README.md

metadata

title: PDF Q&A (Gemini RAG)
emoji: 🧠
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false

PDF Q&A (RAG) with Gemini 2.5 Flash — Hugging Face Space

This Space lets you upload PDFs and ask questions. It uses:

• LangChain text splitters (document-specific splitting for Markdown/Python/JS, plus a generic recursive splitter).
• FAISS for vector search.
• Gemini for embeddings (text-embedding-004) and generation (gemini-2.5-flash).

Quick start (on Hugging Face)

1. Create a new Space → Gradio (Python).
2. Add these files: app.py, requirements.txt, and this README.md.
3. In the Space, go to Settings → Variables and secrets and add:
   • Key: GEMINI_API_KEY
   • Value: your Gemini API key (do not commit it in code).
4. Click Restart to build & launch the Space.

Local dev

python -m venv .venv
source .venv/bin/activate  # on Windows: .venv\Scripts\activate
pip install -r requirements.txt
export GEMINI_API_KEY="YOUR_KEY_HERE"
python app.py

Notes

• The app will try the new from google import genai client first, then fall back to the legacy google-generativeai package.
• The document splitting logic is heuristic-based:
  • Markdown style content → MarkdownTextSplitter
  • Python-like content → PythonCodeTextSplitter
  • JavaScript-like content → RecursiveCharacterTextSplitter.from_language(Language.JS, ...)
  • Otherwise → RecursiveCharacterTextSplitter
• If an answer is not in the context, the model is instructed to say it doesn't know.