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Supabase 混合搜索

Langchain 支持使用 Supabase Postgres 数据库进行混合搜索。该混合搜索结合了 Postgres 的 pgvector 扩展（相似度搜索)和全文搜索（关键词搜索)来检索文档。您可以通过 SupabaseVectorStore 的 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， 和关键词搜索的结果数量作为参数。getRelevantDocuments 函数产生一个去重和按相关性分数排序的文档列表。

设置​

安装库​

npm install -S @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内容:

-- 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​

import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { createClient } from "@supabase/supabase-js";
import { SupabaseHybridSearch } from "langchain/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.getRelevantDocuments("hello bye");

  console.log(results);
};