aiaas-falcon 0.1.2

This python package help to interact with Generative AI - Large Language Models. It interacts with AIaaS LLM , AIaaS embedding , AIaaS Audio set of APIs to cater the request.

AIaaS Falcon

Description

Generative AI - LLM library interacts with specific API, allowing operations such as listing models, creating embeddings, and generating text based on certain configurations.

Installation

Ensure you have the requests and google-api-core libraries installed:

pip install aiaas-falcon

Usage

1. Initialization:

   from aiaas_falcon import Falcon # Assuming the class is saved in a file named falcon_client.py
   
   falcon = Falcon(api_key="<Your_API_Key>", host_name_port="<Your_Host_Name_Port>")
   

2. Listing Models:

   models = falcon_client.list_models()
   print(models)
   

3. Creating an Embedding:

   response = falcon_client.create_embedding(file_path="<Your_File_Path>")
   print(response)
   

4. Generating Text:

   response = falcon_client.generate_text(chat_history=[], query="<Your_Query>")
   print(response)
   

Methods

• list_models(self) - Retrieves available models.
• create_embedding(self, file_path) - Creates embeddings from a provided file.
• generate_text(self, chat_history=[], query="", use_default=1, conversation_config={}, config={}) - Generates text based on provided parameters.

Example usage:

# Example usage

from aiaas_falcon import Falcon  # Make sure the Falcon class is imported

# Initialize the Falcon object with the API key, host name and port
falcon = Falcon(api_key='_____API_KEY_____', host_name_port='34.16.138.59:8888', transport="rest")

# List available models
model = falcon.list_models()['models']

# Check if any model is available
if model:
    # Create an embedding
    response = falcon.create_embedding(['/content/01Aug2023.csv'])
    print(response)
    print('Embedding Success')

    # Define a prompt
    prompt = 'What is Account status key?'

    # Generate text based on the prompt and other parameters
    completion = falcon.generate_text(
         query=prompt,
         chat_history=[],
         use_default=1,
         conversation_config={
            "k": 5,
            "fetch_k": 50000,
            "bot_context_setting": "Do note that Your are a data dictionary bot. Your task is to fully answer the user's query based on the information provided to you."
         },
         config={"max_new_tokens": 1200, "temperature": 0.4, "top_k": 40, "top_p": 0.95, "batch_size": 256}
    )

    print(completion)
    print("Generate Success")

else:
    print("No suitable model found")

Conclusion

The Falcon API Client simplifies interactions with the specified API, providing a straightforward way to perform various operations such as listing models, creating embeddings, and generating text.

Authors

• @Praveengovianalytics
• @zhuofan

Google Colab

• Get start with aiaas_falcon

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