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Ravi
Ravi

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Sample python script: using Text-Bison model via Azure OpenAI

#azure #llm #textbison

To use the Text-Bison model via Azure OpenAI, you need to set up your Azure account and configure the necessary resources. Below is a sample Python script that demonstrates how to interact with the Text-Bison model using Azure OpenAI.

Prerequisites

  1. Azure Account: Create an Azure account if you don’t have one.
  2. Create an OpenAI Resource: Set up an OpenAI resource in the Azure portal and obtain your endpoint and API key.
  3. Install Required Libraries: 
 pip install requests
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Sample Python Script

Here's a basic script to interact with the Text-Bison model:

import requests # Azure OpenAI configuration endpoint = "https://<your-endpoint>.openai.azure.com/" api_key = "<your-api-key>" deployment_name = "text-bison" # Your deployment name  def generate_text(prompt): url = f"{endpoint}openai/deployments/{deployment_name}/completions?api-version=2023-05-15" headers = { "Content-Type": "application/json", "api-key": api_key } # Define the request body  data = { "prompt": prompt, "max_tokens": 100, "temperature": 0.7 } # Make the request to the Azure OpenAI API  response = requests.post(url, headers=headers, json=data) if response.status_code == 200: return response.json()['choices'][0]['text'].strip() else: print(f"Error: {response.status_code} - {response.text}") return None if __name__ == "__main__": prompt = "What are the benefits of using AI in healthcare?" generated_text = generate_text(prompt) if generated_text: print("Generated Text:", generated_text)
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Explanation

  1. Configuration: Replace <your-endpoint> and <your-api-key> with your Azure OpenAI endpoint and API key. The deployment_name should match the name of your Text-Bison model deployment.

  2. Function to Generate Text:

    • The generate_text function constructs the API request.
    • It sets the necessary headers for authentication and specifies the request body, including the prompt and parameters like max_tokens and temperature.

  3. Making the Request:

    • The script uses the requests library to send a POST request to the Azure OpenAI API.
    • If the request is successful, it returns the generated text; otherwise, it prints an error message.

  4. Execution: The script runs a prompt and prints the generated text.

Notes

  • Ensure you have set the proper permissions and configurations in your Azure portal.
  • Adjust parameters such as max_tokens and temperature based on your requirements.
  • Make sure you handle any API limits or quotas as specified by Azure.

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