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4e9727215e95-1500 | You will need the host, port, username, and password.Install the required Node.js peer dependency in your workspace.npmYarnpnpmnpm install -S @clickhouse/clientyarn add @clickhouse/clientpnpm add @clickhouse/clientIndex and Query Docsimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddin... |
4e9727215e95-1501 | langchain/embeddings/openaiQuery Docs From an Existing Collectionimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromExistingIndex( new OpenAIEmbeddings(), { host: process.env.MYSCALE_HOST || "local... |
4e9727215e95-1502 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1503 | You will need the host, port, username, and password.Install the required Node.js peer dependency in your workspace.npmYarnpnpmnpm install -S @clickhouse/clientyarn add @clickhouse/clientpnpm add @clickhouse/clientIndex and Query Docsimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddin... |
4e9727215e95-1504 | from langchain/embeddings/openaiQuery Docs From an Existing Collectionimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromExistingIndex( new OpenAIEmbeddings(), { host: process.env.MYSCALE_HOST || "... |
4e9727215e95-1505 | You will need the host, port, username, and password.Install the required Node.js peer dependency in your workspace.npmYarnpnpmnpm install -S @clickhouse/clientyarn add @clickhouse/clientpnpm add @clickhouse/clientIndex and Query Docsimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddin... |
4e9727215e95-1506 | from langchain/embeddings/openaiQuery Docs From an Existing Collectionimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromExistingIndex( new OpenAIEmbeddings(), { host: process.env.MYSCALE_HOST || "... |
4e9727215e95-1507 | You will need the host, port, username, and password.Install the required Node.js peer dependency in your workspace.npmYarnpnpmnpm install -S @clickhouse/clientyarn add @clickhouse/clientpnpm add @clickhouse/clientIndex and Query Docsimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddin... |
4e9727215e95-1508 | from langchain/vectorstores/myscaleOpenAIEmbeddings from langchain/embeddings/openaiQuery Docs From an Existing Collectionimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromExistingIndex( new OpenAIEmb... |
4e9727215e95-1509 | You will need the host, port, username, and password.Install the required Node.js peer dependency in your workspace.npmYarnpnpmnpm install -S @clickhouse/clientyarn add @clickhouse/clientpnpm add @clickhouse/clientIndex and Query Docsimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddin... |
4e9727215e95-1510 | Reference:MyScaleStore from langchain/vectorstores/myscaleOpenAIEmbeddings from langchain/embeddings/openaiQuery Docs From an Existing Collectionimport { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromExisti... |
4e9727215e95-1511 | yarn add @clickhouse/client
pnpm add @clickhouse/client
import { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [ { id: 2, name: "2" }, { id: 1... |
4e9727215e95-1512 | import { MyScaleStore } from "langchain/vectorstores/myscale";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await MyScaleStore.fromExistingIndex( new OpenAIEmbeddings(), { host: process.env.MYSCALE_HOST || "localhost", port: process.env.MYSCALE_PORT || "8443", username: p... |
4e9727215e95-1513 | Page Title: OpenSearch | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElastic... |
4e9727215e95-1514 | You can also find an example docker-compose file here.Index docsimport { Client } from "@opensearch-project/opensearch";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenSearchVectorStore } from "langchain/vectorstores/opensearch";const client = n... |
4e9727215e95-1515 | "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), { client,});/* Search the vector DB independently with meta filters */const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/* [ { "pageContent": "Hello... |
4e9727215e95-1516 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1517 | "http://127.0.0.1:9200"],});const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "opensearch is also a vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "l... |
4e9727215e95-1518 | "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), { client,});/* Search the vector DB independently with meta filters */const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/* [ { "pageContent": "Hello... |
