Project Overview

The AI News Research Agent is an intelligent system that automates news research and synthesis using modern AI technologies. It combines Google’s Gemini 2.0 Flash model with Tavily’s search capabilities in a type-safe framework powered by Pydantic-AI.

Example image of the AI News Research Agent UI

System Architecture

flowchart TB
    subgraph Development["Development Environment"]
        UV[UV Package Manager] --> |Manages Dependencies| App
    end
    
    subgraph App["Application Core"]
        UI[Streamlit UI] --> Agent
        
        subgraph PydanticAI["Pydantic AI Framework"]
            Agent[Type-Safe Agent System] --> |Structured Prompts| ModelInterface
            ModelInterface[Model Interface] --> |Type Validation| Gemini[Gemini 2.0 Flash]
            Agent --> |Dependency Injection| Tools
            Tools[Tool System] --> Search[Tavily Search]
            
            Results[Research Results] --> |Schema Validation| Agent
        end
        
        Search --> Agent
        Gemini --> ModelInterface
        Results --> UI
    end

    classDef primary fill:#4c75a6,stroke:#fff,stroke-width:2px,color:#fff
    classDef secondary fill:#82b1ff,stroke:#fff,stroke-width:2px,color:#fff
    classDef rust fill:#F74C00,stroke:#fff,stroke-width:2px,color:#fff
    classDef pydantic fill:#E92063,stroke:#fff,stroke-width:2px,color:#fff
    
    class UI,Results primary
    class ModelInterface,Tools secondary
    class UV rust
    class Agent,PydanticAI pydantic

Key Features

  1. Type-Safe Architecture

    • Built with Pydantic-AI for robust type checking
    • Structured data validation throughout the pipeline
    • Clear interface definitions for all components

  2. Advanced LLM Integration

    • Uses Google’s Gemini 2.0 Flash model
    • Efficient prompt management
    • Structured output generation

  3. Real-Time Search

    • Integration with Tavily Search API
    • Configurable search parameters
    • Asynchronous operation

  4. User-Friendly Interface

    • Built with Streamlit
    • Interactive parameter adjustment
    • Clear result presentation

Technical Implementation

Core Components

  1. Type Definitions
class ResearchResult(BaseModel):
    research_title: str = Field(description='Markdown heading describing the article topic')
    research_main: str = Field(description='A detailed news article')
    research_bullets: str = Field(description='Key points summary')

@dataclass
class SearchDataclass:
    max_results: int
    todays_date: str
  1. Agent Configuration
search_agent = Agent(
    model,
    deps_type=ResearchDependencies,
    result_type=ResearchResult,
    system_prompt=(
        "You are a helpful research assistant and an expert in research. "
        "Given a single user query, you will call the 'get_search' tool exactly once, "
        "then combine the results."
    )
)
  1. Search Tool Integration
@search_agent.tool
async def get_search(search_data: RunContext[SearchDataclass], query: str) -> dict:
    """Perform a search using the Tavily client."""
    results = await tavily_client.get_search_context(
        query=query,
        max_results=search_data.deps.max_results
    )
    return json.loads(results)

Technical Implementation details

  1. Dependency Injection

    • Clean separation of concerns
    • Easily testable components
    • Flexible configuration

  2. Async Operations

    • Non-blocking search operations
    • Efficient resource utilization
    • Improved response times

  3. Type Safety

    • Runtime type checking
    • Clear interface definitions
    • Reduced potential for errors

  4. Structured Output

    • Consistent response format
    • Validated data structures
    • Easy integration with frontend

Use Cases

  1. News Research

    • Quick synthesis of current events
    • Multi-source information gathering
    • Automated summarization

  2. Topic Analysis

    • Deep dives into specific subjects
    • Cross-reference multiple sources
    • Structured insights generation

  3. Trend Monitoring

    • Track emerging topics
    • Analyze developing stories
    • Identify key patterns

Future Development

  1. Enhanced Search

    • Multiple search provider support
    • Advanced filtering options
    • Custom search parameters

  2. Improved Processing

    • Advanced content synthesis
    • Better source verification
    • Enhanced summarization

  3. UI Enhancements

    • More interactive features
    • Custom visualization options
    • Advanced result filtering

Source Code

The complete source code is available on GitHub: AI LLM Tutorials Repository

Technical Stack

  • Frontend: Streamlit
  • AI Model: Google Gemini 2.0 Flash
  • Search: Tavily API
  • Framework: Pydantic-AI
  • Language: Python 3.9+

Conclusion

The AI News Research Agent demonstrates the practical application of modern AI technologies in creating useful tools for information gathering and synthesis. Its type-safe architecture ensures reliability while providing powerful capabilities for automated news research.

The project serves as an example of how to combine different AI services into a cohesive, production-ready application while maintaining code quality and type safety.