Welcome to an exciting journey where you learn how to build a robust AI application that fetches real-time discounts from various online marketplaces! This guide will walk you through the process of creating a Python application utilizing the ChatGPT Python API to provide users with current sales information efficiently.

What You Need to Get Started

• Python 3.10 or above
• Pip for managing packages
• OpenAI API Key
• (Optional) Rainforest API Key for additional data
• Basic familiarity with the command line

How to Set Up the Project

We’ll set up the project step-by-step:

Step 1: Clone the Repository

First, you’ll need to clone the GitHub repository using the command:

git clone https://github.com/Boburmirzo/chatgpt-api-python-sales.git

Next, navigate into the project folder:

cd chatgpt-api-python-sales

Step 2: Set Environment Variables

Create a `.env` file in the root directory and add your configuration:

OPENAI_API_TOKEN=YOUR_OPENAI_API_KEY
HOST=0.0.0.0
PORT=8080
...

Be sure to replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API Key.

Step 3: Install Dependencies

Install the required packages using pip:

pip install --upgrade -r requirements.txt

Step 4: Run the Application

Start the application by navigating to the `llm_app` folder and running the main script:

python main.py

If everything works smoothly, you’re ready to go!

Understanding the Code Structure

Let’s dive into the code! The code snippet given is like a recipe in a cookbook. Imagine you want to bake a cake (your real-time AI-enabled data pipeline). Here’s how the process works step-by-step:

• First, you check the ingredients (user queries).
• Next, you prepare the mixing bowl (set up the HTTP REST connector for API interaction).
• You mix the basic ingredients (gather and clean data from sources like JSON lines).
• Then, you fold in the fancy flavors (compute embeddings using the OpenAI API).
• Finally, you pop the cake in the oven (run the data pipeline to get real-time results).

The Code Snippet

query, response_writer = pw.io.http.rest_connector(
    host=host,
    port=port,
    schema=QueryInputSchema,
    autocommit_duration_ms=50,)

sales_data = pw.io.jsonlines.read(
    .examplesdata,
    schema=DataInputSchema,
    mode=streaming)

embedded_data = embeddings(context=sales_data, data_to_embed=sales_data.doc)

index = index_embeddings(embedded_data)

embedded_query = embeddings(context=query, data_to_embed=pw.this.query)

responses = prompt(index, embedded_query, pw.this.query)

response_writer(responses)

pw.run()

Using the Application

Once the application is running, you can ask questions about discounts. For instance, you might query:

"Can you find me discounts this month for Nike men's shoes?"

The application combines real-time data from various sources and responds with the most relevant discounts available!

Troubleshooting Steps

If you run into issues during your setup or usage, try the following:

• Double-check that your API keys are correctly set in the `.env` file.
• Ensure all required packages are installed properly.
• Make sure Docker is running if you are using Docker to set up the application.
• If the application is not responding correctly, Restart the server.

For any persistent issues, feel free to reach out for support or collaboration. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

You have successfully set up a powerful application that utilizes the ChatGPT Python API to fetch real-time sales discounts. The possibilities for enhancement are endless, including support for various data types and sources, making this a robust tool in the realm of AI development.

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.