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4e9727215e95-1300 | Refer to the Supabase blog post for more information.📄️ TigrisTigris makes it easy to build AI applications with vector embeddings.📄️ TypeORMTo enable vector search in a generic PostgreSQL database, LangChainJS supports using TypeORM with the pgvector Postgres extension.📄️ TypesenseVector store that utilizes the Typ... |
4e9727215e95-1301 | ModulesData connectionVector storesIntegrationsVector Stores: Integrations📄️ MemoryMemoryVectorStore is an in-memory, ephemeral vectorstore that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of... |
4e9727215e95-1302 | It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.📄️ RedisRedis is a fast open source, in-memory data store.📄️ SingleStoreSingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-p... |
4e9727215e95-1303 | LangChain connects to Weaviate via the weaviate-ts-client package, the official Typescript client for Weaviate.📄️ XataXata is a serverless data platform, based on PostgreSQL. It provides a type-safe TypeScript/JavaScript SDK for interacting with your database, and a UI for managing your data.📄️ ZepZep is an open sour... |
4e9727215e95-1304 | Vector Stores: Integrations📄️ MemoryMemoryVectorStore is an in-memory, ephemeral vectorstore that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distanc... |
4e9727215e95-1305 | It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.📄️ RedisRedis is a fast open source, in-memory data store.📄️ SingleStoreSingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-p... |
4e9727215e95-1306 | MemoryVectorStore is an in-memory, ephemeral vectorstore that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance.
AnalyticDB for PostgreSQL is a ma... |
4e9727215e95-1307 | Vector store that utilizes the Typesense search engine.
Vectara is a platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by Vectara and is optimized for performance and accuracy.
Weaviate is an open source vector database that stores both objects... |
4e9727215e95-1308 | The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance.UsageCreate a new index from textsimport { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await ... |
4e9727215e95-1309 | MemoryVectorStore.fromDocuments( docs, new OpenAIEmbeddings());// Search for the most similar documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);/* [ Document { pageContent: "Hello world", metadata: { id: 2 } } ]*/API Reference:MemoryVectorStore fro... |
4e9727215e95-1310 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1311 | The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance.UsageCreate a new index from textsimport { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await ... |
4e9727215e95-1312 | the vector storeconst vectorStore = await MemoryVectorStore.fromDocuments( docs, new OpenAIEmbeddings());// Search for the most similar documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);/* [ Document { pageContent: "Hello world", metadata: { id: 2 } ... |
4e9727215e95-1313 | The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance.UsageCreate a new index from textsimport { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await ... |
4e9727215e95-1314 | the vector storeconst vectorStore = await MemoryVectorStore.fromDocuments( docs, new OpenAIEmbeddings());// Search for the most similar documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);/* [ Document { pageContent: "Hello world", metadata: { id: 2 } ... |
4e9727215e95-1315 | The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance.UsageCreate a new index from textsimport { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await ... |
4e9727215e95-1316 | Load the docs into the vector storeconst vectorStore = await MemoryVectorStore.fromDocuments( docs, new OpenAIEmbeddings());// Search for the most similar documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);/* [ Document { pageContent: "Hello world", met... |
