id stringlengths 14 17 | text stringlengths 42 2.11k |
|---|---|
4e9727215e95-2000 | /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: ... |
4e9727215e95-2001 | / structuredQueryTranslator: new FunctionalTranslator(),});/** * Now we can query the vector store. * We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?"... |
4e9727215e95-2002 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to use a translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * translato... |
4e9727215e95-2003 | /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... |
4e9727215e95-2004 | /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: ... |
4e9727215e95-2005 | / structuredQueryTranslator: new FunctionalTranslator(),});/** * Now we can query the vector store. * We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?"... |
4e9727215e95-2006 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to use a translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * translato... |
4e9727215e95-2007 | /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... |
4e9727215e95-2008 | /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: ... |
4e9727215e95-2009 | / structuredQueryTranslator: new FunctionalTranslator(),});/** * Now we can query the vector store. * We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?"... |
4e9727215e95-2010 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to use a translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * translato... |
4e9727215e95-2011 | /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... |
4e9727215e95-2012 | /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: ... |
4e9727215e95-2013 | / structuredQueryTranslator: new FunctionalTranslator(),});/** * Now we can query the vector store. * We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?"... |
4e9727215e95-2014 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to use a translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * translato... |
4e9727215e95-2015 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma ... |
4e9727215e95-2016 | Each document has a pageContent and a metadata field. Make sure your metadata matches the AttributeInfo below. */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 Documen... |
4e9727215e95-2017 | We also provide a description of each attribute and the type of the attribute. * This is used to generate the query prompts. */const attributeInfo: AttributeInfo[] = [ { name: "genre", description: "The genre of the movie", type: "string or array of strings", }, { name: "year", description: "The year ... |
4e9727215e95-2018 | /if ( !process.env.PINECONE_API_KEY || !process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_INDEX) { throw new Error( "PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX must be set" );}const client = new PineconeClient();await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: p... |
4e9727215e95-2019 | The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.getRelevantDocuments( "Which movies are less than 90 minutes? ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are rated higher than 8.5... |
4e9727215e95-2020 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2021 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma Self Query RetrieverHNSWLib Self Query RetrieverMemory Vector Store Self Query RetrieverP... |
4e9727215e95-2022 | Make sure your metadata matches the AttributeInfo below. */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 ... |
4e9727215e95-2023 | This is used to generate the query prompts. */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", descri... |
4e9727215e95-2024 | /if ( !process.env.PINECONE_API_KEY || !process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_INDEX) { throw new Error( "PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX must be set" );}const client = new PineconeClient();await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: p... |
4e9727215e95-2025 | The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.getRelevantDocuments( "Which movies are less than 90 minutes? ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are rated higher than 8.5... |
4e9727215e95-2026 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2027 | ModulesData connectionRetrieversHow-toSelf-queryingPinecone Self Query RetrieverOn this pagePinecone Self Query RetrieverThis example shows how to use a self query retriever with a Pinecone vector store.Usageimport { PineconeClient } from "@pinecone-database/pinecone";import { AttributeInfo } from "langchain/schema/qu... |
4e9727215e95-2028 | /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... |
4e9727215e95-2029 | /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: ... |
4e9727215e95-2030 | /if ( !process.env.PINECONE_API_KEY || !process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_INDEX) { throw new Error( "PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX must be set" );}const client = new PineconeClient();await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: p... |
4e9727215e95-2031 | The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.getRelevantDocuments( "Which movies are less than 90 minutes? ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are rated higher than 8.5... |
4e9727215e95-2032 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2033 | ModulesData connectionRetrieversHow-toSelf-queryingPinecone Self Query RetrieverOn this pagePinecone Self Query RetrieverThis example shows how to use a self query retriever with a Pinecone vector store.Usageimport { PineconeClient } from "@pinecone-database/pinecone";import { AttributeInfo } from "langchain/schema/qu... |
4e9727215e95-2034 | /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... |
4e9727215e95-2035 | /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: ... |
4e9727215e95-2036 | /if ( !process.env.PINECONE_API_KEY || !process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_INDEX) { throw new Error( "PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX must be set" );}const client = new PineconeClient();await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: p... |
4e9727215e95-2037 | The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.getRelevantDocuments( "Which movies are less than 90 minutes? ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are rated higher than 8.5... |
4e9727215e95-2038 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2039 | /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... |
4e9727215e95-2040 | /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: ... |
4e9727215e95-2041 | /if ( !process.env.PINECONE_API_KEY || !process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_INDEX) { throw new Error( "PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX must be set" );}const client = new PineconeClient();await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: p... |
4e9727215e95-2042 | The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.getRelevantDocuments( "Which movies are less than 90 minutes? ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are rated higher than 8.5... |
4e9727215e95-2043 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2044 | /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... |
4e9727215e95-2045 | /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: ... |
4e9727215e95-2046 | /if ( !process.env.PINECONE_API_KEY || !process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_INDEX) { throw new Error( "PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX must be set" );}const client = new PineconeClient();await client.init({ apiKey: process.env.PINECONE_API_KEY, environment: p... |
4e9727215e95-2047 | The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.getRelevantDocuments( "Which movies are less than 90 minutes? ");const query2 = await selfQueryRetriever.getRelevantDocuments( "Which movies are rated higher than 8.5... |
4e9727215e95-2048 | See the official docs for more on how to construct metadata filters.
Supabase Self Query Retriever
Page Title: Supabase Self Query Retriever | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData co... |
4e9727215e95-2049 | RetrieverOn this pageSupabase Self Query RetrieverThis example shows how to use a self query retriever with a Supabase vector store.If you haven't already set up Supabase, please follow the instructions here.Usageimport { createClient } from "@supabase/supabase-js";import { AttributeInfo } from "langchain/schema/query... |
4e9727215e95-2050 | /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... |
4e9727215e95-2051 | /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: ... |
4e9727215e95-2052 | We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". * The retriever will automatically convert these questions into queries that can be used to retrieve ... |
4e9727215e95-2053 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2054 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma Self Query RetrieverHNSWLib Self Query RetrieverMemory Vector Store Self Query RetrieverP... |
4e9727215e95-2055 | Each document has a pageContent and a metadata field. Make sure your metadata matches the AttributeInfo below. */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 Documen... |
4e9727215e95-2056 | We also provide a description of each attribute and the type of the attribute. * This is used to generate the query prompts. */const attributeInfo: AttributeInfo[] = [ { name: "genre", description: "The genre of the movie", type: "string or array of strings", }, { name: "year", description: "The year ... |
4e9727215e95-2057 | / structuredQueryTranslator: new SupabaseTranslator(),});/** * Now we can query the vector store. * We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". ... |
4e9727215e95-2058 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2059 | ModulesData connectionRetrieversHow-toSelf-queryingSupabase Self Query RetrieverOn this pageSupabase Self Query RetrieverThis example shows how to use a self query retriever with a Supabase vector store.If you haven't already set up Supabase, please follow the instructions here.Usageimport { createClient } from "@supa... |
4e9727215e95-2060 | /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... |
4e9727215e95-2061 | /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: ... |
4e9727215e95-2062 | We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". * The retriever will automatically convert these questions into queries that can be used to retrieve ... |
4e9727215e95-2063 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2064 | ModulesData connectionRetrieversHow-toSelf-queryingSupabase Self Query RetrieverOn this pageSupabase Self Query RetrieverThis example shows how to use a self query retriever with a Supabase vector store.If you haven't already set up Supabase, please follow the instructions here.Usageimport { createClient } from "@supa... |
4e9727215e95-2065 | /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... |
4e9727215e95-2066 | /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: ... |
4e9727215e95-2067 | We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". * The retriever will automatically convert these questions into queries that can be used to retrieve ... |
4e9727215e95-2068 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2069 | Supabase Self Query RetrieverThis example shows how to use a self query retriever with a Supabase vector store.If you haven't already set up Supabase, please follow the instructions here.Usageimport { createClient } from "@supabase/supabase-js";import { AttributeInfo } from "langchain/schema/query_constructor";import ... |
4e9727215e95-2070 | /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... |
4e9727215e95-2071 | /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: ... |
4e9727215e95-2072 | We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". * The retriever will automatically convert these questions into queries that can be used to retrieve ... |
4e9727215e95-2073 | addition to the generated query:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * ... |
4e9727215e95-2074 | /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... |
4e9727215e95-2075 | /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: ... |
4e9727215e95-2076 | We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". * The retriever will automatically convert these questions into queries that can be used to retrieve ... |
4e9727215e95-2077 | const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, /** * We need to create a basic translator that translates the queries into a * filter format that the vector store can understand. We provide a basic translator * translator here, but you can cre... |
4e9727215e95-2078 | Self Query RetrieverThis example shows how to use a self query retriever with a Weaviate vector store.If you haven't already set up Weaviate, please follow the instructions here.UsageThis example shows how to intialize a SelfQueryRetriever with a vector store:import weaviate from "weaviate-ts-client";import { Attribut... |
4e9727215e95-2079 | /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... |
4e9727215e95-2080 | /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: ... |
4e9727215e95-2081 | // 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"]... |
4e9727215e95-2082 | ");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... |
4e9727215e95-2083 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma Self Query RetrieverHNSWLib Self Query RetrieverMemory Vector Store Self Query RetrieverP... |
4e9727215e95-2084 | You can load your own documents here instead. * Each document has a pageContent and a metadata field. Make sure your metadata matches the AttributeInfo below. */const docs = [ new Document({ pageContent: "A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata: { year: 1993, rating: 7.... |
4e9727215e95-2085 | in this case, we want to be able to query on the genre, year, director, rating, and length of the movie. * We also provide a description of each attribute and the type of the attribute. * This is used to generate the query prompts. */const attributeInfo: AttributeInfo[] = [ { name: "genre", description: "The gen... |
4e9727215e95-2086 | // 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"]... |
4e9727215e95-2087 | ");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... |
4e9727215e95-2088 | ModulesData connectionRetrieversHow-toSelf-queryingWeaviate Self Query RetrieverOn this pageWeaviate Self Query RetrieverThis example shows how to use a self query retriever with a Weaviate vector store.If you haven't already set up Weaviate, please follow the instructions here.UsageThis example shows how to intialize... |
4e9727215e95-2089 | /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... |
4e9727215e95-2090 | /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: ... |
4e9727215e95-2091 | // 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"]... |
4e9727215e95-2092 | ");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... |
4e9727215e95-2093 | ModulesData connectionRetrieversHow-toSelf-queryingWeaviate Self Query RetrieverOn this pageWeaviate Self Query RetrieverThis example shows how to use a self query retriever with a Weaviate vector store.If you haven't already set up Weaviate, please follow the instructions here.UsageThis example shows how to intialize... |
4e9727215e95-2094 | /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... |
4e9727215e95-2095 | /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: ... |
4e9727215e95-2096 | // 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"]... |
4e9727215e95-2097 | ");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... |
4e9727215e95-2098 | Weaviate Self Query RetrieverThis example shows how to use a self query retriever with a Weaviate vector store.If you haven't already set up Weaviate, please follow the instructions here.UsageThis example shows how to intialize a SelfQueryRetriever with a vector store:import weaviate from "weaviate-ts-client";import {... |
4e9727215e95-2099 | /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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.