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An attempt to build cursor's @codebase feature - RAG on codebases - part 1

An attempt to build cursor's @codebase feature - RAG on codebases - part 2

A powerful code search and query system that lets you explore codebases using natural language. Ask questions about your code and get contextual answers powered by LanceDB, OpenAI gpt4o-mini/gpt4o and Answerdotai's colbert-small-v1 reranker. Supports Python, Rust, JavaScript, CSharp and Java with a clean, minimal UI.

Note: New OpenAI/Anthropic accounts may experience token rate limits. Consider using an established account.

Optimized Branch

Please read this quick write up about the optimizations here This branch runs 2.5x faster than the main branch in worst case.

What is CodeQA?

CodeQA helps you understand codebases by:

• Extracting code structure and metadata using tree-sitter AST parsing
• Indexing the code chunks using OpenAI/Jina embeddings and storing them in LanceDB
• Enabling natural language searches across the codebase by using @codebase in queries
• Providing context-aware answers with references
• Supporting interactive chat-based code exploration

Prerequisites

• Python 3.6 or higher
• Redis server running on localhost:6379

Setup

1. Clone the repository:

   git clone https://github.com/sankalp1999/code_qa.git

2. Navigate to the project directory:

   cd code_qa

3. Set up a Python virtual environment:

Treesitter is supported >=3.8 to 3.11

python3.11 -m venv venv
source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

5. Run the redis server

redis-server

Configuration

You only need to set the OpenAI API key. Jina API key is optional, if you want to use Jina embeddings instead of OpenAI.

Create a .env file and add the following:

OPENAI_API_KEY="your-openai-api-key"
JINA_API_KEY="your-jina-api-key"
SAMBANOVA_API_KEY="your-sambanova-api-key"

This branch uses SambaNova's API for faster LLM processing - 2x speed up over gpt4o-mini timings

Building the Codebase Index

To build the index for the codebase, run the following script:

chmod +x index_codebase.sh

./index_codebase.sh <absolute_path_to_codebase>

This will parse the codebase to get the code chunks, generate embeddings, references and store them in LanceDB.

Usage

To start the server

python app.py <folder_path>

For example, to analyze a JavaScript project located in /Users/sankalp/Documents/code2prompt/twitter-circle, run:

python app.py /Users/sankalp/Documents/code2prompt/twitter-circle

Once the server is running, open a web browser and navigate to http://localhost:5001 to access the code search and query interface.

Use @codebase keyword in queries to fetch context via embeddings Enable re-ranking option to get more relevant results

Technologies Used

• Flask: server and UI
• Treesitter: parsing methods, classes, constructor declarations in a language agnostic way using the abstract syntax tree
• LanceDB: vector db for storing and searching code embeddings
• Redis: in-memory data store for caching and session management
• OpenAI, Jina for chat functionalities and colbert-small-v1 for reranker

License

This project is licensed under the MIT License.