4e9727215e95-1317 | AnalyticDB
Page Title: AnalyticDB | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBC... |
4e9727215e95-1318 | AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.This notebook shows how to use functionality related to the AnalyticDB vector database.To run, you should have an AnalyticDB instance up and running:Using AnalyticDB Cloud Vector Datab... |
4e9727215e95-1319 | [{ page: 1 }, { page: 2 }, { page: 3 }], new OpenAIEmbeddings(), { connectionOptions });const result = await vectorStore.similaritySearch("foo", 1);console.log(JSON.stringify(result));// [{"pageContent":"foo","metadata":{"page":1}}]await vectorStore.addDocuments([{ pageContent: "foo", metadata: { page: 4 } }]);const ... |
4e9727215e95-1320 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1321 | AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.This notebook shows how to use functionality related to the AnalyticDB vector database.To run, you should have an AnalyticDB instance up and running:Using AnalyticDB Cloud Vector Datab... |
4e9727215e95-1322 | AnalyticDBVectorStore.fromTexts( ["foo", "bar", "baz"], [{ page: 1 }, { page: 2 }, { page: 3 }], new OpenAIEmbeddings(), { connectionOptions });const result = await vectorStore.similaritySearch("foo", 1);console.log(JSON.stringify(result));// [{"pageContent":"foo","metadata":{"page":1}}]await vectorStore.addDocumen... |
4e9727215e95-1323 | ModulesData connectionVector storesIntegrationsAnalyticDBOn this pageAnalyticDBAnalyticDB for PostgreSQL is a massively parallel processing (MPP) data warehousing service that is designed to analyze large volumes of data online.AnalyticDB for PostgreSQL is developed based on the open source Greenplum Database project a... |
4e9727215e95-1324 | AnalyticDBVectorStore.fromTexts( ["foo", "bar", "baz"], [{ page: 1 }, { page: 2 }, { page: 3 }], new OpenAIEmbeddings(), { connectionOptions });const result = await vectorStore.similaritySearch("foo", 1);console.log(JSON.stringify(result));// [{"pageContent":"foo","metadata":{"page":1}}]await vectorStore.addDocumen... |
4e9727215e95-1325 | ModulesData connectionVector storesIntegrationsAnalyticDBOn this pageAnalyticDBAnalyticDB for PostgreSQL is a massively parallel processing (MPP) data warehousing service that is designed to analyze large volumes of data online.AnalyticDB for PostgreSQL is developed based on the open source Greenplum Database project a... |
4e9727215e95-1326 | AnalyticDBVectorStore.fromTexts( ["foo", "bar", "baz"], [{ page: 1 }, { page: 2 }, { page: 3 }], new OpenAIEmbeddings(), { connectionOptions });const result = await vectorStore.similaritySearch("foo", 1);console.log(JSON.stringify(result));// [{"pageContent":"foo","metadata":{"page":1}}]await vectorStore.addDocumen... |
4e9727215e95-1327 | AnalyticDB for PostgreSQL processes petabytes of data offline at a high performance level and supports highly concurrent online queries.This notebook shows how to use functionality related to the AnalyticDB vector database.To run, you should have an AnalyticDB instance up and running:Using AnalyticDB Cloud Vector Datab... |
4e9727215e95-1328 | = await AnalyticDBVectorStore.fromTexts( ["foo", "bar", "baz"], [{ page: 1 }, { page: 2 }, { page: 3 }], new OpenAIEmbeddings(), { connectionOptions });const result = await vectorStore.similaritySearch("foo", 1);console.log(JSON.stringify(result));// [{"pageContent":"foo","metadata":{"page":1}}]await vectorStore.ad... |
4e9727215e95-1329 | This notebook shows how to use functionality related to the AnalyticDB vector database.
To run, you should have an AnalyticDB instance up and running:
LangChain.js accepts node-postgres as the connections pool for AnalyticDB vectorstore.