4e9727215e95-1519 | ModulesData connectionVector storesIntegrationsOpenSearchOn this pageOpenSearchCompatibilityOnly available on Node.js.OpenSearch is a fork of Elasticsearch that is fully compatible with the Elasticsearch API. Read more about their support for Approximate Nearest Neighbors here.Langchain.js accepts @opensearch-project/o... |
4e9727215e95-1520 | "http://127.0.0.1:9200"],});const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "opensearch is also a vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "l... |
4e9727215e95-1521 | "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), { client,});/* Search the vector DB independently with meta filters */const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/* [ { "pageContent": "Hello... |
4e9727215e95-1522 | ModulesData connectionVector storesIntegrationsOpenSearchOn this pageOpenSearchCompatibilityOnly available on Node.js.OpenSearch is a fork of Elasticsearch that is fully compatible with the Elasticsearch API. Read more about their support for Approximate Nearest Neighbors here.Langchain.js accepts @opensearch-project/o... |
4e9727215e95-1523 | "http://127.0.0.1:9200"],});const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "opensearch is also a vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "l... |
4e9727215e95-1524 | "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), { client,});/* Search the vector DB independently with meta filters */const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/* [ { "pageContent": "Hello... |
4e9727215e95-1525 | OpenSearchCompatibilityOnly available on Node.js.OpenSearch is a fork of Elasticsearch that is fully compatible with the Elasticsearch API. Read more about their support for Approximate Nearest Neighbors here.Langchain.js accepts @opensearch-project/opensearch as the client for OpenSearch vectorstore.SetupnpmYarnpnpmn... |
4e9727215e95-1526 | "http://127.0.0.1:9200"],});const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "opensearch is also a vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "l... |
4e9727215e95-1527 | "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), { client,});/* Search the vector DB independently with meta filters */const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/* [ { "pageContent": "Hello... |
4e9727215e95-1528 | npm install -S @opensearch-project/opensearch
yarn add @opensearch-project/opensearch
pnpm add @opensearch-project/opensearch
You'll also need to have an OpenSearch instance running. You can use the official Docker image to get started. You can also find an example docker-compose file here.
import { Client } from "... |
4e9727215e95-1529 | import { Client } from "@opensearch-project/opensearch";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { OpenSearchVectorStore } from "langchain/vectorstores/opensearch";const client = new Client({ ... |
4e9727215e95-1530 | Page Title: Pinecone | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticse... |
4e9727215e95-1531 | Install the library with:npmYarnpnpmnpm install -S dotenv @pinecone-database/pineconeyarn add dotenv @pinecone-database/pineconepnpm add dotenv @pinecone-database/pineconeIndex docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document... |
4e9727215e95-1532 | { pineconeIndex,});Query docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { PineconeStore } from "lan... |
4e9727215e95-1533 | ', sourceDocuments: [ Document { pageContent: 'pinecones are the woody fruiting body and of a pine tree', metadata: [Object] } ]}*/Delete docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbe... |
4e9727215e95-1534 | dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "pinecones are the woody fruiting body and of a pine tree", }),];// Also takes an additional {ids: []} parameter for upsertionconst ids = await pinecon... |
4e9727215e95-1535 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1536 | Install the library with:npmYarnpnpmnpm install -S dotenv @pinecone-database/pineconeyarn add dotenv @pinecone-database/pineconepnpm add dotenv @pinecone-database/pineconeIndex docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document... |
4e9727215e95-1537 | { pineconeIndex,});Query docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { PineconeStore } from "lan... |
