aiaas-falcon 0.1.7.1

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

Installation | Quickstart |

Description

AIaaS_Falcon is Generative AI - LLM library interacts with open source LLMs such as llama2 , mistral & Orca APIs, allowing operations such as listing models, creating embeddings, and generating text based on certain configurations.AIaaS_Falcon helps to invoking the RAG pipeline in seconds.

:shield: Installation

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

pip install aiaas-falcon

if you want to install from source

git clone https://github.com/Praveengovianalytics/AIaaS_falcon && cd AIaaS_falcon
pip install -e .

Methods

• health(self) - Check Health Status of Endpoint
• list_models(self) - Retrieves available models.
• create_embedding(self, file_path) - Creates embeddings from a provided file.
• generate_text(self, chat_history=[], query="",use_file=0,type="general", use_default=1, conversation_config={}, config={}) - Generates text based on provided parameters.

:fire: Quickstart

# 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',api_type='aiaas_llm',transport="rest",protocol="http")

# List available models
model = falcon.list_models()
print(model)

# Check if any model is available
if model:
    # Create an embedding
    response = falcon.create_embedding(['/content/01Aug2023.csv'],'general')
    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,
         use_file=1,
         type="general",
         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={"model":"mistral-7b","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")

Azure OpenAI

We also have support for azure OpenAI gpt-3.5-turbo-16k endpoint.

    completion = falcon.generate_text(
         query=prompt,
         chat_history=[],
         use_default=1,
         use_file=0,
         type="general",
         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={"model":"openai","api_key":"AZURE_OPENAI_TOKEN","api_address":"https://XXXXXXXX.openai.azure.com/","max_new_tokens": 1200, "temperature": 0.4, "top_k": 40, "top_p": 0.95, "batch_size": 256}
    )

Conclusion

AIaaS_Falcon library simplifies interactions with the LLM API's, 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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