CodeGeeX4/llamaindex_demo

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RAG Functionality

CodeGeeX4 supports RAG retrieval enhancement and is compatible with the LlamaIndex framework to achieving project-level retrieval Q&A.

Usage Tutorial

1. Install Dependencies

cd llamaindex_demo
pip install -r requirements.txt

Note: This project uses tree-sitter-language, which has compatibility issues with Python 3.10, so please use Python 3.8 or Python 3.9 to run this project.

2. Configure Embedding API Key

This project uses the Zhipu Open Platform's Embedding API to implement vectorization. Please register and obtain an API Key first. Then configure the API Key in models/embedding.py. For details, refer to https://open.bigmodel.cn/dev/api#text_embedding

3. Generate Vector Data

python vectorize.py --workspace . --output_path vectors
>>> File vectorization completed, saved to vectors

4. Run the Q&A Script

python chat.py --vector_path vectors
>>> Running on local URL: http://127.0.0.1:8080

Demo