4e9727215e95-1538 | ', sourceDocuments: [ Document { pageContent: 'pinecones are the woody fruiting body and of a pine tree', metadata: [Object] } ]}*/Delete docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbe... |
4e9727215e95-1539 | "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "pinecones are the woody fruiting body and of a pine tree", }),];// Also takes an additional {ids: []} parame... |
4e9727215e95-1540 | Install the library with:npmYarnpnpmnpm install -S dotenv @pinecone-database/pineconeyarn add dotenv @pinecone-database/pineconepnpm add dotenv @pinecone-database/pineconeIndex docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document... |
4e9727215e95-1541 | { pineconeIndex,});Query docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { PineconeStore } from "lan... |
4e9727215e95-1542 | ', sourceDocuments: [ Document { pageContent: 'pinecones are the woody fruiting body and of a pine tree', metadata: [Object] } ]}*/Delete docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbe... |
4e9727215e95-1543 | "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "pinecones are the woody fruiting body and of a pine tree", }),];// Also takes an additional {ids: []} parame... |
4e9727215e95-1544 | Install the library with:npmYarnpnpmnpm install -S dotenv @pinecone-database/pineconeyarn add dotenv @pinecone-database/pineconepnpm add dotenv @pinecone-database/pineconeIndex docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document... |
4e9727215e95-1545 | { pineconeIndex,});Query docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { PineconeStore } from "lan... |
4e9727215e95-1546 | ', sourceDocuments: [ Document { pageContent: 'pinecones are the woody fruiting body and of a pine tree', metadata: [Object] } ]}*/Delete docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbe... |
4e9727215e95-1547 | }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "pinecones are the woody fruiting body and of a pine tree", }),];// Also takes an addition... |
4e9727215e95-1548 | Install the library with:npmYarnpnpmnpm install -S dotenv @pinecone-database/pineconeyarn add dotenv @pinecone-database/pineconepnpm add dotenv @pinecone-database/pineconeIndex docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document... |
4e9727215e95-1549 | { pineconeIndex,});Query docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { PineconeStore } from "lan... |
4e9727215e95-1550 | ', sourceDocuments: [ Document { pageContent: 'pinecones are the woody fruiting body and of a pine tree', metadata: [Object] } ]}*/Delete docsimport { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbe... |
4e9727215e95-1551 | { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "pinecones are the woody fruiting body and of a pine tree", }),];// Also take... |
4e9727215e95-1552 | yarn add dotenv @pinecone-database/pinecone
pnpm add dotenv @pinecone-database/pinecone
import { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PineconeStore } fr... |
4e9727215e95-1553 | import { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { OpenAI } from "langchain/llms/openai";import { PineconeStore } from "langchain/vectorstores/pinecone";d... |
4e9727215e95-1554 | import { PineconeClient } from "@pinecone-database/pinecone";import * as dotenv from "dotenv";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PineconeStore } from "langchain/vectorstores/pinecone";dotenv.config();const client = new PineconeClient();a... |
4e9727215e95-1555 | }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "pinecones are the woody fruiting body and of a pine tree", }),];// Also takes an additional {ids: []} parameter for upsertionconst ids = await pineconeStore.... |
4e9727215e95-1556 | Page Title: Prisma | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsear... |
4e9727215e95-1557 | with SupabaseRefer to the Prisma and Supabase integration guide to setup a new database instance with Supabase and Prisma.Install PrismanpmYarnpnpmnpm install prismayarn add prismapnpm add prismaSetup pgvector self hosted instance with docker-composepgvector provides a prebuilt Docker image that can be used to quick... |
4e9727215e95-1558 | These fields must be sanitized beforehand to avoid SQL injection.import { PrismaVectorStore } from "langchain/vectorstores/prisma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClient(... |
