README.md

metadata

title: RAG Chatbot — GPT-5.1 + URLs / Files / Text
emoji: 🧠
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
short_description: GPT-5.1 RAG chatbot for URLs, files, and text sources

🔍 RAG Chatbot — GPT-5.1 + URLs / Files / Text

A universal Retrieval-Augmented Generation (RAG) chatbot powered by OpenAI GPT-5.1 and Gradio 5.49.1, enabling users to inject their own knowledge in real time — via URLs, uploaded files, or raw text blocks — and query it conversationally.

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🚀 Features

• OpenAI API key input stored per session (no backend storage)
• GPT-5.1 model for chat; text-embedding-3-large for vector retrieval
• Accepts:
  • Live URLs (auto-fetched text)
  • Uploaded files (.txt, .md, .csv, .json)
  • Custom text areas for notes or raw data
• Interactive presets that auto-fill working examples (ZEN sites, policy QA, etc.)
• Retrieval debugging info displayed transparently for testing relevance
• No external DB — fully in-memory RAG layer for portability
• Works in Hugging Face Space, Google Colab, or local Python

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🧩 How It Works

1. Enter your OpenAI API key and click Save.
2. Add knowledge sources (URLs, uploads, or text).
3. Click Build / Refresh Knowledge Base.
   • Text is chunked → embedded via text-embedding-3-large.
   • Stored in memory as a lightweight vector index.
4. Ask questions — the system retrieves relevant chunks and constrains GPT-5.1 to answer only from those and your system instructions.

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🧱 File Structure