Supabase 混合搜索

Langchain 支持使用 Supabase Postgres 数据库进行混合搜索。混合搜索结合了 postgres pgvector 扩展（相似性搜索）和全文搜索（关键字搜索）来检索文档。您可以通过 SupabaseVectorStore addDocuments 函数添加文档。SupabaseHybridKeyWordSearch 接受嵌入、supabase 客户端、相似性搜索的结果数量和关键字搜索的结果数量作为参数。getRelevantDocuments 函数生成一个去重并按相关性分数排序的文档列表。

设置

使用以下方法安装库

npm

npm install -S @supabase/supabase-js

Yarn

yarn add @supabase/supabase-js

pnpm

pnpm add @supabase/supabase-js

在您的数据库中创建表和搜索函数

在您的数据库中运行以下代码

-- Enable the pgvector extension to work with embedding vectors
create extension vector;

-- Create a table to store your documents
create 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 documents
create 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 plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where metadata @> filter
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

-- Create a function to keyword search for documents
create function kw_match_documents(query_text text, match_count int)
returns table (id bigint, content text, metadata jsonb, similarity real)
as $$

begin
return query execute
format('select id, content, metadata, ts_rank(to_tsvector(content), plainto_tsquery($1)) as similarity
from documents
where to_tsvector(content) @@ plainto_tsquery($1)
order by similarity desc
limit $2')
using query_text, match_count;
end;
$$ language plpgsql;

用法

提示

查看 此部分，了解有关安装集成包的常规说明。

npm

npm install @langchain/openai @langchain/community

Yarn

yarn add @langchain/openai @langchain/community

pnpm

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 参考

• OpenAIEmbeddings 来自 @langchain/openai
• SupabaseHybridSearch 来自 @langchain/community/retrievers/supabase

相关

• 检索器 概念指南
• 检索器 操作指南