From 9e4e5fc509d9136ab43720070f7394f58ec78793 Mon Sep 17 00:00:00 2001 From: ogabrielluiz Date: Wed, 14 Aug 2024 14:22:29 +0000 Subject: [PATCH] deploy: ce17bd4e71f69ddc422a68d156fc12ae34d7600d --- 365085a8-a90a-43f9-a779-f8769ec7eca1.html | 2 +- 404.html | 2 +- assets/js/f66238ae.4590dee3.js | 1 - assets/js/f66238ae.84317656.js | 1 + ...n.3eb60043.js => runtime~main.8de562d6.js} | 2 +- components-custom-components.html | 2 +- components-data.html | 2 +- components-embedding-models.html | 2 +- components-helpers.html | 2 +- components-io.html | 2 +- components-models.html | 2 +- components-prompts.html | 2 +- components-rag.html | 2 +- components-vector-stores.html | 19 +++++++++++++++++-- components.html | 2 +- configuration-api-keys.html | 2 +- configuration-authentication.html | 2 +- configuration-backend-only.html | 2 +- configuration-cli.html | 2 +- contributing-community.html | 2 +- contributing-github-issues.html | 2 +- contributing-how-to-contribute.html | 2 +- contributing-telemetry.html | 2 +- deployment-docker.html | 2 +- deployment-gcp.html | 2 +- deployment-hugging-face-spaces.html | 2 +- deployment-kubernetes.html | 2 +- deployment-railway.html | 2 +- deployment-render.html | 2 +- ...ng-started-common-installation-issues.html | 2 +- getting-started-installation.html | 2 +- getting-started-quickstart.html | 2 +- guides-chat-memory.html | 2 +- guides-data-message.html | 2 +- guides-new-to-llms.html | 2 +- index.html | 2 +- integrations-langsmith.html | 2 +- integrations-langwatch.html | 2 +- settings-global-variables.html | 2 +- settings-project-general-settings.html | 2 +- starter-projects-basic-prompting.html | 2 +- starter-projects-blog-writer.html | 2 +- starter-projects-document-qa.html | 2 +- starter-projects-memory-chatbot.html | 2 +- starter-projects-vector-store-rag.html | 2 +- whats-new-a-new-chapter-langflow.html | 2 +- workspace-api.html | 2 +- workspace-logs.html | 2 +- workspace-playground.html | 2 +- workspace.html | 2 +- 50 files changed, 65 insertions(+), 50 deletions(-) delete mode 100644 assets/js/f66238ae.4590dee3.js create mode 100644 assets/js/f66238ae.84317656.js rename assets/js/{runtime~main.3eb60043.js => runtime~main.8de562d6.js} (99%) diff --git a/365085a8-a90a-43f9-a779-f8769ec7eca1.html b/365085a8-a90a-43f9-a779-f8769ec7eca1.html index 191ecc4f58..cb38ad3847 100644 --- a/365085a8-a90a-43f9-a779-f8769ec7eca1.html +++ b/365085a8-a90a-43f9-a779-f8769ec7eca1.html @@ -7,7 +7,7 @@ - + diff --git a/404.html b/404.html index 175096f962..67cfa69b19 100644 --- a/404.html +++ b/404.html @@ -7,7 +7,7 @@ - + diff --git a/assets/js/f66238ae.4590dee3.js b/assets/js/f66238ae.4590dee3.js deleted file mode 100644 index 7c74b030d0..0000000000 --- a/assets/js/f66238ae.4590dee3.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[5772],{9481:(e,n,r)=>{r.r(n),r.d(n,{assets:()=>d,contentTitle:()=>l,default:()=>h,frontMatter:()=>t,metadata:()=>c,toc:()=>o});var s=r(4848),i=r(8453);const t={title:"Vector Stores",sidebar_position:7,slug:"/components-vector-stores"},l=void 0,c={id:"Components/components-vector-stores",title:"Vector Stores",description:"This page may contain outdated information. It will be updated as soon as possible.",source:"@site/docs/Components/components-vector-stores.md",sourceDirName:"Components",slug:"/components-vector-stores",permalink:"/components-vector-stores",draft:!1,unlisted:!1,tags:[],version:"current",sidebarPosition:7,frontMatter:{title:"Vector Stores",sidebar_position:7,slug:"/components-vector-stores"},sidebar:"defaultSidebar",previous:{title:"Embedding Models",permalink:"/components-embedding-models"},next:{title:"Custom Components",permalink:"/components-custom-components"}},d={},o=[{value:"Astra DB",id:"453bcf5664154e37a920f1b602bd39da",level:3},{value:"Astra DB Search",id:"26f25d1933a9459bad2d6725f87beb11",level:3},{value:"Chroma",id:"74730795605143cba53e1f4c4f2ef5d6",level:3},{value:"Chroma Search",id:"5718072a155441f3a443b944ad4d638f",level:3},{value:"Couchbase",id:"6900a79347164f35af27ae27f0d64a6d",level:3},{value:"Couchbase Search",id:"c77bb09425a3426f9677d38d8237d9ba",level:3},{value:"FAISS",id:"5b3f4e6592a847b69e07df2f674a03f0",level:3},{value:"FAISS Search",id:"81ff12d7205940a3b14e3ddf304630f8",level:3},{value:"MongoDB Atlas",id:"eba8892f7a204b97ad1c353e82948149",level:3},{value:"MongoDB Atlas Search",id:"686ba0e30a54438cbc7153b81ee4b1df",level:3},{value:"PGVector",id:"7ceebdd84ab14f8e8589c13c58370e5b",level:3},{value:"PGVector Search",id:"196bf22ea2844bdbba971b5082750943",level:3},{value:"Pinecone",id:"67abbe3e27c34fb4bcb35926ce831727",level:3},{value:"Pinecone Search",id:"977944558cad4cf2ba332ea4f06bf485",level:3},{value:"Qdrant",id:"88df77f3044e4ac6980950835a919fb0",level:3},{value:"Qdrant Search",id:"5ba5f8dca0f249d7ad00778f49901e6c",level:3},{value:"Redis",id:"a0fb8a9d244a40eb8439d0f8c22a2562",level:3},{value:"Redis Search",id:"80aea4da515f490e979c8576099ee880",level:3},{value:"Supabase",id:"e86fb3cc507e4b5494f0a421f94e853b",level:3},{value:"Supabase Search",id:"fd02d550b9b2457f91f2f4073656cb09",level:3},{value:"Vectara",id:"b4e05230b62a47c792a89c5511af97ac",level:3},{value:"Vectara Search",id:"31a47221c23f4fbba4a7465cf1d89eb0",level:3},{value:"Weaviate",id:"57c7969574b1418dbb079ac5fc8cd857",level:3},{value:"Weaviate Search",id:"6d4e616dfd6143b28dc055bc1c40ecae",level:3}];function a(e){const n={a:"a",admonition:"admonition",code:"code",h3:"h3",hr:"hr",li:"li",p:"p",strong:"strong",ul:"ul",...(0,i.R)(),...e.components};return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"This page may contain outdated information. It will be updated as soon as possible."})}),"\n",(0,s.jsx)(n.h3,{id:"453bcf5664154e37a920f1b602bd39da",children:"Astra DB"}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.code,{children:"Astra DB"}),"\xa0initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data for input."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding or Astra vectorize:"}),"\xa0External or server-side model Astra DB uses."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the Astra DB collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Token:"}),"\xa0Authentication token for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Endpoint:"}),"\xa0API endpoint for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Astra DB namespace."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metric:"}),"\xa0Metric used by Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Batch Size:"}),"\xa0Batch size for operations."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Batch Concurrency:"}),"\xa0Concurrency level for bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Overwrite Concurrency:"}),"\xa0Concurrency level for overwriting during bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Delete Concurrency:"}),"\xa0Concurrency level for bulk deletions."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Setup Mode:"}),"\xa0Setup mode for the vector store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pre Delete Collection:"}),"\xa0Option to delete the collection before setup."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Include:"}),"\xa0Fields to include in metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Exclude:"}),"\xa0Fields to exclude from metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Indexing Policy:"}),"\xa0Indexing policy for the collection."]}),"\n"]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure you configure the necessary Astra DB token and API endpoint before starting."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"26f25d1933a9459bad2d6725f87beb11",children:"Astra DB Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Astra DBSearch"}),"\xa0searches an existing Astra DB vector store for documents similar to the input. It uses the\xa0",(0,s.jsx)(n.code,{children:"Astra DB"}),"component's functionality for efficient retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),"\xa0Type of search, such as Similarity or MMR."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Value to search for."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding or Astra vectorize:"}),"\xa0External or server-side model Astra DB uses."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the Astra DB collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Token:"}),"\xa0Authentication token for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Endpoint:"}),"\xa0API endpoint for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Astra DB namespace."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metric:"}),"\xa0Metric used by Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Batch Size:"}),"\xa0Batch size for operations."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Batch Concurrency:"}),"\xa0Concurrency level for bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Overwrite Concurrency:"}),"\xa0Concurrency level for overwriting during bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Delete Concurrency:"}),"\xa0Concurrency level for bulk deletions."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Setup Mode:"}),"\xa0Setup mode for the vector store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pre Delete Collection:"}),"\xa0Option to delete the collection before setup."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Include:"}),"\xa0Fields to include in metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Exclude:"}),"\xa0Fields to exclude from metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Indexing Policy:"}),"\xa0Indexing policy for the collection."