langchain-retrieval-agent

Name: langchain-retrieval-agent
Availability: InStock
Author: ReByte

Build a document question-answering agent with RAG and LangGraph.

15|5|Updated Dec 5, 2025

Search Tags:#langchain #ai #rag #agent #langgraph #document q&a

Ready to run

npx skills add https://github.com/rebyteai-template/rebyte-skills --skill langchain-retrieval-agent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-retrieval-agent
Source: https://github.com/rebyteai-template/rebyte-skills/tree/main/langchain-retrieval-agent
Command: npx skills add https://github.com/rebyteai-template/rebyte-skills --skill langchain-retrieval-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of extracting specific information and answering questions from a collection of documents efficiently.

Core Features & Use Cases

RAG Implementation: Leverages Retrieval Augmented Generation for accurate, context-aware answers.
LangGraph Integration: Provides a robust framework for building complex AI agents.
Use Case: A legal team can use this Skill to quickly find answers to specific clauses within a large set of contracts, rather than manually searching each document.

Quick Start

Clone the template and set up your Supabase and OpenAI API keys.

Frequently Asked Questions about langchain-retrieval-agent

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

FAQPage Schema

How do I build a RAG agent for document Q&A using LangGraph?▼

A RAG agent for document Q&A uses Retrieval Augmented Generation to extract specific information from a collection of documents efficiently. It provides accurate, context-aware answers by leveraging an interactive querying mechanism over your knowledge base.

What do I need to set up a Retrieval Augmented Generation agent with Supabase pgvector?▼

You need a Supabase project with the pgvector extension enabled and an OpenAI API key for embeddings and LLM processing. These prerequisites allow your RAG agent to store and retrieve document vectors for interactive querying.

Can I use LangGraph to query specific clauses in a large set of contracts?▼

Yes, you can use a LangGraph RAG agent to query specific clauses within a large set of contracts. A legal team can use this approach to quickly find answers within contracts rather than manually searching each document.

What is the best way to extract specific information from a collection of documents efficiently?▼

Using a RAG agent built with LangGraph is an effective way to extract specific information from a collection of documents efficiently. It solves the challenge of finding answers by facilitating interactive querying of knowledge bases stored in Supabase pgvector.

Does this document question answering implementation require an OpenAI API key?▼

Yes, this document question answering implementation requires an OpenAI API key. The key is necessary to generate document embeddings within Supabase pgvector and to power the LLM for generating accurate context-aware answers.

How does Supabase pgvector work with a LangChain retrieval agent?▼

Supabase pgvector stores your document knowledge base as vector embeddings for the LangChain retrieval agent. The agent queries this vector storage to retrieve relevant context, allowing the LLM to generate accurate answers during interactive questioning.