David Van Dijcke - Econometric Research Assistant

An AI-powered assistant specializing in David Van Dijcke’s econometric research. David is an econometrician on the 2025-26 job market who develops novel methods for functional and high-dimensional data.

Available Versions

1. app.py - Original version with basic chunking
2. app_improved.py - Enhanced version with better prompts
3. app_full_context.py - Full paper loading with Gemini’s large context window
4. app_optimized.py - Smart retrieval with section extraction and caching

Features

• Econometric Methods Focus: Detailed information about David’s methodological contributions
• Job Market Paper (R3D): Regression Discontinuity Design with Distribution-Valued Outcomes
• Technical Expertise: Functional data analysis, optimal transport, and geometric measure theory
• Policy Applications: How David applies econometric tools to answer questions with big data
• Research Portfolio: Information on FDR, DISCO, RTO, and other papers

New Improvements

• Full Paper Loading: Reads complete PDFs instead of just first few pages
• Large Context Window: Leverages Gemini 2.0 Flash’s 1M+ token context
• Smart Retrieval: Query-type based retrieval (technical, overview, application)
• Section Extraction: Intelligent parsing of paper sections
• Response Caching: Instant responses for repeated queries
• Hierarchical Search: Both section-level and chunk-level retrieval

Getting the Best Performance

For high quality, accurate responses at very low cost, use Google’s Gemini 2.5 Flash:

Step 1: Get a Google AI API Key

1. Go to https://aistudio.google.com/app/apikey
2. Click “Create API key”
3. Choose “Create API key in new project” (or select existing project)
4. Copy the API key

Step 2: Add API Key to Your Space

1. Go to your Space settings: https://huggingface.co/spaces/dvdijcke/david-research-assistant/settings
2. Scroll down to “Repository secrets”
3. Click “New secret”
4. Name: GOOGLE_API_KEY
5. Value: Paste your API key
6. Click “Add secret”

The Space will automatically restart and use Gemini 2.5 Flash for much more accurate responses!

Performance comparison:

• With Google API: Gemini 2.5 Flash - High accuracy, extremely low cost
• Without API: Limited mode - Lower quality responses

Note: Gemini is much cheaper than OpenAI (~$0.001-0.005 per conversation) while still providing excellent accuracy.

Deployment

This space is designed to run on Hugging Face Spaces with CPU inference.

Local Development

Option 1: Using UV (Recommended)

1. Install UV:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Create virtual environment and install dependencies:

   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   uv pip install -e .
   

3. Copy environment file and add your API key:

   cp .env.example .env
   # Edit .env and add your GOOGLE_API_KEY
   

4. Run the app:

   python app.py
   

Option 2: Using pip

1. Install requirements:

   pip install -r requirements.txt
   

2. Run the app:

   python app.py
   

See README_UV_SETUP.md for detailed UV setup instructions.