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"74730795605143cba53e1f4c4f2ef5d6",children:"Chroma"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Chroma"}),"\xa0sets up a vector store using Chroma for efficient vector storage and retrieval within language processing workflows."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Persist Directory:"}),"\xa0Directory to persist the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server CORS Allow Origins (Optional):"}),"\xa0CORS allow origins for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Host (Optional):"}),"\xa0Host for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Port (Optional):"}),"\xa0Port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server gRPC Port (Optional):"}),"\xa0gRPC port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server SSL Enabled (Optional):"}),"\xa0SSL configuration for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Input data for creating the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Embeddings used for the Vector Store."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For detailed documentation and integration guides, please refer to the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/chroma",children:"Chroma Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"5718072a155441f3a443b944ad4d638f",children:"Chroma Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"ChromaSearch"}),"\xa0searches a Chroma collection for documents similar to the input text. It leverages Chroma to ensure efficient document retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Input text for search."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),"\xa0Type of search, such as Similarity or MMR."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the Chroma collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Directory:"}),"\xa0Directory where the Chroma index is stored."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Embedding model used for vectorization."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server CORS Allow Origins (Optional):"}),"\xa0CORS allow origins for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Host (Optional):"}),"\xa0Host for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Port (Optional):"}),"\xa0Port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server gRPC Port (Optional):"}),"\xa0gRPC port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server SSL Enabled (Optional):"}),"\xa0SSL configuration for the Chroma server."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"6900a79347164f35af27ae27f0d64a6d",children:"Couchbase"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Couchbase"}),"\xa0builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used by Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Connection String:"}),"\xa0Cluster Connection string."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Username:"}),"\xa0Cluster Username."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Password:"}),"\xa0Cluster Password."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bucket Name:"}),"\xa0Bucket identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Scope Name:"}),"\xa0Scope identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For detailed documentation and integration guides, please refer to the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/couchbase",children:"Couchbase Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"c77bb09425a3426f9677d38d8237d9ba",children:"Couchbase Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"CouchbaseSearch"}),"\xa0leverages the Couchbase component to search for documents based on similarity metric."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used in the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Connection String:"}),"\xa0Cluster Connection string."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Username:"}),"\xa0Cluster Username."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Password:"}),"\xa0Cluster Password."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bucket Name:"}),"\xa0Bucket identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Scope Name:"}),"\xa0Scope identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"5b3f4e6592a847b69e07df2f674a03f0",children:"FAISS"}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.code,{children:"FAISS"}),"\xa0component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used for vectorizing inputs."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents to ingest."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Folder Path:"}),"\xa0Save path for the FAISS index, relative to Langflow."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more details, see the\xa0",(0,s.jsx)(n.a,{href:"https://faiss.ai/index.html",children:"FAISS Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"81ff12d7205940a3b14e3ddf304630f8",children:"FAISS Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"FAISSSearch"}),"\xa0searches a FAISS Vector Store for documents similar to a given input, using similarity metrics for efficient retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used in the FAISS Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Folder Path:"}),"\xa0Path to load the FAISS index from, relative to Langflow."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"eba8892f7a204b97ad1c353e82948149",children:"MongoDB Atlas"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"MongoDBAtlas"}),"\xa0builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used by MongoDB Atlas."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier in MongoDB Atlas."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Database Name:"}),"\xa0Database identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"MongoDB Atlas Cluster URI:"}),"\xa0Cluster URI."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n"]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure pymongo is installed for using MongoDB Atlas Vector Store."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"686ba0e30a54438cbc7153b81ee4b1df",children:"MongoDB Atlas Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"MongoDBAtlasSearch"}),"\xa0leverages the MongoDBAtlas component to search for documents based on similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used in the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Database Name:"}),"\xa0Database identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"MongoDB Atlas Cluster URI:"}),"\xa0Cluster URI."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"7ceebdd84ab14f8e8589c13c58370e5b",children:"PGVector"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"PGVector"}),"\xa0integrates a Vector Store within a PostgreSQL database, allowing efficient storage and retrieval of vectors."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Value for the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"PostgreSQL Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table:"}),"\xa0Table name in the PostgreSQL database."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more details, see the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/pgvector",children:"PGVector Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure the PostgreSQL server is accessible and configured correctly."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"196bf22ea2844bdbba971b5082750943",children:"PGVector Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"PGVectorSearch"}),"\xa0extends\xa0",(0,s.jsx)(n.code,{children:"PGVector"}),"\xa0to search for documents based on similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"PostgreSQL Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table:"}),"\xa0Table name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"67abbe3e27c34fb4bcb35926ce831727",children:"Pinecone"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Pinecone"}),"\xa0constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Namespace used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone Environment:"}),"\xa0Environment settings."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pool Threads:"}),"\xa0Number of threads."]}),"\n"]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"Ensure the Pinecone API key and environment are correctly configured."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"977944558cad4cf2ba332ea4f06bf485",children:"Pinecone Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"PineconeSearch"}),"\xa0searches a Pinecone Vector Store for documents similar to the input, using advanced similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Namespace used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone Environment:"}),"\xa0Environment settings."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pool Threads:"}),"\xa0Number of threads."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"88df77f3044e4ac6980950835a919fb0",children:"Qdrant"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Qdrant"}),"\xa0allows efficient similarity searches and retrieval operations, using a list of texts to construct a Qdrant wrapper."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Qdrant API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Advanced Settings:"}),"\xa0Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"5ba5f8dca0f249d7ad00778f49901e6c",children:"Qdrant Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"QdrantSearch"}),"\xa0extends\xa0",(0,s.jsx)(n.code,{children:"Qdrant"}),"\xa0to search for documents similar to the input based on advanced similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Qdrant API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Advanced Settings:"}),"\xa0Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"a0fb8a9d244a40eb8439d0f8c22a2562",children:"Redis"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Redis"}),"\xa0manages a Vector Store in a Redis database, supporting efficient vector storage and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Default index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Data for building the Redis Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Schema:"}),"\xa0Optional schema file (.yaml) for document structure."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Index:"}),"\xa0Optional index name."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For detailed documentation, refer to the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/redis",children:"Redis Documentation"}),"."]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"Ensure the Redis server URL and index name are configured correctly. Provide a schema if no documents are available."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"80aea4da515f490e979c8576099ee880",children:"Redis Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"RedisSearch"}),"\xa0searches a Redis Vector Store for documents similar to the input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Default index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Schema:"}),"\xa0Optional schema file (.yaml) for document structure."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Index:"}),"\xa0Optional index name."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"e86fb3cc507e4b5494f0a421f94e853b",children:"Supabase"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Supabase"}),"\xa0initializes a Supabase Vector Store from texts and embeddings, setting up an environment for efficient document retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Query Name:"}),"\xa0Optional query name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Advanced search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase Service Key:"}),"\xa0Service key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase URL:"}),"\xa0Instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table Name:"}),"\xa0Optional table name."]}),"\n"]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"Ensure the Supabase service key, URL, and table name are properly configured."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"fd02d550b9b2457f91f2f4073656cb09",children:"Supabase Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"SupabaseSearch"}),"\xa0searches a Supabase Vector Store for documents similar to the input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Query Name:"}),"\xa0Optional query name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Advanced search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase Service Key:"}),"\xa0Service key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase URL:"}),"\xa0Instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table Name:"}),"\xa0Optional table name."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"b4e05230b62a47c792a89c5511af97ac",children:"Vectara"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Vectara"}),"\xa0sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Customer ID:"}),"\xa0Customer ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Corpus ID:"}),"\xa0Corpus ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Files Url:"}),"\xa0Optional URLs for file initialization."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Optional data for corpus upsert."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more information, consult the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/vectara",children:"Vectara Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"If inputs or files_url are provided, they will be processed accordingly."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"31a47221c23f4fbba4a7465cf1d89eb0",children:"Vectara Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"VectaraSearch"}),"\xa0searches a Vectara Vector Store for documents based on the provided input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Customer ID:"}),"\xa0Customer ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Corpus ID:"}),"\xa0Corpus ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Files Url:"}),"\xa0Optional URLs for file initialization."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"57c7969574b1418dbb079ac5fc8cd857",children:"Weaviate"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Weaviate"}),"\xa0facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Weaviate URL:"}),"\xa0Default instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search By Text:"}),"\xa0Indicates whether to search by text."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Optional API key for authentication."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Optional index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Text Key:"}),"\xa0Default text extraction key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Document or record."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Attributes:"}),"\xa0Optional additional attributes."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more details, see the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/weaviate",children:"Weaviate Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure Weaviate instance is running and accessible. Verify API key, index name, text key, and attributes are set correctly."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"6d4e616dfd6143b28dc055bc1c40ecae",children:"Weaviate Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"WeaviateSearch"}),"\xa0searches a Weaviate Vector Store for documents similar to the input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Weaviate URL:"}),"\xa0Default instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search By Text:"}),"\xa0Indicates whether to search by text."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Optional API key for authentication."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Optional index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Text Key:"}),"\xa0Default text extraction key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Attributes:"}),"\xa0Optional additional attributes."]}),"\n"]})]})}function h(e={}){const{wrapper:n}={...(0,i.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(a,{...e})}):a(e)}},8453:(e,n,r)=>{r.d(n,{R:()=>l,x:()=>c});var s=r(6540);const i={},t=s.createContext(i);function l(e){const n=s.useContext(t);return s.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function c(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(i):e.components||i:l(e.components),s.createElement(t.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/f66238ae.84317656.js b/assets/js/f66238ae.84317656.js new file mode 100644 index 0000000000..7f30064c21 --- /dev/null +++ b/assets/js/f66238ae.84317656.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunklangflow_docs=self.webpackChunklangflow_docs||[]).push([[5772],{9481:(e,n,r)=>{r.r(n),r.d(n,{assets:()=>d,contentTitle:()=>l,default:()=>h,frontMatter:()=>t,metadata:()=>c,toc:()=>o});var s=r(4848),i=r(8453);const t={title:"Vector Stores",sidebar_position:7,slug:"/components-vector-stores"},l=void 0,c={id:"Components/components-vector-stores",title:"Vector Stores",description:"This page may contain outdated information. It will be updated as soon as possible.",source:"@site/docs/Components/components-vector-stores.md",sourceDirName:"Components",slug:"/components-vector-stores",permalink:"/components-vector-stores",draft:!1,unlisted:!1,tags:[],version:"current",sidebarPosition:7,frontMatter:{title:"Vector Stores",sidebar_position:7,slug:"/components-vector-stores"},sidebar:"defaultSidebar",previous:{title:"Embedding Models",permalink:"/components-embedding-models"},next:{title:"Custom Components",permalink:"/components-custom-components"}},d={},o=[{value:"Astra DB",id:"453bcf5664154e37a920f1b602bd39da",level:3},{value:"Astra DB Search",id:"26f25d1933a9459bad2d6725f87beb11",level:3},{value:"Chroma",id:"74730795605143cba53e1f4c4f2ef5d6",level:3},{value:"Chroma Search",id:"5718072a155441f3a443b944ad4d638f",level:3},{value:"Couchbase",id:"6900a79347164f35af27ae27f0d64a6d",level:3},{value:"Couchbase Search",id:"c77bb09425a3426f9677d38d8237d9ba",level:3},{value:"FAISS",id:"5b3f4e6592a847b69e07df2f674a03f0",level:3},{value:"FAISS Search",id:"81ff12d7205940a3b14e3ddf304630f8",level:3},{value:"MongoDB Atlas",id:"eba8892f7a204b97ad1c353e82948149",level:3},{value:"MongoDB Atlas Search",id:"686ba0e30a54438cbc7153b81ee4b1df",level:3},{value:"PGVector",id:"7ceebdd84ab14f8e8589c13c58370e5b",level:3},{value:"PGVector Search",id:"196bf22ea2844bdbba971b5082750943",level:3},{value:"Pinecone",id:"67abbe3e27c34fb4bcb35926ce831727",level:3},{value:"Pinecone Search",id:"977944558cad4cf2ba332ea4f06bf485",level:3},{value:"Qdrant",id:"88df77f3044e4ac6980950835a919fb0",level:3},{value:"Qdrant Search",id:"5ba5f8dca0f249d7ad00778f49901e6c",level:3},{value:"Redis",id:"a0fb8a9d244a40eb8439d0f8c22a2562",level:3},{value:"Redis Search",id:"80aea4da515f490e979c8576099ee880",level:3},{value:"Supabase",id:"e86fb3cc507e4b5494f0a421f94e853b",level:3},{value:"Supabase Search",id:"fd02d550b9b2457f91f2f4073656cb09",level:3},{value:"Upstash Vector",id:"upstash-vector",level:3},{value:"Vectara",id:"b4e05230b62a47c792a89c5511af97ac",level:3},{value:"Vectara Search",id:"31a47221c23f4fbba4a7465cf1d89eb0",level:3},{value:"Weaviate",id:"57c7969574b1418dbb079ac5fc8cd857",level:3},{value:"Weaviate Search",id:"6d4e616dfd6143b28dc055bc1c40ecae",level:3}];function a(e){const n={a:"a",admonition:"admonition",code:"code",h3:"h3",hr:"hr",li:"li",p:"p",strong:"strong",ul:"ul",...