--- title: Enterprise RAG System emoji: πŸš€ colorFrom: blue colorTo: indigo sdk: streamlit sdk_version: 1.32.2 app_file: app.py pinned: false --- # πŸ” Enterprise RAG System > **A production-ready Retrieval Augmented Generation system featuring Hybrid Search, Reranking, and Hallucination Prevention.** [![Live Demo](https://img.shields.io/badge/πŸ€—%20Hugging%20Face-Live%20Demo-blue)](https://huggingface.co/spaces/yuvis/Enterprise-RAG-System) ## 🌟 Key Differentiators Unlike basic RAG tutorials, this system handles real-world edge cases: 1. **Hybrid Search (BM25 + Semantic)**: accurately retrieves both specific keywords (IDs, names) and conceptual matches. 2. **Safety First**: Implements **Confidence Gating**β€”the system explicitly refuses to answer if retrieved context is insufficient, preventing hallucinations. 3. **Zero-Latency Deployment**: Uses a custom **Build-Time Artifact Injection** pipeline to bake index files into the Docker container, eliminating startup delays. ## πŸ› οΈ Architecture ```mermaid graph LR User[User Query] --> A[Hybrid Retriever] A -->|Keywords| B(BM25 Index) A -->|Semantics| C(Pinecone/FAISS) B & C --> D[Rank Fusion (RRF)] D --> E[Cross-Encoder Reranker] E --> F{Confidence Check} F -->|Low Score| G[Fallback Response] F -->|High Score| H[LLM Generation] ``` ## πŸš€ Quick Start ### Local Development ```bash # 1. Install Dependencies pip install -r requirements.txt # 2. Generate Index python src/ingestion/ingest.py # 3. Run App streamlit run app.py ``` ### Deployment Strategy We treat Data and Code separately for scalability: - **Code**: GitHub (`app.py`, `src/`) - **Artifacts**: Hugging Face Datasets (`data/index/`) The `Dockerfile` automatically fetches the latest index during build, ensuring the deployed container is always ready-to-serve. ## πŸ§ͺ Tech Stack - **LlamaIndex / Custom Pipeline**: Hybrid Retrieval Logic - **Pinecone**: Serverless Vector Database - **Sentence-Transformers**: Embeddings & Reranking - **Streamlit**: Conversational UI - **Docker**: Containerized Deployment