Update README.md

README.md

@@ -11,3 +11,73 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# File: `README.md`
+
+```markdown
+# RAG Document Assistant
+
+A complete Retrieval-Augmented Generation (RAG) Python project using:
+- HuggingFace Transformers
+- sentence-transformers (for embeddings)
+- ChromaDB (vector store)
+- Gradio (UI)
+
+## Project Structure
+```
+
+project/
+├── app.py
+├── rag_pipeline.py
+├── generator.py
+├── utils.py
+├── requirements.txt
+├── README.md
+├── data/
+└── db/
+
+````
+
+## Installation
+1. Create a virtual environment (recommended):
+
+```bash
+python -m venv venv
+source venv/bin/activate  # on Windows use venv\Scripts\activate
+````
+
+2. Install requirements:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Run the app:
+
+```bash
+python app.py
+```
+
+Open the Gradio URL shown in the console (default `http://127.0.0.1:7860`).
+
+## How RAG Works (short)
+
+1. Documents are uploaded and their text is extracted.
+2. Text is chunked into overlapping passages.
+3. Each chunk is embedded using a pretrained sentence-transformer.
+4. Chunks and embeddings are stored in a vector database (ChromaDB).
+5. At query time, the user question is embedded and used to retrieve most relevant chunks.
+6. Retrieved chunks are passed to a generator LLM which composes a grounded answer.
+
+## Notes & Troubleshooting
+
+* Textract may need system-level dependencies for PDF/DOCX parsing on some platforms.
+* Large models may require GPUs. For local CPU usage, prefer small models like `flan-t5-small`.
+* If you see memory errors, reduce model size or run on a machine with more RAM.
+
+## Roadmap / Improvements
+
+* Add user authentication and per-user collections
+* Support incremental indexing and deletion
+* Add streaming generation for long answers
+* Add API endpoints via FastAPI
+