'''

This is the agent file for the Talk2KnowledgeGraphs agent.

'''

import logging

from typing import Annotated

import hydra

from langchain_core.language_models.chat_models import BaseChatModel

from langgraph.checkpoint.memory import MemorySaver

from langgraph.graph import START, StateGraph

from langgraph.prebuilt import create_react_agent, ToolNode, InjectedState

from ..tools.subgraph_extraction import SubgraphExtractionTool

from ..tools.subgraph_summarization import SubgraphSummarizationTool

from ..tools.graphrag_reasoning import GraphRAGReasoningTool

from ..states.state_talk2knowledgegraphs import Talk2KnowledgeGraphs

# Initialize logger

logging.basicConfig(level=logging.INFO)

logger = logging.getLogger(__name__)

def get_app(uniq_id, llm_model: BaseChatModel):

    '''

    This function returns the langraph app.

    '''

    def agent_t2kg_node(state: Annotated[dict, InjectedState]):

        '''

        This function calls the model.

        '''

        logger.log(logging.INFO, "Calling t2kg_agent node with thread_id %s", uniq_id)

        response = model.invoke(state, {"configurable": {"thread_id": uniq_id}})

        return response

    # Load hydra configuration

    logger.log(logging.INFO, "Load Hydra configuration for Talk2KnowledgeGraphs agent.")

    with hydra.initialize(version_base=None, config_path="../configs"):

        cfg = hydra.compose(config_name='config',

                            overrides=['agents/t2kg_agent=default'])

        cfg = cfg.agents.t2kg_agent

    # Define the tools

    subgraph_extraction = SubgraphExtractionTool()

    subgraph_summarization = SubgraphSummarizationTool()

    graphrag_reasoning = GraphRAGReasoningTool()

    tools = ToolNode([

                    subgraph_extraction,

                    subgraph_summarization,

                    graphrag_reasoning,

                    ])

    # Create the agent

    model = create_react_agent(

                llm_model,

                tools=tools,

                state_schema=Talk2KnowledgeGraphs,

                prompt=cfg.state_modifier,

                version='v2',

                checkpointer=MemorySaver()

            )

    # Define a new graph

    workflow = StateGraph(Talk2KnowledgeGraphs)

    # Define the two nodes we will cycle between

    workflow.add_node("agent_t2kg", agent_t2kg_node)

    # Set the entrypoint as the first node

    # This means that this node is the first one called

    workflow.add_edge(START, "agent_t2kg")

    # 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,

                           name="T2KG_Agent")

    logger.log(logging.INFO,

               "Compiled the graph with thread_id %s and llm_model %s",

               uniq_id,

               llm_model)

    return app