This project is a web-based application that allows you to chat with your documents. You can upload a document (PDF, DOCX, TXT, etc.), and the application will process it to answer your questions based on its content.

The application is built with:

• Backend: Python, LlamaIndex, Groq, and Cohere.
• Frontend: Gradio for the user interface.
• Containerization: Docker for easy deployment.

1. Document Parsing: When you upload a document, it's parsed using LlamaParse to extract the text content.
2. Embeddings: The extracted text is then converted into vector embeddings using Cohere's embedding model.
3. LLM Interaction: When you ask a question, the application uses the Groq API (with Llama 3) to find the most relevant information in the document and generate a response.

• Docker installed on your machine.
• API keys for:
  • LlamaParse (LLAMA_CLOUD_API_KEY)
  • Groq (GROQ_API_KEY)
  • Cohere (COHERE_API_KEY)

1. Build the Docker Image:

   docker build -t document-qa .

2. Run the Docker Container:

   Replace your_llama_cloud_key, your_groq_key, and your_cohere_key with your actual API keys.

   docker run -p 7860:7860 \
     -e LLAMA_CLOUD_API_KEY="your_llama_cloud_key" \
     -e GROQ_API_KEY="your_groq_key" \
     -e COHERE_API_KEY="your_cohere_key" \
     document-qa

3. Access the Application:

   Open your web browser and go to http://localhost:7860.

You can deploy this application to Hugging Face Spaces directly from this repository.

1. Create a new Hugging Face Space:

   • Go to huggingface.co/new-space.
   • Give your Space a name.
   • Select Docker as the Space SDK.
   • Choose "Docker from scratch".
   • Create the Space.

2. Upload the files:

   • Upload app.py, requirements.txt, and Dockerfile to your Hugging Face Space repository.

3. Add Secrets:

   • In your Space's settings, go to the Secrets section.
   • Add the following secrets with your API keys:
     • LLAMA_CLOUD_API_KEY
     • GROQ_API_KEY
     • COHERE_API_KEY

4. Deploy:

   • Hugging Face will automatically build the Docker image from your Dockerfile and deploy the application. Once the build is complete, your application will be live.