mirror of
https://github.com/JasonYANG170/CodeGeeX4.git
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43 lines
1.1 KiB
Markdown
43 lines
1.1 KiB
Markdown
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[English](README.md) | [中文](README_zh.md)
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## RAG Functionality
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CodeGeeX4 supports RAG retrieval enhancement and is compatible with the LlamaIndex framework to achieving project-level retrieval Q&A.
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## Usage Tutorial
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### 1. Install Dependencies
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```bash
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cd llamaindex_demo
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pip install -r requirements.txt
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```
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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
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this project.
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### 2. Configure Embedding API Key
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This project uses the Zhipu Open Platform's Embedding API to implement vectorization. Please register and obtain an API Key first.
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Then configure the API Key in `models/embedding.py`.
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For details, refer to https://open.bigmodel.cn/dev/api#text_embedding
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### 3. Generate Vector Data
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```bash
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python vectorize.py --workspace . --output_path vectors
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>>> File vectorization completed, saved to vectors
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```
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### 4. Run the Q&A Script
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```bash
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python chat.py --vector_path vectors
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>>> Running on local URL: http://127.0.0.1:8080
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```
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## Demo
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