DeepSeek Chat RAG

npx mcpbar@latest edit -c claude

DeepSeek Chat

DeepSeek Chat RAG is a project that utilizes advanced retrieval-augmented generation (RAG) models to answer user queries based on documents. The system extracts and indexes content from various file formats (PDF, DOCX, CSV, etc.), storing the data in a Chroma database. It then uses this information to provide relevant answers to user queries using a conversational model.

Features

• Document Extraction: Supports PDF, DOCX, TXT, and CSV formats.
• Document Indexing: Text extracted from documents is indexed in a Chroma database for efficient retrieval.
• Question Answering: Uses the RAG model to answer user questions based on the indexed documents.
• Groq Integration: Powered by Groq's LLM for enhanced response generation.

Requirements

• Python 3.8+
• The following libraries (installed via requirements.txt):
  • langchain
  • langchain-community
  • langchain-huggingface
  • langchain-chroma
  • langchain-groq
  • fitz (PyMuPDF)
  • pandas
  • docx

Installation

1. Clone this repository:

   git clone https://github.com/samaraxmmar/Deepseek_chat_rag.git
   cd Deepseek_chat_rag
   

2. Create a virtual environment and activate it:

   python3 -m venv my_env
   source my_env/bin/activate   # On Windows: my_env\Scripts\activate
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Add Documents: Place your documents (PDF, DOCX, etc.) in the project folder.
2. Run the Document Processing:
   • To process and index the documents, use the following command:

     python streamlit_chat.py
     

3. Ask Questions: After indexing, you can query the system to receive answers based on the documents.
   • Example:

     python streamlit_chat.py "What is the impact of Groq's LLM?"
     

Contributing

Feel free to fork the repository and create a pull request with any improvements, fixes, or features.