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4e9727215e95-1700 | Next, make a note of the clientId and clientSecret, which you can get from the
Application Keys section of the project.Index docsimport { VectorDocumentStore } from "@tigrisdata/vector";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorSt... |
4e9727215e95-1701 | from langchain/embeddings/openaiTigrisVectorStore from langchain/vectorstores/tigrisQuery docsimport { VectorDocumentStore } from "@tigrisdata/vector";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({... |
4e9727215e95-1702 | It is a fully managed cloud-native database that allows you store and
index documents and vector embeddings for fast and scalable vector search.CompatibilityOnly available on Node.js.Setup1. Install the Tigris SDKInstall the SDK as followsnpmYarnpnpmnpm install -S @tigrisdata/vectoryarn add @tigrisdata/vectorpnpm ad... |
4e9727215e95-1703 | Next, make a note of the clientId and clientSecret, which you can get from the
Application Keys section of the project.Index docsimport { VectorDocumentStore } from "@tigrisdata/vector";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorSt... |
4e9727215e95-1704 | from langchain/embeddings/openaiTigrisVectorStore from langchain/vectorstores/tigrisQuery docsimport { VectorDocumentStore } from "@tigrisdata/vector";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({... |
4e9727215e95-1705 | It is a fully managed cloud-native database that allows you store and
index documents and vector embeddings for fast and scalable vector search.CompatibilityOnly available on Node.js.Setup1. Install the Tigris SDKInstall the SDK as followsnpmYarnpnpmnpm install -S @tigrisdata/vectoryarn add @tigrisdata/vectorpnpm ad... |
4e9727215e95-1706 | Next, make a note of the clientId and clientSecret, which you can get from the
Application Keys section of the project.Index docsimport { VectorDocumentStore } from "@tigrisdata/vector";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorSt... |
4e9727215e95-1707 | langchain/documentOpenAIEmbeddings from langchain/embeddings/openaiTigrisVectorStore from langchain/vectorstores/tigrisQuery docsimport { VectorDocumentStore } from "@tigrisdata/vector";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";cons... |
4e9727215e95-1708 | npm install -S @tigrisdata/vectoryarn add @tigrisdata/vectorpnpm add @tigrisdata/vector
npm install -S @tigrisdata/vector
yarn add @tigrisdata/vector
pnpm add @tigrisdata/vector
You can sign up for a free Tigris account here.
Once you have signed up for the Tigris account, create a new project called vectordemo.
... |
4e9727215e95-1709 | Application Keys section of the project.
import { VectorDocumentStore } from "@tigrisdata/vector";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({ conne... |
4e9727215e95-1710 | import { VectorDocumentStore } from "@tigrisdata/vector";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({ connection: { serverUrl: "api.preview.tigrisdata.cloud", projectName: process.env.TIGRI... |
4e9727215e95-1711 | Page Title: TypeORM | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsea... |
4e9727215e95-1712 | Create a file below named docker-compose.yml:services: db: image: ankane/pgvector ports: - 5432:5432 volumes: - ./data:/var/lib/postgresql/data environment: - POSTGRES_PASSWORD=ChangeMe - POSTGRES_USER=myuser - POSTGRES_DB=apiAPI Reference:And then in the same directory, run docker... |
4e9727215e95-1713 | type: "postgres", host: "localhost", port: 5432, username: "myuser", password: "ChangeMe", database: "api", } as DataSourceOptions, }; const typeormVectorStore = await TypeORMVectorStore.fromDataSource( new OpenAIEmbeddings(), args ); await typeormVectorStore.ensureTableInDatabase(... |
4e9727215e95-1714 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1715 | Create a file below named docker-compose.yml:services: db: image: ankane/pgvector ports: - 5432:5432 volumes: - ./data:/var/lib/postgresql/data environment: - POSTGRES_PASSWORD=ChangeMe - POSTGRES_USER=myuser - POSTGRES_DB=apiAPI Reference:And then in the same directory, run docker... |
4e9727215e95-1716 | const args = { postgresConnectionOptions: { type: "postgres", host: "localhost", port: 5432, username: "myuser", password: "ChangeMe", database: "api", } as DataSourceOptions, }; const typeormVectorStore = await TypeORMVectorStore.fromDataSource( new OpenAIEmbeddings(), args ... |
4e9727215e95-1717 | Create a file below named docker-compose.yml:services: db: image: ankane/pgvector ports: - 5432:5432 volumes: - ./data:/var/lib/postgresql/data environment: - POSTGRES_PASSWORD=ChangeMe - POSTGRES_USER=myuser - POSTGRES_DB=apiAPI Reference:And then in the same directory, run docker... |
