In the world of artificial intelligence, OpenAI’s API is a powerful tool that opens up a variety of possibilities for developers looking to create chatbots and other intelligent applications. In this article, we’ll explore how to set up the OpenAI API using Flask, the Baichuan2-13B-Chat model, and ngrok for local development. Let’s walk through the setup step-by-step!

Prerequisites

• Basic knowledge of Python and Flask
• Python installed on your machine
• Access to a GPU (Tesla T4 is recommended)
• Git installed on your machine

Step-by-Step Guide

1. Clone the Repository

First, you will need to clone the repository that contains the required code:

git clone https://github.com/billvsme/my_openai_api.git

2. Set Up Baichuan2-13B-Chat Model

Change your directory to the cloned repository and install any necessary dependencies:

cd my_openai_api
git lfs install
git clone https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits

3. Create a Virtual Environment

To keep your project dependencies organized, create a virtual environment:

mkdir ~/.venv
python -m venv ~/.venv
source ~/.venv/bin/activate
pip install -r requirements.txt

4. Run the Flask Application

Your application is ready to be launched. Here’s the command to start your Flask app:

gunicorn -b 0.0.0.0:5000 --workers=1 my_openai_api:app

5. Configuring the LangChain

Use the LangChain library to set up your API calls. This framework will allow you to easily create chat models and use them:

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import HumanMessage

openai_api_base = "http://127.0.0.1:5000/v1"
openai_api_key = "test"

chat_model = ChatOpenAI(
    streaming=True,
    callbacks=[StreamingStdOutCallbackHandler()],
    openai_api_base=openai_api_base,
    openai_api_key=openai_api_key
)

resp = chat_model([HumanMessage(content="django admin demo")])
print(resp)

Understanding the Code with an Analogy

Think of setting up this application like building a sandwich. Each of the steps above represents a different ingredient you need to make that perfect sandwich:

• Cloning the Repository: This is like selecting the right bread for your sandwich; it’s the foundation.
• Setting Up the Model: Here, you are adding the fillings (the Baichuan2-13B-Chat model) that give your sandwich flavor.
• Creating a Virtual Environment: This step is your sandwich wrapper, keeping everything neat and together.
• Running the Flask Application: This is akin to the moment you take your sandwich out of the kitchen and serve it up—it’s time to enjoy!
• Configuring LangChain: This is where you add the condiments, ensuring everything blends together seamlessly when you take a bite.

Troubleshooting

If you run into issues during your setup, here are some common troubleshooting tips:

• Ensure that the Flask server is running properly by checking your terminal for any error messages.
• Double-check that you have installed all necessary dependencies listed in the requirements file.
• If ngrok is not connecting, verify that you have set the correct authentication token and that ngrok is properly installed.

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Conclusion

Congratulations! You have successfully set up your OpenAI API with Flask using the Baichuan2-13B-Chat model. This tutorial provided a comprehensive guide to get you started with building intelligent applications. With your newfound knowledge, the possibilities are endless!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.