npmYarnpnpmnpm install -S pgyarn add pgpnpm add pg
npm install -S pgyarn add p... |
4e9727215e95-1330 | pnpm add pg-copy-streams
import { AnalyticDBVectorStore } from "langchain/vectorstores/analyticdb";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const connectionOptions = { host: process.env.ANALYTICDB_HOST || "localhost", port: Number(process.env.ANALYTICDB_PORT) || 5432, database: process.env.ANA... |
4e9727215e95-1331 | API Reference:AnalyticDBVectorStore from langchain/vectorstores/analyticdbOpenAIEmbeddings from langchain/embeddings/openai
Chroma
Page Title: Chroma | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/... |
4e9727215e95-1332 | WebsiteDocumentationTwitterDiscordSetupRun Chroma with Docker on your computergit clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --buildInstall the Chroma JS SDK.npmYarnpnpmnpm install -S chromadbyarn add chromadbpnpm add chromadbChroma is fully-typed, fully-tested and fully-documented.Like any other ... |
4e9727215e95-1333 | = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), { collectionName: "a-test-collection", url: "http://localhost:8000", // Optional, will default to this value});// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/*[ Document { page... |
4e9727215e95-1334 | ", ], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { collectionName: "godel-escher-bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What... |
4e9727215e95-1335 | ', metadata: { id: 1 } }]*/API Reference:Chroma from langchain/vectorstores/chromaOpenAIEmbeddings from langchain/embeddings/openaiUsage, delete docsimport { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const embeddings = new OpenAIEmbeddings();const ... |
4e9727215e95-1336 | ", metadata: { speaker: "Tortoise", }, },];// Also supports an additional {ids: []} parameter for upsertionconst ids = await vectorStore.addDocuments(documents);const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata:... |
4e9727215e95-1337 | WebsiteDocumentationTwitterDiscordSetupRun Chroma with Docker on your computergit clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --buildInstall the Chroma JS SDK.npmYarnpnpmnpm install -S chromadbyarn add chromadbpnpm add chromadbChroma is fully-typed, fully-tested and fully-documented.Like any other ... |
4e9727215e95-1338 | = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), { collectionName: "a-test-collection", url: "http://localhost:8000", // Optional, will default to this value});// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/*[ Document { page... |
4e9727215e95-1339 | ", ], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { collectionName: "godel-escher-bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What... |
4e9727215e95-1340 | ', metadata: { id: 1 } }]*/API Reference:Chroma from langchain/vectorstores/chromaOpenAIEmbeddings from langchain/embeddings/openaiUsage, delete docsimport { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const embeddings = new OpenAIEmbeddings();const ... |
4e9727215e95-1341 | ", metadata: { speaker: "Tortoise", }, },];// Also supports an additional {ids: []} parameter for upsertionconst ids = await vectorStore.addDocuments(documents);const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata:... |
4e9727215e95-1342 | WebsiteDocumentationTwitterDiscordSetupRun Chroma with Docker on your computergit clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --buildInstall the Chroma JS SDK.npmYarnpnpmnpm install -S chromadbyarn add chromadbpnpm add chromadbChroma is fully-typed, fully-tested and fully-documented.Like any other ... |
4e9727215e95-1343 | = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), { collectionName: "a-test-collection", url: "http://localhost:8000", // Optional, will default to this value});// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/*[ Document { page... |
4e9727215e95-1344 | ", ], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { collectionName: "godel-escher-bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What... |
4e9727215e95-1345 | ', metadata: { id: 1 } }]*/API Reference:Chroma from langchain/vectorstores/chromaOpenAIEmbeddings from langchain/embeddings/openaiUsage, delete docsimport { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const embeddings = new OpenAIEmbeddings();const ... |
4e9727215e95-1346 | ", metadata: { speaker: "Tortoise", }, },];// Also supports an additional {ids: []} parameter for upsertionconst ids = await vectorStore.addDocuments(documents);const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata:... |
4e9727215e95-1347 | WebsiteDocumentationTwitterDiscordSetupRun Chroma with Docker on your computergit clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --buildInstall the Chroma JS SDK.npmYarnpnpmnpm install -S chromadbyarn add chromadbpnpm add chromadbChroma is fully-typed, fully-tested and fully-documented.Like any other ... |
4e9727215e95-1348 | = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), { collectionName: "a-test-collection", url: "http://localhost:8000", // Optional, will default to this value});// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/*[ Document { page... |
4e9727215e95-1349 | ", ], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { collectionName: "godel-escher-bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What... |
4e9727215e95-1350 | ', metadata: { id: 1 } }]*/API Reference:Chroma from langchain/vectorstores/chromaOpenAIEmbeddings from langchain/embeddings/openaiUsage, delete docsimport { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const embeddings = new OpenAIEmbeddings();const ... |
4e9727215e95-1351 | ", metadata: { speaker: "Tortoise", }, },];// Also supports an additional {ids: []} parameter for upsertionconst ids = await vectorStore.addDocuments(documents);const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata:... |
4e9727215e95-1352 | WebsiteDocumentationTwitterDiscordSetupRun Chroma with Docker on your computergit clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --buildInstall the Chroma JS SDK.npmYarnpnpmnpm install -S chromadbyarn add chromadbpnpm add chromadbChroma is fully-typed, fully-tested and fully-documented.Like any other ... |
4e9727215e95-1353 | = await Chroma.fromDocuments(docs, new OpenAIEmbeddings(), { collectionName: "a-test-collection", url: "http://localhost:8000", // Optional, will default to this value});// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/*[ Document { page... |
4e9727215e95-1354 | ", ], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { collectionName: "godel-escher-bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What... |
4e9727215e95-1355 | ', metadata: { id: 1 } }]*/API Reference:Chroma from langchain/vectorstores/chromaOpenAIEmbeddings from langchain/embeddings/openaiUsage, delete docsimport { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const embeddings = new OpenAIEmbeddings();const ... |
4e9727215e95-1356 | ", metadata: { speaker: "Tortoise", }, },];// Also supports an additional {ids: []} parameter for upsertionconst ids = await vectorStore.addDocuments(documents);const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, no! ', metadata:... |
4e9727215e95-1357 | Like any other database, you can:
View full docs at docs.
import { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";// Create docs with a loaderconst loader = new TextLoader("src/document_loader... |
4e9727215e95-1358 | import { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// text sample from Godel, Escher, Bachconst vectorStore = await Chroma.fromTexts( [ `Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious Little Harmonic Labyrinth of the dreaded... |
4e9727215e95-1359 | filteredResponse = await vectorStore.similaritySearch("scared", 2, { id: 1,});console.log(filteredResponse);/*[ Document { pageContent: 'Achilles: Yiikes! What is that? ', metadata: { id: 1 } }]*/ |
4e9727215e95-1360 | API Reference:Chroma from langchain/vectorstores/chromaOpenAIEmbeddings from langchain/embeddings/openai
import { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await Chroma.fromExistingCollection( new OpenAIEmbeddings(), { collectionN... |
4e9727215e95-1361 | import { Chroma } from "langchain/vectorstores/chroma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const embeddings = new OpenAIEmbeddings();const vectorStore = new Chroma(embeddings, { collectionName: "test-deletion",});const documents = [ { pageContent: `Tortoise: Labyrinth? Labyrinth? Could i... |
4e9727215e95-1362 | ', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What is that? ', metadata: { id: 1 } }]*/// You can also pass a "filter" parameter insteadawait vectorStore.delete({ ids });const response2 = await vectorStore.similaritySearch("scared", 2);console.log(response2);/* []*/
Elasticsearch
SetupUsage, ... |
4e9727215e95-1363 | You can read more about the support of vector search in Elasticsearch here.LangChain.js accepts @elastic/elasticsearch as the client for Elasticsearch vectorstore.SetupnpmYarnpnpmnpm install -S @elastic/elasticsearchyarn add @elastic/elasticsearchpnpm add @elastic/elasticsearchYou'll also need to have an Elasticsearch... |
4e9727215e95-1364 | runs a vector search query, and finally uses an LLM to answer a question in natural language
based on the retrieved documents.import { Client, ClientOptions } from "@elastic/elasticsearch";import { Document } from "langchain/document";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "lan... |
4e9727215e95-1365 | "test_vectorstore", }; // Index documents const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "Elasticsearch is a powerful vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ ... |
4e9727215e95-1366 | ", }), ]; const embeddings = new OpenAIEmbeddings(undefined, { baseOptions: { temperature: 0 }, }); // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs); const vectorStore = new ElasticVectorSearch(embeddings, clientArgs); // Also supports an additional {ids: []} parameter for upsertion ... |
