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CGIAR/fecb-rag

sourceHugging Facemitupdated 4mo agoView on Hugging Face

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App README

FECB RAG Search App

A RAG (Retrieval-Augmented Generation) application that lets you search and query a collection of PDF documents using semantic AI search, powered by Claude (Anthropic).

Project Structure

FECB/
├── app.py           # Gradio web interface
├── ingest.py        # PDF ingestion & FAISS index builder
├── requirements.txt
├── pdfs/            # Drop your PDF files here
├── faiss_index/     # Generated by ingest.py (do not edit)
└── metadata.json    # Generated by ingest.py

Setup

1. Install dependencies

bash

pip install -r requirements.txt

2. Add your PDFs

Copy your PDF files into the pdfs/ folder (subdirectories are supported).

3. Build the vector index

bash

python ingest.py

Options:

--pdf-dir       Path to PDF folder (default: pdfs)
--index-dir     Where to save the FAISS index (default: faiss_index)
--chunk-size    Characters per chunk (default: 800)
--chunk-overlap Overlap between chunks (default: 100)

4. Set your Anthropic API key

bash

export ANTHROPIC_API_KEY=sk-ant-...

5. Run the app

bash

python app.py

Open http://localhost:7860 in your browser.

Configuration

All settings can be overridden with environment variables:

Variable	Default	Description
`ANTHROPIC_API_KEY`	—	Required. Your Anthropic key
`CLAUDE_MODEL`	`claude-sonnet-4-6`	Claude model to use
`EMBED_MODEL`	`BAAI/bge-small-en-v1.5`	HuggingFace embedding model
`TOP_K`	`5`	Number of documents to retrieve
`INDEX_DIR`	`faiss_index`	FAISS index directory
`META_FILE`	`metadata.json`	Metadata file path
`PDF_DIR`	`pdfs`	PDF source directory (ingest)