4e9727215e95-1559 | filter a.k.a {"content": "default"} const resultTwo = await vectorStore.similaritySearch("Hello world", 1); console.log(resultTwo); // Override the local filter const resultThree = await vectorStore.similaritySearchWithScore( "Hello world", 1, { content: { equals: "different_content" } } ); console.log(r... |
4e9727215e95-1560 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1561 | Supabase integration guide to setup a new database instance with Supabase and Prisma.Install PrismanpmYarnpnpmnpm install prismayarn add prismapnpm add prismaSetup pgvector self hosted instance with docker-composepgvector provides a prebuilt Docker image that can be used to quickly setup a self-hosted Postgres instan... |
4e9727215e95-1562 | These fields must be sanitized beforehand to avoid SQL injection.import { PrismaVectorStore } from "langchain/vectorstores/prisma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClient(... |
4e9727215e95-1563 | filter a.k.a {"content": "default"} const resultTwo = await vectorStore.similaritySearch("Hello world", 1); console.log(resultTwo); // Override the local filter const resultThree = await vectorStore.similaritySearchWithScore( "Hello world", 1, { content: { equals: "different_content" } } ); console.log(r... |
4e9727215e95-1564 | ModulesData connectionVector storesIntegrationsPrismaOn this pagePrismaFor augmenting existing models in PostgreSQL database with vector search, Langchain supports using Prisma together with PostgreSQL and pgvector Postgres extension.SetupSetup database instance with SupabaseRefer to the Prisma and Supabase integrati... |
4e9727215e95-1565 | These fields must be sanitized beforehand to avoid SQL injection.import { PrismaVectorStore } from "langchain/vectorstores/prisma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClient(... |
4e9727215e95-1566 | filter a.k.a {"content": "default"} const resultTwo = await vectorStore.similaritySearch("Hello world", 1); console.log(resultTwo); // Override the local filter const resultThree = await vectorStore.similaritySearchWithScore( "Hello world", 1, { content: { equals: "different_content" } } ); console.log(r... |
4e9727215e95-1567 | ModulesData connectionVector storesIntegrationsPrismaOn this pagePrismaFor augmenting existing models in PostgreSQL database with vector search, Langchain supports using Prisma together with PostgreSQL and pgvector Postgres extension.SetupSetup database instance with SupabaseRefer to the Prisma and Supabase integrati... |
4e9727215e95-1568 | These fields must be sanitized beforehand to avoid SQL injection.import { PrismaVectorStore } from "langchain/vectorstores/prisma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClient(... |
4e9727215e95-1569 | filter a.k.a {"content": "default"} const resultTwo = await vectorStore.similaritySearch("Hello world", 1); console.log(resultTwo); // Override the local filter const resultThree = await vectorStore.similaritySearchWithScore( "Hello world", 1, { content: { equals: "different_content" } } ); console.log(r... |
4e9727215e95-1570 | PrismaFor augmenting existing models in PostgreSQL database with vector search, Langchain supports using Prisma together with PostgreSQL and pgvector Postgres extension.SetupSetup database instance with SupabaseRefer to the Prisma and Supabase integration guide to setup a new database instance with Supabase and Prism... |
4e9727215e95-1571 | These fields must be sanitized beforehand to avoid SQL injection.import { PrismaVectorStore } from "langchain/vectorstores/prisma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClient(... |
4e9727215e95-1572 | filter a.k.a {"content": "default"} const resultTwo = await vectorStore.similaritySearch("Hello world", 1); console.log(resultTwo); // Override the local filter const resultThree = await vectorStore.similaritySearchWithScore( "Hello world", 1, { content: { equals: "different_content" } } ); console.log(r... |
4e9727215e95-1573 | Assuming you haven't created a schema yet, create a new model with a vector field of type Unsupported("vector"):
model Document { id String @id @default(cuid()) content String vector Unsupported("vector")?}
Afterwards, create a new migration with --create-only to avoid running the migration d... |
4e9727215e95-1574 | These fields must be sanitized beforehand to avoid SQL injection.