(0,i.R)(),...e.components};return(0,s.jsxs)(s.Fragment,{children:[(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"This page may contain outdated information. It will be updated as soon as possible."})}),"\n",(0,s.jsx)(n.h3,{id:"453bcf5664154e37a920f1b602bd39da",children:"Astra DB"}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.code,{children:"Astra DB"}),"\xa0initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data for input."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding or Astra vectorize:"}),"\xa0External or server-side model Astra DB uses."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the Astra DB collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Token:"}),"\xa0Authentication token for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Endpoint:"}),"\xa0API endpoint for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Astra DB namespace."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metric:"}),"\xa0Metric used by Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Batch Size:"}),"\xa0Batch size for operations."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Batch Concurrency:"}),"\xa0Concurrency level for bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Overwrite Concurrency:"}),"\xa0Concurrency level for overwriting during bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Delete Concurrency:"}),"\xa0Concurrency level for bulk deletions."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Setup Mode:"}),"\xa0Setup mode for the vector store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pre Delete Collection:"}),"\xa0Option to delete the collection before setup."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Include:"}),"\xa0Fields to include in metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Exclude:"}),"\xa0Fields to exclude from metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Indexing Policy:"}),"\xa0Indexing policy for the collection."]}),"\n"]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure you configure the necessary Astra DB token and API endpoint before starting."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"26f25d1933a9459bad2d6725f87beb11",children:"Astra DB Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Astra DBSearch"}),"\xa0searches an existing Astra DB vector store for documents similar to the input. It uses the\xa0",(0,s.jsx)(n.code,{children:"Astra DB"}),"component's functionality for efficient retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),"\xa0Type of search, such as Similarity or MMR."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Value to search for."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding or Astra vectorize:"}),"\xa0External or server-side model Astra DB uses."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the Astra DB collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Token:"}),"\xa0Authentication token for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Endpoint:"}),"\xa0API endpoint for Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Astra DB namespace."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metric:"}),"\xa0Metric used by Astra DB."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Batch Size:"}),"\xa0Batch size for operations."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Batch Concurrency:"}),"\xa0Concurrency level for bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Insert Overwrite Concurrency:"}),"\xa0Concurrency level for overwriting during bulk inserts."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bulk Delete Concurrency:"}),"\xa0Concurrency level for bulk deletions."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Setup Mode:"}),"\xa0Setup mode for the vector store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pre Delete Collection:"}),"\xa0Option to delete the collection before setup."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Include:"}),"\xa0Fields to include in metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Indexing Exclude:"}),"\xa0Fields to exclude from metadata indexing."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Indexing Policy:"}),"\xa0Indexing policy for the collection."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"74730795605143cba53e1f4c4f2ef5d6",children:"Chroma"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Chroma"}),"\xa0sets up a vector store using Chroma for efficient vector storage and retrieval within language processing workflows."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Persist Directory:"}),"\xa0Directory to persist the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server CORS Allow Origins (Optional):"}),"\xa0CORS allow origins for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Host (Optional):"}),"\xa0Host for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Port (Optional):"}),"\xa0Port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server gRPC Port (Optional):"}),"\xa0gRPC port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server SSL Enabled (Optional):"}),"\xa0SSL configuration for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Input data for creating the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Embeddings used for the Vector Store."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For detailed documentation and integration guides, please refer to the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/chroma",children:"Chroma Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"5718072a155441f3a443b944ad4d638f",children:"Chroma Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"ChromaSearch"}),"\xa0searches a Chroma collection for documents similar to the input text. It leverages Chroma to ensure efficient document retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Input text for search."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),"\xa0Type of search, such as Similarity or MMR."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Name of the Chroma collection."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Directory:"}),"\xa0Directory where the Chroma index is stored."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Embedding model used for vectorization."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server CORS Allow Origins (Optional):"}),"\xa0CORS allow origins for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Host (Optional):"}),"\xa0Host for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server Port (Optional):"}),"\xa0Port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server gRPC Port (Optional):"}),"\xa0gRPC port for the Chroma server."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Server SSL Enabled (Optional):"}),"\xa0SSL configuration for the Chroma server."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"6900a79347164f35af27ae27f0d64a6d",children:"Couchbase"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Couchbase"}),"\xa0builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used by Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Connection String:"}),"\xa0Cluster Connection string."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Username:"}),"\xa0Cluster Username."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Password:"}),"\xa0Cluster Password."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bucket Name:"}),"\xa0Bucket identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Scope Name:"}),"\xa0Scope identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For detailed documentation and integration guides, please refer to the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/couchbase",children:"Couchbase Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"c77bb09425a3426f9677d38d8237d9ba",children:"Couchbase Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"CouchbaseSearch"}),"\xa0leverages the Couchbase component to search for documents based on similarity metric."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used in the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Connection String:"}),"\xa0Cluster Connection string."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Username:"}),"\xa0Cluster Username."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Couchbase Cluster Password:"}),"\xa0Cluster Password."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Bucket Name:"}),"\xa0Bucket identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Scope Name:"}),"\xa0Scope identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier in Couchbase."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"5b3f4e6592a847b69e07df2f674a03f0",children:"FAISS"}),"\n",(0,s.jsxs)(n.p,{children:["The\xa0",(0,s.jsx)(n.code,{children:"FAISS"}),"\xa0component manages document ingestion into a FAISS Vector Store, optimizing document indexing and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used for vectorizing inputs."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents to ingest."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Folder Path:"}),"\xa0Save path for the FAISS index, relative to Langflow."