From a0c88317465639aec896433ea5d6b80c65a8bb6a Mon Sep 17 00:00:00 2001 From: "github-merge-queue[bot]" Date: Thu, 17 Apr 2025 14:46:39 +0000 Subject: [PATCH] deploy: ee35003936cc187b83c3f9ed4948df51adf178a3 --- 404.html | 2 +- agents-overview.html | 2 +- agents-tool-calling-agent-component.html | 2 +- api-reference-api-examples.html | 2 +- api.html | 2 +- api/add-user.html | 2 +- api/auto-login.html | 2 +- api/build-flow.html | 2 +- api/build-public-tmp.html | 2 +- api/build-vertex-stream.html | 2 +- api/build-vertex.html | 2 +- api/cancel-build.html | 2 +- api/check-if-store-has-api-key.html | 2 +- api/check-if-store-is-enabled.html | 2 +- api/create-api-key-route.html | 2 +- api/create-flow.html | 2 +- api/create-flows.html | 2 +- api/create-folder.html | 2 +- api/create-upload-file.html | 2 +- api/create-variable.html | 2 +- api/custom-component-update.html | 2 +- api/custom-component.html | 2 +- api/delete-all-files.html | 2 +- api/delete-api-key-route.html | 2 +- api/delete-file-1.html | 2 +- api/delete-file.html | 2 +- api/delete-flow.html | 2 +- api/delete-folder.html | 2 +- api/delete-messages-session.html | 2 +- api/delete-messages.html | 2 +- api/delete-multiple-flows.html | 2 +- api/delete-user.html | 2 +- api/delete-variable.html | 2 +- api/delete-vertex-builds.html | 2 +- api/download-component.html | 2 +- api/download-file-1.html | 2 +- api/download-file-2.html | 2 +- api/download-file.html | 2 +- api/download-image.html | 2 +- api/download-multiple-file.html | 2 +- api/download-profile-picture.html | 2 +- api/edit-file-name.html | 2 +- api/experimental-run-flow.html | 2 +- api/get-all.html | 2 +- api/get-api-keys-route.html | 2 +- api/get-build-events.html | 2 +- api/get-components.html | 2 +- api/get-config.html | 2 +- api/get-elevenlabs-voice-ids.html | 2 +- api/get-list-of-components-liked-by-user.html | 2 +- api/get-messages.html | 2 +- api/get-starter-projects.html | 2 +- api/get-tags.html | 2 +- api/get-task-status.html | 2 +- api/get-transactions.html | 2 +- api/get-version.html | 2 +- api/get-vertex-builds.html | 2 +- api/handle-messages-1.html | 2 +- api/handle-messages.html | 2 +- api/handle-sse-1.html | 2 +- api/handle-sse.html | 2 +- api/health-check.html | 2 +- api/health.html | 2 +- api/like-component.html | 2 +- api/list-files-1.html | 2 +- api/list-files.html | 2 +- api/list-profile-pictures.html | 2 +- api/login-to-get-access-token.html | 2 +- api/logout.html | 2 +- api/logs.html | 2 +- api/patch-user.html | 2 +- api/post-validate-code.html | 2 +- api/post-validate-prompt.html | 2 +- api/process-1.html | 2 +- api/process.html | 2 +- api/read-all-users.html | 2 +- api/read-basic-examples.html | 2 +- api/read-current-user.html | 2 +- api/read-flow.html | 2 +- api/read-flows.html | 2 +- api/read-folder.html | 2 +- api/read-folders.html | 2 +- api/read-public-flow.html | 2 +- api/read-variables.html | 2 +- api/refresh-token.html | 2 +- api/reset-password.html | 2 +- api/retrieve-vertices-order.html | 2 +- api/save-store-api-key.html | 2 +- api/share-component.html | 2 +- api/simplified-run-flow.html | 2 +- api/stream-logs.html | 2 +- api/update-flow.html | 2 +- api/update-folder.html | 2 +- api/update-message.html | 2 +- api/update-session-id.html | 2 +- api/update-shared-component.html | 2 +- api/update-variable.html | 2 +- api/upload-file-1.html | 2 +- api/upload-file-2.html | 2 +- api/upload-file.html | 2 +- api/upload-user-file.html | 2 +- api/webhook-run-flow.html | 2 +- ...ieval-3201f1ea2f13a5f8a87652c81a5386c6.png | Bin 0 -> 462597 bytes ...ieval-3d2d30b0bfef46783987efd758fc5497.png | Bin 377359 -> 0 bytes ...d74f7.915e2f60.js => 22dd74f7.dc682ff7.js} | 2 +- assets/js/2ab0d4f5.432262b8.js | 1 - 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collects and sends tracing data about the flow execution to Langfuse."}),"\n"]}),"\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsx)(n.p,{children:"View the collected data in your Langfuse project dashboard."}),"\n"]}),"\n"]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.img,{src:"https://langfuse.com//images/blog/langflow-langfuse/langflow-example-trace.png",alt:"Example trace in Langfuse"})}),"\n",(0,s.jsxs)(n.p,{children:["For a live public example trace in a Langfuse dashboard, see ",(0,s.jsx)(n.a,{href:"https://cloud.langfuse.com/project/cm0nywmaa005c3ol2msoisiho/traces/f016ae6d-4527-43f5-93ba-9d78388cd3d9?timestamp=2024-11-15T10%3A22%3A56.378Z&observation=c3680212-31f0-46e2-9310-add4352e4cc7",children:"Public example trace in Langfuse"}),"."]}),"\n",(0,s.jsx)(n.h2,{id:"disable-langfuse-tracing",children:"Disable Langfuse Tracing"}),"\n",(0,s.jsx)(n.p,{children:"To disable the Langfuse integration, remove the environment variables you set in the previous steps and restart Langflow."}),"\n",(0,s.jsx)(n.h2,{id:"run-langfuse-and-langflow-with-docker-compose",children:"Run Langfuse and Langflow with Docker Compose"}),"\n",(0,s.jsx)(n.p,{children:"If you prefer to self-host Langfuse, you can run both services with Docker Compose."}),"\n",(0,s.jsxs)(n.ol,{children:["\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsxs)(n.p,{children:["In Langfuse, go to ",(0,s.jsx)(n.strong,{children:"Project Settings"}),", and then create a new set of API keys."]}),"\n"]}),"\n",(0,s.jsxs)(n.li,{children:["\n",(0,s.jsx)(n.p,{children:"Copy the following API key information:"}),"\n"]}),"\n"]}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsx)(n.li,{children:"Secret Key"}),"\n",(0,s.jsx)(n.li,{children:"Public Key"}),"\n",(0,s.jsx)(n.li,{children:"Host URL"}),"\n"]}),"\n",(0,s.jsxs)(n.ol,{start:"3",children:["\n",(0,s.jsxs)(n.li,{children:["Add your Langflow API keys to your ",(0,s.jsx)(n.code,{children:"docker-compose.yml"})," file.\nAn example ",(0,s.jsx)(n.a,{href:"https://github.com/langflow-ai/langflow/blob/main/docker_example/docker-compose.yml",children:"docker-compose.yml"})," file is available in the Langflow GitHub repo."]}),"\n"]}),"\n",(0,s.jsx)(p.Code,{codeConfig:h,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"services",props:{style:{color:"#7EE787"}}},{content:":",props:{style:{color:"#C9D1D9"}}}]},{tokens:[{content:" langflow",props:{style:{color:"#7EE787"}}},{content:":",props:{style:{color:"#C9D1D9"}}}]},{tokens:[{content:" image",props:{style:{color:"#7EE787"}}},{content:": ",props:{style:{color:"#C9D1D9"}}},{content:"langflowai/langflow:latest ",props:{style:{color:"#A5D6FF"}}},{content:"# or another version tag on https://hub.docker.com/r/langflowai/langflow",props:{style:{color:"#8B949E"}}}]},{tokens:[{content:" pull_policy",props:{style:{color:"#7EE787"}}},{content:": ",props:{style:{color:"#C9D1D9"}}},{content:"always 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stored.",props:{style:{color:"#8B949E"}}}]},{tokens:[{content:" - ",props:{style:{color:"#C9D1D9"}}},{content:"LANGFLOW_CONFIG_DIR=app/langflow",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" - ",props:{style:{color:"#C9D1D9"}}},{content:"LANGFUSE_SECRET_KEY=sk-...",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" - ",props:{style:{color:"#C9D1D9"}}},{content:"LANGFUSE_PUBLIC_KEY=pk-...",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" - ",props:{style:{color:"#C9D1D9"}}},{content:"LANGFUSE_HOST=https://us.cloud.langfuse.com",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" volumes",props:{style:{color:"#7EE787"}}},{content:":",props:{style:{color:"#C9D1D9"}}}]},{tokens:[{content:" - ",props:{style:{color:"#C9D1D9"}}},{content:"langflow-data:/app/langflow",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:"",props:{style:{color:"#C9D1D9"}}}]},{tokens:[{content:" postgres",props:{style:{color:"#7EE787"}}},{content:":",props:{style:{color:"#C9D1D9"}}}]},{tokens:[{content:" image",props:{style:{color:"#7EE787"}}},{content:": ",props:{style:{color:"#C9D1D9"}}},{content:"postgres:16",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" environment",props:{style:{color:"#7EE787"}}},{content:":",props:{style:{color:"#C9D1D9"}}}]},{tokens:[{content:" POSTGRES_USER",props:{style:{color:"#7EE787"}}},{content:": ",props:{style:{color:"#C9D1D9"}}},{content:"langflow",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" POSTGRES_PASSWORD",props:{style:{color:"#7EE787"}}},{content:": ",props:{style:{color:"#C9D1D9"}}},{content:"langflow",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" POSTGRES_DB",props:{style:{color:"#7EE787"}}},{content:": ",props:{style:{color:"#C9D1D9"}}},{content:"langflow",props:{style:{color:"#A5D6FF"}}}]},{tokens:[{content:" 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b/assets/js/e6c6a4d2.964336ad.js @@ -1 +1 @@ -"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[8504],{28453:(e,t,n)=>{n.d(t,{R:()=>a,x:()=>l});var s=n(96540);const i={},o=s.createContext(i);function a(e){const t=s.useContext(o);return s.