yk9331/logseq_rag_chatbot

A chatbot that utilizes the RAG technique can generate responses to user questions based on selected Loqseq notes.

Logseq RAG Chatbot Plugin 🤖

A chatbot that utilizes the RAG technique can generate responses to user questions based on selected Loqseq notes.

Features

• Chat with your notes.
• Answer sources citation with links to original note blocks.
• Contextualizing user question with chat history.
• Persistent vector storage on Supabase.
• Prompt tracing with Langsmith

How to get started

1. Clone the repository

2. Make sure you have pnpm installed, install if necessary

3. Execute pnpm install and pnpm build to build the plugin

4. Enable developer-mode in Logseq, go to plugins, select "Load unpacked plugin"

5. Select the directory of your plugin (not the /dist-directory, but the directory which includes your package.json)

6. Setup a Supabase project and execute following SQL in SQL Editor.

   -- Enable the pgvector extension to work with embedding vectors
   create extension vector;
   
   -- Create a table to store page info
   create table pages(
   id bigint generated by default as identity,
   uuid uuid not null primary key,
   name character varying null,
   original_name character varying null,
   updated_at bigint not null,
   );
   
   -- 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 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 $$
   #variable_conflict use_column
   begin
   return query
   select
       id,
       content,
       metadata,
       1 - (documents.embedding <=> query_embedding) as similarity
   from documents
   where metadata @> filter
   order by documents.embedding <=> query_embedding
   limit match_count;
   end;
   $$;

7. Setup Supabase project url, service key, and OpenAI API key in Plugin Settings.

8. Start Chat with your notes.