4e9727215e95-1718 | const args = { postgresConnectionOptions: { type: "postgres", host: "localhost", port: 5432, username: "myuser", password: "ChangeMe", database: "api", } as DataSourceOptions, }; const typeormVectorStore = await TypeORMVectorStore.fromDataSource( new OpenAIEmbeddings(), args ... |
4e9727215e95-1719 | Create a file below named docker-compose.yml:services: db: image: ankane/pgvector ports: - 5432:5432 volumes: - ./data:/var/lib/postgresql/data environment: - POSTGRES_PASSWORD=ChangeMe - POSTGRES_USER=myuser - POSTGRES_DB=apiAPI Reference:And then in the same directory, run docker... |
4e9727215e95-1720 | TypeORMTo enable vector search in a generic PostgreSQL database, LangChainJS supports using TypeORM with the pgvector Postgres extension.SetupTo work with TypeORM, you need to install the typeorm and pg packages:npmYarnpnpmnpm install typeormyarn add typeormpnpm add typeormnpmYarnpnpmnpm install pgyarn add pgpnpm add ... |
4e9727215e95-1721 | const run = async () => { const args = { postgresConnectionOptions: { type: "postgres", host: "localhost", port: 5432, username: "myuser", password: "ChangeMe", database: "api", } as DataSourceOptions, }; const typeormVectorStore = await TypeORMVectorStore.fromDataSource( new O... |
4e9727215e95-1722 | And then in the same directory, run docker compose up to start the container.
You can find more information on how to setup pgvector in the official repository.
One complete example of using TypeORMVectorStore is the following:
import { DataSourceOptions } from "typeorm";import { OpenAIEmbeddings } from "langchain/e... |
4e9727215e95-1723 | Page Title: Typesense | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElastics... |
4e9727215e95-1724 | for the vector column in Typesense but you can change it to whatever you want vector: "vec", // "text" is the default name for the text column in Typesense but you can change it to whatever you want pageContent: "text", // Names of the columns that you will save in your typesense schema and need to be retri... |
4e9727215e95-1725 | Will update documents if there is a document with the same id, at least with the default import function. * @param documents list of documents to create the vector store from * @returns Typesense vector store */const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( doc... |
4e9727215e95-1726 | Add as many other fields as needed for the metadata.constructor(embeddings: Embeddings, config: TypesenseConfig): Constructs a new instance of the Typesense class.embeddings: An instance of the Embeddings class used for embedding documents.config: Configuration object for the Typesense vector store.typesenseClient: Typ... |
4e9727215e95-1727 | Documents are added to the vector store during construction.static async fromTexts(texts: string[], metadatas: object[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>: Creates a Typesense vector store from a list of texts and associated metadata. Texts are converted to documents and added to the ... |
4e9727215e95-1728 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1729 | but you can change it to whatever you want vector: "vec", // "text" is the default name for the text column in Typesense but you can change it to whatever you want pageContent: "text", // Names of the columns that you will save in your typesense schema and need to be retrieved as metadata when searching ... |
4e9727215e95-1730 | Will update documents if there is a document with the same id, at least with the default import function. * @param documents list of documents to create the vector store from * @returns Typesense vector store */const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( doc... |
4e9727215e95-1731 | Add as many other fields as needed for the metadata.constructor(embeddings: Embeddings, config: TypesenseConfig): Constructs a new instance of the Typesense class.embeddings: An instance of the Embeddings class used for embedding documents.config: Configuration object for the Typesense vector store.typesenseClient: Typ... |
4e9727215e95-1732 | ModulesData connectionVector storesIntegrationsTypesenseOn this pageTypesenseVector store that utilizes the Typesense search engine.Basic Usageimport { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { Client } from "typesense";... |
4e9727215e95-1733 | your typesense schema and need to be retrieved as metadata when searching metadataColumnNames: ["foo", "bar", "baz"], }, // Optional search parameters to be passed to Typesense when searching searchParams: { q: "*", filter_by: "foo:[fooo]", query_by: "", }, // You can override the default Typesense imp... |
