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4e9727215e95-2100 | /const attributeInfo: AttributeInfo[] = [ { name: "genre", description: "The genre of the movie", type: "string or array of strings", }, { name: "year", description: "The year the movie was released", type: "number", }, { name: "director", description: "The director of the movie", type: ... |
4e9727215e95-2101 | // eslint-disable-next-line @typescript-eslint/no-explicit-any new (weaviate as any).ApiKey(process.env.WEAVIATE_API_KEY) : undefined,});const vectorStore = await WeaviateStore.fromDocuments(docs, embeddings, { client, indexName: "Test", textKey: "text", metadataKeys: ["year", "director", "rating", "genre"]... |
4e9727215e95-2102 | ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are directed by Greta Gerwig? ");console.log(query1, query2);API Reference:AttributeInfo from langchain/schema/query_constructorDocument from langchain/documentOpenAIEmbeddings from langchain/embeddings/openaiSelfQueryRetriever from langcha... |
4e9727215e95-2103 | This example shows how to intialize a SelfQueryRetriever with a vector store:
import weaviate from "weaviate-ts-client";import { AttributeInfo } from "langchain/schema/query_constructor";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { SelfQueryRetri... |
4e9727215e95-2104 | /const docs = [ new Document({ pageContent: "A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata: { year: 1993, rating: 7.7, genre: "science fiction" }, }), new Document({ pageContent: "Leo DiCaprio gets lost in a dream within a dream within a dream within a ...", meta... |
4e9727215e95-2105 | /const attributeInfo: AttributeInfo[] = [ { name: "genre", description: "The genre of the movie", type: "string or array of strings", }, { name: "year", description: "The year the movie was released", type: "number", }, { name: "director", description: "The director of the movie", type: ... |
4e9727215e95-2106 | // eslint-disable-next-line @typescript-eslint/no-explicit-any new (weaviate as any).ApiKey(process.env.WEAVIATE_API_KEY) : undefined,});const vectorStore = await WeaviateStore.fromDocuments(docs, embeddings, { client, indexName: "Test", textKey: "text", metadataKeys: ["year", "director", "rating", "genre"]... |
4e9727215e95-2107 | API Reference:AttributeInfo from langchain/schema/query_constructorDocument from langchain/documentOpenAIEmbeddings from langchain/embeddings/openaiSelfQueryRetriever from langchain/retrievers/self_queryOpenAI from langchain/llms/openaiWeaviateStore from langchain/vectorstores/weaviateWeaviateTranslator from langchain/... |
4e9727215e95-2108 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingTime-we... |
4e9727215e95-2109 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2110 | ', metadata: {} }] */API Reference:TimeWeightedVectorStoreRetriever from langchain/retrievers/time_weightedMemoryVectorStore from langchain/vectorstores/memoryOpenAIEmbeddings from langchain/embeddings/openaiPreviousWeaviate Self Query RetrieverNextVector store-backed retrieverCommunityDiscordTwitterGitHubPythonJS/TSMo... |
4e9727215e95-2111 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2112 | ModulesData connectionRetrieversHow-toTime-weighted vector store retrieverTime-weighted vector store retrieverThis retriever uses a combination of semantic similarity and a time decay.The algorithm for scoring them is:semantic_similarity + (1.0 - decay_rate) ^ hours_passedNotably, hours_passed refers to the hours passe... |
4e9727215e95-2113 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2114 | Time-weighted vector store retrieverThis retriever uses a combination of semantic similarity and a time decay.The algorithm for scoring them is:semantic_similarity + (1.0 - decay_rate) ^ hours_passedNotably, hours_passed refers to the hours passed since the object in the retriever was last accessed, not since it was cr... |
4e9727215e95-2115 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2116 | The algorithm for scoring them is:
semantic_similarity + (1.0 - decay_rate) ^ hours_passed
Notably, hours_passed refers to the hours passed since the object in the retriever was last accessed, not since it was created. This means that frequently accessed objects remain "fresh."
let score = (1.0 - this.decayRate) ** ... |
4e9727215e95-2117 | import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = new MemoryVectorStore(new OpenAIEmbeddings());const retriever = new TimeWeightedVecto... |
4e9727215e95-2118 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingTime-we... |
4e9727215e95-2119 | await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());// Initialize a retriever wrapper around the vector storeconst vectorStoreRetriever = vectorStore.asRetriever();const docs = retriever.getRelevantDocuments("what did he say about ketanji brown jackson");ConfigurationYou can specify a maximum number of documents... |
4e9727215e95-2120 | Let's walk through an example.const vectorStore = ...const retriever = vectorStore.asRetriever();Here's a more end-to-end example:import { OpenAI } from "langchain/llms/openai";import { RetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "... |
4e9727215e95-2121 | ModulesData connectionRetrieversHow-toVector store-backed retrieverVector store-backed retrieverA vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface.
It uses the search methods imp... |
4e9727215e95-2122 | Let's walk through an example.const vectorStore = ...const retriever = vectorStore.asRetriever();Here's a more end-to-end example:import { OpenAI } from "langchain/llms/openai";import { RetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "... |
4e9727215e95-2123 | Vector store-backed retrieverA vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface.
It uses the search methods implemented by a vector store, like similarity search and MMR, to quer... |
4e9727215e95-2124 | Let's walk through an example.const vectorStore = ...const retriever = vectorStore.asRetriever();Here's a more end-to-end example:import { OpenAI } from "langchain/llms/openai";import { RetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "... |
4e9727215e95-2125 | It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.
Once you construct a Vector store, it's very easy to construct a retriever. Let's walk through an example.
const vectorStore = ...const retriever = vectorStore.asRetriever();
Here's a mor... |
4e9727215e95-2126 | ChatGPT Plugin Retriever
Page Title: ChatGPT Plugin Retriever | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesR... |
4e9727215e95-2127 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2128 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2129 | ChatGPT Plugin RetrieverThis example shows how to use the ChatGPT Retriever Plugin within LangChain.To set up the ChatGPT Retriever Plugin, please follow instructions here.Usageimport { ChatGPTPluginRetriever } from "langchain/retrievers/remote";export const run = async () => { const retriever = new ChatGPTPluginRetr... |
4e9727215e95-2130 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2131 | This reduces the amount of distraction a subsequent chain has to deal with when parsing the retrieved documents and making its final judgements.UsageThis example shows how to intialize a ContextualCompressionRetriever with a vector store and a document compressor:import * as fs from "fs";import { OpenAI } from "langch... |
4e9727215e95-2132 | ",});console.log({ res });API Reference:OpenAI from langchain/llms/openaiRecursiveCharacterTextSplitter from langchain/text_splitterRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiContextualCompressionRetriever from langchain/retrievers/c... |
4e9727215e95-2133 | This reduces the amount of distraction a subsequent chain has to deal with when parsing the retrieved documents and making its final judgements.UsageThis example shows how to intialize a ContextualCompressionRetriever with a vector store and a document compressor:import * as fs from "fs";import { OpenAI } from "langch... |
4e9727215e95-2134 | ",});console.log({ res });API Reference:OpenAI from langchain/llms/openaiRecursiveCharacterTextSplitter from langchain/text_splitterRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiContextualCompressionRetriever from langchain/retrievers/c... |
4e9727215e95-2135 | This reduces the amount of distraction a subsequent chain has to deal with when parsing the retrieved documents and making its final judgements.UsageThis example shows how to intialize a ContextualCompressionRetriever with a vector store and a document compressor:import * as fs from "fs";import { OpenAI } from "langch... |
4e9727215e95-2136 | ",});console.log({ res });API Reference:OpenAI from langchain/llms/openaiRecursiveCharacterTextSplitter from langchain/text_splitterRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiContextualCompressionRetriever from langchain/retrievers/c... |
4e9727215e95-2137 | This reduces the amount of distraction a subsequent chain has to deal with when parsing the retrieved documents and making its final judgements.UsageThis example shows how to intialize a ContextualCompressionRetriever with a vector store and a document compressor:import * as fs from "fs";import { OpenAI } from "langch... |
4e9727215e95-2138 | ",});console.log({ res });API Reference:OpenAI from langchain/llms/openaiRecursiveCharacterTextSplitter from langchain/text_splitterRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiContextualCompressionRetriever from langchain/retrievers/c... |
4e9727215e95-2139 | This reduces the amount of distraction a subsequent chain has to deal with when parsing the retrieved documents and making its final judgements.UsageThis example shows how to intialize a ContextualCompressionRetriever with a vector store and a document compressor:import * as fs from "fs";import { OpenAI } from "langch... |
4e9727215e95-2140 | ",});console.log({ res });API Reference:OpenAI from langchain/llms/openaiRecursiveCharacterTextSplitter from langchain/text_splitterRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiContextualCompressionRetriever from langchain/retrievers/c... |
4e9727215e95-2141 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2142 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2143 | ModulesData connectionRetrieversIntegrationsDataberry RetrieverOn this pageDataberry RetrieverThis example shows how to use the Databerry Retriever in a RetrievalQAChain to retrieve documents from a Databerry.ai datastore.Usageimport { DataberryRetriever } from "langchain/retrievers/databerry";export const run = async... |
4e9727215e95-2144 | ModulesData connectionRetrieversIntegrationsDataberry RetrieverOn this pageDataberry RetrieverThis example shows how to use the Databerry Retriever in a RetrievalQAChain to retrieve documents from a Databerry.ai datastore.Usageimport { DataberryRetriever } from "langchain/retrievers/databerry";export const run = async... |
4e9727215e95-2145 | This example shows how to use the Databerry Retriever in a RetrievalQAChain to retrieve documents from a Databerry.ai datastore.
