[k] = v + # Explicitly drop anything DB- or provider-related even if it matched + # a prefix by accident (e.g. PG* env vars). + for forbidden in ( + "LANGFLOW_DATABASE_URL", + "DATABASE_URL", + "POSTGRES_URL", + "PGHOST", + "PGUSER", + "PGPASSWORD", + "PGDATABASE", + "SQLALCHEMY_DATABASE_URI", + "OPENAI_API_KEY", + "ANTHROPIC_API_KEY", + "GOOGLE_API_KEY", + ): + env.pop(forbidden, None) + env["MT_RUN_TOKEN"] = token + env["MT_RUNTIME_API_URL"] = API_URL + env["FLOW_PATH"] = str(FLOW_PATH) + env["INPUT_TEXT"] = "Hi, who are you?" + return env + + +def _run_worker(env: dict[str, str]) -> dict: + result = subprocess.run( + [sys.executable, str(WORKER_SCRIPT)], + check=False, + capture_output=True, + text=True, + env=env, + cwd=str(PROTOTYPE_ROOT), + ) + last_line = result.stdout.strip().split("\n")[-1] if result.stdout.strip() else "" + try: + outcome = json.loads(last_line) + except json.JSONDecodeError: + return { + "status": "unparseable", + "stdout": result.stdout[-2000:], + "stderr": result.stderr[-2000:], + } + return outcome + + +def main() -> int: + deny_mode = "--deny" in sys.argv + scopes: list[str] = [] if deny_mode else ["variables:read:OPENAI_API_KEY"] + if deny_mode: + print("==> --deny mode: minting token with NO scopes; expecting boundary refusal") + + print(f"==> Starting Runtime API on {API_URL}") + api_proc = _start_runtime_api() + try: + with httpx.Client(base_url=API_URL, timeout=5.0) as admin: + token, provider = _seed_and_mint(admin, scopes=scopes) + print(f"==> Seeded OPENAI_API_KEY (provider={provider}) and minted run token (scopes={scopes})") + + env = _build_worker_env(token) + # Defensive sanity check: confirm the worker env is scrubbed. + leaked = [ + k + for k in env + if k + in ( + "LANGFLOW_DATABASE_URL", + "DATABASE_URL", + "OPENAI_API_KEY", + "PGHOST", + "PGUSER", + "PGPASSWORD", + ) + ] + if leaked: + print(f"FAIL scrub failed; worker would inherit: {leaked}") + return 1 + print("OK worker env scrubbed of DB/provider credentials") + + print(f"==> Spawning worker subprocess: {WORKER_SCRIPT.name}") + outcome = _run_worker(env) + print(f"==> Worker outcome: status={outcome.get('status')}") + + if deny_mode: + # The boundary should hold: worker should report denied (or + # propagate the PermissionError as a build error). Either way, + # not 'ok'. + ok_after_denial = outcome.get("status") == "ok" + error_blob = json.dumps(outcome)[:500] + if ok_after_denial: + print(f"FAIL scope-less token still produced ok: {error_blob}") + return 1 + print(f"OK boundary refused: {error_blob}") + return 0 + + if outcome.get("status") == "ok": + print() + print("Final output:") + print(f" {outcome.get('text') or '(no text returned)'}") + elif outcome.get("status") == "denied": + print(f" denied: {outcome.get('error')}") + return 1 + elif outcome.get("status") == "boot_failed": + print(f" boot_failed: {outcome}") + return 1 + else: + print(json.dumps(outcome, indent=2)[:1500]) + return 1 + + # Verify the variable read went through the Runtime API. + events = admin.get("/admin/events").json() + reads = [ + e + for e in events + if e.get("kind") == "variable_read" and e.get("name") == "OPENAI_API_KEY" and e.get("tenant_id") == "t1" + ] + print() + if reads: + print(f"OK variable lookup went through Runtime API ({len(reads)} read(s) logged)") + return 0 + print("FAIL no Runtime API event logged; the boundary was bypassed") + return 1 + finally: + api_proc.terminate() + try: + api_proc.wait(timeout=3) + except subprocess.TimeoutExpired: + api_proc.kill() + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/prototypes/mt-custom-components/scripts/run.sh b/prototypes/mt-custom-components/scripts/run.sh new file mode 100755 index 0000000000..f7f9e2c6c0 --- /dev/null +++ b/prototypes/mt-custom-components/scripts/run.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +# Run the multitenant lfx prototype end-to-end. +# +# Orchestrator boots the Runtime API, seeds OPENAI_API_KEY, mints a +# scope-bound run token, then spawns an lfx worker subprocess whose env +# has been scrubbed of every DB and provider credential. The worker runs +# the real Basic Prompting flow JSON through real lfx, resolving the +# api_key through the capability-backed VariableService. +# +# Usage: +# bash scripts/run.sh # happy path +# bash scripts/run.sh --deny # mint with no scopes; expect refusal +set -euo pipefail + +cd "$(dirname "$0")/.." + +echo "==> Syncing python deps" +uv sync --quiet +export PATH="$(pwd)/.venv/bin:${PATH}" + +uv run python scripts/orchestrator.py "$@" diff --git a/prototypes/mt-custom-components/scripts/worker.py b/prototypes/mt-custom-components/scripts/worker.py new file mode 100755 index 0000000000..6e73a7835d --- /dev/null +++ b/prototypes/mt-custom-components/scripts/worker.py @@ -0,0 +1,143 @@ +"""Phase B worker subprocess. + +What this proves: +- A real Langflow flow JSON runs through real lfx code in an isolated + subprocess that holds NO database credentials and NO provider keys in + its environment. +- The subprocess receives only a short-lived run token and the Runtime + API URL. Every variable lookup goes through the capability shim. +- If LANGFLOW_DATABASE_URL or similar leaks into the env, the worker + refuses to start, identically to the clean-room worker's boot check. + +Inputs (env): + RUN_TOKEN, RUNTIME_API_URL, FLOW_PATH, INPUT_TEXT + MT_RUN_TOKEN, MT_RUNTIME_API_URL (set by the orchestrator before spawn) + +Output (stdout): a single trailing JSON line with the outcome. +""" + +from __future__ import annotations + +import asyncio +import json +import os +import sys +import traceback +from pathlib import Path + +PROTOTYPE_ROOT = Path(__file__).resolve().parents[1] +if str(PROTOTYPE_ROOT) not in sys.path: + sys.path.insert(0, str(PROTOTYPE_ROOT)) + +# The lfx process IS the worker tier in this prototype. Treat its env as a +# sandbox surface: refuse to boot if anything that smells like DB or +# provider credentials leaked in. +FORBIDDEN_ENV = ( + "LANGFLOW_DATABASE_URL", + "DATABASE_URL", + "POSTGRES_URL", + "PGHOST", + "PGUSER", + "PGPASSWORD", + "PGDATABASE", + "SQLALCHEMY_DATABASE_URI", + # Provider keys must not be in the worker env: the design's whole + # point is fetching them through capabilities. + "OPENAI_API_KEY", + "ANTHROPIC_API_KEY", + "GOOGLE_API_KEY", +) + + +def _emit(outcome: dict) -> None: + sys.stdout.write(json.dumps(outcome) + "\n") + sys.stdout.flush() + + +async def _run() -> dict: + # Register the capability-backed variable service. The decorator on + # import time replaces lfx's default. Must happen BEFORE the graph + # builds any vertex. + import capability_variable_service # noqa: F401 + from lfx.load import aload_flow_from_json + from lfx.run._defaults import apply_run_defaults + from lfx.schema.schema import InputValueRequest + + flow_path = os.environ["FLOW_PATH"] + input_text = os.environ.get("INPUT_TEXT", "Hi, who are you?") + + graph = await aload_flow_from_json(flow_path, disable_logs=True) + apply_run_defaults(graph, session_id=None, user_id=None) + graph.prepare() + + results = [] + async for r in graph.async_start(InputValueRequest(input_value=input_text)): + results.append(r) + + # Pull the assistant text out of the last vertex with a results dict. + for r in reversed(results): + vertex = getattr(r, "vertex", None) + if vertex is None: + continue + vres = getattr(vertex, "results", None) + if isinstance(vres, dict): + for v in vres.values(): + text = _text_of(v) + if text: + return {"text": text} + text = _text_of(vres) + if text: + return {"text": text} + return {"text": None} + + +def _text_of(value) -> str | None: + if value is None: + return None + if isinstance(value, str) and value.strip(): + return value + for attr in ("text", "message", "data"): + sub = getattr(value, attr, None) + if isinstance(sub, str) and sub.strip(): + return sub + if isinstance(sub, dict): + for k in ("text", "message"): + if isinstance(sub.get(k), str) and sub[k].strip(): + return sub[k] + if isinstance(value, dict): + for k in ("text", "message"): + if isinstance(value.get(k), str) and value[k].strip(): + return value[k] + return None + + +def main() -> int: + leaked = [name for name in FORBIDDEN_ENV if name in os.environ] + if leaked: + _emit({"status": "boot_failed", "reason": "forbidden_env", "vars": leaked}) + return 2 + required = ("MT_RUN_TOKEN", "MT_RUNTIME_API_URL", "FLOW_PATH") + missing = [name for name in required if not os.environ.get(name)] + if missing: + _emit({"status": "boot_failed", "reason": "missing_env", "missing": missing}) + return 2 + try: + result = asyncio.run(_run()) + _emit({"status": "ok", **result}) + return 0 + except PermissionError as exc: + _emit({"status": "denied", "error": str(exc)}) + return 0 + except Exception as exc: # noqa: BLE001 + _emit( + { + "status": "error", + "error": f"{type(exc).__name__}: {exc}", + "traceback": traceback.format_exc()[-2000:], + } + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/prototypes/mt-custom-components/uv.lock b/prototypes/mt-custom-components/uv.lock new file mode 100644 index 0000000000..f9e8b152fb --- /dev/null +++ b/prototypes/mt-custom-components/uv.lock @@ -0,0 +1,3191 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.13' and sys_platform == 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + 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"docs/**/API-Reference/python-examples/**", + "prototypes/**", ] line-length = 120