4e9727215e95-1734 | @param documents list of documents to create the vector store from * @returns Typesense vector store */const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( documents, new OpenAIEmbeddings(), typesenseVectorStoreConfig );/** * Returns a Typesense vector store f... |
4e9727215e95-1735 | Add as many other fields as needed for the metadata.constructor(embeddings: Embeddings, config: TypesenseConfig): Constructs a new instance of the Typesense class.embeddings: An instance of the Embeddings class used for embedding documents.config: Configuration object for the Typesense vector store.typesenseClient: Typ... |
4e9727215e95-1736 | ModulesData connectionVector storesIntegrationsTypesenseOn this pageTypesenseVector store that utilizes the Typesense search engine.Basic Usageimport { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { Client } from "typesense";... |
4e9727215e95-1737 | your typesense schema and need to be retrieved as metadata when searching metadataColumnNames: ["foo", "bar", "baz"], }, // Optional search parameters to be passed to Typesense when searching searchParams: { q: "*", filter_by: "foo:[fooo]", query_by: "", }, // You can override the default Typesense imp... |
4e9727215e95-1738 | @param documents list of documents to create the vector store from * @returns Typesense vector store */const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( documents, new OpenAIEmbeddings(), typesenseVectorStoreConfig );/** * Returns a Typesense vector store f... |
4e9727215e95-1739 | Add as many other fields as needed for the metadata.constructor(embeddings: Embeddings, config: TypesenseConfig): Constructs a new instance of the Typesense class.embeddings: An instance of the Embeddings class used for embedding documents.config: Configuration object for the Typesense vector store.typesenseClient: Typ... |
4e9727215e95-1740 | TypesenseVector store that utilizes the Typesense search engine.Basic Usageimport { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { Client } from "typesense";import { Document } from "langchain/document";const vectorTypesenseC... |
4e9727215e95-1741 | be retrieved as metadata when searching metadataColumnNames: ["foo", "bar", "baz"], }, // Optional search parameters to be passed to Typesense when searching searchParams: { q: "*", filter_by: "foo:[fooo]", query_by: "", }, // You can override the default Typesense import function if you want to do som... |
4e9727215e95-1742 | @param documents list of documents to create the vector store from * @returns Typesense vector store */const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( documents, new OpenAIEmbeddings(), typesenseVectorStoreConfig );/** * Returns a Typesense vector store f... |
4e9727215e95-1743 | Add as many other fields as needed for the metadata.constructor(embeddings: Embeddings, config: TypesenseConfig): Constructs a new instance of the Typesense class.embeddings: An instance of the Embeddings class used for embedding documents.config: Configuration object for the Typesense vector store.typesenseClient: Typ... |
4e9727215e95-1744 | import { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { Client } from "typesense";import { Document } from "langchain/document";const vectorTypesenseClient = new Client({ nodes: [ { // Ideally should come from your .e... |
4e9727215e95-1745 | metadataColumnNames: ["foo", "bar", "baz"], }, // Optional search parameters to be passed to Typesense when searching searchParams: { q: "*", filter_by: "foo:[fooo]", query_by: "", }, // You can override the default Typesense import function if you want to do something more complex // Default import func... |
4e9727215e95-1746 | @param documents list of documents to create the vector store from * @returns Typesense vector store */const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( documents, new OpenAIEmbeddings(), typesenseVectorStoreConfig );/** * Returns a Typesense vector store f... |
4e9727215e95-1747 | Page Title: USearch | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsea... |
4e9727215e95-1748 | }], new OpenAIEmbeddings());const resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);API Reference:USearch from langchain/vectorstores/usearchOpenAIEmbeddings from langchain/embeddings/openaiCreate a new index from a loaderimport { USearch } from "langchain/vectorstores/usearch";i... |
