Deep Search Lighting

by positive666

2 stars

230 downloads

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GitHub

About

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 and mcp server for easy develo

Details

Author: positive666
GitHub stars: 2
Downloads: 230
Categories: Search

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Deep Search Lighting

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

From the repository

Install dependencies via pip install -r requirements.txt, rename .env.examples to .env, and fill in your model API information (OpenAI‑style APIs). Run the test case with python test_demo.py, launch the Streamlit demo with streamlit run streamlit_app.py, or start the built‑in MCP server with python mcp_server.py and connect the client via python langgraph_mcp_client.py.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "deep search lighting": {
            "deep_search_lightning": {
                "command": "python",
                "args": [
                    "test_demo.py"
                ]
            }
        }
    }
}

McpServers

{
    "deep_search_lightning": {
        "command": "python",
        "args": [
            "test_demo.py"
        ]
    }
}

Deep Search Lightning

✨ 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.

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About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Deep Search Lightning

✨ Why deepsearch_lightning?

✨ Features

📺 DEMO

🔄 Piepline

🚀 Quick Start

1. Installation

🔧Configuration

🚀 RUN

Planning

License

Install

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