4e9727215e95-1367 | ", "metadata": { "baz": "qux" } } ] } */ await vectorStore.delete({ ids }); const response2 = await chain.call({ query: "What is Elasticsearch?" }); console.log(JSON.stringify(response2, null, 2)); /* [] */}API Reference:Document from langchain/documentOpenAI from ... |
4e9727215e95-1368 | You can read more about the support of vector search in Elasticsearch here.LangChain.js accepts @elastic/elasticsearch as the client for Elasticsearch vectorstore.SetupnpmYarnpnpmnpm install -S @elastic/elasticsearchyarn add @elastic/elasticsearchpnpm add @elastic/elasticsearchYou'll also need to have an Elasticsearch... |
4e9727215e95-1369 | runs a vector search query, and finally uses an LLM to answer a question in natural language
based on the retrieved documents.import { Client, ClientOptions } from "@elastic/elasticsearch";import { Document } from "langchain/document";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "lan... |
4e9727215e95-1370 | "test_vectorstore", }; // Index documents const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "Elasticsearch is a powerful vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ ... |
4e9727215e95-1371 | ", }), ]; const embeddings = new OpenAIEmbeddings(undefined, { baseOptions: { temperature: 0 }, }); // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs); const vectorStore = new ElasticVectorSearch(embeddings, clientArgs); // Also supports an additional {ids: []} parameter for upsertion ... |
4e9727215e95-1372 | ", "metadata": { "baz": "qux" } } ] } */ await vectorStore.delete({ ids }); const response2 = await chain.call({ query: "What is Elasticsearch?" }); console.log(JSON.stringify(response2, null, 2)); /* [] */}API Reference:Document from langchain/documentOpenAI from ... |
4e9727215e95-1373 | runs a vector search query, and finally uses an LLM to answer a question in natural language
based on the retrieved documents.import { Client, ClientOptions } from "@elastic/elasticsearch";import { Document } from "langchain/document";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "lan... |
4e9727215e95-1374 | "test_vectorstore", }; // Index documents const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "Elasticsearch is a powerful vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ ... |
4e9727215e95-1375 | ", }), ]; const embeddings = new OpenAIEmbeddings(undefined, { baseOptions: { temperature: 0 }, }); // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs); const vectorStore = new ElasticVectorSearch(embeddings, clientArgs); // Also supports an additional {ids: []} parameter for upsertion ... |
4e9727215e95-1376 | ", "metadata": { "baz": "qux" } } ] } */ await vectorStore.delete({ ids }); const response2 = await chain.call({ query: "What is Elasticsearch?" }); console.log(JSON.stringify(response2, null, 2)); /* [] */}API Reference:Document from langchain/documentOpenAI from ... |
4e9727215e95-1377 | runs a vector search query, and finally uses an LLM to answer a question in natural language
based on the retrieved documents.import { Client, ClientOptions } from "@elastic/elasticsearch";import { Document } from "langchain/document";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "lan... |
4e9727215e95-1378 | "test_vectorstore", }; // Index documents const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "Elasticsearch is a powerful vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ ... |
4e9727215e95-1379 | ", }), ]; const embeddings = new OpenAIEmbeddings(undefined, { baseOptions: { temperature: 0 }, }); // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs); const vectorStore = new ElasticVectorSearch(embeddings, clientArgs); // Also supports an additional {ids: []} parameter for upsertion ... |
4e9727215e95-1380 | ", "metadata": { "baz": "qux" } } ] } */ await vectorStore.delete({ ids }); const response2 = await chain.call({ query: "What is Elasticsearch?" }); console.log(JSON.stringify(response2, null, 2)); /* [] */}API Reference:Document from langchain/documentOpenAI from ... |
4e9727215e95-1381 | runs a vector search query, and finally uses an LLM to answer a question in natural language
based on the retrieved documents.import { Client, ClientOptions } from "@elastic/elasticsearch";import { Document } from "langchain/document";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "lan... |
4e9727215e95-1382 | "test_vectorstore", }; // Index documents const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "Elasticsearch is a powerful vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ ... |
4e9727215e95-1383 | ", }), ]; const embeddings = new OpenAIEmbeddings(undefined, { baseOptions: { temperature: 0 }, }); // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs); const vectorStore = new ElasticVectorSearch(embeddings, clientArgs); // Also supports an additional {ids: []} parameter for upsertion ... |
4e9727215e95-1384 | ", "metadata": { "baz": "qux" } } ] } */ await vectorStore.delete({ ids }); const response2 = await chain.call({ query: "What is Elasticsearch?" }); console.log(JSON.stringify(response2, null, 2)); /* [] */}API Reference:Document from langchain/documentOpenAI from ... |
4e9727215e95-1385 | based on the retrieved documents.
import { Client, ClientOptions } from "@elastic/elasticsearch";import { Document } from "langchain/document";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { ElasticClientArgs, ElasticVectorSearch,} from "langchai... |
4e9727215e95-1386 | "test_vectorstore", }; // Index documents const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "Elasticsearch is a powerful vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ ... |
4e9727215e95-1387 | ", }), ]; const embeddings = new OpenAIEmbeddings(undefined, { baseOptions: { temperature: 0 }, }); // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs); const vectorStore = new ElasticVectorSearch(embeddings, clientArgs); // Also supports an additional {ids: []} parameter for upsertion ... |
4e9727215e95-1388 | API Reference:Document from langchain/documentOpenAI from langchain/llms/openaiOpenAIEmbeddings from langchain/embeddings/openaiElasticClientArgs from langchain/vectorstores/elasticsearchElasticVectorSearch from langchain/vectorstores/elasticsearchVectorDBQAChain from langchain/chains
Faiss
SetupExample: index docs, ... |
4e9727215e95-1389 | It also provides the ability to read the saved file from Python's implementation.SetupInstall the faiss-node, which is a Node.js bindings for Faiss.npmYarnpnpmnpm install -S faiss-nodeyarn add faiss-nodepnpm add faiss-nodeTo enable the ability to read the saved file from Python's implementation, the pickleparser also ... |
4e9727215e95-1390 | documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);API Reference:FaissStore from langchain/vectorstores/faissOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/textMerging indexes and creating new index from another instance... |
4e9727215e95-1391 | "langchain/vectorstores/faiss";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await FaissStore.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddin... |
4e9727215e95-1392 | langchain/embeddings/openaiPreviousElasticsearchNextHNSWLibSetupUsageCreate a new index from textsCreate a new index from a loaderMerging indexes and creating new index from another instanceSave an index to file and load it againLoad the saved file from Python's implementationCommunityDiscordTwitterGitHubPythonJS/TSMor... |
4e9727215e95-1393 | It also provides the ability to read the saved file from Python's implementation.SetupInstall the faiss-node, which is a Node.js bindings for Faiss.npmYarnpnpmnpm install -S faiss-nodeyarn add faiss-nodepnpm add faiss-nodeTo enable the ability to read the saved file from Python's implementation, the pickleparser also ... |
4e9727215e95-1394 | documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);API Reference:FaissStore from langchain/vectorstores/faissOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/textMerging indexes and creating new index from another instance... |
4e9727215e95-1395 | "langchain/vectorstores/faiss";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await FaissStore.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddin... |
4e9727215e95-1396 | ModulesData connectionVector storesIntegrationsFaissOn this pageFaissCompatibilityOnly available on Node.js.Faiss is a library for efficient similarity search and clustering of dense vectors.Langchainjs supports using Faiss as a vectorstore that can be saved to file. |
4e9727215e95-1397 | It also provides the ability to read the saved file from Python's implementation.SetupInstall the faiss-node, which is a Node.js bindings for Faiss.npmYarnpnpmnpm install -S faiss-nodeyarn add faiss-nodepnpm add faiss-nodeTo enable the ability to read the saved file from Python's implementation, the pickleparser also ... |
4e9727215e95-1398 | documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);API Reference:FaissStore from langchain/vectorstores/faissOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/textMerging indexes and creating new index from another instance... |
4e9727215e95-1399 | "langchain/vectorstores/faiss";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await FaissStore.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddin... |
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