import { DataberryRetriever } from "langchain/retrievers/databerry";export const run = async () => { const retriever = new DataberryRetriever({ datastoreUrl: "https://api.databerry.ai/qu... |
4e9727215e95-2146 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2147 | By default, the HyDE class comes with some default prompts to use (see the paper for more details on them), but we can also create our own, which should have a single input variable {question}.Usageimport { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { Me... |
4e9727215e95-2148 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2149 | By default, the HyDE class comes with some default prompts to use (see the paper for more details on them), but we can also create our own, which should have a single input variable {question}.Usageimport { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { Me... |
4e9727215e95-2150 | ModulesData connectionRetrieversIntegrationsHyDE RetrieverHyDE RetrieverThis example shows how to use the HyDE Retriever, which implements Hypothetical Document Embeddings (HyDE) as described in this paper.At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then embe... |
4e9727215e95-2151 | ");console.log(results);/*[ Document { pageContent: 'My favourite food is pasta. ', metadata: {} }]*/API Reference:OpenAI from langchain/llms/openaiOpenAIEmbeddings from langchain/embeddings/openaiMemoryVectorStore from langchain/vectorstores/memoryHydeRetriever from langchain/retrievers/hydeDocument from langchain/do... |
4e9727215e95-2152 | HyDE RetrieverThis example shows how to use the HyDE Retriever, which implements Hypothetical Document Embeddings (HyDE) as described in this paper.At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then embeds that generated document and uses that as the final exam... |
4e9727215e95-2153 | ");console.log(results);/*[ Document { pageContent: 'My favourite food is pasta. ', metadata: {} }]*/API Reference:OpenAI from langchain/llms/openaiOpenAIEmbeddings from langchain/embeddings/openaiMemoryVectorStore from langchain/vectorstores/memoryHydeRetriever from langchain/retrievers/hydeDocument from langchain/do... |
4e9727215e95-2154 | import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { HydeRetriever } from "langchain/retrievers/hyde";import { Document } from "langchain/document";const embeddings = new OpenAIEmbeddings(... |
4e9727215e95-2155 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2156 | It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.SetupnpmYarnpnpmnpm i @aws-sdk/client-kendrayarn add @aws-sdk/client-kendrapnpm add @aws-sdk/client-kendraUsageimport { AmazonKendraRetriever } from "langchain/retrievers/amaz... |
4e9727215e95-2157 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2158 | It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.SetupnpmYarnpnpmnpm i @aws-sdk/client-kendrayarn add @aws-sdk/client-kendrapnpm add @aws-sdk/client-kendraUsageimport { AmazonKendraRetriever } from "langchain/retrievers/amaz... |
4e9727215e95-2159 | ModulesData connectionRetrieversIntegrationsAmazon Kendra RetrieverAmazon Kendra RetrieverAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various dat... |
4e9727215e95-2160 | Amazon Kendra RetrieverAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users ... |
4e9727215e95-2161 | With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.
