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4e9727215e95-1600 | Redis
SetupUsageCreate a new index from textsCreate a new index from docsQuery docs from existing collection
Page Title: Redis | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocume... |
4e9727215e95-1601 | "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageConte... |
4e9727215e95-1602 | "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), { redisClient: client, indexName: "docs",});/* Simple standalone search in the vector DB */const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/*[ Document { page... |
4e9727215e95-1603 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1604 | "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageConte... |
4e9727215e95-1605 | "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), { redisClient: client, indexName: "docs",});/* Simple standalone search in the vector DB */const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/*[ Document { page... |
4e9727215e95-1606 | As part of the Redis Stack, RediSearch is the module that enables vector similarity semantic search, as well as many other types of searching.CompatibilityOnly available on Node.js.LangChain.js accepts node-redis as the client for Redis vectorstore.SetupRun Redis with Docker on your computer following the docsInstall ... |
4e9727215e95-1607 | "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageConte... |
4e9727215e95-1608 | "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), { redisClient: client, indexName: "docs",});/* Simple standalone search in the vector DB */const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/*[ Document { page... |
4e9727215e95-1609 | As part of the Redis Stack, RediSearch is the module that enables vector similarity semantic search, as well as many other types of searching.CompatibilityOnly available on Node.js.LangChain.js accepts node-redis as the client for Redis vectorstore.SetupRun Redis with Docker on your computer following the docsInstall ... |
4e9727215e95-1610 | "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageConte... |
4e9727215e95-1611 | "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), { redisClient: client, indexName: "docs",});/* Simple standalone search in the vector DB */const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/*[ Document { page... |
4e9727215e95-1612 | RedisRedis is a fast open source, in-memory data store.
As part of the Redis Stack, RediSearch is the module that enables vector similarity semantic search, as well as many other types of searching.CompatibilityOnly available on Node.js.LangChain.js accepts node-redis as the client for Redis vectorstore.SetupRun Redi... |
4e9727215e95-1613 | "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageConte... |
4e9727215e95-1614 | "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), { redisClient: client, indexName: "docs",});/* Simple standalone search in the vector DB */const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/*[ Document { page... |
4e9727215e95-1615 | Redis is a fast open source, in-memory data store.
As part of the Redis Stack, RediSearch is the module that enables vector similarity semantic search, as well as many other types of searching.
LangChain.js accepts node-redis as the client for Redis vectorstore.
npmYarnpnpmnpm install -S redisyarn add redispnpm add ... |
4e9727215e95-1616 | import { createClient } from "redis";import { OpenAI } from "langchain/llms/openai";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RetrievalQAChain } from "langchain/chains";import { RedisVectorStore } from "langchain/vectorstores/redis";const client = createClient({ url: process.env.REDIS_URL... |
4e9727215e95-1617 | Document { pageContent: "the quick brown fox jumped over the lazy dog", metadata: [Object] } ]}*/await client.disconnect(); |
4e9727215e95-1618 | API Reference:OpenAI from langchain/llms/openaiOpenAIEmbeddings from langchain/embeddings/openaiRetrievalQAChain from langchain/chainsRedisVectorStore from langchain/vectorstores/redis
SingleStore
Page Title: SingleStore | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPytho... |
4e9727215e95-1619 | It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.Standard usageBelow is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the Single... |
4e9727215e95-1620 | "], [ { id: 1, language: "English" }, { id: 2, language: "English" }, { id: 3, language: "Portugese" }, { id: 4, language: "Portugese" }, ], new OpenAIEmbeddings(), { connectionOptions: { host: process.env.SINGLESTORE_HOST, port: Number(process.env.SINGLESTORE_PORT), ... |
4e9727215e95-1621 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1622 | It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.Standard usageBelow is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the Single... |
4e9727215e95-1623 | "], [ { id: 1, language: "English" }, { id: 2, language: "English" }, { id: 3, language: "Portugese" }, { id: 4, language: "Portugese" }, ], new OpenAIEmbeddings(), { connectionOptions: { host: process.env.SINGLESTORE_HOST, port: Number(process.env.SINGLESTORE_PORT), ... |
4e9727215e95-1624 | It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.Standard usageBelow is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the Single... |
