README.md

metadata

title: Lets Talk
emoji: 🐨
colorFrom: green
colorTo: blue
sdk: docker
pinned: false

Welcome to TheDataGuy Chat! 👋

This is a Q&A chatbot powered by TheDataGuy blog blog posts. Ask questions about topics covered in the blog, such as:

• RAGAS and RAG evaluation
• Building research agents
• Metric-driven development
• Data science best practices

How it works

Under the hood, this application uses:

1. Snowflake Arctic Embeddings: To convert text into vector representations

   • Base model: Snowflake/snowflake-arctic-embed-l
   • Fine-tuned model: mafzaal/thedataguy_arctic_ft (custom-tuned using blog-specific query-context pairs)

2. Qdrant Vector Database: To store and search for similar content

   • Efficiently indexes blog post content for fast semantic search
   • Supports real-time updates when new blog posts are published

3. GPT-4o-mini: To generate helpful responses based on retrieved content

   • Primary model: OpenAI gpt-4o-mini for production inference
   • Evaluation model: OpenAI gpt-4.1 for complex tasks including synthetic data generation and evaluation

4. LangChain: For building the RAG workflow

   • Orchestrates the retrieval and generation components
   • Provides flexible components for LLM application development
   • Structured for easy maintenance and future enhancements

5. Chainlit: For the chat interface

   • Offers an interactive UI with message threading
   • Supports file uploads and custom components

Technology Stack

Core Components

• Vector Database: Qdrant (stores embeddings via pipeline.py)
• Embedding Model: Snowflake Arctic Embeddings
• LLM: OpenAI GPT-4o-mini
• Framework: LangChain + Chainlit
• Development Language: Python 3.13

Advanced Features

• Evaluation: Ragas metrics for evaluating RAG performance:
  • Faithfulness
  • Context Relevancy
  • Answer Relevancy
  • Topic Adherence
• Synthetic Data Generation: For training and testing
• Vector Store Updates: Automated pipeline to update when new blog content is published
• Fine-tuned Embeddings: Custom embeddings tuned for technical content

Project Structure

lets-talk/
├── data/                  # Raw blog post content
├── py-src/                # Python source code
│   ├── lets_talk/         # Core application modules
│   │   ├── agent.py       # Agent implementation
│   │   ├── config.py      # Configuration settings
│   │   ├── models.py      # Data models
│   │   ├── prompts.py     # LLM prompt templates
│   │   ├── rag.py         # RAG implementation
│   │   ├── rss_tool.py    # RSS feed integration
│   │   └── tools.py       # Tool implementations
│   ├── app.py             # Main application entry point
│   └── pipeline.py        # Data processing pipeline
├── db/                    # Vector database storage
├── evals/                 # Evaluation datasets and results
└── notebooks/             # Jupyter notebooks for analysis

Environment Setup

The application requires the following environment variables:

OPENAI_API_KEY=your_openai_api_key
VECTOR_STORAGE_PATH=./db/vector_store_tdg
LLM_MODEL=gpt-4o-mini
EMBEDDING_MODEL=Snowflake/snowflake-arctic-embed-l
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

Additional configuration options for vector database creation:

# Vector Database Creation Configuration
FORCE_RECREATE=False      # Whether to force recreation of the vector store
OUTPUT_DIR=./stats        # Directory to save stats and artifacts
USE_CHUNKING=True         # Whether to split documents into chunks
SHOULD_SAVE_STATS=True    # Whether to save statistics about the documents

Running Locally

Using Docker

docker build -t lets-talk .
docker run -p 7860:7860 \
    --env-file ./.env \
    lets-talk

Using Python

# Install dependencies
uv init && uv sync

# Run the application
chainlit run py-src/app.py --host 0.0.0.0 --port 7860

Deployment

The application is designed to be deployed on:

• Development: Hugging Face Spaces (Live Demo)
• Production: Azure Container Apps (planned)

Evaluation

This project includes extensive evaluation capabilities using the Ragas framework:

• Synthetic Data Generation: For creating test datasets
• Metric Evaluation: Measuring faithfulness, relevance, and more
• Fine-tuning Analysis: Comparing different embedding models

Future Enhancements

• Agentic Reasoning: Adding more sophisticated agent capabilities
• Web UI Integration: Custom Svelte component for the blog
• CI/CD: GitHub Actions workflow for automated deployment
• Monitoring: LangSmith integration for observability

License

This project is available under the MIT License.

Acknowledgements

• TheDataGuy blog for the content
• Ragas for evaluation framework
• LangChain for RAG components
• Chainlit for the chat interface

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference