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Keras Agent

An Agent is an executor of a specific action. The KerasAgent provide the capabilities to load offline open source models using keras to be part of the tasks flow.

Parameters​

When creating an KerasAgent, you need to provide several parameters that define its behavior:

  • agent_type: Determines the kind of actions it can perform. Accepted types are defined in AgentTypes.
  • mission (optional): A mission statement guiding the agent's task. It often serves as the system input for AI models.
  • model_params: A dictionary containing the keras model parameters, such as model and max_length.
  • log: Track the model stage for debugging, default is False.

Supported Models​

  • gemma_2b_en.
  • gemma_instruct_2b_en.
  • gemma_7b_en.
  • gemma_instruct_7b_en.
  • mistral_7b_en.
  • mistral_instruct_7b_en.

Example​

Creating Keras Agents​

Instantiate the KerasAgent class with the appropriate parameters:

from intelli.flow.agents.kagent import KerasAgent

Gemma agent:

gemma_params = {
            "model": "gemma_2b_en",
            "max_length": 200
        }
gemma_agent = KerasAgent(agent_type="text",
                        mission="writing assistant",
                        model_params=gemma_params,
                        log=True)

Mistral agent:

mistral_params = {
            "model": "mistral_instruct_7b_en",
            "max_length": 200
        }
mistral_agent = KerasAgent(agent_type="text",
                        mission="social media assistant",
                        model_params=mistral_params,
                        log=True)

Executing Keras Agents​

The task handle the agent execution as part of the flow, below is full example to execute the agent:

# create the tasks with the prompt instructions, you can asign the same agent to all tasks.
task1 = Task(
            TextTaskInput("write blog post about electric cars"), gemma_agent, log=True
        )
task2 = Task(
            TextTaskInput("write short social media post from the context"), mistral_agent, log=True
        )

# create and execute the flow
flow = SequenceFlow(order=[task1, task2], log=True)
final_result = flow.start()