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https://github.com/sankalp1999/code_qa
RAG on codebases using treesitter and LanceDB
https://github.com/sankalp1999/code_qa
jina lancedb openai-embeddings rag tree-sitter
Last synced: 3 days ago
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RAG on codebases using treesitter and LanceDB
- Host: GitHub
- URL: https://github.com/sankalp1999/code_qa
- Owner: sankalp1999
- License: mit
- Created: 2024-04-29T20:43:34.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T18:12:03.000Z (3 months ago)
- Last Synced: 2025-02-08T21:39:34.663Z (12 days ago)
- Topics: jina, lancedb, openai-embeddings, rag, tree-sitter
- Language: Python
- Homepage:
- Size: 147 KB
- Stars: 80
- Watchers: 3
- Forks: 13
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
**Update 14th November 2024**: Check the branch [`feature/optimization`](https://github.com/sankalp1999/code_qa/tree/feature/optimization) which runs 2.5x faster than this branch's code (what you saw in the demo video).
The optimized branch achieves 2.5x faster performance (10-20 seconds range) through two key improvements:
1. **Optimized Token Management** - Reduced HYDE max tokens to 400 and HYDE-v2 to 768. Keeps hallucinated output concise while maintaining relevance. Result: ~5-10 seconds saved
2. **Enhanced Context Processing** - Implemented SambaNova Llama 3.1 8b (1000 tok/s) for context filtering and changed gpt4o -> SambaNova Llama 3.1-70b (400 tok/s) for chat responses. Improved context relevance and processing speed. Result: ~20 seconds saved
Worst case scenario codeQA works under 20 seconds as compared to the previous 40 seconds.
## What is CodeQA?
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 and Java with a clean, minimal UI.
Blog Links:
[An attempt to build cursor's @codebase feature - RAG on codebases - part 1](https://blog.lancedb.com/rag-codebase-1/)
[An attempt to build cursor's @codebase feature - RAG on codebases - part 2](https://blog.lancedb.com/building-rag-on-codebases-part-2/)
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:
```bash
git clone https://github.com/sankalp1999/code_qa.git
```
2. Navigate to the project directory:
```bash
cd code_qa
```
3. Set up a Python virtual environment:
Treesitter is supported >=3.8 to 3.11
```bash
python3.11 -m venv venv
source venv/bin/activate
```
4. Install the required packages:
```bash
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"
```
## Building the Codebase Index
To build the index for the codebase, run the following script:
```
chmod +x index_codebase.sh
```
```bash
./index_codebase.sh
```
This will parse the codebase to get the code chunks, generate embeddings, references and store them in LanceDB.
## Usage
To start the server
```bash
python app.py
```
For example, to analyze a JavaScript project located in `/Users/sankalp/Documents/code2prompt/twitter-circle`, run:
```bash
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](LICENSE).