deep_search_lightning

# Deep Search Lighting [](README_zh.md) 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 ```bash 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 ```bash 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](LICENSE).