id stringlengths 14 17 | text stringlengths 42 2.11k |
|---|---|
4e9727215e95-1400 | 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. |
4e9727215e95-1401 | 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 ... |
4e9727215e95-1402 | 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... |
4e9727215e95-1403 | "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... |
4e9727215e95-1404 | 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 ... |
4e9727215e95-1405 | 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... |
4e9727215e95-1406 | "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... |
4e9727215e95-1407 | Install the faiss-node, which is a Node.js bindings for Faiss.
npmYarnpnpmnpm install -S faiss-nodeyarn add faiss-nodepnpm add faiss-node
npm install -S faiss-nodeyarn add faiss-nodepnpm add faiss-node
npm install -S faiss-node
yarn add faiss-node
pnpm add faiss-node
To enable the ability to read the saved file f... |
4e9727215e95-1408 | import { FaissStore } from "langchain/vectorstores/faiss";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 loader.lo... |
4e9727215e95-1409 | import { FaissStore } from "langchain/vectorstores/faiss";import { OpenAIEmbeddings } from "langchain/embeddings/openai";export const run = async () => { // Create an initial vector store const vectorStore = await FaissStore.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [{ id: 2 }, { id: 1 }, { id:... |
4e9727215e95-1410 | import { FaissStore } from "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... |
4e9727215e95-1411 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB Atl... |
4e9727215e95-1412 | It uses HNSWLib.SetupcautionOn Windows, you might need to install Visual Studio first in order to properly build the hnswlib-node package.You can install it withnpmYarnpnpmnpm install hnswlib-nodeyarn add hnswlib-nodepnpm add hnswlib-nodeUsageCreate a new index from textsimport { HNSWLib } from "langchain/vectorstor... |
4e9727215e95-1413 | langchain/document_loaders/fs/textSave an index to a file and load it againimport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await HNSWLib.fromTexts( ["He... |
4e9727215e95-1414 | indeximport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Save the vector store to a directoryconst directory = "your/directory/here";// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIE... |
4e9727215e95-1415 | It uses HNSWLib.SetupcautionOn Windows, you might need to install Visual Studio first in order to properly build the hnswlib-node package.You can install it withnpmYarnpnpmnpm install hnswlib-nodeyarn add hnswlib-nodepnpm add hnswlib-nodeUsageCreate a new index from textsimport { HNSWLib } from "langchain/vectorstor... |
4e9727215e95-1416 | langchain/document_loaders/fs/textSave an index to a file and load it againimport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await HNSWLib.fromTexts( ["He... |
4e9727215e95-1417 | indeximport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Save the vector store to a directoryconst directory = "your/directory/here";// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIE... |
4e9727215e95-1418 | It uses HNSWLib.SetupcautionOn Windows, you might need to install Visual Studio first in order to properly build the hnswlib-node package.You can install it withnpmYarnpnpmnpm install hnswlib-nodeyarn add hnswlib-nodepnpm add hnswlib-nodeUsageCreate a new index from textsimport { HNSWLib } from "langchain/vectorstor... |
4e9727215e95-1419 | langchain/document_loaders/fs/textSave an index to a file and load it againimport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await HNSWLib.fromTexts( ["He... |
4e9727215e95-1420 | indeximport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Save the vector store to a directoryconst directory = "your/directory/here";// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIE... |
4e9727215e95-1421 | It uses HNSWLib.SetupcautionOn Windows, you might need to install Visual Studio first in order to properly build the hnswlib-node package.You can install it withnpmYarnpnpmnpm install hnswlib-nodeyarn add hnswlib-nodepnpm add hnswlib-nodeUsageCreate a new index from textsimport { HNSWLib } from "langchain/vectorstor... |
4e9727215e95-1422 | langchain/document_loaders/fs/textSave an index to a file and load it againimport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await HNSWLib.fromTexts( ["He... |
4e9727215e95-1423 | indeximport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Save the vector store to a directoryconst directory = "your/directory/here";// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIE... |
4e9727215e95-1424 | It uses HNSWLib.SetupcautionOn Windows, you might need to install Visual Studio first in order to properly build the hnswlib-node package.You can install it withnpmYarnpnpmnpm install hnswlib-nodeyarn add hnswlib-nodepnpm add hnswlib-nodeUsageCreate a new index from textsimport { HNSWLib } from "langchain/vectorstor... |
