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Talk2Biomodels agent

This is the agent file for the Talk2BioModels agent.

get_app(uniq_id, llm_model='gpt-4o-mini')

This function returns the langraph app.

Source code in aiagents4pharma/talk2biomodels/agents/t2b_agent.py 
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def get_app(uniq_id, llm_model='gpt-4o-mini'):
    '''
    This function returns the langraph app.
    '''
    def agent_t2b_node(state: Annotated[dict, InjectedState]):
        '''
        This function calls the model.
        '''
        logger.log(logging.INFO, "Calling t2b_agent node with thread_id %s", uniq_id)
        response = model.invoke(state, {"configurable": {"thread_id": uniq_id}})
        return response

    # Define the tools
    tools = ToolNode([
                    SimulateModelTool(),
                    AskQuestionTool(),
                    CustomPlotterTool(),
                    SearchModelsTool(),
                    GetModelInfoTool(),
                    SteadyStateTool(),
                    ParameterScanTool()
                    ])

    # Define the model
    llm = ChatOpenAI(model=llm_model, temperature=0)
    # Load hydra configuration
    logger.log(logging.INFO, "Load Hydra configuration for Talk2BioModels agent.")
    with hydra.initialize(version_base=None, config_path="../../configs"):
        cfg = hydra.compose(config_name='config',
                            overrides=['talk2biomodels/agents/t2b_agent=default'])
        cfg = cfg.talk2biomodels.agents.t2b_agent
    logger.log(logging.INFO, "state_modifier: %s", cfg.state_modifier)
    # Create the agent
    model = create_react_agent(
                llm,
                tools=tools,
                state_schema=Talk2Biomodels,
                state_modifier=cfg.state_modifier,
                checkpointer=MemorySaver()
            )

    # Define a new graph
    workflow = StateGraph(Talk2Biomodels)

    # Define the two nodes we will cycle between
    workflow.add_node("agent_t2b", agent_t2b_node)

    # Set the entrypoint as the first node
    # This means that this node is the first one called
    workflow.add_edge(START, "agent_t2b")

    # Initialize memory to persist state between graph runs
    checkpointer = MemorySaver()

    # Finally, we compile it!
    # This compiles it into a LangChain Runnable,
    # meaning you can use it as you would any other runnable.
    # Note that we're (optionally) passing the memory
    # when compiling the graph
    app = workflow.compile(checkpointer=checkpointer)
    logger.log(logging.INFO,
               "Compiled the graph with thread_id %s and llm_model %s",
               uniq_id,
               llm_model)

    return app