adalflow with OpenAI models through a developer platform.

[vijayendra-g]

Hey

I have a question about using OpenAI models through a developer platform.
With dspy , I can pass the developer platform endpoint ( base_url="https://llm.prod.xxx.com"), which looks something like

turbo = dspy.OpenAI(
            model='gpt-4o',
            api_key=bearer_token,
            base_url="https://llm.prod.xxx.com",
            temperature=0,
            max_tokens=1000,
        )
dspy.settings.configure(lm=turbo)

How to do similar thing in adalflow?

[ahsan3219]

Hello!

To configure adalflow to use a custom OpenAI endpoint similar to how you do it with dspy, you'll need to initialize the OpenAI client with your custom parameters, including the base_url, model, api_key, temperature, and max_tokens. Here's how you can achieve this:

Step-by-Step Guide
Install adalflow (if not already installed):

Ensure you have adalflow installed. If not, you can install it using pip:

pip install adalflow

Initialize the OpenAI Client with Custom Endpoint:

Assuming adalflow provides a similar interface to dspy, you can initialize the OpenAI client by specifying the custom base_url along with other necessary parameters.

import adalflow

Initialize the OpenAI client with your custom endpoint

openai_client = adalflow.OpenAI(
model='gpt-4o', # Replace with your specific model name
api_key='your_bearer_token_here', # Replace with your actual API key
base_url="https://llm.prod.xxx.com", # Your custom endpoint
temperature=0, # Adjust as needed
max_tokens=1000, # Adjust as needed
)
Configure adalflow Settings to Use the Custom Client:

After initializing the client, you need to configure adalflow to use this client as its language model.

Configure adalflow to use the custom OpenAI client

adalflow.settings.configure(lm=openai_client)
Using the Configured Client:

Now, you can use adalflow as you normally would, and it will route requests through your specified base_url.

response = adalflow.generate("Your prompt here")
print(response)
Complete Example
Putting it all together, here's a complete example:

import adalflow

Replace these variables with your actual configuration

MODEL_NAME = 'gpt-4o'
BEARER_TOKEN = 'your_bearer_token_here'
CUSTOM_BASE_URL = "https://llm.prod.xxx.com"
TEMPERATURE = 0
MAX_TOKENS = 1000

Initialize the OpenAI client with custom endpoint

openai_client = adalflow.OpenAI(
model=MODEL_NAME,
api_key=BEARER_TOKEN,
base_url=CUSTOM_BASE_URL,
temperature=TEMPERATURE,
max_tokens=MAX_TOKENS,
)

Configure adalflow to use this client

adalflow.settings.configure(lm=openai_client)

Example usage

prompt = "Explain the theory of relativity in simple terms."
response = adalflow.generate(prompt)
print(response)
Additional Considerations
Authentication: Ensure that your bearer_token has the necessary permissions to access the custom endpoint.

Endpoint Compatibility: Verify that your custom endpoint (https://llm.prod.xxx.com) is fully compatible with OpenAI's API specifications. Differences in API behavior might require additional adjustments.

Error Handling: Implement appropriate error handling to manage potential issues like network errors, authentication failures, or unexpected responses.

Documentation Reference: If adalflow has specific documentation regarding custom endpoints or advanced configurations, it's a good idea to refer to it for more detailed instructions or additional parameters that might be required.

Troubleshooting
Invalid URL or Token Errors: Double-check the base_url and api_key to ensure they're correct and have the necessary access rights.

Model Compatibility: Ensure that the model name (gpt-4o in your case) is supported by your custom endpoint.

Logging: Enable logging within adalflow to get more insights into any issues that arise during the API calls.

If you encounter any specific errors or issues while setting this up, feel free to share the error messages, and I can help troubleshoot further!

[liyin2015]

@ahsan3219 we are not the same with dspy, we dont use global config, need a new proposal here.

[ahsan3219]

@liyin2015

Hello!

I understand you're looking to configure adalflow to use a custom OpenAI endpoint similarly to how you've set it up with dspy. Since adalflow doesn't utilize a global configuration like dspy, the approach will differ slightly. Below, I'll guide you through configuring adalflow with a custom OpenAI endpoint, ensuring it aligns with adalflow's architecture.

Step-by-Step Guide to Configure adalflow with a Custom OpenAI Endpoint

1. Install adalflow (if not already installed)

First, ensure that you have adalflow installed in your environment. If not, you can install it using pip:

pip install adalflow

2. Initialize the OpenAI Client with Custom Parameters

Since adalflow doesn't use a global configuration, you'll need to create an instance of the OpenAI client with your custom parameters each time you need to interact with the API. Here's how you can do it:

import adalflow

# Replace these variables with your actual configuration
MODEL_NAME = 'gpt-4o'
BEARER_TOKEN = 'your_bearer_token_here'
CUSTOM_BASE_URL = "https://llm.prod.xxx.com"
TEMPERATURE = 0
MAX_TOKENS = 1000