4e9727215e95-1749 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1750 | id: 3 }], new OpenAIEmbeddings());const resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);API Reference:USearch from langchain/vectorstores/usearchOpenAIEmbeddings from langchain/embeddings/openaiCreate a new index from a loaderimport { USearch } from "langchain/vectorstores/usea... |
4e9727215e95-1751 | ModulesData connectionVector storesIntegrationsUSearchOn this pageUSearchCompatibilityOnly available on Node.js.USearch is a library for efficient similarity search and clustering of dense vectors.SetupInstall the usearch package, which is a Node.js binding for USearch.npmYarnpnpmnpm install -S usearchyarn add usearch... |
4e9727215e95-1752 | ModulesData connectionVector storesIntegrationsUSearchOn this pageUSearchCompatibilityOnly available on Node.js.USearch is a library for efficient similarity search and clustering of dense vectors.SetupInstall the usearch package, which is a Node.js binding for USearch.npmYarnpnpmnpm install -S usearchyarn add usearch... |
4e9727215e95-1753 | USearchCompatibilityOnly available on Node.js.USearch is a library for efficient similarity search and clustering of dense vectors.SetupInstall the usearch package, which is a Node.js binding for USearch.npmYarnpnpmnpm install -S usearchyarn add usearchpnpm add usearchUsageCreate a new index from textsimport { USear... |
4e9727215e95-1754 | Install the usearch package, which is a Node.js binding for USearch.
npmYarnpnpmnpm install -S usearchyarn add usearchpnpm add usearch
npm install -S usearchyarn add usearchpnpm add usearch
npm install -S usearch
yarn add usearch
pnpm add usearch
import { USearch } from "langchain/vectorstores/usearch";import { O... |
4e9727215e95-1755 | Page Title: Vectara | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsea... |
4e9727215e95-1756 | new OpenAIEmbeddings(), args);SetupYou'll need to:Create a free Vectara account.Create a corpus to store your dataCreate an API key with QueryService and IndexService access so you can access this corpusConfigure your .env file or provide args to connect LangChain to your Vectara corpus:VECTARA_CUSTOMER_ID=your_custo... |
4e9727215e95-1757 | ", 1, { lambda: 0.025, });// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));// [// [// {// "pageContent": "In the room the women come and go talking of Michelangelo",// "metadata": [// {// "name": "lang",// "value": "eng"// },// ... |
4e9727215e95-1758 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1759 | new OpenAIEmbeddings(), args);SetupYou'll need to:Create a free Vectara account.Create a corpus to store your dataCreate an API key with QueryService and IndexService access so you can access this corpusConfigure your .env file or provide args to connect LangChain to your Vectara corpus:VECTARA_CUSTOMER_ID=your_custo... |
4e9727215e95-1760 | ", 1, { lambda: 0.025, });// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));// [// [// {// "pageContent": "In the room the women come and go talking of Michelangelo",// "metadata": [// {// "name": "lang",// "value": "eng"// },// ... |
4e9727215e95-1761 | ModulesData connectionVector storesIntegrationsVectaraOn this pageVectaraVectara 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.You can use Vectara as a vector store with LangChain.... |
4e9727215e95-1762 | new OpenAIEmbeddings(), args);SetupYou'll need to:Create a free Vectara account.Create a corpus to store your dataCreate an API key with QueryService and IndexService access so you can access this corpusConfigure your .env file or provide args to connect LangChain to your Vectara corpus:VECTARA_CUSTOMER_ID=your_custo... |
4e9727215e95-1763 | ", 1, { lambda: 0.025, });// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));// [// [// {// "pageContent": "In the room the women come and go talking of Michelangelo",// "metadata": [// {// "name": "lang",// "value": "eng"// },// ... |
4e9727215e95-1764 | ModulesData connectionVector storesIntegrationsVectaraOn this pageVectaraVectara 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.You can use Vectara as a vector store with LangChain.... |
4e9727215e95-1765 | new OpenAIEmbeddings(), args);SetupYou'll need to:Create a free Vectara account.Create a corpus to store your dataCreate an API key with QueryService and IndexService access so you can access this corpusConfigure your .env file or provide args to connect LangChain to your Vectara corpus:VECTARA_CUSTOMER_ID=your_custo... |
4e9727215e95-1766 | ", 1, { lambda: 0.025, });// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));// [// [// {// "pageContent": "In the room the women come and go talking of Michelangelo",// "metadata": [// {// "name": "lang",// "value": "eng"// },// ... |
