Update README.md

README.md

@@ -11,4 +11,178 @@ license: apache-2.0
 short_description: deepseek-chat-bot
 ---
 
+# RAG-Powered Chatbot with Streamlit
+
+This project is a Retrieval-Augmented Generation (RAG) chatbot built using Streamlit. It allows users to upload a PDF document, process it, and ask questions about its content. The application efficiently processes the document once and uses vector-based retrieval to answer queries.
+
+---
+
+## Features
+
+- Upload PDF documents and process them into chunks for efficient querying.
+- Generate semantic embeddings using `sentence-transformers`.
+- Store embeddings in a `FAISS` vector database for efficient retrieval.
+- Use the `DeepSeek` API for question-answering capabilities.
+- Built with Streamlit for an interactive and user-friendly UI.
+
+---
+
+## Requirements
+
+- Python 3.8 or higher
+
+### Dependencies
+
+Install the required Python libraries:
+
+```plaintext
+streamlit==1.25.0
+langchain==0.81.0
+langchain-community==0.1.2
+faiss-cpu==1.7.4
+sentence-transformers==2.2.2
+pypdf==3.8.1
+```
+
+To install all dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## Setup and Usage
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/your-username/rag-chatbot.git
+cd rag-chatbot
+```
+
+### 2. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Run the Application
+
+Run the Streamlit application:
+
+```bash
+streamlit run app.py
+```
+
+### 4. Interact with the Chatbot
+
+1. Enter your `DeepSeek API Key` in the provided input field.
+2. Upload a PDF document.
+3. Ask questions about the content of the document.
+
+---
+
+## Project Structure
+
+```plaintext
+.
+├── app.py              # Main application code
+├── requirements.txt    # List of dependencies
+├── README.md           # Documentation
+```
+
+---
+
+## Key Technologies Used
+
+1. **Streamlit**:
+   - For building a user-friendly web interface.
+
+2. **LangChain**:
+   - For document loading, text splitting, and RAG pipeline.
+
+3. **FAISS**:
+   - For storing and querying vector embeddings.
+
+4. **Sentence Transformers**:
+   - For generating semantic embeddings of text chunks.
+
+5. **PyPDF**:
+   - For parsing PDF files.
+
+6. **DeepSeek API**:
+   - For question-answering capabilities.
+
+---
+
+## How It Works
+
+1. **PDF Upload**:
+   - The user uploads a PDF document.
+   - The document is split into manageable text chunks.
+
+2. **Embeddings Generation**:
+   - Semantic embeddings are generated using `sentence-transformers`.
+
+3. **Vector Storage**:
+   - The embeddings are stored in a `FAISS` vector database for efficient retrieval.
+
+4. **Question Answering**:
+   - The user asks a question about the uploaded document.
+   - The RAG pipeline retrieves relevant chunks and generates a response using the `DeepSeek` API.
+
+---
+
+## Troubleshooting
+
+- **Error: `pypdf package not found`**
+  Ensure `pypdf` is installed. Run:
+  ```bash
+  pip install pypdf
+  ```
+
+- **Error: `langchain-community module not found`**
+  Ensure `langchain-community` is installed. Run:
+  ```bash
+  pip install langchain-community
+  ```
+
+- **Reprocessing PDF on Every Query**
+  This issue is resolved by using `st.session_state` to persist the processed `vector_store`.
+
+---
+
+## Future Improvements
+
+1. Add support for multiple file uploads.
+2. Integrate additional language models.
+3. Enhance the UI with better visualization of document content.
+4. Add support for other document formats (e.g., Word, TXT).
+
+---
+
+## License
+
+This project is licensed under the MIT License. See the `LICENSE` file for more details.
+
+---
+
+## Contributions
+
+Contributions are welcome! Feel free to fork the repository and submit a pull request.
+
+---
+
+## Contact
+
+For any queries or support, please contact:
+
+- Name: [Sagun Chalise]
+- Email: [[email protected]]
+
+
+---
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference