Merge remote changes, keep local app.py improvements

README.md

@@ -1,27 +1,125 @@
 ---
 license: mit
-title: RAG RH (Gradio)
+title: 🇫🇷 Assistant RH — RAG Chatbot
 sdk: gradio
-emoji: 💻
-colorFrom: blue
-colorTo: indigo
+emoji: 📚
+colorFrom: indigo
+colorTo: purple
 app_file: app.py
-pinned: false
+pinned: true
+short_description: 👉 RAG-powered AI assistant for French Human Resources
+tags:
+- gradio
+- rag
+- faiss
+- openai
+- hr
+- human-resources
+- law
+- france
+- french
+- chatbot
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/6668057ef7604601278857f5/JeivLn409aMRCqx6RwO2J.png
 ---
 
-# RAG RH (Gradio)
+# 🇫🇷 RAG-powered HR Assistant
 
-- Embeddings via **HF Inference API** (`feature-extraction`) with `HF_EMBEDDINGS_MODEL` (default `BAAI/bge-m3`)
-- Datasets:
-  - `edouardfoussier/travail-emploi-clean`
-  - `edouardfoussier/service-public-filtered`
+👉 **An AI assistant specialised in French Human Resources, powered by Retrieval-Augmented Generation (RAG) and based on official public datasets.**  
 
-## Space Variables
+[![Hugging Face Space](https://img.shields.io/badge/🤗-HuggingFace%20Space-blue)](https://huggingface.co/spaces/edouardfoussier/rag-rh-assistant)
 
-Set in **Settings → Variables**:
+![App Screenshot](assets/screenshot2.png)
 
-- `HF_API_TOKEN` (Write token) — required
-- Optional:
-  - `HF_EMBEDDINGS_MODEL` (default `BAAI/bge-m3`)
-  - `EMBED_COL` (default `embeddings_bge-m3`)
-  - `MAX_ROWS_PER_DATASET` (e.g., `2000` to cap memory during testing)
+---
+
+## ✨ What is this?
+
+This project is an **AI assistant** for HR topics in the **French labor law and public administration HR practices**.  
+It combines **retrieval** over trusted sources with **LLM synthesis**, and cites its sources.
+
+- UI: **Gradio**
+- Retrieval: **FAISS** (fallback: NumPy)
+- Embeddings: **HF Inference API**
+- LLM: **OpenAI** (BYO API Key)
+
+---
+
+## 📚 Datasets & Attribution
+
+This space relies on **public HR datasets** curated by [**AgentPublic**](https://huggingface.co/datasets/AgentPublic):
+- [Service-Public dataset](https://huggingface.co/datasets/AgentPublic/service-public)
+- [Travail-Emploi dataset](https://huggingface.co/datasets/AgentPublic/travail-emploi)
+
+For this project, I built **cleaned and filtered derivatives** hosted under my profile:
+- [edouardfoussier/service-public-filtered](https://huggingface.co/datasets/edouardfoussier/service-public-filtered)
+- [edouardfoussier/travail-emploi-clean](https://huggingface.co/datasets/edouardfoussier/travail-emploi-clean)
+
+---
+
+## ⚙️ How it works
+
+1. **Question** → User asks in French (e.g., “DPAE : quelles obligations ?”).  
+2. **Retrieve** → FAISS searches semantic vectors from the datasets.  
+3. **Synthesize** → The LLM writes a concise, factual answer with citations `[1], [2], …`.  
+4. **Explain** → The “Sources” panel shows the original articles used for answer generation
+
+---
+
+## 🔑 BYOK
+
+The app never stores your OpenAI key; it’s used in-session only.
+
+---
+
+## 🧩 Configuration notes
+
+- FAISS is used when available; otherwise we fall back to NumPy dot-product search.
+- The retriever loads vectors from the datasets and keeps a compressed cache at runtime (/tmp/rag_index.npz) to speed up cold starts.
+- You can change the Top-K slider in the UI; it controls both retrieval and the number of passages given to the LLM.
+
+---
+
+## 🚀 Run locally
+
+### 1) Clone & install
+```bash
+git clone https://huggingface.co/spaces/edouardfoussier/rag-rh-assistant
+cd rag-rh-assistant
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+### 2) Configure environment
+Key env vars:
+- HF_API_TOKEN → required for embeddings via HF Inference API
+- HF_EMBEDDINGS_MODEL → defaults to BAAI/bge-m3
+- EMBED_COL → name of the embedding column in the dataset (defaults to embeddings_bge-m3)
+- OPENAI_API_KEY → optional at startup (you can also enter it in the UI)
+- LLM_MODEL → e.g. gpt-4o-mini (configurable)
+- LLM_BASE_URL → default https://api.openai.com/v1
+
+### 3) Launch
+```bash
+python app.py
+```
+
+Open http://127.0.0.1:7860 and enter your OpenAI API key in the sidebar (or set it in .env).
+
+---
+
+## 📊 Roadmap
+
+- Reranking (cross-encoder)
+- Multi-turn memory
+- More datasets (other ministries, codes)
+- Hallucination checks & eval (faithfulness)
+- Multi-LLM backends
+
+---
+
+## 🙌 Credits
+
+- Original data: [**AgentPublic**](https://huggingface.co/datasets/AgentPublic)
+- Built with: Hugging Face Spaces, Gradio, FAISS, OpenAI