import os

from llama_index.core.agent import AgentRunner, ReActChatFormatter
from llama_index.core.settings import Settings
from llama_index.core.tools.query_engine import QueryEngineTool

from app.engine.engine import create_query_engine, create_summary_query_engine
from app.engine.index import get_index
from app.engine.prompt import ReActChatFormatter_messages, tree_summary_query_engine_tool_messages, \
    query_engine_tool_messages, summary_query_tool_messages
#from app.engine.loaders.db import makeDescriptionByEngine
from app.engine.tools import ToolFactory
from app.api.routers.request.base import ProjectInfo
from llama_index.core.response_synthesizers import ResponseMode

def getPrjFalg(params:dict=None)->str:
    prjFlag = ''
    if params is not None:
        prjFlag = ProjectInfo().prjFalg(params.get('projectname'))     
    return prjFlag


def get_chat_engine(filters=None, params:dict=None):
    system_prompt = os.getenv("SYSTEM_PROMPT")
    top_k = int(os.getenv("TOP_K", "3"))
    use_reranker = os.getenv("RERANK_ENABLED")
    tools = []
    # 创建SQL查询工具
#    sql_query_engine = create_summary_query_engine(index)
    # sql_query_tool = QueryEngineTool.from_defaults(query_engine=sql_query_engine,
    #                                                name="zjdata_query_tool",
    #                                                description="来源于一个由博微公司电力造价软件编制的造价工程文件。该文件以多张表格的形式存储存储了整个工程的全部数据内容。适用于以详细的自然语言查询表格数据方式查询造价工程各项具体属性、费用的数值。请先使用“zj_query_tool”无法解决才使用本工具"
    #                                                )
    #tools.append(sql_query_tool)

    # Add query tool if index exists
    index = get_index(getPrjFalg(params))
    if index is not None:


        summary_query_engine = create_summary_query_engine(index,top_k,use_reranker,filters)
        summary_query_tool = QueryEngineTool.from_defaults( query_engine=summary_query_engine, name="summary_query_tool",
                                                            description=summary_query_tool_messages,
                                                            )

        query_engine = create_query_engine(index,top_k,use_reranker,filters,response_mode = ResponseMode.TREE_SUMMARIZE)
        query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine, name="zj_query_tool",
                                                          description=query_engine_tool_messages)

        query_engine = create_query_engine(index,top_k,use_reranker,filters,response_mode = ResponseMode.TREE_SUMMARIZE)
        query_engine_tool_1 = QueryEngineTool.from_defaults(query_engine=query_engine, name="zj_query_tool_1",
                                                          description=tree_summary_query_engine_tool_messages)

        tools.append(query_engine_tool)
        #tools.append(query_engine_tool_1)
        #tools.append(summary_query_tool)

    # Add additional tools
    tools += ToolFactory.from_env()

    react_chat_formatter = ReActChatFormatter.from_defaults(ReActChatFormatter_messages)
    agentrunner = AgentRunner.from_llm(
        llm=Settings.llm,
        tools=tools,     
        #react_chat_formatter=react_chat_formatter,
        system_prompt=system_prompt,
        verbose=True,
    )
    return agentrunner

    # create the function calling worker for reasoning
    # worker = FunctionCallingAgentWorker.from_tools(
    #     tools, verbose=True
    # )
    #
    # # wrap the worker in the top-level planner
    # return StructuredPlannerAgent(worker, tools)