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lance_rag_task

langroid/agent/special/lance_rag/lance_rag_task.py

The LanceRAGTaskCreator.new() method creates a 3-Agent system that uses this agent. It takes a LanceDocChatAgent instance as argument, and adds two more agents: - LanceQueryPlanAgent, which is given the LanceDB schema in LanceDocChatAgent, and based on this schema, for a given user query, creates a Query Plan using the QueryPlanTool, which contains a filter, a rephrased query, and a dataframe_calc. - QueryPlanCritic, which is given the LanceDB schema in LanceDocChatAgent, and gives feedback on the Query Plan and Result using the QueryPlanFeedbackTool.

The LanceRAGTaskCreator.new() method sets up the given LanceDocChatAgent and QueryPlanCritic as sub-tasks of the LanceQueryPlanAgent's task.

Langroid's built-in task orchestration ensures that: - the LanceQueryPlanAgent reformulates the plan based on the QueryPlanCritics's feedback, - LLM deviations are corrected via tools and overrides of ChatAgent methods.

LanceRAGTaskCreator

new(agent, interactive=True) staticmethod

Add a LanceFilterAgent to the LanceDocChatAgent, set up the corresponding Tasks, connect them, and return the top-level query_plan_task.

Source code in langroid/agent/special/lance_rag/lance_rag_task.py 
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@staticmethod
def new(
    agent: LanceDocChatAgent,
    interactive: bool = True,
) -> Task:
    """
    Add a LanceFilterAgent to the LanceDocChatAgent,
    set up the corresponding Tasks, connect them,
    and return the top-level query_plan_task.
    """
    doc_agent_name = "LanceRAG"
    critic_name = "QueryPlanCritic"
    query_plan_agent_config = LanceQueryPlanAgentConfig(
        critic_name=critic_name,
        doc_agent_name=doc_agent_name,
        doc_schema=agent._get_clean_vecdb_schema(),
        llm=agent.config.llm,
    )
    query_plan_agent_config.set_system_message()

    critic_config = QueryPlanCriticConfig(
        doc_schema=agent._get_clean_vecdb_schema(),
        llm=agent.config.llm,
    )
    critic_config.set_system_message()

    query_planner = LanceQueryPlanAgent(query_plan_agent_config)
    query_plan_task = Task(
        query_planner,
        interactive=interactive,
    )
    critic_agent = QueryPlanCritic(critic_config)
    critic_task = Task(
        critic_agent,
        interactive=False,
    )
    rag_task = Task(
        agent,
        name="LanceRAG",
        interactive=False,
        done_if_response=[Entity.LLM],  # done when non-null response from LLM
        done_if_no_response=[Entity.LLM],  # done when null response from LLM
    )
    query_plan_task.add_sub_task([critic_task, rag_task])
    return query_plan_task