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Local Code Vector Database based on ChatGPT

Introduction

🌍 中文文档

🤖️ This project utilizes the fully open-source milvus to build a local code vector database, enabling local, personal, and company-level code vectorization. It supports the creation of function and feature-level code vector databases by parsing code files at the function level. It leverages ChatGPT to obtain the semantic meaning of functions and utilizes Sentence-BERT for word embeddings.

Scenarios

  • Building personal or company-level code vector libraries
  • Uploading code files to establish function and feature-level code vector databases
  • Searching for your own code using prompts
  • Generating frameworks and code based on requirement documents

List

  • support openai api key pool，Support for multi threading Requests to OpenAI API.
  • Support uploading Python, Java, and C++ code files
  • Parse code at the function level
  • Obtain semantic meaning of parsed functions using ChatGPT
  • Build code vector libraries based on semantic meaning
  • Support various open-source vector databases
  • Support searching on a web interface
  • Support code optimization using ChatGPT
  • Support translation of custom code to other languages using ChatGPT
  • Support supervised adjustments

Dependencies

  • python = 3.10
  • antlr4-python3-runtime
  • milvus
  • openai
  • Sentence-BERT

Local Deployment

1. Setting up the environment

# you can use conda to install the environment
$ conda create -p /your_path/env_name python=3.10
# Activate the environment
$ source activate /your_path/env_name
# Deactivate the environment
$ source deactivate /your_path/env_name
# Remove the environment
$ conda env remove -p  /your_path/env_name
  • Project dependencies
# Clone the repository
$ git clone https://github.com/Trree/code-vector-database.git

$ cd code-vector-database
# Install dependencies
$ pip install -r requirements.txt

2. Install Milvus

3. Configuration

  1. Find the file named .env.template in the main code-vector-database folder. This file may be hidden by default in some operating systems due to the dot prefix. To reveal hidden files, follow the instructions for your specific operating system: Windows, macOS.
  2. Create a copy of .env.template and call it .env; if you're already in a command prompt/terminal window: cp .env.template .env.
  3. Open the .env file in a text editor.
  4. Find the line that says OPENAI_API_KEY=.
  5. After the =, enter your unique OpenAI API Key without any quotes or spaces.
  6. Please provide the Milvus keys services you would like to use.
  7. Save and close the .env file.

4. Run Scripts to Experience Web UI

  • Start python app.py.
  • Connect to 127.0.0.1:5000 on your browser.