Chino is a terminal based chatbot based on ChatGPT.
Project description
Chino 🌸
🤔 Pre-requisites
python3
pdm
🐍 Python Version Support
This project is designed to be compatible with specific versions of Python for optimal performance and stability.
Supported Python Version
- Python 3.11.7
❗️ For the best experience and performance, it is recommended to use the version mentioned above.
Before diving into the project, ensure that you have the correct Python version installed. To check the version of Python you currently have, execute the following command in your terminal:
python --version
🐍 Installing Python 3.11.7 with pyenv
Protip: Managing multiple Python versions is a breeze with pyenv. It allows you to seamlessly switch between different Python versions without the need to reinstall them.
If you haven't installed pyenv
yet, follow their official guide to set it up.
Once you have pyenv
ready, install the recommended Python version by running:
pyenv install 3.11.7
When you navigate to this project's directory in the future,
pyenv
will automatically select the recommended Python version, thanks to the.python-version
file in the project root.
Installation 🛠️
Coming soon!
📦 Setup
Local setup 🛠️ with Docker 🐳
Coming soon!
Local setup 🛠️ without Docker 🐳
Setting Up the Project with PDM
PDM (Python Development Master) is utilized for dependency management in this project. To set up and run the project:
-
Installing PDM: Before you begin, ensure you have PDM installed. If not, refer to the official documentation to install PDM.
-
Clone the Repository: Get the project source code from GitHub:
git clone https://github.com/SAMAD101/Chino.git
-
Navigate to the Project Directory:
cd Chino
-
Install Dependencies: Use PDM to install the project's dependencies:
pdm install
To install dev dependencies:
pdm install -G dev
-
Start the Project: Use PDM to run the project:
pdm run start
Other commands are in
pyproject.toml
[tool.pdm.scripts]
⚠️ Note:
You will need an OpenAI API key to make it work. Get your API key from OpenAI website and set it as an environment variable:
export OPENAI_API_KEY="<your_api_key>"
Usage 📖
For using the Retrieval Augmented Generation (RAG) features, follow these steps:
-
You will need to put your documents in the
data/
directory in the root of the project. Createdata/
directory in the root of the project if it doesn't exist.mkdir data
-
Create a directory in the root of the project called
chroma/
. This directory will contain the OpenAI embeddings (embedding vectors) for the documents. -
Process the documents and create the embeddings using the following command:
pdm run process
Using Query mode:
Once your documents are processed. You can use the query mode to give prompts for the documents [RAG].
pdm run -q
or, you can use \q:
before your prompt to use it in query mode.
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