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more details, see the\xa0",(0,s.jsx)(n.a,{href:"https://faiss.ai/index.html",children:"FAISS Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"81ff12d7205940a3b14e3ddf304630f8",children:"FAISS Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"FAISSSearch"}),"\xa0searches a FAISS Vector Store for documents similar to a given input, using similarity metrics for efficient retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used in the FAISS Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Folder Path:"}),"\xa0Path to load the FAISS index from, relative to Langflow."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"eba8892f7a204b97ad1c353e82948149",children:"MongoDB Atlas"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"MongoDBAtlas"}),"\xa0builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used by MongoDB Atlas."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier in MongoDB Atlas."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Database Name:"}),"\xa0Database identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"MongoDB Atlas Cluster URI:"}),"\xa0Cluster URI."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n"]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure pymongo is installed for using MongoDB Atlas Vector Store."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"686ba0e30a54438cbc7153b81ee4b1df",children:"MongoDB Atlas Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"MongoDBAtlasSearch"}),"\xa0leverages the MongoDBAtlas component to search for documents based on similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used in the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Database Name:"}),"\xa0Database identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"MongoDB Atlas Cluster URI:"}),"\xa0Cluster URI."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"7ceebdd84ab14f8e8589c13c58370e5b",children:"PGVector"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"PGVector"}),"\xa0integrates a Vector Store within a PostgreSQL database, allowing efficient storage and retrieval of vectors."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Value for the Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"PostgreSQL Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table:"}),"\xa0Table name in the PostgreSQL database."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more details, see the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/pgvector",children:"PGVector Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure the PostgreSQL server is accessible and configured correctly."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"196bf22ea2844bdbba971b5082750943",children:"PGVector Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"PGVectorSearch"}),"\xa0extends\xa0",(0,s.jsx)(n.code,{children:"PGVector"}),"\xa0to search for documents based on similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"PostgreSQL Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table:"}),"\xa0Table name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"67abbe3e27c34fb4bcb35926ce831727",children:"Pinecone"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Pinecone"}),"\xa0constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Namespace used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone Environment:"}),"\xa0Environment settings."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pool Threads:"}),"\xa0Number of threads."]}),"\n"]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"Ensure the Pinecone API key and environment are correctly configured."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"977944558cad4cf2ba332ea4f06bf485",children:"Pinecone Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"PineconeSearch"}),"\xa0searches a Pinecone Vector Store for documents similar to the input, using advanced similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Index identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0Namespace used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pinecone Environment:"}),"\xa0Environment settings."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Additional search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Pool Threads:"}),"\xa0Number of threads."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"88df77f3044e4ac6980950835a919fb0",children:"Qdrant"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Qdrant"}),"\xa0allows efficient similarity searches and retrieval operations, using a list of texts to construct a Qdrant wrapper."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or Data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Qdrant API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Advanced Settings:"}),"\xa0Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"5ba5f8dca0f249d7ad00778f49901e6c",children:"Qdrant Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"QdrantSearch"}),"\xa0extends\xa0",(0,s.jsx)(n.code,{children:"Qdrant"}),"\xa0to search for documents similar to the input based on advanced similarity metrics."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Qdrant API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Collection Name:"}),"\xa0Collection identifier."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Advanced Settings:"}),"\xa0Includes content payload key, distance function, gRPC port, host, HTTPS, location, metadata payload key, path, port, prefer gRPC, prefix, search kwargs, timeout, URL."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"a0fb8a9d244a40eb8439d0f8c22a2562",children:"Redis"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Redis"}),"\xa0manages a Vector Store in a Redis database, supporting efficient vector storage and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Default index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Data for building the Redis Vector Store."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Schema:"}),"\xa0Optional schema file (.yaml) for document structure."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Index:"}),"\xa0Optional index name."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For detailed documentation, refer to the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/redis",children:"Redis Documentation"}),"."]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"Ensure the Redis server URL and index name are configured correctly. Provide a schema if no documents are available."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"80aea4da515f490e979c8576099ee880",children:"Redis Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"RedisSearch"}),"\xa0searches a Redis Vector Store for documents similar to the input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Default index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Schema:"}),"\xa0Optional schema file (.yaml) for document structure."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Server Connection String:"}),"\xa0Server URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Redis Index:"}),"\xa0Optional index name."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"e86fb3cc507e4b5494f0a421f94e853b",children:"Supabase"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Supabase"}),"\xa0initializes a Supabase Vector Store from texts and embeddings, setting up an environment for efficient document retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Documents or data."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Query Name:"}),"\xa0Optional query name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Advanced search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase Service Key:"}),"\xa0Service key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase URL:"}),"\xa0Instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table Name:"}),"\xa0Optional table name."]}),"\n"]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"Ensure the Supabase service key, URL, and table name are properly configured."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"fd02d550b9b2457f91f2f4073656cb09",children:"Supabase Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"SupabaseSearch"}),"\xa0searches a Supabase Vector Store for documents similar to the input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Query Name:"}),"\xa0Optional query name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Kwargs:"}),"\xa0Advanced search parameters."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase Service Key:"}),"\xa0Service key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Supabase URL:"}),"\xa0Instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Table Name:"}),"\xa0Optional table name."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"upstash-vector",children:"Upstash Vector"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"UpstashVector"})," searches a Upstash Vector Store for documents similar to the input. It has it's own embedding\nmodel which can be used to search documents without needing an external embedding model."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index URL:"}),"\xa0The URL of the Upstash index."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Token:"}),"\xa0The token for the Upstash index."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Text Key:"}),"\xa0The key in the record to use as text."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Namespace:"}),"\xa0The namespace name. A new namespace is created if not found. Leave empty for default namespace."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Query:"}),"\xa0The search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Metadata Filter:"}),"\xa0The metadata filter. Filters documents by metadata. Look at the ",(0,s.jsx)(n.a,{href:"https://upstash.com/docs/vector/features/filtering",children:"docs"})," for more information."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0The embedding model used. To use Upstash's embeddings, don't provide an embedding."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Number of Results:"}),"\xa0The number of results to return."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"b4e05230b62a47c792a89c5511af97ac",children:"Vectara"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Vectara"}),"\xa0sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Customer ID:"}),"\xa0Customer ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Corpus ID:"}),"\xa0Corpus ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Files Url:"}),"\xa0Optional URLs for file initialization."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Optional data for corpus upsert."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more information, consult the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/vectara",children:"Vectara Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.admonition,{type:"info",children:(0,s.jsx)(n.p,{children:"If inputs or files_url are provided, they will be processed accordingly."})}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"31a47221c23f4fbba4a7465cf1d89eb0",children:"Vectara Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"VectaraSearch"}),"\xa0searches a Vectara Vector Store for documents based on the provided input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Customer ID:"}),"\xa0Customer ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara Corpus ID:"}),"\xa0Corpus ID."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Vectara API Key:"}),"\xa0API key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Files Url:"}),"\xa0Optional URLs for file initialization."]}),"\n"]}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"57c7969574b1418dbb079ac5fc8cd857",children:"Weaviate"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"Weaviate"}),"\xa0facilitates a Weaviate Vector Store setup, optimizing text and document indexing and retrieval."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Weaviate URL:"}),"\xa0Default instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search By Text:"}),"\xa0Indicates whether to search by text."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Optional API key for authentication."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Optional index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Text Key:"}),"\xa0Default text extraction key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input:"}),"\xa0Document or record."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Attributes:"}),"\xa0Optional additional attributes."]}),"\n"]}),"\n",(0,s.jsxs)(n.p,{children:["For more details, see the\xa0",(0,s.jsx)(n.a,{href:"https://python.langchain.com/docs/integrations/vectorstores/weaviate",children:"Weaviate Component Documentation"}),"."]}),"\n",(0,s.jsx)(n.p,{children:"NOTE"}),"\n",(0,s.jsx)(n.p,{children:"Ensure Weaviate instance is running and accessible. Verify API key, index name, text key, and attributes are set correctly."}),"\n",(0,s.jsx)(n.hr,{}),"\n",(0,s.jsx)(n.h3,{id:"6d4e616dfd6143b28dc055bc1c40ecae",children:"Weaviate Search"}),"\n",(0,s.jsxs)(n.p,{children:[(0,s.jsx)(n.code,{children:"WeaviateSearch"}),"\xa0searches a Weaviate Vector Store for documents similar to the input."]}),"\n",(0,s.jsx)(n.p,{children:(0,s.jsx)(n.strong,{children:"Parameters:"})}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search Type:"}),'\xa0Type of search, such as "Similarity" or "MMR".']}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Input Value:"}),"\xa0Search query."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Weaviate URL:"}),"\xa0Default instance URL."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Search By Text:"}),"\xa0Indicates whether to search by text."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"API Key:"}),"\xa0Optional API key for authentication."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Index Name:"}),"\xa0Optional index name."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Text Key:"}),"\xa0Default text extraction key."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Embedding:"}),"\xa0Model used."]}),"\n",(0,s.jsxs)(n.li,{children:[(0,s.jsx)(n.strong,{children:"Attributes:"}),"\xa0Optional additional attributes."]}),"\n"]})]})}function h(e={}){const{wrapper:n}={...(0,i.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(a,{...e})}):a(e)}},8453:(e,n,r)=>{r.d(n,{R:()=>l,x:()=>c});var s=r(6540);const i={},t=s.createContext(i);function l(e){const n=s.useContext(t);return s.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function c(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(i):e.components||i:l(e.components),s.createElement(t.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/runtime~main.3eb60043.js b/assets/js/runtime~main.8de562d6.js similarity index 99% rename from assets/js/runtime~main.3eb60043.js rename to assets/js/runtime~main.8de562d6.js index 3e820922ea..736e1f1acc 100644 --- a/assets/js/runtime~main.3eb60043.js +++ b/assets/js/runtime~main.8de562d6.js @@ -1 +1 @@ -(()=>{"use strict";var e,a,r,c,t,d={},f={};function b(e){var a=f[e];if(void 0!==a)return a.exports;var r=f[e]={id:e,loaded:!1,exports:{}};return d[e].call(r.exports,r,r.exports,b),r.loaded=!0,r.exports}b.m=d,b.c=f,e=[],b.O=(a,r,c,t)=>{if(!r){var d=1/0;for(n=0;n=t)&&Object.keys(b.O).every((e=>b.O[e](r[o])))?r.splice(o--,1):(f=!1,t0&&e[n-1][2]>t;n--)e[n]=e[n-1];e[n]=[r,c,t]},b.n=e=>{var a=e&&e.__esModule?()=>e.default:()=>e;return b.d(a,{a:a}),a},r=Object.getPrototypeOf?e=>Object.getPrototypeOf(e):e=>e.__proto__,b.t=function(e,c){if(1&c&&(e=this(e)),8&c)return e;if("object"==typeof e&&e){if(4&c&&e.__esModule)return e;if(16&c&&"function"==typeof e.then)return e}var t=Object.create(null);b.r(t);var d={};a=a||[null,r({}),r([]),r(r)];for(var f=2&c&&e;"object"==typeof f&&!~a.indexOf(f);f=r(f))Object.getOwnPropertyNames(f).forEach((a=>d[a]=()=>e[a]));return d.default=()=>e,b.d(t,d),t},b.d=(e,a)=>{for(var r in a)b.o(a,r)&&!b.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:a[r]})},b.f={},b.e=e=>Promise.all(Object.keys(b.f).reduce(((a,r)=>(b.f[r](e,a),a)),[])),b.u=e=>"assets/js/"+({144:"e2a386ca",145:"0be1d5fe",784:"fe965b62",804:"7fcd30b6",868:"dc7b1ef3",1039:"16d61ab3",1227:"647965d5",1246:"1b523369",1456:"ecd98ae0",1563:"3ef76b56",1567:"22dd74f7",1917:"c3616f7f",2005:"0575dfc8",2042:"reactPlayerTwitch",2431:"c0d3c6ab",2657:"a3ab51d1",2691:"2efb9d3a",2723:"reactPlayerMux",3392:"reactPlayerVidyard",3659:"4618a71f",4028:"c2ebd62a",4046:"9ac856ad",4133:"5cf11f26",5562:"20e9af62",5572:"873ebc27",5628:"a83bc7c0",5742:"aba21aa0",5772:"f66238ae",6008:"445668ec",6147:"ab17fe15",6173:"reactPlayerVimeo",6328:"reactPlayerDailyMotion",6353:"reactPlayerPreview",6463:"reactPlayerKaltura",6494:"b453f7b0",6677:"2f617b24",6887:"reactPlayerFacebook",6900:"3f8193f3",7098:"a7bd4aaa",7211:"cacdc615",7258:"11478de3",7338:"c39b795e",7408:"3ae94ad4",7453:"fdefa9a8",7458:"reactPlayerFilePlayer",7462:"921b5fc1",7570:"reactPlayerMixcloud",7627:"reactPlayerStreamable",8031:"25bf2d67",8054:"85112c90",8127:"53ed7db4",8183:"d59b5b70",8261:"474d53cd",8401:"17896441",8446:"reactPlayerYouTube",8598:"44dfcf75",8929:"172b3cfb",9048:"a94703ab",9304:"c329cc2b",9340:"reactPlayerWistia",9455:"54175a6c",9461:"23d0e682",9580:"f81a91eb",9642:"eae80ce0",9647:"5e95c892",9979:"reactPlayerSoundCloud"}[e]||e)+"."+{144:"7e8e09aa",145:"4524db73",784:"bf3c3b32",804:"052d3ec1",868:"d7c01dec",900:"cec9b91d",1039:"6b5d9275",1227:"f67bd117",1246:"9ab26cae",1456:"f59b808e",1563:"19a1df84",1567:"47c4b73d",1917:"7225d5e0",2005:"e702fe64",2042:"38dd7b7d",2237:"4bb44bc4",2431:"a3030b6b",2657:"8e67cffa",2691:"c7804f19",2723:"8f6391ba",3392:"cb42cdfc",3554:"c7291073",3659:"65567128",4028:"4c8ebf9d",4046:"54f4209d",4133:"f0925cd4",5562:"8d63ec91",5572:"9f836f52",5628:"31f63b7e",5742:"7728a8b8",5772:"4590dee3",6008:"5070ebd4",6147:"2502f6c4",6173:"291c2909",6328:"f614ee4c",6353:"8cd2b61f",6463:"8e37235b",6494:"404054b3",6677:"6a39e3b4",6887:"a60ee927",6900:"ed789b0e",7098:"71beb8e2",7211:"69ff3bb2",7258:"cada45ea",7338:"894a81dc",7408:"8c54d710",7453:"cc4cfb89",7458:"c0e08054",7462:"8828b31d",7570:"34c84b39",7627:"b6323134",8031:"9469dd38",8054:"2d20ecfb",8127:"7f91271f",8183:"7baaad08",8261:"4737b75e",8401:"2ed86b26",8446:"a89f408c",8598:"44362993",8929:"e136d1f2",9048:"38aef81a",9304:"db4c4794",9340:"7f514310",9455:"77251598",9461:"f73e4482",9580:"1279fd6d",9642:"b1d36097",9647:"8641e05b",9979:"9ddea34f"}[e]+".js",b.miniCssF=e=>{},b.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(e){if("object"==typeof window)return window}}(),b.o=(e,a)=>Object.prototype.hasOwnProperty.call(e,a),c={},t="langflow-docs:",b.l=(e,a,r,d)=>{if(c[e])c[e].push(a);else{var f,o;if(void 0!==r)for(var l=document.getElementsByTagName("script"),n=0;n{f.onerror=f.onload=null,clearTimeout(s);var t=c[e];if(delete c[e],f.parentNode&&f.parentNode.removeChild(f),t&&t.forEach((e=>e(r))),a)return a(r)},s=setTimeout(u.bind(null,void 0,{type:"timeout",target:f}),12e4);f.onerror=u.bind(null,f.onerror),f.onload=u.bind(null,f.onload),o&&document.head.appendChild(f)}},b.