4e9727215e95-1425 | langchain/document_loaders/fs/textSave an index to a file and load it againimport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await HNSWLib.fromTexts( ["He... |
4e9727215e95-1426 | indeximport { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// Save the vector store to a directoryconst directory = "your/directory/here";// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIE... |
4e9727215e95-1427 | API Reference:HNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openai
import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";// Create docs with a loader... |
4e9727215e95-1428 | import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await HNSWLib.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings());const result = await vectorStore.similaritySe... |
4e9727215e95-1429 | Page Title: LanceDB | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsea... |
4e9727215e95-1430 | It is open source and distributed with an Apache-2.0 license.LanceDB datasets are persisted to disk and can be shared between Node.js and Python.SetupInstall the LanceDB Node.js bindings:npmYarnpnpmnpm install -S vectordbyarn add vectordbpnpm add vectordbUsageCreate a new index from textsimport { LanceDB } from "lan... |
4e9727215e95-1431 | { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import |
4e9727215e95-1432 | path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();export const run = async () => { const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"))... |
4e9727215e95-1433 | db.openTable("vectors"); const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table }); const resultOne = await vectorStore.similaritySearch("hello world", 1); console.log(resultOne); // [ Document { pageContent: 'Hello world', metadata: { id: 1 } } ]};async function createdTestDb(): Promise<string> { const d... |
4e9727215e95-1434 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1435 | It is open source and distributed with an Apache-2.0 license.LanceDB datasets are persisted to disk and can be shared between Node.js and Python.SetupInstall the LanceDB Node.js bindings:npmYarnpnpmnpm install -S vectordbyarn add vectordbpnpm add vectordbUsageCreate a new index from textsimport { LanceDB } from "lan... |
4e9727215e95-1436 | { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import |
4e9727215e95-1437 | path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();export const run = async () => { const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"))... |
4e9727215e95-1438 | db.openTable("vectors"); const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table }); const resultOne = await vectorStore.similaritySearch("hello world", 1); console.log(resultOne); // [ Document { pageContent: 'Hello world', metadata: { id: 1 } } ]};async function createdTestDb(): Promise<string> { const d... |
4e9727215e95-1439 | It is open source and distributed with an Apache-2.0 license.LanceDB datasets are persisted to disk and can be shared between Node.js and Python.SetupInstall the LanceDB Node.js bindings:npmYarnpnpmnpm install -S vectordbyarn add vectordbpnpm add vectordbUsageCreate a new index from textsimport { LanceDB } from "lan... |
4e9727215e95-1440 | { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import |
4e9727215e95-1441 | path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();export const run = async () => { const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"))... |
4e9727215e95-1442 | db.openTable("vectors"); const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table }); const resultOne = await vectorStore.similaritySearch("hello world", 1); console.log(resultOne); // [ Document { pageContent: 'Hello world', metadata: { id: 1 } } ]};async function createdTestDb(): Promise<string> { const d... |
4e9727215e95-1443 | It is open source and distributed with an Apache-2.0 license.LanceDB datasets are persisted to disk and can be shared between Node.js and Python.SetupInstall the LanceDB Node.js bindings:npmYarnpnpmnpm install -S vectordbyarn add vectordbpnpm add vectordbUsageCreate a new index from textsimport { LanceDB } from "lan... |
4e9727215e95-1444 | { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import |
4e9727215e95-1445 | path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();export const run = async () => { const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"))... |
4e9727215e95-1446 | db.openTable("vectors"); const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table }); const resultOne = await vectorStore.similaritySearch("hello world", 1); console.log(resultOne); // [ Document { pageContent: 'Hello world', metadata: { id: 1 } } ]};async function createdTestDb(): Promise<string> { const d... |
4e9727215e95-1447 | It is open source and distributed with an Apache-2.0 license.LanceDB datasets are persisted to disk and can be shared between Node.js and Python.SetupInstall the LanceDB Node.js bindings:npmYarnpnpmnpm install -S vectordbyarn add vectordbpnpm add vectordbUsageCreate a new index from textsimport { LanceDB } from "lan... |
4e9727215e95-1448 | { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import |