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function l(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(i):e.components||i:a(e.components),s.createElement(o.Provider,{value:t},e.children)}},94910:(e,t,n)=>{n.r(t),n.d(t,{contentTitle:()=>l,default:()=>d,frontMatter:()=>a,metadata:()=>s,toc:()=>r});const s=JSON.parse('{"type":"api","id":"experimental-run-flow","title":"Experimental Run Flow","description":"","slug":"/experimental-run-flow","frontMatter":{},"api":{"tags":["Base"],"description":"Executes a specified flow by ID with optional input values, output selection, tweaks, and streaming capability.\\n\\nThis endpoint supports running flows with 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Atlas",id:"mongodb-atlas",level:2},{value:"Inputs",id:"inputs-12",level:3},{value:"Outputs",id:"outputs-12",level:3},{value:"Opensearch",id:"opensearch",level:2},{value:"Inputs",id:"inputs-13",level:3},{value:"Outputs",id:"outputs-13",level:3},{value:"PGVector",id:"pgvector",level:2},{value:"Inputs",id:"inputs-14",level:3},{value:"Outputs",id:"outputs-14",level:3},{value:"Pinecone",id:"pinecone",level:2},{value:"Inputs",id:"inputs-15",level:3},{value:"Outputs",id:"outputs-15",level:3},{value:"Qdrant",id:"qdrant",level:2},{value:"Inputs",id:"inputs-16",level:3},{value:"Outputs",id:"outputs-16",level:3},{value:"Redis",id:"redis",level:2},{value:"Inputs",id:"inputs-17",level:3},{value:"Outputs",id:"outputs-17",level:3},{value:"Supabase",id:"supabase",level:2},{value:"Inputs",id:"inputs-18",level:3},{value:"Outputs",id:"outputs-18",level:3},{value:"Upstash",id:"upstash",level:2},{value:"Inputs",id:"inputs-19",level:3},{value:"Outputs",id:"outputs-19",level:3},{value:"Vectara",id:"vectara",level:2},{value:"Inputs",id:"inputs-20",level:3},{value:"Outputs",id:"outputs-20",level:3},{value:"Vectara Search",id:"vectara-search",level:2},{value:"Inputs",id:"inputs-21",level:3},{value:"Outputs",id:"outputs-21",level:3},{value:"Weaviate",id:"weaviate",level:2},{value:"Inputs",id:"inputs-22",level:3},{value:"Outputs",id:"outputs-22",level:3},{value:"Weaviate Search",id:"weaviate-search",level:2},{value:"Inputs",id:"inputs-23",level:3},{value:"Outputs",id:"outputs-23",level:3}];function u(e){const t={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",h3:"h3",header:"header",img:"img",li:"li",ol:"ol",p:"p",strong:"strong",table:"table",tbody:"tbody",td:"td",th:"th",thead:"thead",tr:"tr",ul:"ul",...(0,n.R)(),...e.components};return o||m("CH",!1),o.Code||m("CH.Code",!0),(0,d.jsxs)(d.Fragment,{children:[(0,d.jsx)("style",{dangerouslySetInnerHTML:{__html:'[data-ch-theme="github-dark"] { --ch-t-colorScheme: dark;--ch-t-foreground: #c9d1d9;--ch-t-background: #0d1117;--ch-t-lighter-inlineBackground: #0d1117e6;--ch-t-editor-background: #0d1117;--ch-t-editor-foreground: 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}'}}),"\n","\n",(0,d.jsx)(t.header,{children:(0,d.jsx)(t.h1,{id:"vector-store-components-in-langflow",children:"Vector store components in Langflow"})}),"\n",(0,d.jsx)(t.p,{children:"Vector databases store vector data, which backs AI workloads like chatbots and Retrieval Augmented Generation."}),"\n",(0,d.jsx)(t.p,{children:"Vector database components establish connections to existing vector databases or create in-memory vector stores for storing and retrieving vector data."}),"\n",(0,d.jsxs)(t.p,{children:["Vector database components are distinct from ",(0,d.jsx)(t.a,{href:"/components-memories",children:"memory components"}),", which are built specifically for storing and retrieving chat messages from external databases."]}),"\n",(0,d.jsx)(t.h2,{id:"use-a-vector-store-component-in-a-flow",children:"Use a vector store component in a flow"}),"\n",(0,d.jsxs)(t.p,{children:["This example uses the ",(0,d.jsx)(t.strong,{children:"Astra DB vector store"})," component. Your vector store component's parameters and authentication may be different, but the document ingestion workflow is the same. A document is loaded from a local machine and chunked. The Astra DB vector store generates embeddings with the connected ",(0,d.jsx)(t.a,{href:"/components-models",children:"model"})," component, and stores them in the connected Astra DB database."]}),"\n",(0,d.jsx)(t.p,{children:"This vector data can then be retrieved for workloads like Retrieval Augmented Generation."}),"\n",(0,d.jsx)(t.p,{children:(0,d.jsx)(t.img,{src:r(67719).A+"",width:"2894",height:"1014"})}),"\n",(0,d.jsxs)(t.p,{children:["The user's chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search.\nThe results are output from the vector database component as a ",(0,d.jsx)(t.a,{href:"/concepts-objects",children:"Data"})," object and parsed into text.\nThis text fills the ",(0,d.jsx)(t.code,{children:"{context}"})," variable in the ",(0,d.jsx)(t.strong,{children:"Prompt"})," component, which informs the ",(0,d.jsx)(t.strong,{children:"Open AI model"})," component's responses."]}),"\n",(0,d.jsxs)(t.p,{children:["Alternatively, connect the vector database component's ",(0,d.jsx)(t.strong,{children:"Retriever"})," port to a ",(0,d.jsx)(t.a,{href:"components-tools#retriever-tool",children:"retriever tool"}),", and then to an ",(0,d.jsx)(t.a,{href:"/components-agents",children:"agent"})," component. This enables the agent to use your vector database as a tool and make decisions based on the available data."]}),"\n",(0,d.jsx)(t.p,{children:(0,d.jsx)(t.img,{src:r(59072).A+"",width:"2414",height:"1602"})}),"\n",(0,d.jsx)(t.h2,{id:"astra-db-vector-store",children:"Astra DB Vector Store"}),"\n",(0,d.jsx)(t.p,{children:"This component implements a Vector Store using Astra DB with search capabilities."}),"\n",(0,d.jsxs)(t.p,{children:["For more information, see the ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html",children:"DataStax documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Display Name"}),(0,d.jsx)(t.th,{children:"Info"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"token"}),(0,d.jsx)(t.td,{children:"Astra DB Application Token"}),(0,d.jsx)(t.td,{children:"The authentication token for accessing Astra DB."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"environment"}),(0,d.jsx)(t.td,{children:"Environment"}),(0,d.jsxs)(t.td,{children:["The environment for the Astra DB API Endpoint. For example, ",(0,d.jsx)(t.code,{children:"dev"})," or ",(0,d.jsx)(t.code,{children:"prod"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"database_name"}),(0,d.jsx)(t.td,{children:"Database"}),(0,d.jsx)(t.td,{children:"The database name for the Astra DB instance."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"api_endpoint"}),(0,d.jsx)(t.td,{children:"Astra DB API Endpoint"}),(0,d.jsx)(t.td,{children:"The API endpoint for the Astra DB instance. This supersedes the database selection."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"collection_name"}),(0,d.jsx)(t.td,{children:"Collection"}),(0,d.jsx)(t.td,{children:"The name of the collection within Astra DB where the vectors are stored."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"keyspace"}),(0,d.jsx)(t.td,{children:"Keyspace"}),(0,d.jsx)(t.td,{children:"An optional keyspace within Astra DB to use for the collection."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"embedding_choice"}),(0,d.jsx)(t.td,{children:"Embedding Model or Astra Vectorize"}),(0,d.jsx)(t.td,{children:"Choose an embedding model or use Astra vectorize."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"embedding_model"}),(0,d.jsx)(t.td,{children:"Embedding Model"}),(0,d.jsx)(t.td,{children:"Specify the embedding model. Not required for Astra vectorize collections."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Number of Search Results"}),(0,d.jsxs)(t.td,{children:["The number of search results to return (default: ",(0,d.jsx)(t.code,{children:"4"}),")."