!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

You are viewing the **preview** v0.2 docs. View the **stable** v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386).

[

![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png)

](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com)

[More](#)

*   [People](/v0.2/docs/people/)
*   [Community](/v0.2/docs/community)
*   [Tutorials](/v0.2/docs/additional_resources/tutorials)
*   [Contributing](/v0.2/docs/contributing)

[v0.2](#)

*   [v0.2](/v0.2/docs/introduction)
*   [v0.1](https://js.langchain.com/v0.1/docs/get_started/introduction)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Providers](/v0.2/docs/integrations/platforms/)
    
    *   [Providers](/v0.2/docs/integrations/platforms/)
    *   [Anthropic](/v0.2/docs/integrations/platforms/anthropic)
    *   [AWS](/v0.2/docs/integrations/platforms/aws)
    *   [Google](/v0.2/docs/integrations/platforms/google)
    *   [Microsoft](/v0.2/docs/integrations/platforms/microsoft)
    *   [OpenAI](/v0.2/docs/integrations/platforms/openai)
*   [Components](/v0.2/docs/integrations/components)
    
    *   [Chat models](/v0.2/docs/integrations/chat/)
        
    *   [LLMs](/v0.2/docs/integrations/llms/)
        
    *   [Embedding models](/v0.2/docs/integrations/text_embedding)
        
    *   [Document loaders](/v0.2/docs/integrations/document_loaders)
        
    *   [Document transformers](/v0.2/docs/integrations/document_transformers)
        
    *   [Vector stores](/v0.2/docs/integrations/vectorstores)
        
    *   [Retrievers](/v0.2/docs/integrations/retrievers)
        
        *   [Knowledge Bases for Amazon Bedrock](/v0.2/docs/integrations/retrievers/bedrock-knowledge-bases)
        *   [Chaindesk Retriever](/v0.2/docs/integrations/retrievers/chaindesk-retriever)
        *   [ChatGPT Plugin Retriever](/v0.2/docs/integrations/retrievers/chatgpt-retriever-plugin)
        *   [Dria Retriever](/v0.2/docs/integrations/retrievers/dria)
        *   [Exa Search](/v0.2/docs/integrations/retrievers/exa)
        *   [HyDE Retriever](/v0.2/docs/integrations/retrievers/hyde)
        *   [Amazon Kendra Retriever](/v0.2/docs/integrations/retrievers/kendra-retriever)
        *   [Metal Retriever](/v0.2/docs/integrations/retrievers/metal-retriever)
        *   [Supabase Hybrid Search](/v0.2/docs/integrations/retrievers/supabase-hybrid)
        *   [Tavily Search API](/v0.2/docs/integrations/retrievers/tavily)
        *   [Time-Weighted Retriever](/v0.2/docs/integrations/retrievers/time-weighted-retriever)
        *   [Vector Store](/v0.2/docs/integrations/retrievers/vectorstore)
        *   [Vespa Retriever](/v0.2/docs/integrations/retrievers/vespa-retriever)
        *   [Zep Retriever](/v0.2/docs/integrations/retrievers/zep-retriever)
    *   [Tools](/v0.2/docs/integrations/tools)
        
    *   [Toolkits](/v0.2/docs/integrations/toolkits)
        
    *   [Stores](/v0.2/docs/integrations/stores/)
        

*   [](/v0.2/)
*   [Components](/v0.2/docs/integrations/components)
*   [Retrievers](/v0.2/docs/integrations/retrievers)
*   Supabase Hybrid Search

On this page

Supabase Hybrid Search
======================

Langchain supports hybrid search with a Supabase Postgres database. The hybrid search combines the postgres `pgvector` extension (similarity search) and Full-Text Search (keyword search) to retrieve documents. You can add documents via SupabaseVectorStore `addDocuments` function. SupabaseHybridKeyWordSearch accepts embedding, supabase client, number of results for similarity search, and number of results for keyword search as parameters. The `getRelevantDocuments` function produces a list of documents that has duplicates removed and is sorted by relevance score.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

### Install the library with[​](#install-the-library-with "Direct link to Install the library with")

*   npm
*   Yarn
*   pnpm

    npm install -S @supabase/supabase-js

    yarn add @supabase/supabase-js

    pnpm add @supabase/supabase-js

### Create a table and search functions in your database[​](#create-a-table-and-search-functions-in-your-database "Direct link to Create a table and search functions in your database")

Run this in your database:

    -- Enable the pgvector extension to work with embedding vectorscreate extension vector;-- Create a table to store your documentscreate table documents (  id bigserial primary key,  content text, -- corresponds to Document.pageContent  metadata jsonb, -- corresponds to Document.metadata  embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed);-- Create a function to similarity search for documentscreate function match_documents (  query_embedding vector(1536),  match_count int DEFAULT null,  filter jsonb DEFAULT '{}') returns table (  id bigint,  content text,  metadata jsonb,  similarity float)language plpgsqlas $$#variable_conflict use_columnbegin  return query  select    id,    content,    metadata,    1 - (documents.embedding <=> query_embedding) as similarity  from documents  where metadata @> filter  order by documents.embedding <=> query_embedding  limit match_count;end;$$;-- Create a function to keyword search for documentscreate function kw_match_documents(query_text text, match_count int)returns table (id bigint, content text, metadata jsonb, similarity real)as $$beginreturn query executeformat('select id, content, metadata, ts_rank(to_tsvector(content), plainto_tsquery($1)) as similarityfrom documentswhere to_tsvector(content) @@ plainto_tsquery($1)order by similarity desclimit $2')using query_text, match_count;end;$$ language plpgsql;

Usage[​](#usage "Direct link to Usage")
---------------------------------------

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

    import { OpenAIEmbeddings } from "@langchain/openai";import { createClient } from "@supabase/supabase-js";import { SupabaseHybridSearch } from "@langchain/community/retrievers/supabase";export const run = async () => {  const client = createClient(    process.env.SUPABASE_URL || "",    process.env.SUPABASE_PRIVATE_KEY || ""  );  const embeddings = new OpenAIEmbeddings();  const retriever = new SupabaseHybridSearch(embeddings, {    client,    //  Below are the defaults, expecting that you set up your supabase table and functions according to the guide above. Please change if necessary.    similarityK: 2,    keywordK: 2,    tableName: "documents",    similarityQueryName: "match_documents",    keywordQueryName: "kw_match_documents",  });  const results = await retriever.invoke("hello bye");  console.log(results);};

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [SupabaseHybridSearch](https://v02.api.js.langchain.com/classes/langchain_community_retrievers_supabase.SupabaseHybridSearch.html) from `@langchain/community/retrievers/supabase`

* * *

#### Was this page helpful?

  

#### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E).

[

Previous

Metal Retriever

](/v0.2/docs/integrations/retrievers/metal-retriever)[

Next

Tavily Search API

](/v0.2/docs/integrations/retrievers/tavily)

*   [Setup](#setup)
    *   [Install the library with](#install-the-library-with)
    *   [Create a table and search functions in your database](#create-a-table-and-search-functions-in-your-database)
*   [Usage](#usage)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.