# Initialize the OpenAI client with the custom endpoint
openai_client = adalflow.OpenAI(
    model=MODEL_NAME,
    api_key=BEARER_TOKEN,
    base_url=CUSTOM_BASE_URL,
    temperature=TEMPERATURE,
    max_tokens=MAX_TOKENS,
)

3. Use the Configured Client for Generating Responses

Instead of configuring adalflow globally, you'll use the openai_client instance directly when generating responses. Here's an example of how to use it:

# Example usage
prompt = "Explain the theory of relativity in simple terms."
response = openai_client.generate(prompt)
print(response)

4. Encapsulate Configuration for Reusability (Optional)

To make your code cleaner and more reusable, consider encapsulating the client initialization in a function or a class. Here's an example using a function:

import adalflow

def create_openai_client():
    return adalflow.OpenAI(
        model='gpt-4o',
        api_key='your_bearer_token_here',
        base_url="https://llm.prod.xxx.com",
        temperature=0,
        max_tokens=1000,
    )

# Usage
client = create_openai_client()
response = client.generate("Your prompt here")
print(response)

Alternatively, using a class:

import adalflow

class OpenAIClient:
    def __init__(self, model, api_key, base_url, temperature, max_tokens):
        self.client = adalflow.OpenAI(
            model=model,
            api_key=api_key,
            base_url=base_url,
            temperature=temperature,
            max_tokens=max_tokens,
        )
    
    def generate_response(self, prompt):
        return self.client.generate(prompt)

# Usage
openai_client = OpenAIClient(
    model='gpt-4o',
    api_key='your_bearer_token_here',
    base_url="https://llm.prod.xxx.com",
    temperature=0,
    max_tokens=1000,
)

response = openai_client.generate_response("Your prompt here")
print(response)

5. Handling Multiple Configurations

If you need to work with multiple configurations or endpoints, creating separate client instances as shown above ensures that each configuration remains isolated and doesn't interfere with others.

Additional Considerations

1. Authentication:

   • Ensure that your BEARER_TOKEN has the necessary permissions to access the custom endpoint.
   • If your custom endpoint requires additional authentication mechanisms (e.g., OAuth tokens, API keys in headers), make sure to include them as per adalflow's requirements.

2. Endpoint Compatibility:

   • Verify that your custom endpoint (https://llm.prod.xxx.com) adheres to OpenAI's API specifications. Any deviations might require additional parameters or handling in your client setup.

3. Error Handling:

   • Implement robust error handling to manage scenarios like network failures, authentication issues, or unexpected API responses. For example:

     try:
         response = openai_client.generate("Your prompt here")
         print(response)
     except adalflow.OpenAIError as e:
         print(f"An error occurred: {e}")

4. Logging:

   • Enable logging within adalflow to monitor API calls, debug issues, and track usage. Check adalflow's documentation for logging configurations.

5. Refer to adalflow Documentation:

   • Since adalflow might have specific requirements or additional configuration options, refer to its official documentation for more detailed instructions and best practices.

Troubleshooting

• Invalid URL or Token Errors:

  • Double-check the base_url and api_key to ensure they're correct.
  • Ensure that the endpoint is accessible from your network environment.

• Model Compatibility:

  • Confirm that the model name (gpt-4o in your case) is supported by your custom endpoint.

• API Specification Differences:

  • If your custom endpoint has variations from OpenAI's standard API, you might need to adjust parameters or handle responses differently.

• Enable Debugging:

  • Utilize adalflow's debugging or verbose modes to get more insights into the API interactions.

Example: Complete Implementation

Here's a complete example combining the steps above:

import adalflow

# Configuration variables
MODEL_NAME = 'gpt-4o'
BEARER_TOKEN = 'your_bearer_token_here'
CUSTOM_BASE_URL = "https://llm.prod.xxx.com"
TEMPERATURE = 0
MAX_TOKENS = 1000

def create_openai_client():
    return adalflow.OpenAI(
        model=MODEL_NAME,
        api_key=BEARER_TOKEN,
        base_url=CUSTOM_BASE_URL,
        temperature=TEMPERATURE,
        max_tokens=MAX_TOKENS,
    )

def generate_response(prompt):
    client = create_openai_client()
    try:
        response = client.generate(prompt)
        return response
    except adalflow.OpenAIError as e:
        print(f"Error generating response: {e}")
        return None

if __name__ == "__main__":
    prompt = "Explain the theory of relativity in simple terms."
    response = generate_response(prompt)
    if response:
        print(response)

Conclusion

Configuring adalflow to use a custom OpenAI endpoint involves initializing the OpenAI client with your specific parameters each time you need to make a request, rather than relying on a global configuration. By following the steps outlined above, you can effectively set up adalflow to communicate with your custom endpoint, ensuring seamless integration and usage within your applications.

If you encounter specific issues or have further questions, feel free to share more details, and I'd be happy to assist!

[NOWSHAD76]

It throws module 'adalflow' has no attribute 'OpenAI' error.