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_type"}),(0,d.jsx)(t.td,{children:"Search Type"}),(0,d.jsxs)(t.td,{children:["The search type to use. The options are ",(0,d.jsx)(t.code,{children:"Similarity"}),", ",(0,d.jsx)(t.code,{children:"Similarity with score threshold"}),", and ",(0,d.jsx)(t.code,{children:"MMR (Max Marginal Relevance)"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_score_threshold"}),(0,d.jsx)(t.td,{children:"Search Score Threshold"}),(0,d.jsxs)(t.td,{children:["The minimum similarity score threshold for search results when using the ",(0,d.jsx)(t.code,{children:"Similarity with score threshold"})," option."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"advanced_search_filter"}),(0,d.jsx)(t.td,{children:"Search Metadata Filter"}),(0,d.jsx)(t.td,{children:"An optional dictionary of filters to apply to the search query."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"autodetect_collection"}),(0,d.jsx)(t.td,{children:"Autodetect Collection"}),(0,d.jsx)(t.td,{children:"A boolean flag to determine whether to autodetect the collection."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"content_field"}),(0,d.jsx)(t.td,{children:"Content Field"}),(0,d.jsx)(t.td,{children:"A field to use as the text content field for the vector store."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"deletion_field"}),(0,d.jsx)(t.td,{children:"Deletion Based On Field"}),(0,d.jsx)(t.td,{children:"When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new data is loaded."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"ignore_invalid_documents"}),(0,d.jsx)(t.td,{children:"Ignore Invalid Documents"}),(0,d.jsx)(t.td,{children:"A boolean flag to determine whether to ignore invalid documents at runtime."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"astradb_vectorstore_kwargs"}),(0,d.jsx)(t.td,{children:"AstraDBVectorStore Parameters"}),(0,d.jsx)(t.td,{children:"An optional dictionary of additional parameters for the AstraDBVectorStore."})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Display Name"}),(0,d.jsx)(t.th,{children:"Info"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"Vector Store"}),(0,d.jsx)(t.td,{children:"Astra DB vector store instance configured with the specified parameters."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_results"}),(0,d.jsx)(t.td,{children:"Search Results"}),(0,d.jsxs)(t.td,{children:["The results of the similarity search as a list of ",(0,d.jsx)(t.a,{href:"/concepts-objects#data-object",children:"Data"})," objects."]})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"generate-embeddings",children:"Generate embeddings"}),"\n",(0,d.jsxs)(t.p,{children:["The ",(0,d.jsx)(t.strong,{children:"Astra DB Vector Store"})," component offers two methods for generating embeddings."]}),"\n",(0,d.jsxs)(t.ol,{children:["\n",(0,d.jsxs)(t.li,{children:["\n",(0,d.jsxs)(t.p,{children:[(0,d.jsx)(t.strong,{children:"Embedding Model"}),": Use your own embedding model by connecting an ",(0,d.jsx)(t.a,{href:"/components-embedding-models",children:"Embeddings"})," component in Langflow."]}),"\n"]}),"\n",(0,d.jsxs)(t.li,{children:["\n",(0,d.jsxs)(t.p,{children:[(0,d.jsx)(t.strong,{children:"Astra Vectorize"}),": Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's ",(0,d.jsx)(t.code,{children:"NV-Embed-QA"})," model hosted by Datastax."]}),"\n"]}),"\n"]}),"\n",(0,d.jsx)(t.admonition,{type:"important",children:(0,d.jsx)(t.p,{children:"The embedding model selection is made when creating a new collection and cannot be changed later."})}),"\n",(0,d.jsxs)(t.p,{children:["For an example of using the ",(0,d.jsx)(t.strong,{children:"Astra DB Vector Store"})," component with an embedding model, see the ",(0,d.jsx)(t.a,{href:"/starter-projects-vector-store-rag",children:"Vector Store RAG starter project"}),"."]}),"\n",(0,d.jsxs)(t.p,{children:["For more information, see the ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html",children:"Astra DB Serverless documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"hybrid-search",children:"Hybrid search"}),"\n",(0,d.jsxs)(t.p,{children:["The ",(0,d.jsx)(t.strong,{children:"Astra DB"})," component includes ",(0,d.jsx)(t.strong,{children:"hybrid search"}),", which is enabled by default."]}),"\n",(0,d.jsxs)(t.p,{children:["The component fields related to hybrid search are ",(0,d.jsx)(t.strong,{children:"Search Query"}),", ",(0,d.jsx)(t.strong,{children:"Lexical Terms"}),", and ",(0,d.jsx)(t.strong,{children:"Reranker"}),"."]}),"\n",(0,d.jsxs)(t.ul,{children:["\n",(0,d.jsxs)(t.li,{children:[(0,d.jsx)(t.strong,{children:"Search Query"})," finds results by vector similarity."]}),"\n",(0,d.jsxs)(t.li,{children:[(0,d.jsx)(t.strong,{children:"Lexical Terms"})," is a comma-separated string of keywords, like ",(0,d.jsx)(t.code,{children:"features, data, attributes, characteristics"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:[(0,d.jsx)(t.strong,{children:"Reranker"})," is the re-ranker model used in the hybrid search.\nThe re-ranker model is ",(0,d.jsx)(t.code,{children:"nvidia/llama-3.2-nv.reranker"}),"."]}),"\n"]}),"\n",(0,d.jsxs)(t.p,{children:[(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html",children:"Hybrid search"})," performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall."]}),"\n",(0,d.jsxs)(t.p,{children:["To use ",(0,d.jsx)(t.strong,{children:"Hybrid search"})," in the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," component, do the following:"]}),"\n",(0,d.jsxs)(t.ol,{children:["\n",(0,d.jsxs)(t.li,{children:["Click ",(0,d.jsx)(t.strong,{children:"New Flow"})," > ",(0,d.jsx)(t.strong,{children:"RAG"})," > ",(0,d.jsx)(t.strong,{children:"Hybrid Search RAG"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," model component, add your ",(0,d.jsx)(t.strong,{children:"OpenAI API key"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," vector store component, add your ",(0,d.jsx)(t.strong,{children:"Astra DB Application Token"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Database"})," field, select your database."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Collection"})," field, select the collection you want to search.\nYou must enable support for hybrid search when you create the collection."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Playground"}),", enter a question about your data, such as ",(0,d.jsx)(t.code,{children:"What are the features of my data?"}),"\nYour query is sent to two components: an ",(0,d.jsx)(t.strong,{children:"OpenAI"})," model component and the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," vector database component.\nThe ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component contains a prompt for creating the lexical query from your input:"]}),"\n"]}),"\n",(0,d.jsx)(o.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.",props:{}}]},{tokens:[{content:"You should convert the query into:",props:{}}]},{tokens:[{content:"1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.",props:{}}]},{tokens:[{content:"2. A question to use as the basis for a QA embedding engine.",props:{}}]},{tokens:[{content:"Avoid common keywords associated with the user's subject matter.",props:{}}]}],lang:"text"},annotations:[]}]}),"\n",(0,d.jsxs)(t.ol,{start:"7",children:["\n",(0,d.jsxs)(t.li,{children:["To view the keywords and questions the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component generates from your collection, in the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component, click ",(0,d.jsx)(c.A,{name:"TextSearch","aria-label":"Inspect icon"}),"."]}),"\n"]}),"\n",(0,d.jsx)(o.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"1. Keywords: features, data, attributes, characteristics",props:{}}]},{tokens:[{content:"2. Question: What characteristics can be identified in my data?",props:{}}]}],lang:"text"},annotations:[]}]}),"\n",(0,d.jsxs)(t.ol,{start:"8",children:["\n",(0,d.jsxs)(t.li,{children:["\n",(0,d.jsxs)(t.p,{children:["To view the ",(0,d.jsx)(t.a,{href:"/concepts-objects#dataframe-object",children:"DataFrame"})," generated from the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component's response, in the ",(0,d.jsx)(t.strong,{children:"Structured Output"})," component, click ",(0,d.jsx)(c.A,{name:"TextSearch","aria-label":"Inspect icon"}),".