README.md

metadata

title: GAIA Agent with Deep Research
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.25.2
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_expiration_minutes: 480
license: mit

GAIA Agent with Deep Research

An intelligent agent built with LangGraph for the GAIA Benchmark, featuring multi-source research capabilities.

🌟 Features

• Multi-Source Research: Combines Wikipedia, Web Search, and Academic papers
• RAG Enhancement: Uses vector database for few-shot learning
• Tool Orchestration: Smart tool selection based on question type
• Optimized System Prompt: Guides the agent to choose appropriate tools

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Configure API Keys

Create a .env file:

HUGGINGFACEHUB_API_TOKEN=your_hf_token
TAVILY_API_KEY=your_tavily_key

3. Setup Vector Database

python setup_chromadb.py

4. Run the Agent

python app.py

📁 Project Structure

hf_submission/
├── agent.py                 # Main agent implementation
├── deep_research_tool.py    # Multi-source research tool
├── app.py                   # Gradio UI
├── system_prompt.txt        # Optimized system prompt
├── setup_chromadb.py        # Vector DB initialization
├── requirements.txt         # Dependencies
└── metadata.jsonl           # Training data

🔧 Configuration

Choosing LLM Provider

Edit agent.py line 147:

graph = build_graph(provider="huggingface")  # or "groq", "google"

Adjusting Deep Research

Edit deep_research_tool.py:

# Change search sources
WikipediaLoader(query=query, load_max_docs=2)  # Adjust max_docs
TavilySearchResults(max_results=10)             # Adjust max_results
ArxivLoader(query=query, load_max_docs=5)      # Adjust max_docs

# Change content length
"content": doc.page_content[:2000]  # Adjust truncation

🎯 How It Works

Agent Architecture

START → Retriever → Assistant → Tools → Assistant → END
           ↓           ↓           ↓
      Vector DB    LLM Decision  Execute

Tool Selection Strategy

The agent automatically selects tools based on question type:

• Complex topics → deep_research (multi-source verification)
• Simple facts → wiki_search (quick lookup)
• Recent events → web_search (latest info)
• Academic topics → arxiv_search (papers)
• Calculations → math tools (multiply, add, etc.)

Deep Research Process

1. Multi-Source Search: Queries Wikipedia, Web, and Arxiv in parallel
2. Deduplication: Removes redundant information using content hashing
3. Formatting: Creates structured report with source attribution

📊 Example Usage

from agent import build_graph
from langchain_core.messages import HumanMessage

# Build agent
graph = build_graph(provider="huggingface")

# Ask question
question = "What is quantum computing and its applications?"
result = graph.invoke({"messages": [HumanMessage(content=question)]})

# Get answer
print(result["messages"][-1].content)

🛠️ Advanced Features

System Prompt

The optimized system prompt (system_prompt.txt) guides the agent to:

• Choose appropriate tools for different question types
• Prioritize deep_research for complex topics
• Cross-validate important information
• Cite sources when possible

Vector Database (RAG)

Uses ChromaDB to store past questions and answers:

• Provides few-shot examples to the agent
• Improves accuracy on similar questions
• Automatically populated from metadata.jsonl

🎓 Educational Resources

This project includes comprehensive documentation:

• Architecture deep dive
• Tool calling mechanism explained
• Deep research tutorial
• Integration guide

(See full project repository for detailed docs)

📝 License

MIT

🤝 Contributing

This is an educational project for the GAIA Benchmark. Feel free to:

• Report issues
• Suggest improvements
• Share your results

📮 Contact

For questions or feedback, please open an issue on the repository.

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Built with: LangGraph, LangChain, HuggingFace, ChromaD