Deep Search Lighting

Deep Search Lightning

A lightweight, pure web search solution for large language models, supporting multi-engine aggregated search, deep reflection and result evaluation. A balanced approach between web search and deep research, providing a framework-free implementation for easy developer integration.

✨ Why deepsearch_lightning?

Web search is a common feature for large language models, but traditional solutions have limitations:

• Limited search result quality and reflection effectiveness
• Requires powerful models and paid search engines
• Small models often struggle with tool calling patterns
• Contextual understanding can be unstable across different model sizes

Deep Search Lighting provides:

• Framework-free implementation with no restrictions
• Works with free APIs while maintaining good query quality
• Adjustable depth parameters to balance speed and results
• Reflection mechanism for model self-evaluation
• Supports models of any size, including smaller ones

[Experimental Planning]:

• Simplified design without web parsing or text chunking
• Considering adding RL-trained small recall models

✨ Features

• Multi-engine aggregated search:

  • ✅ Baidu (free)
  • ✅ DuckDuckGo (free but requires VPN)
  • ✅ Bocha (requires API key)
  • ✅ Tavily (requires registration key)

• Reflection strategies and controllable evaluation

• Custom pipelines for all LLM models

• OpenAI-style API compatibility

• Pure model source code for easy integration

• Built-in MCP server support

📺 DEMO

🔄 Piepline

🚀 Quick Start

1. Installation

    conda create -n deepsearch_lightning python==3.11
    conda activate deepsearch_lightning
    pip install -r requirements.txt
    # Optional: For langchain support
    pip install -r requirements_langchain.txt

🔧Configuration

1. Rename .env.examples to .env
2. Fill in your model information (currently supports OpenAI-style APIs)
3. Baidu search is enabled by default - configure other engines as needed

🚀 RUN

        1. test case
                python test_demo.py
        2. streamlit demo
                streamlit run streamlit_app.py
        3. run mcp server
                python mcp_server.py 
                python langgraph_mcp_client.py

Planning

🧪 RL-trained small recall QA model validation
🧪 Strategy improvements
🧪 Multi-agent framework implementation

🙌 Welcome to contribute your ideas! Participate in the project via [Issues] or [Pull Requests].

License

This repository is licensed under the Apache-2.0 License.