\nThe DataFrame is passed to a ",(0,d.jsx)(t.strong,{children:"Parser"})," component, which parses the contents of the ",(0,d.jsx)(t.strong,{children:"Keywords"})," column into a string."]}),"\n",(0,d.jsxs)(t.p,{children:["This string of comma-separated words is passed to the ",(0,d.jsx)(t.strong,{children:"Lexical Terms"})," port of the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," component.\nNote that the ",(0,d.jsx)(t.strong,{children:"Search Query"})," port of the Astra DB port is connected to the ",(0,d.jsx)(t.strong,{children:"Chat Input"})," component from step 6.\nThis ",(0,d.jsx)(t.strong,{children:"Search Query"})," is vectorized, and both the ",(0,d.jsx)(t.strong,{children:"Search Query"})," and ",(0,d.jsx)(t.strong,{children:"Lexical Terms"})," content are sent to the reranker at the ",(0,d.jsx)(t.code,{children:"find_and_rerank"})," endpoint."]}),"\n",(0,d.jsxs)(t.p,{children:["The reranker compares the vector search results against the string of terms from the lexical search.\nThe highest-ranked results of your hybrid search are returned to the ",(0,d.jsx)(t.strong,{children:"Playground"}),"."]}),"\n"]}),"\n"]}),"\n",(0,d.jsxs)(t.p,{children:["For more information, see the ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html",children:"DataStax documentation"}),"."]}),"\n",(0,d.jsx)(t.h2,{id:"astradb-graph-vector-store",children:"AstraDB Graph vector 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documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-6",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"couchbase_connection_string"}),(0,d.jsx)(t.td,{children:"SecretString"}),(0,d.jsx)(t.td,{children:"Couchbase Cluster connection string (required)."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"couchbase_username"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Couchbase username (required)."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"couchbase_password"}),(0,d.jsx)(t.td,{children:"SecretString"}),(0,d.jsx)(t.td,{children:"Couchbase password 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Default: 4 (advanced)."})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs-6",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsx)(t.tbody,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"CouchbaseVectorStore"}),(0,d.jsx)(t.td,{children:"A Couchbase vector store instance configured with the specified parameters."})]})})]}),"\n",(0,d.jsx)(t.h2,{id:"elasticsearch",children:"Elasticsearch"}),"\n",(0,d.jsxs)(t.p,{children:["This component creates an Elasticsearch Vector Store with search capabilities.\nFor more information, see the ",(0,d.jsx)(t.a,{href:"https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html",children:"Elasticsearch documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-7",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"es_url"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Elasticsearch server URL"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"es_user"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Username for Elasticsearch authentication"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"es_password"}),(0,d.jsx)(t.td,{children:"SecretString"}),(0,d.jsx)(t.td,{children:"Password for Elasticsearch authentication"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"index_name"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Name of the Elasticsearch index"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"strategy"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:'Strategy for vector search ("approximate_k_nearest_neighbors" or "script_scoring")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"distance_strategy"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:'Strategy for distance calculation ("COSINE", "EUCLIDEAN_DISTANCE", "DOT_PRODUCT")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_query"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Query for similarity search"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"ingest_data"}),(0,d.jsx)(t.td,{children:"Data"}),(0,d.jsx)(t.td,{children:"Data to be ingested into the vector store"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"embedding"}),(0,d.jsx)(t.td,{children:"Embeddings"}),(0,d.jsx)(t.td,{children:"Embedding function to use"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Integer"}),(0,d.jsx)(t.td,{children:"Number of results to return in search (default: 4)"})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs-7",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"ElasticsearchStore"}),(0,d.jsx)(t.td,{children:"Elasticsearch vector store instance"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_results"}),(0,d.jsx)(t.td,{children:"List[Data]"}),(0,d.jsx)(t.td,{children:"Results of similarity search"})]})]})]}),"\n",(0,d.jsx)(t.h2,{id:"faiss",children:"FAISS"}),"\n",(0,d.jsxs)(t.p,{children:["This component creates a FAISS Vector Store with search capabilities.\nFor more information, see the ",(0,d.jsx)(t.a,{href:"https://faiss.ai/index.html",children:"FAISS documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-8",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"index_name"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:'The name of the FAISS index. 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This is not required for collections embedded with ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html",children:"Astra vectorize"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"Vector Store Connection"}),(0,d.jsx)(t.td,{children:"Connection to the vector store."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"edge_definition"}),(0,d.jsx)(t.td,{children:"Edge Definition"}),(0,d.jsxs)(t.td,{children:["Edge definition for the graph traversal. For more information, see the ",(0,d.jsx)(t.a,{href:"https://datastax.github.io/graph-rag/reference/graph_retriever/edges/",children:"GraphRAG documentation"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"strategy"}),(0,d.jsx)(t.td,{children:"Traversal Strategies"}),(0,d.jsx)(t.td,{children:"The strategy to use for graph traversal. 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For more information, see the ",(0,d.jsx)(t.a,{href:"https://datastax.github.io/graph-rag/reference/graph_retriever/strategies/",children:"strategy documentation"}),"."]})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs-9",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsx)(t.tbody,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_results"}),(0,d.jsx)(t.td,{children:"List[Data]"}),(0,d.jsxs)(t.td,{children:["Results of the graph-based document retrieval as a list of ",(0,d.jsx)(t.a,{href:"/concepts-objects#data-object",children:"Data"})," objects."]})]})})]}),"\n",(0,d.jsx)(t.h2,{id:"hyper-converged-database-hcd-vector-store",children:"Hyper-Converged Database (HCD) Vector Store"}),"\n",(0,d.jsx)(t.p,{children:"This component implements a Vector Store using HCD."}),"\n",(0,d.jsxs)(t.p,{children:["To use the HCD vector store, add your deployment's collection name, username, password, and HCD Data API endpoint.\nThe endpoint must be formatted like ",(0,d.jsx)(t.code,{children:"http[s]://**DOMAIN_NAME** or **IP_ADDRESS**[:port]"}),", for example, ",(0,d.jsx)(t.code,{children:"http://192.0.2.250:8181"}),"."]}),"\n",(0,d.jsxs)(t.p,{children:["Replace ",(0,d.jsx)(t.strong,{children:"DOMAIN_NAME"})," or ",(0,d.jsx)(t.strong,{children:"IP_ADDRESS"})," with the domain name or IP address of your HCD Data API connection."]}),"\n",(0,d.jsx)(t.p,{children:"To use the HCD vector store for embeddings ingestion, connect it to an embeddings model and a file loader:"}),"\n",(0,d.jsx)(t.p,{children:(0,d.jsx)(t.img,{alt:"HCD vector store embeddings ingestion",src:r(42638).A+"",width:"2294",height:"1684"})}),"\n",(0,d.jsx)(t.h3,{id:"inputs-10",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Display Name"}),(0,d.jsx)(t.th,{children:"Info"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"collection_name"}),(0,d.jsx)(t.td,{children:"Collection Name"}),(0,d.jsx)(t.td,{children:"The name of the collection within HCD where the vectors will be stored (required)"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"username"}),(0,d.jsx)(t.td,{children:"HCD Username"}),(0,d.jsx)(t.td,{children:'Authentication username for accessing HCD (default: "hcd-superuser", required)'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"password"}),(0,d.jsx)(t.td,{children:"HCD Password"}),(0,d.jsx)(t.td,{children:"Authentication password for accessing HCD 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Not required for Astra vectorize collections."