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--- |
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title: AB Testing RAG Agent |
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emoji: 🤖 |
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colorFrom: blue |
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colorTo: green |
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sdk: docker |
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sdk_version: 3.14 |
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app_port: 8501 |
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pinned: false |
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--- |
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# AB Testing RAG Agent |
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This application is a Streamlit-based frontend for an AB Testing QA system that uses a carefully designed retrieval-augmented generation (RAG) approach with a LangGraph architecture. |
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## Features |
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- QA system specialized in AB Testing topics |
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- Intelligent query routing with LangGraph |
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- Source citations for all answers |
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- Streamlit interface for easy interaction |
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## Setup for Development |
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### Prerequisites |
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- Python 3.9+ |
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- OpenAI API key |
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- Huggingface account and token (for deployment) |
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### Environment Setup |
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1. Clone this repository |
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2. Create a `.env` file in the root directory with the following content: |
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``` |
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OPENAI_API_KEY=your_openai_api_key_here |
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HF_TOKEN=your_huggingface_token_here |
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``` |
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### Process the PDFs |
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Before running the app, you need to process the PDF files to create the vectorstore: |
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```bash |
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python process_data.py |
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``` |
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This will: |
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1. Load PDFs from `notebook_version/data/` |
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2. Process, chunk, and embed the documents |
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3. Create a Qdrant vectorstore in `data/processed_data/` |
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### Running the App Locally |
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Once the data is processed, you can run the Streamlit app: |
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```bash |
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streamlit run app/app.py |
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``` |
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## Deployment to Huggingface Spaces |
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### Prerequisites for Deployment |
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1. Huggingface account |
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2. Docker installed locally |
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### Steps to Deploy |
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1. Process the PDFs locally: `python process_data.py` |
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2. Build the Docker image: `docker build -t ab-testing-qa .` |
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3. Create a new Huggingface Space (Docker-based) |
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4. Add your Huggingface token and OpenAI API key as secrets in the space |
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5. Push the Docker image to Huggingface |
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### Huggingface Spaces Configuration |
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The application is configured to use the following secrets: |
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- `OPENAI_API_KEY`: Your OpenAI API key |
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- `HF_TOKEN`: Your Huggingface token |
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## System Architecture |
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The AB Testing QA system uses a sophisticated LangGraph architecture: |
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1. **Initial RAG Node**: Retrieves documents and attempts to answer the query |
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2. **Helpfulness Judge**: Determines if: |
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- The query is related to AB Testing |
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- The initial response is helpful enough |
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3. **Agent Node**: If needed, uses specialized tools to improve the answer: |
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- Standard retrieval tool |
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- Query-rephrasing retrieval tool |
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- ArXiv search tool |
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## Data Processing |
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The system processes PDFs using a specific approach: |
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1. Merges PDF pages while maintaining page metadata |
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2. Uses RecursiveCharacterTextSplitter with specific parameters |
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3. Embeds using OpenAI's text-embedding-3-small model |
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4. Stores in a Qdrant vectorstore |