4e9727215e95-1625 | "], [ { id: 1, language: "English" }, { id: 2, language: "English" }, { id: 3, language: "Portugese" }, { id: 4, language: "Portugese" }, ], new OpenAIEmbeddings(), { connectionOptions: { host: process.env.SINGLESTORE_HOST, port: Number(process.env.SINGLESTORE_PORT), ... |
4e9727215e95-1626 | It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.Standard usageBelow is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the Single... |
4e9727215e95-1627 | "], [ { id: 1, language: "English" }, { id: 2, language: "English" }, { id: 3, language: "Portugese" }, { id: 4, language: "Portugese" }, ], new OpenAIEmbeddings(), { connectionOptions: { host: process.env.SINGLESTORE_HOST, port: Number(process.env.SINGLESTORE_PORT), ... |
4e9727215e95-1628 | It is recommended to call await store.end(); before terminating your application to assure all connections are appropriately closed and prevent any possible resource leaks.Standard usageBelow is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the Single... |
4e9727215e95-1629 | "], [ { id: 1, language: "English" }, { id: 2, language: "English" }, { id: 3, language: "Portugese" }, { id: 4, language: "Portugese" }, ], new OpenAIEmbeddings(), { connectionOptions: { host: process.env.SINGLESTORE_HOST, port: Number(process.env.SINGLESTORE_PORT), ... |
4e9727215e95-1630 | Below is a straightforward example showcasing how to import the relevant module and perform a base similarity search using the SingleStoreVectorStore:
import { SingleStoreVectorStore } from "langchain/vectorstores/singlestore";import { OpenAIEmbeddings } from "langchain/embeddings/openai";export const run = async () =... |
4e9727215e95-1631 | import { SingleStoreVectorStore } from "langchain/vectorstores/singlestore";import { OpenAIEmbeddings } from "langchain/embeddings/openai";export const run = async () => { const vectorStore = await SingleStoreVectorStore.fromTexts( ["Good afternoon", "Bye bye", "Boa tarde! ", "Até logo! "], [ { id: 1, langu... |
4e9727215e95-1632 | Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB Atl... |
4e9727215e95-1633 | Refer to the Supabase blog post for more information.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a table and search function in your databaseRun this in your database:-- Enable the pgvector extension to work with embed... |
4e9727215e95-1634 | 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;UsageStandard UsageThe below example shows how to perform a basic similarity search with Supabase:import { SupabaseVectorStore } from "langc... |
4e9727215e95-1635 | "], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { client, tableName: "documents", queryName: "match_documents", } ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne);};API Reference:SupabaseVectorStore from langchain/vectorstores... |
4e9727215e95-1636 | SUPABASE_URL`);export const run = async () => { const client = createClient(url, privateKey); const vectorStore = await SupabaseVectorStore.fromTexts( ["Hello world", "Hello world", "Hello world"], [{ user_id: 2 }, { user_id: 1 }, { user_id: 3 }], new OpenAIEmbeddings(), { client, tableName: "do... |
4e9727215e95-1637 | the column should look something like metadata->some_int_value::int).import { SupabaseFilterRPCCall, SupabaseVectorStore,} from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at/... |
4e9727215e95-1638 | So I would need to proceed by discussing the echo of virtual tweets in the binary corridors of the digital universe. Each tweet, like a pixelated canary, hums in an unseen frequency, a fascinatingly perplexing phenomenon that, while conjuring vivid imagery, lacks any concrete implication or real-world relevance, portra... |
4e9727215e95-1639 | ", metadata: { b: 2, c: 9, stuff: "right" }, }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "right" } }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "wrong" } }, { pageContent: "hi", metadata: { b: 2, c: 8, stuff: "right" } }, { pageContent: "bye", metadata: { b: 3, c: 7, stuff... |
4e9727215e95-1640 | .filter("metadata->c::int", "gt", 7) .filter("metadata->>stuff", "eq", "right"); const resultB = await store.similaritySearch("hello", 2, funcFilterB); console.log(resultA, resultB);};API Reference:SupabaseFilterRPCCall from langchain/vectorstores/supabaseSupabaseVectorStore from langchain/vectorstores/supabaseO... |
4e9727215e95-1641 | "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabaseconst privateKey = process.env.SUPABASE_PRIVATE_KEY;if (!privateKey) throw new Error(`Expected env var SUPABASE_PRIVATE_KEY`);const url = process.env.SUPABASE_URL;if (!url) ... |
4e9727215e95-1642 | /* [] */};API Reference:SupabaseVectorStore from langchain/vectorstores/supabaseOpenAIEmbeddings from langchain/embeddings/openaiPreviousSingleStoreNextTigrisSetupInstall the library withCreate a table and search function in your databaseUsageStandard |
4e9727215e95-1643 | UsageMetadata FilteringMetadata Query Builder FilteringDocument deletionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegratio... |
4e9727215e95-1644 | Refer to the Supabase blog post for more information.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a table and search function in your databaseRun this in your database:-- Enable the pgvector extension to work with embed... |
4e9727215e95-1645 | 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;UsageStandard UsageThe below example shows how to perform a basic similarity search with Supabase:import { SupabaseVectorStore } from "langc... |
4e9727215e95-1646 | "], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { client, tableName: "documents", queryName: "match_documents", } ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne);};API Reference:SupabaseVectorStore from langchain/vectorstores... |
4e9727215e95-1647 | SUPABASE_URL`);export const run = async () => { const client = createClient(url, privateKey); const vectorStore = await SupabaseVectorStore.fromTexts( ["Hello world", "Hello world", "Hello world"], [{ user_id: 2 }, { user_id: 1 }, { user_id: 3 }], new OpenAIEmbeddings(), { client, tableName: "do... |
4e9727215e95-1648 | the column should look something like metadata->some_int_value::int).import { SupabaseFilterRPCCall, SupabaseVectorStore,} from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at/... |
4e9727215e95-1649 | So I would need to proceed by discussing the echo of virtual tweets in the binary corridors of the digital universe. Each tweet, like a pixelated canary, hums in an unseen frequency, a fascinatingly perplexing phenomenon that, while conjuring vivid imagery, lacks any concrete implication or real-world relevance, portra... |
4e9727215e95-1650 | ", metadata: { b: 2, c: 9, stuff: "right" }, }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "right" } }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "wrong" } }, { pageContent: "hi", metadata: { b: 2, c: 8, stuff: "right" } }, { pageContent: "bye", metadata: { b: 3, c: 7, stuff... |
4e9727215e95-1651 | .filter("metadata->c::int", "gt", 7) .filter("metadata->>stuff", "eq", "right"); const resultB = await store.similaritySearch("hello", 2, funcFilterB); console.log(resultA, resultB);};API Reference:SupabaseFilterRPCCall from langchain/vectorstores/supabaseSupabaseVectorStore from langchain/vectorstores/supabaseO... |
4e9727215e95-1652 | "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabaseconst privateKey = process.env.SUPABASE_PRIVATE_KEY;if (!privateKey) throw new Error(`Expected env var SUPABASE_PRIVATE_KEY`);const url = process.env.SUPABASE_URL;if (!url) ... |
4e9727215e95-1653 | /* [] */};API Reference:SupabaseVectorStore from langchain/vectorstores/supabaseOpenAIEmbeddings from langchain/embeddings/openaiPreviousSingleStoreNextTigrisSetupInstall the library withCreate a table and search function in your databaseUsageStandard |
4e9727215e95-1654 | UsageMetadata FilteringMetadata Query Builder FilteringDocument deletion
ModulesData connectionVector storesIntegrationsSupabaseOn this pageSupabaseLangchain supports using Supabase Postgres database as a vector store, using the pgvector postgres extension.
Refer to the Supabase blog post for more information.SetupI... |
4e9727215e95-1655 | 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;UsageStandard UsageThe below example shows how to perform a basic similarity search with Supabase:import { SupabaseVectorStore } from "langc... |
4e9727215e95-1656 | "], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { client, tableName: "documents", queryName: "match_documents", } ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne);};API Reference:SupabaseVectorStore from langchain/vectorstores... |
4e9727215e95-1657 | SUPABASE_URL`);export const run = async () => { const client = createClient(url, privateKey); const vectorStore = await SupabaseVectorStore.fromTexts( ["Hello world", "Hello world", "Hello world"], [{ user_id: 2 }, { user_id: 1 }, { user_id: 3 }], new OpenAIEmbeddings(), { client, tableName: "do... |
4e9727215e95-1658 | the column should look something like metadata->some_int_value::int).import { SupabaseFilterRPCCall, SupabaseVectorStore,} from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at/... |
4e9727215e95-1659 | So I would need to proceed by discussing the echo of virtual tweets in the binary corridors of the digital universe. Each tweet, like a pixelated canary, hums in an unseen frequency, a fascinatingly perplexing phenomenon that, while conjuring vivid imagery, lacks any concrete implication or real-world relevance, portra... |
4e9727215e95-1660 | ", metadata: { b: 2, c: 9, stuff: "right" }, }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "right" } }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "wrong" } }, { pageContent: "hi", metadata: { b: 2, c: 8, stuff: "right" } }, { pageContent: "bye", metadata: { b: 3, c: 7, stuff... |
4e9727215e95-1661 | .filter("metadata->c::int", "gt", 7) .filter("metadata->>stuff", "eq", "right"); const resultB = await store.similaritySearch("hello", 2, funcFilterB); console.log(resultA, resultB);};API Reference:SupabaseFilterRPCCall from langchain/vectorstores/supabaseSupabaseVectorStore from langchain/vectorstores/supabaseO... |
4e9727215e95-1662 | "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabaseconst privateKey = process.env.SUPABASE_PRIVATE_KEY;if (!privateKey) throw new Error(`Expected env var SUPABASE_PRIVATE_KEY`);const url = process.env.SUPABASE_URL;if (!url) ... |
4e9727215e95-1663 | /* [] */};API Reference:SupabaseVectorStore from langchain/vectorstores/supabaseOpenAIEmbeddings from langchain/embeddings/openaiPreviousSingleStoreNextTigrisSetupInstall the library withCreate a table and search function in your databaseUsageStandard |
4e9727215e95-1664 | UsageMetadata FilteringMetadata Query Builder FilteringDocument deletion
ModulesData connectionVector storesIntegrationsSupabaseOn this pageSupabaseLangchain supports using Supabase Postgres database as a vector store, using the pgvector postgres extension.
Refer to the Supabase blog post for more information.SetupI... |
4e9727215e95-1665 | 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;UsageStandard UsageThe below example shows how to perform a basic similarity search with Supabase:import { SupabaseVectorStore } from "langc... |
4e9727215e95-1666 | "], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { client, tableName: "documents", queryName: "match_documents", } ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne);};API Reference:SupabaseVectorStore from langchain/vectorstores... |
4e9727215e95-1667 | SUPABASE_URL`);export const run = async () => { const client = createClient(url, privateKey); const vectorStore = await SupabaseVectorStore.fromTexts( ["Hello world", "Hello world", "Hello world"], [{ user_id: 2 }, { user_id: 1 }, { user_id: 3 }], new OpenAIEmbeddings(), { client, tableName: "do... |
4e9727215e95-1668 | the column should look something like metadata->some_int_value::int).import { SupabaseFilterRPCCall, SupabaseVectorStore,} from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at/... |
4e9727215e95-1669 | So I would need to proceed by discussing the echo of virtual tweets in the binary corridors of the digital universe. Each tweet, like a pixelated canary, hums in an unseen frequency, a fascinatingly perplexing phenomenon that, while conjuring vivid imagery, lacks any concrete implication or real-world relevance, portra... |
4e9727215e95-1670 | ", metadata: { b: 2, c: 9, stuff: "right" }, }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "right" } }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "wrong" } }, { pageContent: "hi", metadata: { b: 2, c: 8, stuff: "right" } }, { pageContent: "bye", metadata: { b: 3, c: 7, stuff... |
4e9727215e95-1671 | .filter("metadata->c::int", "gt", 7) .filter("metadata->>stuff", "eq", "right"); const resultB = await store.similaritySearch("hello", 2, funcFilterB); console.log(resultA, resultB);};API Reference:SupabaseFilterRPCCall from langchain/vectorstores/supabaseSupabaseVectorStore from langchain/vectorstores/supabaseO... |
4e9727215e95-1672 | "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabaseconst privateKey = process.env.SUPABASE_PRIVATE_KEY;if (!privateKey) throw new Error(`Expected env var SUPABASE_PR... |
4e9727215e95-1673 | 2); console.log(resultB); /* [] */};API Reference:SupabaseVectorStore from langchain/vectorstores/supabaseOpenAIEmbeddings from langchain/embeddings/openaiPreviousSingleStoreNextTigris |
4e9727215e95-1674 | SupabaseLangchain supports using Supabase Postgres database as a vector store, using the pgvector postgres extension.
Refer to the Supabase blog post for more information.SetupInstall the library withnpmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-jsCreate a t... |
4e9727215e95-1675 | 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count;end;$$;UsageStandard UsageThe below example shows how to perform a basic similarity search with Supabase:import { SupabaseVectorStore } from "langc... |
4e9727215e95-1676 | "], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings(), { client, tableName: "documents", queryName: "match_documents", } ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne);};API Reference:SupabaseVectorStore from langchain/vectorstores... |
4e9727215e95-1677 | SUPABASE_URL`);export const run = async () => { const client = createClient(url, privateKey); const vectorStore = await SupabaseVectorStore.fromTexts( ["Hello world", "Hello world", "Hello world"], [{ user_id: 2 }, { user_id: 1 }, { user_id: 3 }], new OpenAIEmbeddings(), { client, tableName: "do... |
4e9727215e95-1678 | the column should look something like metadata->some_int_value::int).import { SupabaseFilterRPCCall, SupabaseVectorStore,} from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at/... |
4e9727215e95-1679 | So I would need to proceed by discussing the echo of virtual tweets in the binary corridors of the digital universe. Each tweet, like a pixelated canary, hums in an unseen frequency, a fascinatingly perplexing phenomenon that, while conjuring vivid imagery, lacks any concrete implication or real-world relevance, portra... |
4e9727215e95-1680 | ", metadata: { b: 2, c: 9, stuff: "right" }, }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "right" } }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "wrong" } }, { pageContent: "hi", metadata: { b: 2, c: 8, stuff: "right" } }, { pageContent: "bye", metadata: { b: 3, c: 7, stuff... |
4e9727215e95-1681 | .filter("metadata->c::int", "gt", 7) .filter("metadata->>stuff", "eq", "right"); const resultB = await store.similaritySearch("hello", 2, funcFilterB); console.log(resultA, resultB);};API Reference:SupabaseFilterRPCCall from langchain/vectorstores/supabaseSupabaseVectorStore from langchain/vectorstores/supabaseO... |
4e9727215e95-1682 | "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabaseconst privateKey = process.env.SUPABASE_PRIVATE_KEY;if (!privateKey) throw new Error(`Expected env var SUPABASE_PR... |
4e9727215e95-1683 | 2); console.log(resultB); /* [] */};API Reference:SupabaseVectorStore from langchain/vectorstores/supabaseOpenAIEmbeddings from langchain/embeddings/openai |
4e9727215e95-1684 | npmYarnpnpmnpm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-js
npm install -S @supabase/supabase-jsyarn add @supabase/supabase-jspnpm add @supabase/supabase-js
npm install -S @supabase/supabase-js
yarn add @supabase/supabase-js
pnpm add @supabase/supabase-js
Run this in... |
4e9727215e95-1685 | The below example shows how to perform a basic similarity search with Supabase:
import { SupabaseVectorStore } from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at// https://js.... |
4e9727215e95-1686 | Note: If you've previously been using SupabaseVectorStore, you may need to drop and recreate the match_documents function per the updated SQL above to use this functionality.
import { SupabaseVectorStore } from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { cr... |
4e9727215e95-1687 | import { SupabaseFilterRPCCall, SupabaseVectorStore,} from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integra... |
4e9727215e95-1688 | So I would need to proceed by discussing the echo of virtual tweets in the binary corridors of the digital universe. Each tweet, like a pixelated canary, hums in an unseen frequency, a fascinatingly perplexing phenomenon that, while conjuring vivid imagery, lacks any concrete implication or real-world relevance, portra... |
4e9727215e95-1689 | ", metadata: { b: 2, c: 9, stuff: "right" }, }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "right" } }, { pageContent: "hello", metadata: { b: 1, c: 9, stuff: "wrong" } }, { pageContent: "hi", metadata: { b: 2, c: 8, stuff: "right" } }, { pageContent: "bye", metadata: { b: 3, c: 7, stuff... |
4e9727215e95-1690 | API Reference:SupabaseFilterRPCCall from langchain/vectorstores/supabaseSupabaseVectorStore from langchain/vectorstores/supabaseOpenAIEmbeddings from langchain/embeddings/openai |
4e9727215e95-1691 | import { SupabaseVectorStore } from "langchain/vectorstores/supabase";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { createClient } from "@supabase/supabase-js";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabaseconst priva... |
4e9727215e95-1692 | }, ] */ await store.delete({ ids }); const resultB = await store.similaritySearch("hello", 2); console.log(resultB); /* [] */}; |
4e9727215e95-1693 | Tigris
SetupInstall the library withCreate a table and search function in your databaseUsageStandard UsageMetadata FilteringMetadata Query Builder FilteringDocument deletion
Page Title: Tigris | 🦜️🔗 Langchain
Paragraphs:
Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntrod... |
4e9727215e95-1694 | 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... |
4e9727215e95-1695 | 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({... |
4e9727215e95-1696 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionDocument loadersDocument transformersText embedding modelsVector storesIntegrationsMemoryAnalyticDBChromaElasticsearchFaissHNSWLibLanceDBMilvusMongoDB AtlasMyScaleOpenSearchPineconePrismaQdrantRedisSingleStoreSupabaseTigrisTypeORMTypesenseUSea... |
4e9727215e95-1697 | 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... |
4e9727215e95-1698 | 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({... |
4e9727215e95-1699 | 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... |
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