4e9727215e95-1767 | VectaraVectara 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.You can use Vectara as a vector store with LangChain.js.👉 Embeddings IncludedVectara uses its own embeddings under th... |
4e9727215e95-1768 | new OpenAIEmbeddings(), args);SetupYou'll need to:Create a free Vectara account.Create a corpus to store your dataCreate an API key with QueryService and IndexService access so you can access this corpusConfigure your .env file or provide args to connect LangChain to your Vectara corpus:VECTARA_CUSTOMER_ID=your_custo... |
4e9727215e95-1769 | ", 1, { lambda: 0.025, });// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));// [// [// {// "pageContent": "In the room the women come and go talking of Michelangelo",// "metadata": [// {// "name": "lang",// "value": "eng"// },// ... |
4e9727215e95-1770 | You'll need to:
Configure your .env file or provide args to connect LangChain to your Vectara corpus:
VECTARA_CUSTOMER_ID=your_customer_idVECTARA_CORPUS_ID=your_corpus_idVECTARA_API_KEY=your-vectara-api-key |
4e9727215e95-1771 | import { VectaraStore } from "langchain/vectorstores/vectara";import { Document } from "langchain/document";// Create the Vectara store.const store = new VectaraStore({ customerId: Number(process.env.VECTARA_CUSTOMER_ID), corpusId: Number(process.env.VECTARA_CORPUS_ID), apiKey: String(process.env.VECTARA_API_KEY), ... |
4e9727215e95-1772 | API Reference:VectaraStore from langchain/vectorstores/vectaraDocument from langchain/document
Note that lambda is a parameter related to Vectara's hybrid search capbility, providing a tradeoff between neural search and boolean/exact match as described here. We recommend the value of 0.025 as a default, while providin... |
4e9727215e95-1773 | LangChain connects to Weaviate via the weaviate-ts-client package, the official Typescript client for Weaviate.LangChain inserts vectors directly to Weaviate, and queries Weaviate for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Weaviate.SetupnpmYarnpnpmnp... |
4e9727215e95-1774 | Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage, query documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { WeaviateStore } from "langchain/vectorstores/weaviate";import { OpenAIEmbeddings }... |
4e9727215e95-1775 | [ Document { pageContent: 'hi there', metadata: { foo: 'baz' } } ] */}API Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage delete documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { Weaviat... |
4e9727215e95-1776 | ", 1); console.log(results2); /* [] */ const docs2 = [ { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "hi there", metadata: { foo: "baz" } }, { pageContent: "how are you", metadata: { foo: "qux" } }, { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "... |
4e9727215e95-1777 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1778 | LangChain connects to Weaviate via the weaviate-ts-client package, the official Typescript client for Weaviate.LangChain inserts vectors directly to Weaviate, and queries Weaviate for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Weaviate.SetupnpmYarnpnpmnp... |
4e9727215e95-1779 | Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage, query documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { WeaviateStore } from "langchain/vectorstores/weaviate";import { OpenAIEmbeddings }... |
4e9727215e95-1780 | [ Document { pageContent: 'hi there', metadata: { foo: 'baz' } } ] */}API Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage delete documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { Weaviat... |
4e9727215e95-1781 | ", 1); console.log(results2); /* [] */ const docs2 = [ { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "hi there", metadata: { foo: "baz" } }, { pageContent: "how are you", metadata: { foo: "qux" } }, { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "... |
4e9727215e95-1782 | LangChain connects to Weaviate via the weaviate-ts-client package, the official Typescript client for Weaviate.LangChain inserts vectors directly to Weaviate, and queries Weaviate for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Weaviate.SetupnpmYarnpnpmnp... |
4e9727215e95-1783 | Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage, query documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { WeaviateStore } from "langchain/vectorstores/weaviate";import { OpenAIEmbeddings }... |