4e9727215e95-1449 | path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();export const run = async () => { const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"))... |
4e9727215e95-1450 | db.openTable("vectors"); const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table }); const resultOne = await vectorStore.similaritySearch("hello world", 1); console.log(resultOne); // [ Document { pageContent: 'Hello world', metadata: { id: 1 } } ]};async function createdTestDb(): Promise<string> { const d... |
4e9727215e95-1451 | yarn add vectordb
pnpm add vectordb
import { LanceDB } from "langchain/vectorstores/lancedb";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { connect } from "vectordb";import * as fs from "node:fs/promises";import * as path from "node:path";import os from "node:os";export const run = async () =... |
4e9727215e95-1452 | import { LanceDB } from "langchain/vectorstores/lancedb";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create doc... |
4e9727215e95-1453 | import { LanceDB } from "langchain/vectorstores/lancedb";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { connect } from "vectordb";import * as fs from "node:fs/promises";import * as path from "node:path";import os from "node:os";//// You can open a LanceDB dataset created elsewhere, such as Lan... |
4e9727215e95-1454 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB Atl... |
4e9727215e95-1455 | code3.1 OpenAIexport OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HEREexport MILVUS_URL=YOUR_MILVUS_URL_HERE # for example http://localhost:195303.2 Azure OpenAIexport AZURE_OPENAI_API_KEY=YOUR_AZURE_OPENAI_API_KEY_HEREexport AZURE_OPENAI_API_INSTANCE_NAME=YOUR_AZURE_OPENAI_INSTANCE_NAME_HEREexport AZURE_OPENAI_API_DEPLOYMENT_NA... |
4e9727215e95-1456 | ", "Tortoise: They say-although I person never believed it myself-that an I\ Majotaur has created a tiny labyrinth sits in a pit in the middle of\ it, waiting innocent victims to get lost in its fears complexity.\ Then, when they wander and dazed into the center, he laughs and\ ... |
4e9727215e95-1457 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1458 | OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HEREexport MILVUS_URL=YOUR_MILVUS_URL_HERE # for example http://localhost:195303.2 Azure OpenAIexport AZURE_OPENAI_API_KEY=YOUR_AZURE_OPENAI_API_KEY_HEREexport AZURE_OPENAI_API_INSTANCE_NAME=YOUR_AZURE_OPENAI_INSTANCE_NAME_HEREexport AZURE_OPENAI_API_DEPLOYMENT_NAME=YOUR_AZURE_OPENAI_... |
4e9727215e95-1459 | ", "Tortoise: They say-although I person never believed it myself-that an I\ Majotaur has created a tiny labyrinth sits in a pit in the middle of\ it, waiting innocent victims to get lost in its fears complexity.\ Then, when they wander and dazed into the center, he laughs and\ ... |
4e9727215e95-1460 | ModulesData connectionVector storesIntegrationsMilvusOn this pageMilvusMilvus is a vector database built for embeddings similarity search and AI applications.CompatibilityOnly available on Node.js.SetupRun Milvus instance with Docker on your computer docsInstall the Milvus Node.js SDK.npmYarnpnpmnpm install -S @zilliz... |
4e9727215e95-1461 | Escher, Bachconst vectorStore = await Milvus.fromTexts( [ "Tortoise: Labyrinth? Labyrinth? |
4e9727215e95-1462 | Could it Are we in the notorious Little\ Harmonic Labyrinth of the dreaded Majotaur? ", "Achilles: Yiikes! What is that? ", "Tortoise: They say-although I person never believed it myself-that an I\ Majotaur has created a tiny labyrinth sits in a pit in the middle of\ it, waiting i... |
4e9727215e95-1463 | ModulesData connectionVector storesIntegrationsMilvusOn this pageMilvusMilvus is a vector database built for embeddings similarity search and AI applications.CompatibilityOnly available on Node.js.SetupRun Milvus instance with Docker on your computer docsInstall the Milvus Node.js SDK.npmYarnpnpmnpm install -S @zilliz... |
4e9727215e95-1464 | Escher, Bachconst vectorStore = await Milvus.fromTexts( [ "Tortoise: Labyrinth? Labyrinth? |
4e9727215e95-1465 | Could it Are we in the notorious Little\ Harmonic Labyrinth of the dreaded Majotaur? ", "Achilles: Yiikes! What is that? ", "Tortoise: They say-although I person never believed it myself-that an I\ Majotaur has created a tiny labyrinth sits in a pit in the middle of\ it, waiting i... |
4e9727215e95-1466 | MilvusMilvus is a vector database built for embeddings similarity search and AI applications.CompatibilityOnly available on Node.js.SetupRun Milvus instance with Docker on your computer docsInstall the Milvus Node.js SDK.npmYarnpnpmnpm install -S @zilliz/milvus2-sdk-nodeyarn add @zilliz/milvus2-sdk-nodepnpm add @zilli... |
4e9727215e95-1467 | Could it Are we in the notorious Little\ Harmonic Labyrinth of the dreaded Majotaur? ", "Achilles: Yiikes! What is that? ", "Tortoise: They say-although I person never believed it myself-that an I\ Majotaur has created a tiny labyrinth sits in a pit in the middle of\ it, waiting i... |
4e9727215e95-1468 | Install the Milvus Node.js SDK.