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Number of Search Results"}),(0,d.jsxs)(t.td,{children:["The number of search results to return (default: ",(0,d.jsx)(t.code,{children:"4"}),")."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_type"}),(0,d.jsx)(t.td,{children:"Search Type"}),(0,d.jsxs)(t.td,{children:["The search type to use. The options are ",(0,d.jsx)(t.code,{children:"Similarity"}),", ",(0,d.jsx)(t.code,{children:"Similarity with score threshold"}),", and ",(0,d.jsx)(t.code,{children:"MMR (Max Marginal Relevance)"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_score_threshold"}),(0,d.jsx)(t.td,{children:"Search Score Threshold"}),(0,d.jsxs)(t.td,{children:["The minimum similarity score threshold for search results when using the ",(0,d.jsx)(t.code,{children:"Similarity with score threshold"})," option."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"advanced_search_filter"}),(0,d.jsx)(t.td,{children:"Search Metadata Filter"}),(0,d.jsx)(t.td,{children:"An optional dictionary of filters to apply to the search query."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"autodetect_collection"}),(0,d.jsx)(t.td,{children:"Autodetect Collection"}),(0,d.jsx)(t.td,{children:"A boolean flag to determine whether to autodetect the collection."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"content_field"}),(0,d.jsx)(t.td,{children:"Content Field"}),(0,d.jsx)(t.td,{children:"A field to use as the text content field for the vector store."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"deletion_field"}),(0,d.jsx)(t.td,{children:"Deletion Based On Field"}),(0,d.jsx)(t.td,{children:"When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new data is loaded."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"ignore_invalid_documents"}),(0,d.jsx)(t.td,{children:"Ignore Invalid Documents"}),(0,d.jsx)(t.td,{children:"A boolean flag to determine whether to ignore invalid documents at runtime."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"astradb_vectorstore_kwargs"}),(0,d.jsx)(t.td,{children:"AstraDBVectorStore Parameters"}),(0,d.jsx)(t.td,{children:"An optional dictionary of additional parameters for the AstraDBVectorStore."})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Display Name"}),(0,d.jsx)(t.th,{children:"Info"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"Vector Store"}),(0,d.jsx)(t.td,{children:"Astra DB vector store instance configured with the specified parameters."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_results"}),(0,d.jsx)(t.td,{children:"Search Results"}),(0,d.jsxs)(t.td,{children:["The results of the similarity search as a list of ",(0,d.jsx)(t.a,{href:"/concepts-objects#data-object",children:"Data"})," objects."]})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"generate-embeddings",children:"Generate embeddings"}),"\n",(0,d.jsxs)(t.p,{children:["The ",(0,d.jsx)(t.strong,{children:"Astra DB Vector Store"})," component offers two methods for generating embeddings."]}),"\n",(0,d.jsxs)(t.ol,{children:["\n",(0,d.jsxs)(t.li,{children:["\n",(0,d.jsxs)(t.p,{children:[(0,d.jsx)(t.strong,{children:"Embedding Model"}),": Use your own embedding model by connecting an ",(0,d.jsx)(t.a,{href:"/components-embedding-models",children:"Embeddings"})," component in Langflow."]}),"\n"]}),"\n",(0,d.jsxs)(t.li,{children:["\n",(0,d.jsxs)(t.p,{children:[(0,d.jsx)(t.strong,{children:"Astra Vectorize"}),": Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's ",(0,d.jsx)(t.code,{children:"NV-Embed-QA"})," model hosted by Datastax."]}),"\n"]}),"\n"]}),"\n",(0,d.jsx)(t.admonition,{type:"important",children:(0,d.jsx)(t.p,{children:"The embedding model selection is made when creating a new collection and cannot be changed later."})}),"\n",(0,d.jsxs)(t.p,{children:["For an example of using the ",(0,d.jsx)(t.strong,{children:"Astra DB Vector Store"})," component with an embedding model, see the ",(0,d.jsx)(t.a,{href:"/starter-projects-vector-store-rag",children:"Vector Store RAG starter project"}),"."]}),"\n",(0,d.jsxs)(t.p,{children:["For more information, see the ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html",children:"Astra DB Serverless documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"hybrid-search",children:"Hybrid search"}),"\n",(0,d.jsxs)(t.p,{children:["The ",(0,d.jsx)(t.strong,{children:"Astra DB"})," component includes ",(0,d.jsx)(t.strong,{children:"hybrid search"}),", which is enabled by default."]}),"\n",(0,d.jsxs)(t.p,{children:["The component fields related to hybrid search are ",(0,d.jsx)(t.strong,{children:"Search Query"}),", ",(0,d.jsx)(t.strong,{children:"Lexical Terms"}),", and ",(0,d.jsx)(t.strong,{children:"Reranker"}),"."]}),"\n",(0,d.jsxs)(t.ul,{children:["\n",(0,d.jsxs)(t.li,{children:[(0,d.jsx)(t.strong,{children:"Search Query"})," finds results by vector similarity."]}),"\n",(0,d.jsxs)(t.li,{children:[(0,d.jsx)(t.strong,{children:"Lexical Terms"})," is a comma-separated string of keywords, like ",(0,d.jsx)(t.code,{children:"features, data, attributes, characteristics"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:[(0,d.jsx)(t.strong,{children:"Reranker"})," is the re-ranker model used in the hybrid search.\nThe re-ranker model is ",(0,d.jsx)(t.code,{children:"nvidia/llama-3.2-nv.reranker"}),"."]}),"\n"]}),"\n",(0,d.jsxs)(t.p,{children:[(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html",children:"Hybrid search"})," performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall."]}),"\n",(0,d.jsxs)(t.p,{children:["To use ",(0,d.jsx)(t.strong,{children:"Hybrid search"})," in the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," component, do the following:"]}),"\n",(0,d.jsxs)(t.ol,{children:["\n",(0,d.jsxs)(t.li,{children:["Click ",(0,d.jsx)(t.strong,{children:"New Flow"})," > ",(0,d.jsx)(t.strong,{children:"RAG"})," > ",(0,d.jsx)(t.strong,{children:"Hybrid Search RAG"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," model component, add your ",(0,d.jsx)(t.strong,{children:"OpenAI API key"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," vector store component, add your ",(0,d.jsx)(t.strong,{children:"Astra DB Application Token"}),"."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Database"})," field, select your database."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Collection"})," field, select the collection you want to search.\nYou must enable support for hybrid search when you create the collection."]}),"\n",(0,d.jsxs)(t.li,{children:["In the ",(0,d.jsx)(t.strong,{children:"Playground"}),", enter a question about your data, such as ",(0,d.jsx)(t.code,{children:"What are the features of my data?"}),"\nYour query is sent to two components: an ",(0,d.jsx)(t.strong,{children:"OpenAI"})," model component and the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," vector database component.\nThe ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component contains a prompt for creating the lexical query from your input:"]}),"\n"]}),"\n",(0,d.jsx)(o.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.",props:{}}]},{tokens:[{content:"You should convert the query into:",props:{}}]},{tokens:[{content:"1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.",props:{}}]},{tokens:[{content:"2. A question to use as the basis for a QA embedding engine.",props:{}}]},{tokens:[{content:"Avoid common keywords associated with the user's subject matter.",props:{}}]}],lang:"text"},annotations:[]}]}),"\n",(0,d.jsxs)(t.ol,{start:"7",children:["\n",(0,d.jsxs)(t.li,{children:["To view the keywords and questions the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component generates from your collection, in the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component, click ",(0,d.jsx)(c.A,{name:"TextSearch","aria-label":"Inspect icon"}),"."]