4e9727215e95-1784 | [ Document { pageContent: 'hi there', metadata: { foo: 'baz' } } ] */}API Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage delete documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { Weaviat... |
4e9727215e95-1785 | ", 1); console.log(results2); /* [] */ const docs2 = [ { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "hi there", metadata: { foo: "baz" } }, { pageContent: "how are you", metadata: { foo: "qux" } }, { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "... |
4e9727215e95-1786 | LangChain connects to Weaviate via the weaviate-ts-client package, the official Typescript client for Weaviate.LangChain inserts vectors directly to Weaviate, and queries Weaviate for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Weaviate.SetupnpmYarnpnpmnp... |
4e9727215e95-1787 | Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage, query documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { WeaviateStore } from "langchain/vectorstores/weaviate";import { OpenAIEmbeddings }... |
4e9727215e95-1788 | [ Document { pageContent: 'hi there', metadata: { foo: 'baz' } } ] */}API Reference:WeaviateStore from langchain/vectorstores/weaviateOpenAIEmbeddings from langchain/embeddings/openaiUsage delete documents/* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { Weaviat... |
4e9727215e95-1789 | ", 1); console.log(results2); /* [] */ const docs2 = [ { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "hi there", metadata: { foo: "baz" } }, { pageContent: "how are you", metadata: { foo: "qux" } }, { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "... |
4e9727215e95-1790 | npm install weaviate-ts-client graphqlyarn add weaviate-ts-client graphqlpnpm add weaviate-ts-client graphql
npm install weaviate-ts-client graphql
yarn add weaviate-ts-client graphql
pnpm add weaviate-ts-client graphql
You'll need to run Weaviate either locally or on a server, see the Weaviate documentation for mo... |
4e9727215e95-1791 | /* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { WeaviateStore } from "langchain/vectorstores/weaviate";import { OpenAIEmbeddings } from "langchain/embeddings/openai";export async function run() { // Something wrong with the weaviate-ts-client types, so we need ... |
4e9727215e95-1792 | /* eslint-disable @typescript-eslint/no-explicit-any */import weaviate from "weaviate-ts-client";import { WeaviateStore } from "langchain/vectorstores/weaviate";import { OpenAIEmbeddings } from "langchain/embeddings/openai";export async function run() { // Something wrong with the weaviate-ts-client types, so we need ... |
4e9727215e95-1793 | ", 1); console.log(results2); /* [] */ const docs2 = [ { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "hi there", metadata: { foo: "baz" } }, { pageContent: "how are you", metadata: { foo: "qux" } }, { pageContent: "hello world", metadata: { foo: "bar" } }, { pageContent: "... |
4e9727215e95-1794 | Page Title: Xata | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearch... |
4e9727215e95-1795 | Create a table, again you can name it anything, but we will use vectors. Add the following columns via the UI:content of type "Long text". This is used to store the Document.pageContent values.embedding of type "Vector". Use the dimension used by the model you plan to use (1536 for OpenAI).any other columns you want to... |
4e9727215e95-1796 | See the Xata getting started docs for more details on using the Xata JavaScript/TypeScript SDK.UsageExample: Q&A chatbot using OpenAI and Xata as vector storeThis example uses the VectorDBQAChain to search the documents stored in Xata and then pass them as context to the OpenAI model, in order to answer the question ... |
4e9727215e95-1797 | not set"); } const xata = new BaseClient({ databaseURL: process.env.XATA_DB_URL, apiKey: process.env.XATA_API_KEY, branch: process.env.XATA_BRANCH || "main", }); return xata;};export async function run() { const client = getXataClient(); const table = "vectors"; const embeddings = new OpenAIEmbeddings()... |
4e9727215e95-1798 | Before running it, make sure to add an author column of type String to the vectors table in Xata.import { XataVectorSearch } from "langchain/vectorstores/xata";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { BaseClient } from "@xata.io/client";import { Document } from "langchain/document";// Fir... |
4e9727215e95-1799 | docs = [ new Document({ pageContent: "Xata works great with Langchain.js", metadata: { author: "Xata" }, }), new Document({ pageContent: "Xata works great with Langchain", metadata: { author: "Langchain" }, }), new Document({ pageContent: "Xata includes similarity search", m... |
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