npmYarnpnpmnpm i @aws-sdk/client-kendray... |
4e9727215e95-2162 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2163 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2164 | ModulesData connectionRetrieversIntegrationsMetal RetrieverMetal RetrieverThis example shows how to use the Metal Retriever in a RetrievalQAChain to retrieve documents from a Metal index.SetupnpmYarnpnpmnpm i @getmetal/metal-sdkyarn add @getmetal/metal-sdkpnpm add @getmetal/metal-sdkUsage/* eslint-disable @typescript... |
4e9727215e95-2165 | Metal RetrieverThis example shows how to use the Metal Retriever in a RetrievalQAChain to retrieve documents from a Metal index.SetupnpmYarnpnpmnpm i @getmetal/metal-sdkyarn add @getmetal/metal-sdkpnpm add @getmetal/metal-sdkUsage/* eslint-disable @typescript-eslint/no-non-null-assertion */import Metal from "@getmeta... |
4e9727215e95-2166 | yarn add @getmetal/metal-sdk
pnpm add @getmetal/metal-sdk
/* eslint-disable @typescript-eslint/no-non-null-assertion */import Metal from "@getmetal/metal-sdk";import { MetalRetriever } from "langchain/retrievers/metal";export const run = async () => { const MetalSDK = Metal; const client = new MetalSDK( process.... |
4e9727215e95-2167 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2168 | const chain = RetrievalQAChain.fromLLM(model, retriever); // Call the chain with a query. const res = await chain.call({ query: "What did the president say about Justice Breyer? ", }); console.log({ res }); /* { res: { text: 'The president said that Justice Breyer was an Army veteran, Constitutional sch... |
4e9727215e95-2169 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2170 | const res = await chain.call({ query: "What did the president say about Justice Breyer? ", }); console.log({ res }); /* { res: { text: 'The president said that Justice Breyer was an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court and thanked him for his ... |
4e9727215e95-2171 | ModulesData connectionRetrieversIntegrationsRemote RetrieverRemote RetrieverThis example shows how to use a Remote Retriever in a RetrievalQAChain to retrieve documents from a remote server.Usageimport { OpenAI } from "langchain/llms/openai";import { RetrievalQAChain } from "langchain/chains";import { RemoteLangChainR... |
4e9727215e95-2172 | Remote RetrieverThis example shows how to use a Remote Retriever in a RetrievalQAChain to retrieve documents from a remote server.Usageimport { OpenAI } from "langchain/llms/openai";import { RetrievalQAChain } from "langchain/chains";import { RemoteLangChainRetriever } from "langchain/retrievers/remote";export const r... |
4e9727215e95-2173 | import { OpenAI } from "langchain/llms/openai";import { RetrievalQAChain } from "langchain/chains";import { RemoteLangChainRetriever } from "langchain/retrievers/remote";export const run = async () => { // Initialize the LLM to use to answer the question. const model = new OpenAI({}); // Initialize the remote retriev... |
4e9727215e95-2174 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2175 | The getRelevantDocuments function produces a list of documents that has duplicates removed and is sorted by relevance score.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a table and search functions in your databaseRun t... |
4e9727215e95-2176 | where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;-- Create a function to keyword search for documentscreate function kw_match_documents(query_text text, match_count int)returns table (id bigint, content text, metadata jsonb, similarity real)as $$beginreturn query exec... |
4e9727215e95-2177 | similarityK: 2, keywordK: 2, tableName: "documents", similarityQueryName: "match_documents", keywordQueryName: "kw_match_documents", }); const results = await retriever.getRelevantDocuments("hello bye"); console.log(results);};API Reference:OpenAIEmbeddings from langchain/embeddings/openaiSupabaseHybridS... |
4e9727215e95-2178 | The getRelevantDocuments function produces a list of documents that has duplicates removed and is sorted by relevance score.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a table and search functions in your databaseRun t... |
4e9727215e95-2179 | where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;-- Create a function to keyword search for documentscreate function kw_match_documents(query_text text, match_count int)returns table (id bigint, content text, metadata jsonb, similarity real)as $$beginreturn query exec... |
4e9727215e95-2180 | similarityK: 2, keywordK: 2, tableName: "documents", similarityQueryName: "match_documents", keywordQueryName: "kw_match_documents", }); const results = await retriever.getRelevantDocuments("hello bye"); console.log(results);};API Reference:OpenAIEmbeddings from langchain/embeddings/openaiSupabaseHybridS... |
4e9727215e95-2181 | The getRelevantDocuments function produces a list of documents that has duplicates removed and is sorted by relevance score.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a table and search functions in your databaseRun t... |
4e9727215e95-2182 | where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;-- Create a function to keyword search for documentscreate function kw_match_documents(query_text text, match_count int)returns table (id bigint, content text, metadata jsonb, similarity real)as $$beginreturn query exec... |
4e9727215e95-2183 | similarityK: 2, keywordK: 2, tableName: "documents", similarityQueryName: "match_documents", keywordQueryName: "kw_match_documents", }); const results = await retriever.getRelevantDocuments("hello bye"); console.log(results);};API Reference:OpenAIEmbeddings from langchain/embeddings/openaiSupabaseHybridS... |
4e9727215e95-2184 | The getRelevantDocuments function produces a list of documents that has duplicates removed and is sorted by relevance score.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a table and search functions in your databaseRun t... |
4e9727215e95-2185 | where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;-- Create a function to keyword search for documentscreate function kw_match_documents(query_text text, match_count int)returns table (id bigint, content text, metadata jsonb, similarity real)as $$beginreturn query exec... |
4e9727215e95-2186 | Supabase Hybrid SearchLangchain supports hybrid search with a Supabase Postgres database. The hybrid search combines the postgres pgvector extension (similarity search) and Full-Text Search (keyword search) to retrieve documents. You can add documents via SupabaseVectorStore addDocuments function. SupabaseHybridKeyWord... |
4e9727215e95-2187 | where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;-- Create a function to keyword search for documentscreate function kw_match_documents(query_text text, match_count int)returns table (id bigint, content text, metadata jsonb, similarity real)as $$beginreturn query exec... |
4e9727215e95-2188 | Langchain supports hybrid search with a Supabase Postgres database. The hybrid search combines the postgres pgvector extension (similarity search) and Full-Text Search (keyword search) to retrieve documents. You can add documents via SupabaseVectorStore addDocuments function. SupabaseHybridKeyWordSearch accepts embeddi... |
4e9727215e95-2189 | -- Enable the pgvector extension to work with embedding vectorscreate extension vector;-- Create a table to store your documentscreate table documents ( id bigserial primary key, content text, -- corresponds to Document.pageContent metadata jsonb, -- corresponds to Document.metadata embedding vector(1536) -- 1536 w... |
4e9727215e95-2190 | import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";import { SupabaseHybridSearch } from "langchain/retrievers/supabase";export const run = async () => { const client = createClient( process.env.SUPABASE_URL || "", process.env.SUPABASE_PRIVATE_KEY |... |
4e9727215e95-2191 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression Retrieve... |
4e9727215e95-2192 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2193 | ', metadata: {} }] */API Reference:TimeWeightedVectorStoreRetriever from langchain/retrievers/time_weightedMemoryVectorStore from langchain/vectorstores/memoryOpenAIEmbeddings from langchain/embeddings/openaiPreviousSupabase Hybrid SearchNextVector StoreUsageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyr... |
4e9727215e95-2194 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toIntegrationsChatGPT Plugin RetrieverContextual Compression RetrieverDataberry RetrieverHyDE RetrieverAmazon Kendra RetrieverMetal RetrieverRemote RetrieverS... |
4e9727215e95-2195 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2196 | ModulesData connectionRetrieversIntegrationsTime-Weighted RetrieverOn this pageTime-Weighted RetrieverA Time-Weighted Retriever is a retriever that takes into account recency in addition to similarity. The scoring algorithm is:let score = (1.0 - this.decayRate) ** hoursPassed + vectorRelevance;Notably, hoursPassed abov... |
4e9727215e95-2197 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
4e9727215e95-2198 | ModulesData connectionRetrieversIntegrationsTime-Weighted RetrieverOn this pageTime-Weighted RetrieverA Time-Weighted Retriever is a retriever that takes into account recency in addition to similarity. The scoring algorithm is:let score = (1.0 - this.decayRate) ** hoursPassed + vectorRelevance;Notably, hoursPassed abov... |
4e9727215e95-2199 | It is important to note that due to required metadata, all documents must be added to the backing vector store using the addDocuments method on the retriever, not the vector store itself.import { TimeWeightedVectorStoreRetriever } from "langchain/retrievers/time_weighted";import { MemoryVectorStore } from "langchain/ve... |
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