Multi-Query Web Search Application

An AI-augmented search application that expands your search query and explores multiple perspectives of your original question.

Features

• Query Expansion: Uses an OpenAI-compatible LLM to generate 5 diverse, related search queries
• Parallel Search: Performs multiple web searches using the DuckDuckGo search engine
• Result Deduplication: Removes duplicate results based on URLs
• AI Re-ranking: Re-ranks the combined search results based on relevance to your original query
• Clean UI: Simple, responsive user interface

How It Works

1. You enter a search query in the web interface
2. The backend generates 5 diverse, related search queries using an LLM
3. The application performs 5 parallel web searches using DuckDuckGo
4. Results are deduplicated and then re-ranked by the LLM based on relevance to your original query
5. The final, re-ranked results are displayed in the web interface

Setup Instructions

Prerequisites

• Python 3.9 or higher
• An OpenAI API key (or compatible API key for a local LLM)

Installation

1. Clone this repository or download the source code:

git clone https://github.com/yourusername/multi-query-search.git
cd multi-query-search

2. Create a virtual environment:

python -m venv venv

3. Activate the virtual environment:

• Windows:

  venv\Scripts\activate

• macOS/Linux:

  source venv/bin/activate

4. Install the required dependencies:

pip install -r requirements.txt

5. Create a .env file based on the .env.example file:

cp .env.example .env

6. Edit the .env file and add your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

Running the Application

1. Start the Flask server:

python app.py

2. Open your web browser and navigate to:

http://localhost:5000

Using a Local LLM

To use a local LLM instead of OpenAI's API:

1. Set up your local LLM with an OpenAI-compatible API (e.g., LM Studio, LocalAI, Ollama, etc.)
2. In your .env file, add the base URL of your local LLM API:

OPENAI_API_BASE=http://localhost:8000/v1
OPENAI_MODEL=your_local_model_name

Customization

• Search Results: You can adjust the number of results per query in the search_duckduckgo function in app.py
• Model Selection: Change the LLM model by setting the OPENAI_MODEL environment variable
• UI Styling: Modify the CSS in templates/index.html to change the appearance

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Uses the DuckDuckGo Search Python library
• Powered by OpenAI API or compatible LLM providers