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langchain-retrieval

Answer document questions using RAG with LangChain.js and Supabase pgvector.

15|5|Updated Dec 5, 2025
Search Tags:#langchain#ai#rag#supabase#document q&a#vector store
One-click install
npx skills add https://github.com/rebyteai-template/rebyte-skills --skill langchain-retrieval-rebyteai-template
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-retrieval
Source: https://github.com/rebyteai-template/rebyte-skills/tree/main/langchain-retrieval
Command: npx skills add https://github.com/rebyteai-template/rebyte-skills --skill langchain-retrieval-rebyteai-template

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables users to ask questions about their documents and receive answers powered by AI, leveraging Retrieval Augmented Generation (RAG).

Core Features & Use Cases

  • Document Q&A: Ask natural language questions about the content of your documents.
  • RAG Implementation: Utilizes LangChain.js and Supabase pgvector for efficient retrieval and generation.
  • Use Case: Upload a collection of research papers and ask specific questions about their findings, methodologies, or conclusions.

Quick Start

Use the langchain-retrieval skill to ask questions about the documents in the Supabase vector store.

Frequently Asked Questions about langchain-retrieval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a document Q&A application using RAG with LangChain and Supabase?▼

Document Q&A using RAG with LangChain.js and Supabase pgvector allows interactive data exploration and knowledge retrieval from a corpus of documents. You ask natural language questions and receive answers generated from your document content.

Do I need an OpenAI API key for Retrieval Augmented Generation with Supabase pgvector?▼

Yes, Retrieval Augmented Generation with Supabase pgvector requires an OpenAI API key to generate document embeddings and power the LLM for question answering. A configured Supabase project with the pgvector extension is also necessary.

What is the best way to query a collection of research papers using natural language?▼

Querying a collection of research papers using natural language is best achieved by uploading the documents to a Supabase vector store and asking specific questions about their findings, methodologies, or conclusions through a RAG pipeline.

Does LangChain.js work with Supabase for vector store document retrieval?▼

Yes, LangChain.js works with Supabase for vector store document retrieval. It facilitates efficient retrieval by using the pgvector extension to store document embeddings and fetch relevant context for generating accurate answers.

What are the prerequisites for setting up a Supabase vector store for document embeddings?▼

Prerequisites for setting up a Supabase vector store for document embeddings include an active Supabase project with the pgvector extension enabled and a valid OpenAI API key to process and embed the text data.

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