id stringlengths 14 17 | text stringlengths 42 2.11k |
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4e9727215e95-1900 | childK: 20,});const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();// We must add the parent documents via the retriever's addDocuments methodawait retriever.addDocuments(parentDocuments);const retrievedDocs = await retriever.getRelevantDocuments("justi... |
4e9727215e95-1901 | ', metadata: { source: '../examples/state_of_the_union.txt', loc: [Object] } }, Document { pageContent: 'Justice Breyer, thank you for your service. Thank you, thank you, thank you. I mean it. Get up. Stand — let me see you. Thank you.\n' + '\n' + 'And we all know — no matter what your ide... |
4e9727215e95-1902 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverIntegrationsExperimentalCaching... |
4e9727215e95-1903 | This can either be the whole raw document OR a larger chunk.Usageimport { OpenAIEmbeddings } from "langchain/embeddings/openai";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { InMemoryDocstore } from "langchain/stores/doc/in_memory";import { ParentDocumentRetriever } from "langchain/retrieve... |
4e9727215e95-1904 | childK: 20,});const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();// We must add the parent documents via the retriever's addDocuments methodawait retriever.addDocuments(parentDocuments);const retrievedDocs = await retriever.getRelevantDocuments("justi... |
4e9727215e95-1905 | ', metadata: { source: '../examples/state_of_the_union.txt', loc: [Object] } }, Document { pageContent: 'Justice Breyer, thank you for your service. Thank you, thank you, thank you. I mean it. Get up. Stand — let me see you. Thank you.\n' + '\n' + 'And we all know — no matter what your ide... |
4e9727215e95-1906 | This can either be the whole raw document OR a larger chunk.Usageimport { OpenAIEmbeddings } from "langchain/embeddings/openai";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { InMemoryDocstore } from "langchain/stores/doc/in_memory";import { ParentDocumentRetriever } from "langchain/retrieve... |
4e9727215e95-1907 | childK: 20,});const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();// We must add the parent documents via the retriever's addDocuments methodawait retriever.addDocuments(parentDocuments);const retrievedDocs = await retriever.getRelevantDocuments("justi... |
4e9727215e95-1908 | ', metadata: { source: '../examples/state_of_the_union.txt', loc: [Object] } }, Document { pageContent: 'Justice Breyer, thank you for your service. Thank you, thank you, thank you. I mean it. Get up. Stand — let me see you. Thank you.\n' + '\n' + 'And we all know — no matter what your ide... |
4e9727215e95-1909 | This can either be the whole raw document OR a larger chunk.Usageimport { OpenAIEmbeddings } from "langchain/embeddings/openai";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { InMemoryDocstore } from "langchain/stores/doc/in_memory";import { ParentDocumentRetriever } from "langchain/retrieve... |
4e9727215e95-1910 | childK: 20,});const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();// We must add the parent documents via the retriever's addDocuments methodawait retriever.addDocuments(parentDocuments);const retrievedDocs = await retriever.getRelevantDocuments("justi... |
4e9727215e95-1911 | ', metadata: { source: '../examples/state_of_the_union.txt', loc: [Object] } }, Document { pageContent: 'Justice Breyer, thank you for your service. Thank you, thank you, thank you. I mean it. Get up. Stand — let me see you. Thank you.\n' + '\n' + 'And we all know — no matter what your ide... |
4e9727215e95-1912 | import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { InMemoryDocstore } from "langchain/stores/doc/in_memory";import { ParentDocumentRetriever } from "langchain/retrievers/parent_document";import { RecursiveCharacterTextSplitter } from... |
4e9727215e95-1913 | childK: 20,});const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();// We must add the parent documents via the retriever's addDocuments methodawait retriever.addDocuments(parentDocuments);const retrievedDocs = await retriever.getRelevantDocuments("justi... |
4e9727215e95-1914 | ', metadata: { source: '../examples/state_of_the_union.txt', loc: [Object] } }, Document { pageContent: 'Justice Breyer, thank you for your service. Thank you, thank you, thank you. I mean it. Get up. Stand — let me see you. Thank you.\n' + '\n' + 'And we all know — no matter what your ide... |
4e9727215e95-1915 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma ... |
4e9727215e95-1916 | This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters.All Self Query retrievers require peggy as a peer dependency:npmYarnp... |
4e9727215e95-1917 | /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... |
4e9727215e95-1918 | /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: ... |
4e9727215e95-1919 | / 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?"... |
4e9727215e95-1920 | you could initialize the above retriever as follows:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, structuredQueryTranslator: new FunctionalTranslator(), searchParams: { filter: (doc: Document) => doc.metadata && doc.metadata.genre === "animated... |
4e9727215e95-1921 | This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters.All Self Query retrievers require peggy as a peer dependency:npmYarnp... |
4e9727215e95-1922 | /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... |
4e9727215e95-1923 | /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: ... |
4e9727215e95-1924 | / 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?"... |
4e9727215e95-1925 | you could initialize the above retriever as follows:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, structuredQueryTranslator: new FunctionalTranslator(), searchParams: { filter: (doc: Document) => doc.metadata && doc.metadata.genre === "animated... |
4e9727215e95-1926 | ModulesData connectionRetrieversHow-toSelf-queryingSelf-queryingA self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured que... |
4e9727215e95-1927 | /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... |
4e9727215e95-1928 | /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: ... |
4e9727215e95-1929 | / 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?"... |
4e9727215e95-1930 | you could initialize the above retriever as follows:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, structuredQueryTranslator: new FunctionalTranslator(), searchParams: { filter: (doc: Document) => doc.metadata && doc.metadata.genre === "animated... |
4e9727215e95-1931 | Self-queryingA self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the ... |
4e9727215e95-1932 | /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... |
