Document Processor and RAG System

This project implements a Streamlit-based web application that processes various document types (PDF, TXT, and web pages) and creates a Retrieval-Augmented Generation (RAG) system for question answering.

Features

• Support for multiple document types:
  • PDF files
  • Text files
  • Web pages (via URL)
• Flexible embedding model selection:
  • Ollama
  • FastEmbeddings
  • HuggingFace
• Customizable local LLM model selection
• Vector store creation for efficient document retrieval
• RAG-based question answering system
• Streamlit-based user interface for easy interaction
• Comprehensive logging system

Requirements

• Python 3.7+
• Streamlit
• LangChain
• PyPDF2
• BeautifulSoup4
• Requests
• Ollama (for local LLM serving)
• Other dependencies (see requirements.txt)

Setup

1. Clone this repository:

   git clone https://github.com/yourusername/document-processor-rag-system.git
   cd document-processor-rag-system
   

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Install Ollama by following the instructions at Ollama's official website.

5. Start the Ollama server:

   ollama serve
   

6. Pull the necessary models. For example, to use the 'mistral' model:

   ollama pull mistral
   

Usage

1. Ensure the Ollama server is running (step 5 in Setup).

2. Start the Streamlit app:

   streamlit run app.py
   

3. Open your web browser and navigate to the URL provided by Streamlit (usually http://localhost:8501).

4. Use the sidebar to configure:

   • Log level
   • Embedding model
   • Local LLM model (enter the name of an Ollama-compatible model, e.g., 'mistral')

5. Upload PDF or TXT files using the file uploader.

6. (Optional) Enter a website URL to process.

7. Click "Process Files/URLs and Create Vector Store" to process the documents and create the RAG system.

8. Once processing is complete, use the "Ask a Question" section to query the system about the processed documents.

Model Constraints

When selecting a local LLM model, keep in mind the following constraints:

• The model must be compatible with Ollama.
• Ensure you have sufficient system resources (RAM, GPU) to run the chosen model.
• Some larger models may require more processing time, affecting the responsiveness of the application.

To use a specific model:

1. Pull the model using Ollama (e.g., ollama pull mistral)
2. Enter the model name in the "Local LLM Model" field in the Streamlit sidebar.

Project Structure

• app.py: Main Streamlit application file
• uploaded_files/: Directory for temporarily storing uploaded files
• logs/: Directory for storing application logs
• vector_database/: Directory for storing the Chroma vector database

Customization

• To add support for additional document types, modify the process_file function in app.py.
• To change the embedding models, update the respective sections in the sidebar configuration.
• To use different LLM models, ensure they are pulled via Ollama and enter their names in the sidebar.

Logging

The application generates detailed logs in the logs/ directory. Check these logs for debugging and monitoring purposes.

Troubleshooting

• If you encounter issues with Ollama, ensure the server is running (ollama serve).
• If a model is not found, make sure you've pulled it using ollama pull [model-name].
• For performance issues, try using a smaller or more efficient model.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details."# Local-RAG-System"