Add AI research agent using smolagents

[codebyNJ]

This PR introduces a powerful, interactive Web Research Assistant built using Streamlit, integrating enhanced search tools, web scraping, AI agents, and markdown-based summarization. The tool is designed to provide recent, relevant, and readable information from the web, driven by the Hugging Face Qwen2.5-Coder-32B-Instruct model and DuckDuckGo search APIs.

🧠 Key Features:

Streamlit Interface: Intuitive, real-time search assistant with multi-tab results display (Results, Sources, Analysis, Logs).

• Enhanced Search Agent
• Webpage Content Extraction
• Agent Framework
• Logging & Debugging
• Export Capabilities
• Sidebar Settings

Install the dependencies

pip install streamlit pandas requests beautifulsoup4 markdownify python-dotenv smolagents huggingface_hub

Add your hugging face token

HF_TOKEN="hf_..."

Summary by CodeRabbit

• New Features
  • Introduced a web-based research assistant that performs enhanced web searches, retrieves and summarizes content, and prioritizes recent sources through an interactive interface.
• Documentation
  • Added a README with an overview, usage instructions, and a demo video link.
  • Included an example environment file for setting up authentication tokens.
• Chores
  • Added a requirements file listing necessary Python packages for the project.

[coderabbitai[bot]]

Walkthrough

A new research assistant application is introduced using the Smol Agents framework. The update adds a Streamlit-based interface, agent logic for enhanced web search and webpage retrieval, supporting utilities, and documentation. Dependency management and environment variable configuration are provided via requirements and example environment files.

Changes

File(s)	Change Summary
researchagent-smolagents/README.md	Added a README introducing the project, its purpose, usage, and demo link.
researchagent-smolagents/agents.py	Implemented the main Streamlit app, agent logic, search and webpage tools, logging utilities, and UI components.
researchagent-smolagents/requirements.txt	Added required Python dependencies for the project.
researchagent-smolagents/.env.example	Provided an example environment file with a placeholder for the Hugging Face token.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Streamlit UI
    participant ManagerAgent
    participant ToolCallingAgent
    participant EnhancedSearchTool
    participant VisitWebpageTool

    User->>Streamlit UI: Enter search query and settings
    Streamlit UI->>ManagerAgent: Run manager agent with query and parameters
    ManagerAgent->>ToolCallingAgent: Delegate search task
    ToolCallingAgent->>EnhancedSearchTool: Perform enhanced search
    EnhancedSearchTool->>ToolCallingAgent: Return search results
    ToolCallingAgent->>VisitWebpageTool: Fetch webpage content (as needed)
    VisitWebpageTool->>ToolCallingAgent: Return webpage content
    ToolCallingAgent->>ManagerAgent: Return aggregated results
    ManagerAgent->>Streamlit UI: Provide formatted response
    Streamlit UI->>User: Display results, sources, analysis, and logs

Poem

A rabbit hopped to Streamlit’s door,
With agents and tools, ready to explore.
It searched the web, fetched pages anew,
Summarized findings, all for you!
With tokens and logs, dependencies set,
This research bunny’s your best bet.
🐇✨

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[codegen-sh[bot]]

PR Review: Add AI research agent using smolagents

Thank you for this contribution! I've reviewed the PR which adds a new AI research agent using the smolagents framework. Here's my feedback:

Strengths

• Well-structured application: The Streamlit app is well-organized with clear separation of concerns between UI, search functionality, and agent orchestration.
• Comprehensive features: The implementation includes enhanced search, content extraction, recency scoring, and export capabilities.
• User-friendly interface: The multi-tab display (Results, Sources, Analysis, Logs) provides a good user experience.
• Error handling: The code includes proper error handling for web requests and agent operations.

Suggested Improvements

1. Security & Best Practices

• Environment Variables: As CodeRabbit suggested, rename .env to .env.example and use a placeholder for the token. Add .env to .gitignore to prevent accidental token commits.
• Web Requests: Add timeouts and proper user-agent headers to the visit_webpage function to prevent hanging on slow sites and avoid being blocked.
• Unused Code: Remove the unused HTTP request in the enhanced_search function.

2. Dependencies

• Missing Requirements: Add pandas and beautifulsoup4 to requirements.txt as they are imported but not listed.
• Version Pinning: Consider pinning dependency versions for more deterministic builds, e.g., streamlit>=1.34,<2.0.

3. Documentation

• Demo Link: The demo video link in the README appears to be broken. Please update with a working link.
• Setup Instructions: Consider adding more detailed setup instructions in the README, including:
  • How to create and activate a virtual environment
  • How to run the application (streamlit run agents.py)
  • Example queries to try

4. Performance Considerations

• Large Page Handling: Consider limiting download size for web pages (e.g., via stream=True and iter_content) to handle very large pages gracefully.
• Caching Strategy: The current caching implementation is good, but you might want to add TTL (time-to-live) for cached content.

Minor Issues

• There's a typo in the README title: "AI Reseach Agent" should be "AI Research Agent"

Overall, this is a valuable addition to the repository. With the suggested improvements, it will be even more robust and user-friendly. Great work!