4e9727215e95-1933 | /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: ... |
4e9727215e95-1934 | / 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?"... |
4e9727215e95-1935 | you could initialize the above retriever as follows:const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, structuredQueryTranslator: new FunctionalTranslator(), searchParams: { filter: (doc: Document) => doc.metadata && doc.metadata.genre === "animated... |
4e9727215e95-1936 | Here's a basic example with an in-memory, unoptimized vector store:
import { MemoryVectorStore } from "langchain/vectorstores/memory";import { AttributeInfo } from "langchain/schema/query_constructor";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { ... |
4e9727215e95-1937 | /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... |
4e9727215e95-1938 | /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: ... |
4e9727215e95-1939 | / 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?"... |
4e9727215e95-1940 | you could initialize the above retriever as follows:
const selfQueryRetriever = await SelfQueryRetriever.fromLLM({ llm, vectorStore, documentContents, attributeInfo, structuredQueryTranslator: new FunctionalTranslator(), searchParams: { filter: (doc: Document) => doc.metadata && doc.metadata.genre === "animat... |
4e9727215e95-1941 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma ... |
4e9727215e95-1942 | 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 ... |
4e9727215e95-1943 | 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... |
4e9727215e95-1944 | / structuredQueryTranslator: new ChromaTranslator(),});/** * 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?". * ... |
4e9727215e95-1945 | 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 * ... |
4e9727215e95-1946 | 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... |
4e9727215e95-1947 | /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... |
4e9727215e95-1948 | /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: ... |
4e9727215e95-1949 | / structuredQueryTranslator: new ChromaTranslator(),});/** * 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?". * ... |
4e9727215e95-1950 | 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 * ... |
4e9727215e95-1951 | /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... |
4e9727215e95-1952 | /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: ... |
4e9727215e95-1953 | / structuredQueryTranslator: new ChromaTranslator(),});/** * 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?". * ... |
4e9727215e95-1954 | 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 * ... |
4e9727215e95-1955 | /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... |
4e9727215e95-1956 | /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: ... |
4e9727215e95-1957 | / structuredQueryTranslator: new ChromaTranslator(),});/** * 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?". * ... |
4e9727215e95-1958 | 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 * ... |
4e9727215e95-1959 | /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... |
4e9727215e95-1960 | /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: ... |
4e9727215e95-1961 | / structuredQueryTranslator: new ChromaTranslator(),});/** * 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?". * ... |
4e9727215e95-1962 | 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 * ... |
4e9727215e95-1963 | /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... |
4e9727215e95-1964 | /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: ... |
4e9727215e95-1965 | / structuredQueryTranslator: new ChromaTranslator(),});/** * 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?". * ... |
4e9727215e95-1966 | 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 ... |
4e9727215e95-1967 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma ... |
4e9727215e95-1968 | 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 ... |
4e9727215e95-1969 | 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... |
4e9727215e95-1970 | / 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?"... |
4e9727215e95-1971 | 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... |
4e9727215e95-1972 | 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... |
4e9727215e95-1973 | /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... |
4e9727215e95-1974 | /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: ... |
4e9727215e95-1975 | 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 ... |
4e9727215e95-1976 | 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... |
4e9727215e95-1977 | /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... |
4e9727215e95-1978 | /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: ... |
4e9727215e95-1979 | 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 ... |
4e9727215e95-1980 | 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... |
4e9727215e95-1981 | /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... |
4e9727215e95-1982 | /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: ... |
4e9727215e95-1983 | 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 ... |
4e9727215e95-1984 | 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... |
4e9727215e95-1985 | /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... |
4e9727215e95-1986 | /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: ... |
4e9727215e95-1987 | 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 ... |
4e9727215e95-1988 | 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... |
4e9727215e95-1989 | /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... |
4e9727215e95-1990 | /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: ... |
4e9727215e95-1991 | 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 ... |
4e9727215e95-1992 | 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 * translator here, but you can create your ... |
4e9727215e95-1993 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesRetrieversHow-toContextual compressionParent Document RetrieverSelf-queryingChroma ... |
4e9727215e95-1994 | 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... |
4e9727215e95-1995 | 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 ... |
4e9727215e95-1996 | Note that the vector store needs to support filtering on the metadata * attributes you want to query on. */ 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... |
4e9727215e95-1997 | 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... |
4e9727215e95-1998 | 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... |
4e9727215e95-1999 | /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... |
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