npmYarnpnpmnpm install -S @zilliz/milvus2-sdk-nodeyarn add @zilliz/milvus2-sdk-nodepnpm add @zilliz/milvus2-sdk-node
npm install -S @zilliz/milvus2-sdk-nodeyarn add @zilliz/milvus2-sdk-nodepnpm add @zilliz/milvus2-sdk-node
npm install -S @zilliz/milvus2-sdk-node
yarn add @zilliz/milv... |
4e9727215e95-1469 | import { Milvus } from "langchain/vectorstores/milvus";import { OpenAIEmbeddings } from "langchain/embeddings/openai";// text sample from Godel, Escher, Bachconst vectorStore = await Milvus.fromTexts( [ "Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious Little\ Harmonic Labyrinth of the dr... |
4e9727215e95-1470 | import { Milvus } from "langchain/vectorstores/milvus";import { OpenAIEmbeddings } from "langchain/embeddings/openai";const vectorStore = await Milvus.fromExistingCollection( new OpenAIEmbeddings(), { collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);
Mongo... |
4e9727215e95-1471 | which takes a combination of documents are most similar to the inputs, then reranks and optimizes for diversity.SetupInstallationFirst, add the Node MongoDB SDK to your project:npmYarnpnpmnpm install -S mongodbyarn add mongodbpnpm add mongodbInitial Cluster ConfigurationNext, you'll need create a MongoDB Atlas clust... |
4e9727215e95-1472 | You should initialize the vector store with field names matching your collection schema as shown below.Finally, proceed to build the index.UsageIngestionimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient }... |
4e9727215e95-1473 | Defaults to "embedding" });await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereSearchimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/... |
4e9727215e95-1474 | Defaults to "embedding"});const resultOne = await vectorStore.similaritySearch("Hello world", 1);console.log(resultOne);await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereMaximal marginal relevanceimport { MongoDBAtlasVe... |
4e9727215e95-1475 | Defaults to "embedding"});const resultOne = await vectorStore.maxMarginalRelevanceSearch("Hello world", { k: 4, fetchK: 20, // The number of documents to return on initial fetch});console.log(resultOne);// Using MMR in a vector store retrieverconst retriever = await vectorStore.asRetriever({ searchType: "mmr", sear... |
4e9727215e95-1476 | which takes a combination of documents are most similar to the inputs, then reranks and optimizes for diversity.SetupInstallationFirst, add the Node MongoDB SDK to your project:npmYarnpnpmnpm install -S mongodbyarn add mongodbpnpm add mongodbInitial Cluster ConfigurationNext, you'll need create a MongoDB Atlas clust... |
4e9727215e95-1477 | You should initialize the vector store with field names matching your collection schema as shown below.Finally, proceed to build the index.UsageIngestionimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient }... |
4e9727215e95-1478 | Defaults to "embedding" });await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereSearchimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/... |
4e9727215e95-1479 | Defaults to "embedding"});const resultOne = await vectorStore.similaritySearch("Hello world", 1);console.log(resultOne);await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereMaximal marginal relevanceimport { MongoDBAtlasVe... |
4e9727215e95-1480 | Defaults to "embedding"});const resultOne = await vectorStore.maxMarginalRelevanceSearch("Hello world", { k: 4, fetchK: 20, // The number of documents to return on initial fetch});console.log(resultOne);// Using MMR in a vector store retrieverconst retriever = await vectorStore.asRetriever({ searchType: "mmr", sear... |
4e9727215e95-1481 | which takes a combination of documents are most similar to the inputs, then reranks and optimizes for diversity.SetupInstallationFirst, add the Node MongoDB SDK to your project:npmYarnpnpmnpm install -S mongodbyarn add mongodbpnpm add mongodbInitial Cluster ConfigurationNext, you'll need create a MongoDB Atlas clust... |
4e9727215e95-1482 | You should initialize the vector store with field names matching your collection schema as shown below.Finally, proceed to build the index.UsageIngestionimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient }... |
4e9727215e95-1483 | Defaults to "embedding" });await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereSearchimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/... |
