FROAV:Framework for RAG Observation and Agent Verification
FROAV is an advanced document analysis ecosystem designed to bridge the gap between autonomous AI agents and human expertise.
FROAV:Framework for RAG Observation and Agent Verification
Project Overview
FROAV is an advanced document analysis ecosystem designed to bridge the gap between autonomous AI agents and human expertise. While initially focused on analyzing complex financial filings (SEC 10-K, 10-Q, 8-K), the platform is material-agnostic and adaptable to any domain requiring deep semantic analysis.
It leverages a multi-stage Retrieval-Augmented Generation (RAG) workflow to analyze documents and subjects the results to a rigorous “LLM-as-a-Judge” evaluation process.
By integrating n8n for orchestration, PostgreSQL for granular data management, FastAPI for backend logic, and Streamlit for human interaction, FROAV provides a transparent laboratory for researchers to experiment with prompts, refine RAG strategies, and validate agent performance.
🎯 Good Scenarios
- ⏳ If you don’t want to spend hundreds of hours to implement infrastructures for your LLM agent analysis
- 🔬 If you don’t have much understanding of frontend, backend, and database but you are a good researcher and just want to focus on what you are interested in
A ready-to-go research lab for AI document analysis that handles all the infrastructure headaches so you can focus on the fun stuff — experimenting with RAG pipelines and watching LLMs judge each others work.
Key features:
- 🔬 Multi-stage RAG workflow with built-in LLM-as-a-Judge evaluation
- 🎛️ No-code workflow control via n8n visual interface
- 🐳 One docker-compose command spins up the whole ecosystem
- 📊 Clean Streamlit UI for human feedback and agent report comparison
This summary was generated by GitHub Copilot based on the project README.