AI-Powered Documentation Search

A powerful search tool that uses AI embeddings to search through documentation and provide contextual answers using various LLM providers.

Features

• Semantic search using AI embeddings
• Multiple LLM provider support (OpenAI, Groq, Ollama)
• Contextual answers from documentation
• GitHub link generation for source references
• Configurable search parameters
• Error handling and graceful fallbacks

Prerequisites

• Python 3.x
• Access to one of the supported LLM providers:
  • OpenAI API key
  • Groq API key
  • Ollama local installation

Installation

1. Clone the repository:

2. Install dependencies:

   pip install -r requirements.txt

3. Create a .env file with your API keys: env OPENAI_API_KEY=your_openai_key_here GROQ_API_KEY=your_groq_key_here

Configuration

• Set the default LLM provider in app.py:

  LLM_PROVIDER = 'openai'  # or 'groq' or 'ollama'

Usage

You can interact with the tool in two ways:

1. Interactive Mode

Run the script without a query.txt file:

python app.py 

This will start an interactive session where you can type queries and receive responses in real-time.

2. Query File Mode

Create a query.txt file in the root directory with your query:

echo "How do I implement Magic SDK authentication?" > query.txt
python app.py

The tool will:

• Automatically detect and read the query from query.txt
• Process the query and generate a response
• Display the answer along with relevant source references
• Exit after processing the query