import { PrismaVectorStore } from "langchain/vectorstores/prisma";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClien... |
4e9727215e95-1575 | "]; await vectorStore.addModels( await db.$transaction( texts.map((content) => db.document.create({ data: { content } })) ) ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne); // create an instance with default filter const vectorStore2 = PrismaVectorStore... |
4e9727215e95-1576 | The samples above uses the following schema:
// This is your Prisma schema file,// learn more about it in the docs: https://pris.ly/d/prisma-schemagenerator client { provider = "prisma-client-js"}datasource db { provider = "postgresql" url = env("DATABASE_URL")}model Document { id String ... |
4e9727215e95-1577 | It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.CompatibilityOnly available on Node.js.SetupRun a Qdrant instance with Docker on your computer by following the Qdrant setup instructions.Install the Qdrant Node.js SDK.npmYarnpnpmnpm i... |
4e9727215e95-1578 | AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPEN... |
4e9727215e95-1579 | Courage, Achilles.`, ], [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }], new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, n... |
4e9727215e95-1580 | 1);console.log(response);/*[ Document { pageContent: 'Foo\nBar\nBaz\n\n', metadata: { source: 'src/document_loaders/example_data/example.txt' } }]*/API Reference:QdrantVectorStore from langchain/vectorstores/qdrantOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/tex... |
4e9727215e95-1581 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1582 | AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPEN... |
4e9727215e95-1583 | Courage, Achilles.`, ], [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }], new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, n... |
4e9727215e95-1584 | 1);console.log(response);/*[ Document { pageContent: 'Foo\nBar\nBaz\n\n', metadata: { source: 'src/document_loaders/example_data/example.txt' } }]*/API Reference:QdrantVectorStore from langchain/vectorstores/qdrantOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/tex... |
4e9727215e95-1585 | It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.CompatibilityOnly available on Node.js.SetupRun a Qdrant instance with Docker on your computer by following the Qdrant setup instructions.Install the Qdrant Node.js SDK.npmYarnpnpmnpm i... |
4e9727215e95-1586 | AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPEN... |
4e9727215e95-1587 | Courage, Achilles.`, ], [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }], new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, n... |
4e9727215e95-1588 | 1);console.log(response);/*[ Document { pageContent: 'Foo\nBar\nBaz\n\n', metadata: { source: 'src/document_loaders/example_data/example.txt' } }]*/API Reference:QdrantVectorStore from langchain/vectorstores/qdrantOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/tex... |
4e9727215e95-1589 | It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.CompatibilityOnly available on Node.js.SetupRun a Qdrant instance with Docker on your computer by following the Qdrant setup instructions.Install the Qdrant Node.js SDK.npmYarnpnpmnpm i... |
4e9727215e95-1590 | AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPEN... |
4e9727215e95-1591 | Courage, Achilles.`, ], [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }], new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, n... |
4e9727215e95-1592 | 1);console.log(response);/*[ Document { pageContent: 'Foo\nBar\nBaz\n\n', metadata: { source: 'src/document_loaders/example_data/example.txt' } }]*/API Reference:QdrantVectorStore from langchain/vectorstores/qdrantOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/tex... |
4e9727215e95-1593 | QdrantQdrant is a vector similarity search engine.
It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.CompatibilityOnly available on Node.js.SetupRun a Qdrant instance with Docker on your computer by following the Qdrant setup instruct... |
4e9727215e95-1594 | AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_COMPLETIONS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME_HEREexport AZURE_OPENAI_API_VERSION=YOUR_AZURE_OPEN... |
4e9727215e95-1595 | Courage, Achilles.`, ], [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }], new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[ Document { pageContent: 'Achilles: Oh, n... |
4e9727215e95-1596 | 1);console.log(response);/*[ Document { pageContent: 'Foo\nBar\nBaz\n\n', metadata: { source: 'src/document_loaders/example_data/example.txt' } }]*/API Reference:QdrantVectorStore from langchain/vectorstores/qdrantOpenAIEmbeddings from langchain/embeddings/openaiTextLoader from langchain/document_loaders/fs/tex... |
4e9727215e95-1597 | yarn add @qdrant/js-client-rest
pnpm add @qdrant/js-client-rest
Setup Env variables for Qdrant before running the code
export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HEREexport QDRANT_URL=YOUR_QDRANT_URL_HERE # for example http://localhost:6333
export AZURE_OPENAI_API_KEY=YOUR_AZURE_OPENAI_API_KEY_HEREexport AZURE_OPENA... |
4e9727215e95-1598 | import { QdrantVectorStore } from "langchain/vectorstores/qdrant";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// text sample from Godel, Escher, Bachconst vectorStore = await QdrantVectorStore.fromTexts( [ `Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious LittleHarmonic Labyrinth ... |
4e9727215e95-1599 | import { QdrantVectorStore } from "langchain/vectorstores/qdrant";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await l... |
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