}),"\n"]}),"\n",(0,d.jsx)(o.Code,{codeConfig:x,northPanel:{tabs:[""],active:"",heightRatio:1},files:[{name:"",focus:"",code:{lines:[{tokens:[{content:"1. Keywords: features, data, attributes, characteristics",props:{}}]},{tokens:[{content:"2. Question: What characteristics can be identified in my data?",props:{}}]}],lang:"text"},annotations:[]}]}),"\n",(0,d.jsxs)(t.ol,{start:"8",children:["\n",(0,d.jsxs)(t.li,{children:["\n",(0,d.jsxs)(t.p,{children:["To view the ",(0,d.jsx)(t.a,{href:"/concepts-objects#dataframe-object",children:"DataFrame"})," generated from the ",(0,d.jsx)(t.strong,{children:"OpenAI"})," component's response, in the ",(0,d.jsx)(t.strong,{children:"Structured Output"})," component, click ",(0,d.jsx)(c.A,{name:"TextSearch","aria-label":"Inspect icon"}),".\nThe DataFrame is passed to a ",(0,d.jsx)(t.strong,{children:"Parser"})," component, which parses the contents of the ",(0,d.jsx)(t.strong,{children:"Keywords"})," column into a string."]}),"\n",(0,d.jsxs)(t.p,{children:["This string of comma-separated words is passed to the ",(0,d.jsx)(t.strong,{children:"Lexical Terms"})," port of the ",(0,d.jsx)(t.strong,{children:"Astra DB"})," component.\nNote that the ",(0,d.jsx)(t.strong,{children:"Search Query"})," port of the Astra DB port is connected to the ",(0,d.jsx)(t.strong,{children:"Chat Input"})," component from step 6.\nThis ",(0,d.jsx)(t.strong,{children:"Search Query"})," is vectorized, and both the ",(0,d.jsx)(t.strong,{children:"Search Query"})," and ",(0,d.jsx)(t.strong,{children:"Lexical Terms"})," content are sent to the reranker at the ",(0,d.jsx)(t.code,{children:"find_and_rerank"})," endpoint."]}),"\n",(0,d.jsxs)(t.p,{children:["The reranker compares the vector search results against the string of terms from the lexical search.\nThe highest-ranked results of your hybrid search are returned to the ",(0,d.jsx)(t.strong,{children:"Playground"}),"."]}),"\n"]}),"\n"]}),"\n",(0,d.jsxs)(t.p,{children:["For more information, see the ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html",children:"DataStax documentation"}),"."]}),"\n",(0,d.jsx)(t.h2,{id:"astradb-graph-vector-store",children:"AstraDB Graph vector store"}),"\n",(0,d.jsxs)(t.p,{children:["This component implements a Vector Store using AstraDB with graph capabilities.\nFor more information, see the ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/tutorials/graph-rag.html",children:"Astra DB Serverless documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-1",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Display Name"}),(0,d.jsx)(t.th,{children:"Info"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"collection_name"}),(0,d.jsx)(t.td,{children:"Collection Name"}),(0,d.jsx)(t.td,{children:"The name of the collection within AstraDB where the vectors will be stored (required)"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"token"}),(0,d.jsx)(t.td,{children:"Astra DB Application Token"}),(0,d.jsx)(t.td,{children:"Authentication token for 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use"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"metric"}),(0,d.jsx)(t.td,{children:"Metric"}),(0,d.jsx)(t.td,{children:'Distance metric for vector comparisons (options: "cosine", "euclidean", "dot_product")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"setup_mode"}),(0,d.jsx)(t.td,{children:"Setup Mode"}),(0,d.jsx)(t.td,{children:'Configuration mode for setting up the vector store (options: "Sync", "Async", "Off")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"pre_delete_collection"}),(0,d.jsx)(t.td,{children:"Pre Delete Collection"}),(0,d.jsx)(t.td,{children:"Boolean flag to determine whether to delete the collection before creating a new one"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Number of Results"}),(0,d.jsx)(t.td,{children:"Number of results to return in similarity search (default: 4)"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_type"}),(0,d.jsx)(t.td,{children:"Search Type"}),(0,d.jsx)(t.td,{children:'Search type to use (options: "Similarity", "Graph Traversal", "Hybrid")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"traversal_depth"}),(0,d.jsx)(t.td,{children:"Traversal Depth"}),(0,d.jsx)(t.td,{children:"Maximum depth for graph traversal searches (default: 1)"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_score_threshold"}),(0,d.jsx)(t.td,{children:"Search Score Threshold"}),(0,d.jsx)(t.td,{children:"Minimum similarity score threshold for search results"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_filter"}),(0,d.jsx)(t.td,{children:"Search Metadata Filter"}),(0,d.jsx)(t.td,{children:"Optional dictionary of filters to apply to the search 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",(0,d.jsx)(t.a,{href:"https://cassandra.apache.org/doc/latest/cassandra/vector-search/overview.html",children:"Cassandra documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-2",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"database_ref"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Contact points for the database or AstraDB database ID"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"username"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Username for the database (leave empty for AstraDB)"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"token"}),(0,d.jsx)(t.td,{children:"SecretString"}),(0,d.jsx)(t.td,{children:"User password for the database or AstraDB 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table"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"cluster_kwargs"}),(0,d.jsx)(t.td,{children:"Dict"}),(0,d.jsx)(t.td,{children:"Additional keyword arguments for the Cassandra cluster"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_query"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Query for similarity search"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"ingest_data"}),(0,d.jsx)(t.td,{children:"Data"}),(0,d.jsx)(t.td,{children:"Data to be ingested into the vector store"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"embedding"}),(0,d.jsx)(t.td,{children:"Embeddings"}),(0,d.jsx)(t.td,{children:"Embedding function to use"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Integer"}),(0,d.jsx)(t.td,{children:"Number of results to return in 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Default: 4 (advanced)."})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs-6",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsx)(t.tbody,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"CouchbaseVectorStore"}),(0,d.jsx)(t.td,{children:"A Couchbase vector store instance configured with the specified parameters."})]})})]}),"\n",(0,d.jsx)(t.h2,{id:"elasticsearch",children:"Elasticsearch"}),"\n",(0,d.jsxs)(t.p,{children:["This component creates an Elasticsearch Vector Store with search capabilities.\nFor more information, see the ",(0,d.jsx)(t.a,{href:"https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html",children:"Elasticsearch documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-7",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"es_url"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Elasticsearch server URL"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"es_user"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Username for Elasticsearch authentication"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"es_password"}),(0,d.jsx)(t.td,{children:"SecretString"}),(0,d.jsx)(t.td,{children:"Password for Elasticsearch authentication"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"index_name"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Name of the Elasticsearch index"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"strategy"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:'Strategy for vector search ("approximate_k_nearest_neighbors" or "script_scoring")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"distance_strategy"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:'Strategy for distance calculation ("COSINE", "EUCLIDEAN_DISTANCE", "DOT_PRODUCT")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_query"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:"Query for similarity search"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"ingest_data"}),(0,d.jsx)(t.td,{children:"Data"}),(0,d.jsx)(t.td,{children:"Data to be ingested into the vector store"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"embedding"}),(0,d.jsx)(t.td,{children:"Embeddings"}),(0,d.jsx)(t.td,{children:"Embedding function to use"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Integer"}),(0,d.jsx)(t.td,{children:"Number of results to return in search (default: 4)"})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs-7",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"ElasticsearchStore"}),(0,d.jsx)(t.td,{children:"Elasticsearch vector store instance"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_results"}),(0,d.jsx)(t.td,{children:"List[Data]"}),(0,d.jsx)(t.td,{children:"Results of similarity search"})]})]})]}),"\n",(0,d.jsx)(t.h2,{id:"faiss",children:"FAISS"}),"\n",(0,d.jsxs)(t.p,{children:["This component creates a FAISS Vector Store with search capabilities.