Spaces:
Running
Running
| license: mit | |
| title: DocuMind | |
| sdk: docker | |
| colorFrom: red | |
| colorTo: indigo | |
| short_description: Enterprise RAG Chatbot | |
| emoji: 📃 | |
| # 🧠 DocuMind: Enterprise RAG Chatbot | |
| DocuMind is a production-ready, highly accurate Enterprise Retrieval-Augmented Generation (RAG) system. It allows organizations to ingest `.pdf`, `.docx`, and `.txt` documents and interact with them securely using a ChatGPT-style interface, completely circumventing LLM hallucinations by enforcing strict source-grounding. | |
| ## 🌟 Key Features | |
| - **Multi-Format Document Ingestion**: Seamlessly upload APIs and TXTs locally or through the UI drag-and-drop. | |
| - **High-Accuracy RAG Pipeline**: Combines dense retrieval via ChromaDB (`all-MiniLM-L6-v2`) with precision reranking via a CrossEncoder (`ms-marco-MiniLM-L-6-v2`) to pull only the 3 most strictly relevant context chunks. | |
| - **Citation-Backed UI**: The Streamlit interface displays the exact ReRanker context chunks drawn from the documents, allowing users to verify LLM claims instantly. | |
| - **Conversation Memory**: Maintains multi-turn context awareness seamlessly using LangChain's conversational buffer memory. | |
| - **Enterprise-Ready Middleware**: API endpoints are secured with `slowapi` rate limiting. | |
| - **Instant Deployment**: Fully dockerized with multi-container `docker-compose` routing, alongside a unified `Dockerfile` for HuggingFace Space hosting. | |
| --- | |
| ## 🛠️ Tech Stack | |
| - **Backend Framework**: FastAPI, Uvicorn | |
| - **Frontend UI**: Streamlit | |
| - **RAG Orchestrator**: LangChain | |
| - **Embeddings & ReRanker**: HuggingFace `sentence-transformers` | |
| - **Vector Database**: ChromaDB (Local SQLite Persistence) | |
| - **Generation LLM**: LLaMA-3.1-8B (via Groq API) | |
| --- | |
| ## 🚀 Getting Started Locally | |
| ### 1. Requirements | |
| Ensure you have Docker installed. Clone this repository and execute: | |
| ```bash | |
| git clone https://your-repo-link/DocuMind.git | |
| cd DocuMind | |
| ``` | |
| ### 2. Configure Environment Variables | |
| Create a `.env` file in the root directory and add your Groq inference key: | |
| ```env | |
| GROQ_API_KEY=gsk_YOUR_GROQ_API_KEY_HERE | |
| ``` | |
| ### 3. Spin up the Containers | |
| Boot the unified backend, vector store, and frontend seamlessly using Docker Compose: | |
| ```bash | |
| docker-compose up --build -d | |
| ``` | |
| Access the application by navigating to **`http://localhost:8501`** in your browser. | |
| --- | |
| ## 🌐 Deploying to Hugging Face Spaces | |
| This project contains a unified `Dockerfile` and `run.sh` script precisely tuned to bypass HF networking permissions and boot the application perfectly. | |
| 1. Create a New Space on Hugging Face using the **Docker** SDK (Blank Template). | |
| 2. Clone your Space, copy the contents of this repository into it, and `git push`. | |
| 3. Go to the **Settings > Variables and secrets** tab of the Space. | |
| 4. Create a new Secret named `GROQ_API_KEY` and provide your token. | |
| 5. Hugging Face will automatically execute the build and render the Streamlit app! | |
| --- | |
| ## 🔒 Security & Model Boundaries | |
| Please review [MODEL_CARD.md](MODEL_CARD.md) for detailed descriptions on token chunking logic, LLM fallback behaviors limiting false generation, and privacy constraints. |