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},b.p="/",b.gca=function(e){return e={17896441:"8401",e2a386ca:"144","0be1d5fe":"145",fe965b62:"784","7fcd30b6":"804",dc7b1ef3:"868","16d61ab3":"1039","647965d5":"1227","1b523369":"1246",ecd98ae0:"1456","3ef76b56":"1563","22dd74f7":"1567",c3616f7f:"1917","0575dfc8":"2005",reactPlayerTwitch:"2042",c0d3c6ab:"2431",a3ab51d1:"2657","2efb9d3a":"2691",reactPlayerMux:"2723",reactPlayerVidyard:"3392","4618a71f":"3659",c2ebd62a:"4028","9ac856ad":"4046","5cf11f26":"4133","20e9af62":"5562","873ebc27":"5572",a83bc7c0:"5628",aba21aa0:"5742",f66238ae:"5772","445668ec":"6008",ab17fe15:"6147",reactPlayerVimeo:"6173",reactPlayerDailyMotion:"6328",reactPlayerPreview:"6353",reactPlayerKaltura:"6463",b453f7b0:"6494","2f617b24":"6677",reactPlayerFacebook:"6887","3f8193f3":"6900",a7bd4aaa:"7098",cacdc615:"7211","11478de3":"7258",c39b795e:"7338","3ae94ad4":"7408",fdefa9a8:"7453",reactPlayerFilePlayer:"7458","921b5fc1":"7462",reactPlayerMixcloud:"7570",reactPlayerStreamable:"7627","25bf2d67":"8031","85112c90":"8054","53ed7db4":"8127",d59b5b70:"8183","474d53cd":"8261",reactPlayerYouTube:"8446","44dfcf75":"8598","172b3cfb":"8929",a94703ab:"9048",c329cc2b:"9304",reactPlayerWistia:"9340","54175a6c":"9455","23d0e682":"9461",f81a91eb:"9580",eae80ce0:"9642","5e95c892":"9647",reactPlayerSoundCloud:"9979"}[e]||e,b.p+b.u(e)},(()=>{var e={5354:0,1869:0};b.f.j=(a,r)=>{var c=b.o(e,a)?e[a]:void 0;if(0!==c)if(c)r.push(c[2]);else if(/^(1869|5354)$/.test(a))e[a]=0;else{var t=new Promise(((r,t)=>c=e[a]=[r,t]));r.push(c[2]=t);var d=b.p+b.u(a),f=new Error;b.l(d,(r=>{if(b.o(e,a)&&(0!==(c=e[a])&&(e[a]=void 0),c)){var t=r&&("load"===r.type?"missing":r.type),d=r&&r.target&&r.target.src;f.message="Loading chunk "+a+" failed.\n("+t+": "+d+")",f.name="ChunkLoadError",f.type=t,f.request=d,c[1](f)}}),"chunk-"+a,a)}},b.O.j=a=>0===e[a];var a=(a,r)=>{var c,t,d=r[0],f=r[1],o=r[2],l=0;if(d.some((a=>0!==e[a]))){for(c in f)b.o(f,c)&&(b.m[c]=f[c]);if(o)var n=o(b)}for(a&&a(r);l{"use strict";var e,a,r,c,t,d={},f={};function b(e){var a=f[e];if(void 0!==a)return a.exports;var r=f[e]={id:e,loaded:!1,exports:{}};return d[e].call(r.exports,r,r.exports,b),r.loaded=!0,r.exports}b.m=d,b.c=f,e=[],b.O=(a,r,c,t)=>{if(!r){var d=1/0;for(n=0;n=t)&&Object.keys(b.O).every((e=>b.O[e](r[o])))?r.splice(o--,1):(f=!1,t0&&e[n-1][2]>t;n--)e[n]=e[n-1];e[n]=[r,c,t]},b.n=e=>{var a=e&&e.__esModule?()=>e.default:()=>e;return b.d(a,{a:a}),a},r=Object.getPrototypeOf?e=>Object.getPrototypeOf(e):e=>e.__proto__,b.t=function(e,c){if(1&c&&(e=this(e)),8&c)return e;if("object"==typeof e&&e){if(4&c&&e.__esModule)return e;if(16&c&&"function"==typeof e.then)return e}var t=Object.create(null);b.r(t);var d={};a=a||[null,r({}),r([]),r(r)];for(var f=2&c&&e;"object"==typeof f&&!~a.indexOf(f);f=r(f))Object.getOwnPropertyNames(f).forEach((a=>d[a]=()=>e[a]));return d.default=()=>e,b.d(t,d),t},b.d=(e,a)=>{for(var r in a)b.o(a,r)&&!b.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:a[r]})},b.f={},b.e=e=>Promise.all(Object.keys(b.f).reduce(((a,r)=>(b.f[r](e,a),a)),[])),b.u=e=>"assets/js/"+({144:"e2a386ca",145:"0be1d5fe",784:"fe965b62",804:"7fcd30b6",868:"dc7b1ef3",1039:"16d61ab3",1227:"647965d5",1246:"1b523369",1456:"ecd98ae0",1563:"3ef76b56",1567:"22dd74f7",1917:"c3616f7f",2005:"0575dfc8",2042:"reactPlayerTwitch",2431:"c0d3c6ab",2657:"a3ab51d1",2691:"2efb9d3a",2723:"reactPlayerMux",3392:"reactPlayerVidyard",3659:"4618a71f",4028:"c2ebd62a",4046:"9ac856ad",4133:"5cf11f26",5562:"20e9af62",5572:"873ebc27",5628:"a83bc7c0",5742:"aba21aa0",5772:"f66238ae",6008:"445668ec",6147:"ab17fe15",6173:"reactPlayerVimeo",6328:"reactPlayerDailyMotion",6353:"reactPlayerPreview",6463:"reactPlayerKaltura",6494:"b453f7b0",6677:"2f617b24",6887:"reactPlayerFacebook",6900:"3f8193f3",7098:"a7bd4aaa",7211:"cacdc615",7258:"11478de3",7338:"c39b795e",7408:"3ae94ad4",7453:"fdefa9a8",7458:"reactPlayerFilePlayer",7462:"921b5fc1",7570:"reactPlayerMixcloud",7627:"reactPlayerStreamable",8031:"25bf2d67",8054:"85112c90",8127:"53ed7db4",8183:"d59b5b70",8261:"474d53cd",8401:"17896441",8446:"reactPlayerYouTube",8598:"44dfcf75",8929:"172b3cfb",9048:"a94703ab",9304:"c329cc2b",9340:"reactPlayerWistia",9455:"54175a6c",9461:"23d0e682",9580:"f81a91eb",9642:"eae80ce0",9647:"5e95c892",9979:"reactPlayerSoundCloud"}[e]||e)+"."+{144:"7e8e09aa",145:"4524db73",784:"bf3c3b32",804:"052d3ec1",868:"d7c01dec",900:"cec9b91d",1039:"6b5d9275",1227:"f67bd117",1246:"9ab26cae",1456:"f59b808e",1563:"19a1df84",1567:"47c4b73d",1917:"7225d5e0",2005:"e702fe64",2042:"38dd7b7d",2237:"4bb44bc4",2431:"a3030b6b",2657:"8e67cffa",2691:"c7804f19",2723:"8f6391ba",3392:"cb42cdfc",3554:"c7291073",3659:"65567128",4028:"4c8ebf9d",4046:"54f4209d",4133:"f0925cd4",5562:"8d63ec91",5572:"9f836f52",5628:"31f63b7e",5742:"7728a8b8",5772:"84317656",6008:"5070ebd4",6147:"2502f6c4",6173:"291c2909",6328:"f614ee4c",6353:"8cd2b61f",6463:"8e37235b",6494:"404054b3",6677:"6a39e3b4",6887:"a60ee927",6900:"ed789b0e",7098:"71beb8e2",7211:"69ff3bb2",7258:"cada45ea",7338:"894a81dc",7408:"8c54d710",7453:"cc4cfb89",7458:"c0e08054",7462:"8828b31d",7570:"34c84b39",7627:"b6323134",8031:"9469dd38",8054:"2d20ecfb",8127:"7f91271f",8183:"7baaad08",8261:"4737b75e",8401:"2ed86b26",8446:"a89f408c",8598:"44362993",8929:"e136d1f2",9048:"38aef81a",9304:"db4c4794",9340:"7f514310",9455:"77251598",9461:"f73e4482",9580:"1279fd6d",9642:"b1d36097",9647:"8641e05b",9979:"9ddea34f"}[e]+".js",b.miniCssF=e=>{},b.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(e){if("object"==typeof window)return window}}(),b.o=(e,a)=>Object.prototype.hasOwnProperty.call(e,a),c={},t="langflow-docs:",b.l=(e,a,r,d)=>{if(c[e])c[e].push(a);else{var f,o;if(void 0!==r)for(var l=document.getElementsByTagName("script"),n=0;n{f.onerror=f.onload=null,clearTimeout(s);var t=c[e];if(delete c[e],f.parentNode&&f.parentNode.removeChild(f),t&&t.forEach((e=>e(r))),a)return a(r)},s=setTimeout(u.bind(null,void 0,{type:"timeout",target:f}),12e4);f.onerror=u.bind(null,f.onerror),f.onload=u.bind(null,f.onload),o&&document.head.appendChild(f)}},b.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},b.p="/",b.gca=function(e){return e={17896441:"8401",e2a386ca:"144","0be1d5fe":"145",fe965b62:"784","7fcd30b6":"804",dc7b1ef3:"868","16d61ab3":"1039","647965d5":"1227","1b523369":"1246",ecd98ae0:"1456","3ef76b56":"1563","22dd74f7":"1567",c3616f7f:"1917","0575dfc8":"2005",reactPlayerTwitch:"2042",c0d3c6ab:"2431",a3ab51d1:"2657","2efb9d3a":"2691",reactPlayerMux:"2723",reactPlayerVidyard:"3392","4618a71f":"3659",c2ebd62a:"4028","9ac856ad":"4046","5cf11f26":"4133","20e9af62":"5562","873ebc27":"5572",a83bc7c0:"5628",aba21aa0:"5742",f66238ae:"5772","445668ec":"6008",ab17fe15:"6147",reactPlayerVimeo:"6173",reactPlayerDailyMotion:"6328",reactPlayerPreview:"6353",reactPlayerKaltura:"6463",b453f7b0:"6494","2f617b24":"6677",reactPlayerFacebook:"6887","3f8193f3":"6900",a7bd4aaa:"7098",cacdc615:"7211","11478de3":"7258",c39b795e:"7338","3ae94ad4":"7408",fdefa9a8:"7453",reactPlayerFilePlayer:"7458","921b5fc1":"7462",reactPlayerMixcloud:"7570",reactPlayerStreamable:"7627","25bf2d67":"8031","85112c90":"8054","53ed7db4":"8127",d59b5b70:"8183","474d53cd":"8261",reactPlayerYouTube:"8446","44dfcf75":"8598","172b3cfb":"8929",a94703ab:"9048",c329cc2b:"9304",reactPlayerWistia:"9340","54175a6c":"9455","23d0e682":"9461",f81a91eb:"9580",eae80ce0:"9642","5e95c892":"9647",reactPlayerSoundCloud:"9979"}[e]||e,b.p+b.u(e)},(()=>{var e={5354:0,1869:0};b.f.j=(a,r)=>{var c=b.o(e,a)?e[a]:void 0;if(0!==c)if(c)r.push(c[2]);else if(/^(1869|5354)$/.test(a))e[a]=0;else{var t=new Promise(((r,t)=>c=e[a]=[r,t]));r.push(c[2]=t);var d=b.p+b.u(a),f=new Error;b.l(d,(r=>{if(b.o(e,a)&&(0!==(c=e[a])&&(e[a]=void 0),c)){var t=r&&("load"===r.type?"missing":r.type),d=r&&r.target&&r.target.src;f.message="Loading chunk "+a+" failed.\n("+t+": "+d+")",f.name="ChunkLoadError",f.type=t,f.request=d,c[1](f)}}),"chunk-"+a,a)}},b.O.j=a=>0===e[a];var a=(a,r)=>{var c,t,d=r[0],f=r[1],o=r[2],l=0;if(d.some((a=>0!==e[a]))){for(c in f)b.o(f,c)&&(b.m[c]=f[c]);if(o)var n=o(b)}for(a&&a(r);l - + diff --git a/components-data.html b/components-data.html index f70d6fc2a9..703783bdaa 100644 --- a/components-data.html +++ b/components-data.html @@ -7,7 +7,7 @@ - + diff --git a/components-embedding-models.html b/components-embedding-models.html index 89f01a5030..b15f2f3a5b 100644 --- a/components-embedding-models.html +++ b/components-embedding-models.html @@ -7,7 +7,7 @@ - + diff --git a/components-helpers.html b/components-helpers.html index 32c598adf2..62c6ad49cf 100644 --- a/components-helpers.html +++ b/components-helpers.html @@ -7,7 +7,7 @@ - + diff --git a/components-io.html b/components-io.html index 58caf020c2..2f60f58e39 100644 --- a/components-io.html +++ b/components-io.html @@ -7,7 +7,7 @@ - + diff --git a/components-models.html b/components-models.html index 9638d147de..e5d44c3a24 100644 --- a/components-models.html +++ b/components-models.html @@ -7,7 +7,7 @@ - + diff --git a/components-prompts.html b/components-prompts.html index e4fa529593..fa3acfee47 100644 --- a/components-prompts.html +++ b/components-prompts.html @@ -7,7 +7,7 @@ - + diff --git a/components-rag.html b/components-rag.html index 119575ec58..8312c2e869 100644 --- a/components-rag.html +++ b/components-rag.html @@ -7,7 +7,7 @@ - + diff --git a/components-vector-stores.html b/components-vector-stores.html index 82a45fe3eb..c89a681cf5 100644 --- a/components-vector-stores.html +++ b/components-vector-stores.html @@ -7,7 +7,7 @@ - + @@ -303,6 +303,21 @@
  • Table Name: Optional table name.