\nFor more information, see the ",(0,d.jsx)(t.a,{href:"https://faiss.ai/index.html",children:"FAISS documentation"}),"."]}),"\n",(0,d.jsx)(t.h3,{id:"inputs-8",children:"Inputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsxs)(t.tbody,{children:[(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"index_name"}),(0,d.jsx)(t.td,{children:"String"}),(0,d.jsx)(t.td,{children:'The name of the FAISS index. 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Default: True (advanced)."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"embedding"}),(0,d.jsx)(t.td,{children:"Embeddings"}),(0,d.jsx)(t.td,{children:"The embedding function to use for the vector store."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"number_of_results"}),(0,d.jsx)(t.td,{children:"Integer"}),(0,d.jsx)(t.td,{children:"Number of results to return from the search. 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This is not required for collections embedded with ",(0,d.jsx)(t.a,{href:"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html",children:"Astra vectorize"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"vector_store"}),(0,d.jsx)(t.td,{children:"Vector Store Connection"}),(0,d.jsx)(t.td,{children:"Connection to the vector store."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"edge_definition"}),(0,d.jsx)(t.td,{children:"Edge Definition"}),(0,d.jsxs)(t.td,{children:["Edge definition for the graph traversal. For more information, see the ",(0,d.jsx)(t.a,{href:"https://datastax.github.io/graph-rag/reference/graph_retriever/edges/",children:"GraphRAG documentation"}),"."]})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"strategy"}),(0,d.jsx)(t.td,{children:"Traversal Strategies"}),(0,d.jsx)(t.td,{children:"The strategy to use for graph traversal. Strategy options are dynamically loaded from available strategies."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_query"}),(0,d.jsx)(t.td,{children:"Search Query"}),(0,d.jsx)(t.td,{children:"The query to search for in the vector store."})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"graphrag_strategy_kwargs"}),(0,d.jsx)(t.td,{children:"Strategy Parameters"}),(0,d.jsxs)(t.td,{children:["Optional dictionary of additional parameters for the retrieval strategy. For more information, see the ",(0,d.jsx)(t.a,{href:"https://datastax.github.io/graph-rag/reference/graph_retriever/strategies/",children:"strategy documentation"}),"."]})]})]})]}),"\n",(0,d.jsx)(t.h3,{id:"outputs-9",children:"Outputs"}),"\n",(0,d.jsxs)(t.table,{children:[(0,d.jsx)(t.thead,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.th,{children:"Name"}),(0,d.jsx)(t.th,{children:"Type"}),(0,d.jsx)(t.th,{children:"Description"})]})}),(0,d.jsx)(t.tbody,{children:(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"search_results"}),(0,d.jsx)(t.td,{children:"List[Data]"}),(0,d.jsxs)(t.td,{children:["Results of the graph-based document retrieval as a list of ",(0,d.jsx)(t.a,{href:"/concepts-objects#data-object",children:"Data"})," objects."]})]})})]}),"\n",(0,d.jsx)(t.h2,{id:"hyper-converged-database-hcd-vector-store",children:"Hyper-Converged Database (HCD) Vector Store"}),"\n",(0,d.jsx)(t.p,{children:"This component implements a Vector Store using HCD."}),"\n",(0,d.jsxs)(t.p,{children:["To use the HCD vector store, add your deployment's collection name, username, password, and HCD Data API endpoint.\nThe endpoint must be formatted like ",(0,d.jsx)(t.code,{children:"http[s]://**DOMAIN_NAME** or **IP_ADDRESS**[:port]"}),", for example, ",(0,d.jsx)(t.code,{children:"http://192.0.2.250:8181"}),"."]}),"\n",(0,d.jsxs)(t.p,{children:["Replace ",(0,d.jsx)(t.strong,{children:"DOMAIN_NAME"})," or ",(0,d.jsx)(t.strong,{children:"IP_ADDRESS"})," with the domain name or IP address of your HCD Data API connection."]}),"\n",(0,d.jsx)(t.p,{children:"To use the HCD vector store for embeddings ingestion, connect it to an embeddings model and a file loader:"}),"\n",(0,d.jsx)(t.p,{children:(0,d.jsx)(t.img,{alt:"HCD vector store embeddings 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data"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"bulk_delete_concurrency"}),(0,d.jsx)(t.td,{children:"Bulk Delete Concurrency"}),(0,d.jsx)(t.td,{children:"Optional concurrency level for bulk delete operations"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"setup_mode"}),(0,d.jsx)(t.td,{children:"Setup Mode"}),(0,d.jsx)(t.td,{children:'Configuration mode for setting up the vector store (options: "Sync", "Async", "Off", default: "Sync")'})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"pre_delete_collection"}),(0,d.jsx)(t.td,{children:"Pre Delete Collection"}),(0,d.jsx)(t.td,{children:"Boolean flag to determine whether to delete the collection before creating a new one"})]}),(0,d.jsxs)(t.tr,{children:[(0,d.jsx)(t.td,{children:"metadata_indexing_include"}),(0,d.jsx)(t.td,{children:"Metadata Indexing Include"}),(0,d.jsx)(t.td,{children:"Optional list of metadata fields to include in the 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b/components-bundle-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-custom-components.html b/components-custom-components.html index e2011d527b..eca0898dcd 100644 --- a/components-custom-components.html +++ b/components-custom-components.html @@ -21,7 +21,7 @@ - + diff --git a/components-data.html b/components-data.html index e2b3eb8dce..3d882649ca 100644 --- a/components-data.html +++ b/components-data.html @@ -21,7 +21,7 @@ - + diff --git a/components-embedding-models.html b/components-embedding-models.html index f9b63470ff..e10c7c858f 100644 --- a/components-embedding-models.html +++ b/components-embedding-models.html @@ -21,7 +21,7 @@ - + diff --git a/components-helpers.html b/components-helpers.html index 0e1a0e5968..9717a8e4d0 100644 --- a/components-helpers.html +++ b/components-helpers.html @@ -21,7 +21,7 @@ - + diff --git a/components-io.html b/components-io.html index 879429c785..756bd9dcd9 100644 --- a/components-io.html +++ b/components-io.html @@ -21,7 +21,7 @@ - + diff --git a/components-loaders.html b/components-loaders.html index 50be7dd43f..5c052cd2c2 100644 --- a/components-loaders.html +++ b/components-loaders.html @@ -21,7 +21,7 @@ - + diff --git a/components-logic.html b/components-logic.html index 73e9e63cf6..7b32ad9b44 100644 --- a/components-logic.html +++ b/components-logic.html @@ -21,7 +21,7 @@ - + diff --git a/components-memories.html b/components-memories.html index 7c29007585..9df50e58b1 100644 --- a/components-memories.html +++ b/components-memories.html @@ -21,7 +21,7 @@ - + diff --git a/components-models.html b/components-models.html index 66f6a0ec42..a710078914 100644 --- a/components-models.html +++ b/components-models.html @@ -21,7 +21,7 @@ - + diff --git a/components-processing.html b/components-processing.html index 56b8f32bd7..086fd8f1e7 100644 --- a/components-processing.html +++ b/components-processing.html @@ -21,7 +21,7 @@ - + diff --git a/components-prompts.html b/components-prompts.html index 26e4f7e04a..b404f45b19 100644 --- a/components-prompts.html +++ b/components-prompts.html @@ -21,7 +21,7 @@ - + diff --git a/components-tools.html b/components-tools.html index b59dc50d9f..749a87b6bb 100644 --- a/components-tools.html +++ b/components-tools.html @@ -21,7 +21,7 @@ - + diff --git a/components-vector-stores.html b/components-vector-stores.html index 794f5bcfc0..435a7e58cf 100644 --- a/components-vector-stores.html +++ b/components-vector-stores.html @@ -21,7 +21,7 @@ - + @@ -42,7 +42,7 @@

    Use a vector store component in a flow​

    This example uses the Astra DB vector store component. Your vector store component's parameters and authentication may be different, but the document ingestion workflow is the same. A document is loaded from a local machine and chunked. The Astra DB vector store generates embeddings with the connected model component, and stores them in the connected Astra DB database.

    This vector data can then be retrieved for workloads like Retrieval Augmented Generation.

    -

    +

    The user's chat input is embedded and compared to the vectors embedded during document ingestion for a similarity search. The results are output from the vector database component as a Data object and parsed into text. This text fills the {context} variable in the Prompt component, which informs the Open AI model component's responses.

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b/install-custom-dependencies.html index 78f3439ba2..a2a75f79df 100644 --- a/install-custom-dependencies.html +++ b/install-custom-dependencies.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-apify.html b/integrations-apify.html index d97b649757..bc87dc5e2f 100644 --- a/integrations-apify.html +++ b/integrations-apify.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-arize.html b/integrations-arize.html index 0c0a9e3e33..d5e0075343 100644 --- a/integrations-arize.html +++ b/integrations-arize.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-assemblyai.html b/integrations-assemblyai.html index c09a7183ab..daf6c485ee 100644 --- a/integrations-assemblyai.html +++ b/integrations-assemblyai.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-composio.html b/integrations-composio.html index 06a548cf7f..366be3807a 100644 --- a/integrations-composio.html +++ b/integrations-composio.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-langfuse.html b/integrations-langfuse.html index 74d4710bf0..3d393703a2 100644 --- a/integrations-langfuse.html +++ b/integrations-langfuse.html @@ -3,7 +3,7 @@ -Langfuse | Langflow Documentation +Langfuse | Langflow Documentation @@ -21,7 +21,7 @@ - + @@ -36,47 +36,83 @@
      • +

        Example trace in Langfuse

        +

        For a live public example trace in a Langfuse dashboard, see Public example trace in Langfuse.

        +

        Disable Langfuse Tracing​

        +

        To disable the Langfuse integration, remove the environment variables you set in the previous steps and restart Langflow.

        +

        Run Langfuse and Langflow with Docker Compose​

        +

        If you prefer to self-host Langfuse, you can run both services with Docker Compose.

        +
          +
        1. +

          In Langfuse, go to Project Settings, and then create a new set of API keys.

          +
          2. +
        2. +

          Copy the following API key information:

          +
          3. +
        +
          +
        • Secret Key
          • +
        • Public Key
          • +
        • Host URL
          • +
        +
          +
        1. Add your Langflow API keys to your docker-compose.yml file. +An example docker-compose.yml file is available in the Langflow GitHub repo.
          2. +
        +

        _32
        services:
        _32
        langflow:
        _32
        image: langflowai/langflow:latest # or another version tag on https://hub.docker.com/r/langflowai/langflow
        _32
        pull_policy: always # set to 'always' when using 'latest' image
        _32
        ports:
        _32
        - "7860:7860"
        _32
        depends_on:
        _32
        - postgres
        _32
        environment:
        _32
        - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
        _32
        # This variable defines where the logs, file storage, monitor data and secret keys are stored.
        _32
        - LANGFLOW_CONFIG_DIR=app/langflow
        _32
        - LANGFUSE_SECRET_KEY=sk-...
        _32
        - LANGFUSE_PUBLIC_KEY=pk-...
        _32
        - LANGFUSE_HOST=https://us.cloud.langfuse.com
        _32
        volumes:
        _32
        - langflow-data:/app/langflow
        _32
        _32
        postgres:
        _32
        image: postgres:16
        _32
        environment:
        _32
        POSTGRES_USER: langflow
        _32
        POSTGRES_PASSWORD: langflow
        _32
        POSTGRES_DB: langflow
        _32
        ports:
        _32
        - "5432:5432"
        _32
        volumes:
        _32
        - langflow-postgres:/var/lib/postgresql/data
        _32
        _32
        volumes:
        _32
        langflow-postgres:
        _32
        langflow-data:

        +
          +
        1. Start the Docker container.
          2. +
        +

        _10
        docker-compose up

        +
          +
        1. To confirm Langfuse is connected to your Langflow container, run this command. +Ensure you've exported LANGFLOW_HOST as a variable in your terminal.
          2. +
        +

        _10
        docker compose exec langflow python -c "import requests, os; addr = os.environ.get('LANGFUSE_HOST'); print(addr); res = requests.get(addr, timeout=5); print(res.status_code)"

        +

        An output similar to this indicates success:

        +

        _10
        https://us.cloud.langfuse.com
        _10
        200

      Search
      diff --git a/integrations-langsmith.html b/integrations-langsmith.html index 8e712bb3d9..eb88305e63 100644 --- a/integrations-langsmith.html +++ b/integrations-langsmith.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-langwatch.html b/integrations-langwatch.html index 2b80a5e35c..94ae0a33fb 100644 --- a/integrations-langwatch.html +++ b/integrations-langwatch.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-mcp.html b/integrations-mcp.html index 2136ce98a1..4947200494 100644 --- a/integrations-mcp.html +++ b/integrations-mcp.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-nvidia-ingest-wsl2.html b/integrations-nvidia-ingest-wsl2.html index 489dc385ae..1576375708 100644 --- a/integrations-nvidia-ingest-wsl2.html +++ b/integrations-nvidia-ingest-wsl2.html @@ -21,7 +21,7 @@ - + diff --git a/integrations-nvidia-ingest.html b/integrations-nvidia-ingest.html index e03aa1c21b..9ecb20a7fc 100644 --- a/integrations-nvidia-ingest.html +++ b/integrations-nvidia-ingest.html @@ 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--- a/webhook.html +++ b/webhook.html @@ -21,7 +21,7 @@ - +

      Integrate Langfuse with Langflow

      -

      Langfuse is an observability and analytics platform specifically designed for language models and AI applications.

      +

      Langfuse (GitHub) is an open-source platform for LLM observability. It provides tracing and monitoring capabilities for AI applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks such as workflows builders like Langflow.

      This guide walks you through how to configure Langflow to collect tracing data about your flow executions and automatically send the data to Langfuse.

      +

      Prerequisites​

      -

      Create Langfuse project credentials​

      +

      Set Langfuse credentials as environment variables​

      1. -

        In Langfuse, go to your project settings, and then create a new set of API keys.

        +

        In Langfuse, go to Project Settings, and then create a new set of API keys.

      2. Copy the following API key information:

        +
        3. +
      • Secret Key
      • Public Key
      • Host URL
      - +
        +
      1. Set your Langfuse project credentials as environment variables in the same environment where you run Langflow.
      -

      Set your Langfuse credentials as environment variables​

      -

      Set your Langfuse project credentials as environment variables in the same environment where you run Langflow.

      -

      You can use any method you prefer to set environment variables. -The following examples show how to set environment variables in a terminal session (Linux or macOS) and in a command prompt session (Windows):

      +

      The following examples set environment variables in a Linux or macOS terminal session or in a Windows command prompt session: +Replace SECRET_KEY, PUBLIC_KEY, and HOST_URL with the API key information you copied from Langfuse.


      _10
      export LANGFUSE_SECRET_KEY=SECRET_KEY
      _10
      export LANGFUSE_PUBLIC_KEY=PUBLIC_KEY
      _10
      export LANGFUSE_HOST=HOST_URL

      -

      Replace SECRET_KEY, PUBLIC_KEY, and HOST_URL with the API key information you copied from Langfuse.

      -

      Start Langflow and run a flow​

      +

      Start Langflow and view traces in Langfuse​

      1. Start Langflow in the same terminal or environment where you set the environment variables:

      _10
      uv run langflow run

        -
      1. In Langflow, open an existing project, and then run a flow.
        2. +
      2. +

        In Langflow, open an existing project, and then run a flow.

        +

        Langflow automatically collects and sends tracing data about the flow execution to Langfuse.

        +
        3. +
      3. +

        View the collected data in your Langfuse project dashboard.

        +
      -

      View tracing data in Langfuse​

      -

      Langflow automatically collects and sends tracing data about the flow execution to Langfuse. -You can view the collected data in your Langfuse project dashboard.

      -

      Disable the Langfuse integration​

      -

      To disable the Langfuse integration, remove the environment variables you set in the previous steps and restart Langflow.