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RAG Demo App: Presidential History Chatbot

What is this?

Welcome to the RAG Demo App, a chatbot that's like having a White House insider in your pocket (minus the security clearance)! This project showcases the power of Retrieval-Augmented Generation (RAG) in creating a specialized AI assistant focused on U.S. Presidential history, governmental structure, and up-to-date political insights.

What does it do?

Our chatbot is designed to:

  • Educate users on U.S. Presidential history
  • Provide accurate information about governmental structure and foundations
  • Reference recent resources that might not be in a pre-trained model's dataset

For example, you can ask questions like:

  • "What did Teddy Roosevelt say about nature?"
  • "What is Joe Biden doing about Ukraine now?"

How does it work?

  1. User Query: The app takes in a user's question about U.S. politics or history.

  2. Smart Search: It then searches through a curated collection of:

    • Speeches delivered by U.S. Presidents
    • Biographies of Presidents and First Ladies
    • Q&A from the White House website

  3. Context Building: The most relevant hits (including context and metadata) are formatted and fed into an LLM.

  4. AI Magic: The LLM, armed with the original query, relevant context, and specific instructions, crafts a response.

Technologies Used

  • LangChain: For orchestrating the whole show
  • HuggingFace: Providing models (Embedding, Tokenizer, Foundation Model)
  • LLM: LLaMa 3.2 3B for Q&A (Note: In production, I'd use a model with >8B parameters)
  • ChromaDB: For vector store and retrieval

The Process: From Concept to Chatbot

1. Data Preprocessing

  • Collected and curated a diverse set of presidential speeches, biographies, and official Q&As
  • Processed and formatted the data for optimal retrieval

2. RAG Architecture Implementation

  • Designed a system that efficiently retrieves relevant information based on user queries
  • Integrated the retrieval process with the LLM for coherent and informed responses

3. Continuous Improvement

  • Regularly evaluated and fine-tuned the system for better performance
  • Focused on enhancing retrieval accuracy and response quality

Challenges and Lessons Learned

Challenges:

  • Developing a robust retriever that goes beyond surface-level content searching
  • Creating an effective evaluation process for both retrieval and response quality
  • Optimizing the system to work within the constraints of Google Colab for extended coding sessions

Key Takeaways:

  1. Retrieval is King: A robust retriever is crucial for high-quality answers. Focus on retrieving the most important information (80/20 rule).
  2. Evaluation Matters: Invest time in creating ground truth retrieval examples and hand-made Q&A pairs for thorough testing.
  3. LLM Considerations:
    • LLMs can be resource-intensive. Consider quantization techniques to reduce computational costs.
    • For handling long messages, state-of-the-art LLMs (like those from OpenAI, Cohere, or Anthropic) might be necessary.
  4. It's Still Just a Web App: At its core, this AI system is similar to other web applications in terms of resource management and API design.

Results and Comparisons

While our specialized RAG Demo App shines in its focused domain, I found that ChatGPT-4 outperforms our bot in several areas:

  • General historical Q&A
  • Comparative analysis
  • Complex reasoning tasks

However, our system excels in:

  • Providing specialized information on U.S. Presidential history
  • Offering up-to-date insights on current governmental affairs
  • Delivering consistent and focused responses in its specialized domain

Future Improvements

  1. Implement more advanced retrieval techniques (e.g., Langchain SelfQueryRetriever, LlamaIndex AutoRetriever)
  2. Expand the knowledge base to cover more aspects of U.S. politics and history
  3. Explore fine-tuning options for even more accurate and contextually relevant responses
  4. Optimize deployment for better scalability and reduced latency
  5. Expand front end from basic Gradio demo interface

Get Involved

I'm always looking to improve! If you're interested in contributing or have suggestions, please:

  1. Fork the repository
  2. Create a new branch for your feature
  3. Submit a pull request with a clear description of your changes

Contact

For any questions or feedback, reach out at [email protected].

Demo Coming Soon!