  • +

    Upstash Vector

    +

    UpstashVector searches a Upstash Vector Store for documents similar to the input. It has it's own embedding +model which can be used to search documents without needing an external embedding model.

    +

    Parameters:

    +
      +
    • Index URL: The URL of the Upstash index.
    • +
    • Index Token: The token for the Upstash index.
    • +
    • Text Key: The key in the record to use as text.
    • +
    • Namespace: The namespace name. A new namespace is created if not found. Leave empty for default namespace.
    • +
    • Search Query: The search query.
    • +
    • Metadata Filter: The metadata filter. Filters documents by metadata. Look at the docs for more information.
    • +
    • Embedding: The embedding model used. To use Upstash's embeddings, don't provide an embedding.
    • +
    • Number of Results: The number of results to return.
    • +
    +

    Vectara

    Vectara sets up a Vectara Vector Store from files or upserted data, optimizing document retrieval.

    Parameters:

    @@ -358,6 +373,6 @@
  • Text Key: Default text extraction key.
  • Embedding: Model used.
  • Attributes: Optional additional attributes.
  • -

    Hi, how can I help you?

    +

    Hi, how can I help you?

    \ No newline at end of file diff --git a/components.html b/components.html index 8ce8f71fe2..ec762c53e8 100644 --- a/components.html +++ b/components.html @@ -7,7 +7,7 @@ - + diff --git a/configuration-api-keys.html b/configuration-api-keys.html index f63abcc7b0..3928c5db62 100644 --- a/configuration-api-keys.html +++ b/configuration-api-keys.html @@ -7,7 +7,7 @@ - + diff --git a/configuration-authentication.html b/configuration-authentication.html index ecebbf2369..5ce77eb520 100644 --- a/configuration-authentication.html +++ b/configuration-authentication.html @@ -7,7 +7,7 @@ - + diff --git a/configuration-backend-only.html b/configuration-backend-only.html index 6f8f56616f..33d4eca3c3 100644 --- a/configuration-backend-only.html +++ b/configuration-backend-only.html @@ -7,7 +7,7 @@ - + diff --git a/configuration-cli.html b/configuration-cli.html index 76329b414f..15b9a65aa7 100644 --- a/configuration-cli.html +++ b/configuration-cli.html @@ -7,7 +7,7 @@ - + diff --git a/contributing-community.html b/contributing-community.html index 6d0bd33a99..04120e4138 100644 --- a/contributing-community.html +++ b/contributing-community.html @@ -7,7 +7,7 @@ - + diff --git a/contributing-github-issues.html b/contributing-github-issues.html index b3a2e23630..0965ba8af5 100644 --- a/contributing-github-issues.html +++ b/contributing-github-issues.html @@ -7,7 +7,7 @@ - + diff --git a/contributing-how-to-contribute.html b/contributing-how-to-contribute.html index 9dc8e31848..2c2950e8eb 100644 --- a/contributing-how-to-contribute.html +++ b/contributing-how-to-contribute.html @@ -7,7 +7,7 @@ - + diff --git a/contributing-telemetry.html b/contributing-telemetry.html index 099d84affa..317e331de3 100644 --- a/contributing-telemetry.html +++ b/contributing-telemetry.html @@ -7,7 +7,7 @@ - + diff --git a/deployment-docker.html b/deployment-docker.html index 3a8065659c..47f0f9feb0 100644 --- a/deployment-docker.html +++ b/deployment-docker.html @@ -7,7 +7,7 @@ - + diff --git a/deployment-gcp.html b/deployment-gcp.html index 53da87e679..94d7461000 100644 --- a/deployment-gcp.html +++ b/deployment-gcp.html @@ -7,7 +7,7 @@ - + diff --git a/deployment-hugging-face-spaces.html b/deployment-hugging-face-spaces.html index b5d41b812d..f7e9052b8f 100644 --- a/deployment-hugging-face-spaces.html +++ b/deployment-hugging-face-spaces.html @@ -7,7 +7,7 @@ - + diff --git a/deployment-kubernetes.html b/deployment-kubernetes.html index fc29947580..eb794af04a 100644 --- a/deployment-kubernetes.html +++ b/deployment-kubernetes.html @@ -7,7 +7,7 @@ - + diff --git a/deployment-railway.html b/deployment-railway.html index f3281fa211..371add357f 100644 --- a/deployment-railway.html +++ b/deployment-railway.html @@ -7,7 +7,7 @@ - + diff --git a/deployment-render.html b/deployment-render.html index 98e372b3dd..1b6013a7c5 100644 --- a/deployment-render.html +++ b/deployment-render.html @@ -7,7 +7,7 @@ - + diff --git a/getting-started-common-installation-issues.html b/getting-started-common-installation-issues.html index 9c50984ed5..0f782a9ef4 100644 --- a/getting-started-common-installation-issues.html +++ b/getting-started-common-installation-issues.html @@ -7,7 +7,7 @@ - + diff --git a/getting-started-installation.html b/getting-started-installation.html index e9f002a836..d5fcd8b8db 100644 --- a/getting-started-installation.html +++ b/getting-started-installation.html @@ -7,7 +7,7 @@ - + diff --git a/getting-started-quickstart.html b/getting-started-quickstart.html index dd0e28adfd..087b2b38e0 100644 --- a/getting-started-quickstart.html +++ b/getting-started-quickstart.html @@ -7,7 +7,7 @@ - + diff --git a/guides-chat-memory.html b/guides-chat-memory.html index d68980cc3c..201a5ad0a5 100644 --- a/guides-chat-memory.html +++ b/guides-chat-memory.html @@ -7,7 +7,7 @@ - + diff --git a/guides-data-message.html b/guides-data-message.html index aa06532919..a7c83bf21f 100644 --- a/guides-data-message.html +++ b/guides-data-message.html @@ -7,7 +7,7 @@ - + diff --git a/guides-new-to-llms.html b/guides-new-to-llms.html index 0fdbe37a43..54d4e6c733 100644 --- a/guides-new-to-llms.html +++ b/guides-new-to-llms.html @@ -7,7 +7,7 @@ - + diff --git a/index.html b/index.html index db56656804..7cf1ca28f0 100644 --- a/index.html +++ b/index.html @@ -7,7 +7,7 @@ - + diff --git a/integrations-langsmith.html b/integrations-langsmith.html index 1b76570f82..e5f572327b 100644 --- a/integrations-langsmith.html +++ b/integrations-langsmith.html @@ -7,7 +7,7 @@ - + diff --git a/integrations-langwatch.html b/integrations-langwatch.html index a46bfbefbf..6bde87c79c 100644 --- a/integrations-langwatch.html +++ b/integrations-langwatch.html @@ -7,7 +7,7 @@ - + diff --git a/settings-global-variables.html b/settings-global-variables.html index 0986cfa728..5411397c96 100644 --- a/settings-global-variables.html +++ b/settings-global-variables.html @@ -7,7 +7,7 @@ - + diff --git a/settings-project-general-settings.html b/settings-project-general-settings.html index 7089326381..efe5c6c681 100644 --- a/settings-project-general-settings.html +++ b/settings-project-general-settings.html @@ -7,7 +7,7 @@ - + diff --git a/starter-projects-basic-prompting.html b/starter-projects-basic-prompting.html index 88cef0f76a..d02ec042b9 100644 --- a/starter-projects-basic-prompting.html +++ b/starter-projects-basic-prompting.html @@ -7,7 +7,7 @@ - + diff --git a/starter-projects-blog-writer.html b/starter-projects-blog-writer.html index 847da0d93d..d958cc6595 100644 --- a/starter-projects-blog-writer.html +++ b/starter-projects-blog-writer.html @@ -7,7 +7,7 @@ - + diff --git a/starter-projects-document-qa.html b/starter-projects-document-qa.html index cb90736c1f..ab69036d5e 100644 --- a/starter-projects-document-qa.html +++ b/starter-projects-document-qa.html @@ -7,7 +7,7 @@ - + diff --git a/starter-projects-memory-chatbot.html b/starter-projects-memory-chatbot.html index 6914a049c7..429e650d07 100644 --- a/starter-projects-memory-chatbot.html +++ b/starter-projects-memory-chatbot.html @@ -7,7 +7,7 @@ - + diff --git a/starter-projects-vector-store-rag.html b/starter-projects-vector-store-rag.html index 234c046f72..ddf64839cd 100644 --- a/starter-projects-vector-store-rag.html +++ b/starter-projects-vector-store-rag.html @@ -7,7 +7,7 @@ - + diff --git a/whats-new-a-new-chapter-langflow.html b/whats-new-a-new-chapter-langflow.html index bfc3f2cee8..b6a7e2dc01 100644 --- a/whats-new-a-new-chapter-langflow.html +++ b/whats-new-a-new-chapter-langflow.html @@ -7,7 +7,7 @@ - + diff --git a/workspace-api.html b/workspace-api.html index 43cf0064c8..5a8726bd69 100644 --- a/workspace-api.html +++ b/workspace-api.html @@ -7,7 +7,7 @@ - + diff --git a/workspace-logs.html b/workspace-logs.html index d34bad2e8c..1714b2c650 100644 --- a/workspace-logs.html +++ b/workspace-logs.html @@ -7,7 +7,7 @@ - + diff --git a/workspace-playground.html b/workspace-playground.html index 4c74a93eb7..bd5c7e68bc 100644 --- a/workspace-playground.html +++ b/workspace-playground.html @@ -7,7 +7,7 @@ - + diff --git a/workspace.html b/workspace.html index 7da2d00e30..dba894a878 100644 --- a/workspace.html +++ b/workspace.html @@ -7,7 +7,7 @@ - +