Developing a RAG-Based Chatbot

I really enjoyed building this project over the last couple of weeks. My interest in the All-In Podcast, particularly the recent episodes interviewing 2024 Presidential candidates, fueled my curiosity to learn more about them. Although I've experimented with one-off scripts using OpenAI and RAG, I hadn't built anything substantial until now. Constructing a chatbot gave me the chance to integrate various elements together, allowing me to understand the nuances and interactions between these different components.

In the video below, I walk through the decisions made and considerationsactual code behind building the application. These include:

1. Speech to text with Distil

2. Data cleanup and preparation: automating it, deciding on structure and manual review

3. Vector database embedding using FAISS

4. Model development: developing and testing multiple models at scale

5. UI setup with Streamlit

Watch the video

Here is the slide deck I covered in the video. I would suggest reviewing this as your primary source of information.

The repository is hosted here: https://github.com/eoinclancy1/allin-rag-chatbot

Overview

** Take the below notes with a pinch of salt. I used GPT to generate them so please use the video, deck and the GitHub repository as your primary sources of information. **

This comprehensive exploration delves into the development of a chatbot based on Retrieval-Augmented Generation (RAG) technology. It covers the journey from initial data collection to the final deployment, focusing on integrating RAG with a language model like GPT. The exploration also touches upon the challenges faced and the methods employed to overcome them.

Concept and Process

The project's goal was to assess the effectiveness of RAG in enhancing chatbot interactions, particularly with a focus on discussions around the 2024 presidential candidates. This exploration details the entire process, from sourcing data to the chatbot's final implementation.

Approach

1. Data Handling and Preparation: Podcasts were the main data source, converted into text and rigorously cleaned to form a structured question-answer format.

2. RAG Integration: The core of the chatbot's design was the RAG architecture, merging a retriever model with a generative language model. This allowed the chatbot to use external data sources to provide updated and context-relevant responses.

3. Model Development and Trials: Various models and methods were tested. The process included single model approaches and more complex conversational models, highlighting the importance of iterative testing to enhance accuracy and responsiveness.

Key Findings

Application and Performance

The chatbot, implemented using Streamlit, demonstrated proficiency in accurately answering queries about specific presidential candidates, showing an ability to handle complex queries with contextually relevant responses.

Challenges and Solutions

Notable challenges encountered and addressed included:

• Transcription and Data Cleaning: Converting podcast audio to text and ensuring data accuracy was a significant challenge, requiring meticulous attention to detail.

• Data Optimization: Key decisions regarding data chunk size and overlap were essential for effective data querying and response generation, requiring careful optimization.

• Prompt Engineering and Model Refinement: Ongoing adjustments to prompts and model tuning were crucial in reducing errors and improving response relevance.

Insights for Future Development

Potential areas for enhancement identified included:

• Diversifying Data Sources: Broadening the range of data sources could substantially improve the chatbot's knowledge base and the quality of its responses.

• Language Model Customization: Tailoring the language model for specific contexts and removing any biased or inaccurate content could significantly boost chatbot performance.

• Automation in Testing: Implementing more sophisticated methods for prompt testing and feedback could streamline the model improvement process.

Implications and Potential Applications

The insights from this exploration are valuable for those developing sophisticated chatbots using RAG architecture. The approaches and solutions presented can inform similar AI projects, especially in scenarios requiring nuanced and context-aware responses.

Conclusion

This exploration contributes important insights into the creation of a RAG-based chatbot. The methods, challenges, and solutions discussed offer foundational knowledge for future projects in AI and chatbot development, particularly for those aiming to leverage retrieval-augmented models for advanced conversational AI capabilities. It underscores the importance of iterative development and the fine-tuning process in AI-driven chatbot technology.