codebase-intelligence

🧩 Tibo – a powerful command-line tool designed to index your codebase, generate call graphs, and chunk code into a vector database. With tibo, you can query your codebase using natural language and retrieve contextually relevant files, functions, and code snippets effortlessly.

Features

• Codebase Indexing: Scans and organizes your project for easy querying.
• Call Graph Generation: Maps relationships between functions and files.
• Vector Database: Embeds code chunks for fast, intelligent retrieval.
• Natural Language Queries: Ask questions about your code in plain English.
• Context-Aware Results: Returns relevant files and snippets with added context from the call graph.

Installation

Get started with tibo by installing:

pip install tibo

Find the latest version and additional details on the PyPI project page.

Usage

Follow these steps to integrate tibo into your workflow:

1. Configure the Tool - Set up tibo with your OpenAI API key:

tibo config

2. Index Your Project - Navigate to your project directory and index your codebase:

cd /path/to/your/project
tibo index

Note: This creates a .tibo folder in your project root to store indexed data, call graphs, and vector embeddings.

3. Query Your Codebase - Fetch relevant context by asking questions in natural language:

tibo fetch "my query to the codebase"

Results include the most relevant file names and code chunks. Full output is saved in .tibo/query_output/query_output.json.

4. NEW Interact with Tibo Agent - chat with the ai agent to understnad the codebase better and get help with implementing new features:

tibo agent

NOTE: requires running tibo config and adding ANTHROPIC_API_KEY when prompted. The agent can use the tibo fetching tools if you have run 'tibo index' before. In the shell:

• type 'exit' or 'quit' to quit the agent shell
• type '#' followed by a command to execute a command directly in your terminal
• type 'reset' to reset the conversation history

How It Works

Configuration: Link Tibo to your OpenAI API for LLM-powered enhancements.

Indexing: Processes codebase, builds call graph, chunks files, enhances with GPT-4o-mini, and stores vector embeddings locally.

Querying: Enhances your query with an LLM, matches it to the top relevant chunks, and supplements results with call graph context.

Requirements

Python 3.7+ An OpenAI API key (required for LLM functionality)

Contributing

We welcome contributions! Feel free to open issues or submit pull requests on our GitHub repository.

License

MIT License