CodeGeeX4/llamaindex_demo/README.md
2024-07-05 09:33:53 +08:00

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![](../resources/logo.jpeg)
[English](README.md) | [中文](README_zh.md)
## 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
```bash
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
```bash
python vectorize.py --workspace . --output_path vectors
>>> File vectorization completed, saved to vectors
```
### 4. Run the Q&A Script
```bash
python chat.py --vector_path vectors
>>> Running on local URL: http://127.0.0.1:8080
```
## Demo
![](resources/demo.png)