4e9727215e95-1484 | Defaults to "embedding"});const resultOne = await vectorStore.similaritySearch("Hello world", 1);console.log(resultOne);await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereMaximal marginal relevanceimport { MongoDBAtlasVe... |
4e9727215e95-1485 | Defaults to "embedding"});const resultOne = await vectorStore.maxMarginalRelevanceSearch("Hello world", { k: 4, fetchK: 20, // The number of documents to return on initial fetch});console.log(resultOne);// Using MMR in a vector store retrieverconst retriever = await vectorStore.asRetriever({ searchType: "mmr", sear... |
4e9727215e95-1486 | which takes a combination of documents are most similar to the inputs, then reranks and optimizes for diversity.SetupInstallationFirst, add the Node MongoDB SDK to your project:npmYarnpnpmnpm install -S mongodbyarn add mongodbpnpm add mongodbInitial Cluster ConfigurationNext, you'll need create a MongoDB Atlas clust... |
4e9727215e95-1487 | You should initialize the vector store with field names matching your collection schema as shown below.Finally, proceed to build the index.UsageIngestionimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient }... |
4e9727215e95-1488 | Defaults to "embedding" });await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereSearchimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/... |
4e9727215e95-1489 | Defaults to "embedding"});const resultOne = await vectorStore.similaritySearch("Hello world", 1);console.log(resultOne);await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereMaximal marginal relevanceimport { MongoDBAtlasVe... |
4e9727215e95-1490 | Defaults to "embedding"});const resultOne = await vectorStore.maxMarginalRelevanceSearch("Hello world", { k: 4, fetchK: 20, // The number of documents to return on initial fetch});console.log(resultOne);// Using MMR in a vector store retrieverconst retriever = await vectorStore.asRetriever({ searchType: "mmr", sear... |
4e9727215e95-1491 | which takes a combination of documents are most similar to the inputs, then reranks and optimizes for diversity.SetupInstallationFirst, add the Node MongoDB SDK to your project:npmYarnpnpmnpm install -S mongodbyarn add mongodbpnpm add mongodbInitial Cluster ConfigurationNext, you'll need create a MongoDB Atlas clust... |
4e9727215e95-1492 | You should initialize the vector store with field names matching your collection schema as shown below.Finally, proceed to build the index.UsageIngestionimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient }... |
4e9727215e95-1493 | Defaults to "embedding" });await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereSearchimport { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/... |
4e9727215e95-1494 | Defaults to "embedding"});const resultOne = await vectorStore.similaritySearch("Hello world", 1);console.log(resultOne);await client.close();API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohereMaximal marginal relevanceimport { MongoDBAtlasVe... |
4e9727215e95-1495 | Defaults to "embedding"});const resultOne = await vectorStore.maxMarginalRelevanceSearch("Hello world", { k: 4, fetchK: 20, // The number of documents to return on initial fetch});console.log(resultOne);// Using MMR in a vector store retrieverconst retriever = await vectorStore.asRetriever({ searchType: "mmr", sear... |
4e9727215e95-1496 | { "mappings": { "fields": { // Default value, should match the name of the field within your collection that contains embeddings "embedding": [ { "dimensions": 1024, "similarity": "euclidean", "type": "knnVector" } ] } }}
The dimensions property should ma... |
4e9727215e95-1497 | API Reference:MongoDBAtlasVectorSearch from langchain/vectorstores/mongodb_atlasCohereEmbeddings from langchain/embeddings/cohere
import { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient } from "mongodb";const cl... |
4e9727215e95-1498 | import { MongoDBAtlasVectorSearch } from "langchain/vectorstores/mongodb_atlas";import { CohereEmbeddings } from "langchain/embeddings/cohere";import { MongoClient } from "mongodb";const client = new MongoClient(process.env.MONGODB_ATLAS_URI || "");const namespace = "langchain.test";const [dbName, collectionName] = nam... |
4e9727215e95-1499 | Page Title: MyScale | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsea... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.