LQDataGovernance/views/test_views.py

# ！/usr/bin/ python
# -*- coding: utf-8 -*-
'''
@Project    : lq-agent-api
@File       :cattle_farm_views.py
@IDE        :PyCharm
@Author     :
@Date       :2025/7/10 17:32
'''
import json
from typing import Optional

from fastapi import Depends, Response, Header
from sse_starlette import EventSourceResponse
from starlette.responses import JSONResponse

from foundation.agent.test_agent import test_agent_client
from foundation.agent.generate.model_generate import test_generate_model_client
from foundation.logger.loggering import server_logger
from foundation.schemas.test_schemas import TestForm
from foundation.utils.common import return_json, handler_err
from views import test_router, get_operation_id
from foundation.agent.workflow.test_workflow_graph import test_workflow_graph
from file_processors.pdf_processor import PDFProcessor
from file_processors.bfp_pdf_processor import BfpPDFProcessor
from file_processors.pdf_mineru_md import BfpPDFMineruMdProcessor
from file_processors.bfp_md_processor import BfpMarkdownProcessor

from foundation.models.silicon_flow import SiliconFlowAPI
from foundation.rag.vector.pg_vector_mananger import PGVectorManager
from foundation.rag.vector.pg_vector import PGVectorDB
from foundation.rag.vector.milvus_vector import MilvusVectorManager


@test_router.post("/generate/chat", response_model=TestForm)
async def generate_chat_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        生成类模型
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        output = test_generate_model_client.get_model_generate_invoke(trace_id , task_prompt_info, 
                                                                                 input_query, context)
        # 直接执行
        server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))


@test_router.post("/generate/stream", response_model=TestForm)
async def generate_stream_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        生成类模型
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")

        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        # 创建 SSE 流式响应
        async def event_generator():
            try:
                # 流式处理查询 trace_id, task_prompt_info: dict, input_query, context=None
                for chunk in test_generate_model_client.get_model_generate_stream(trace_id , task_prompt_info, 
                                                                                 input_query, context):
                    # 发送数据块
                    yield {
                        "event": "message",
                        "data": json.dumps({
                            "output": chunk,
                            "completed": False,
                        }, ensure_ascii=False)
                    }
                     # 获取缓存数据
                result_data = {}
                # 发送结束事件
                yield {
                    "event": "message_end",
                    "data": json.dumps({
                        "completed": True,
                        "message": json.dumps(result_data, ensure_ascii=False),
                        "code": 0,
                        "trace_id": trace_id,
                    }, ensure_ascii=False),
                }
            except Exception as e:
                # 错误处理
                yield {
                    "event": "error",
                    "data": json.dumps({
                        "trace_id": trace_id,
                        "message": str(e),
                        "code": 1
                    }, ensure_ascii=False)
                }
            finally:
                # 不需要关闭客户端，因为它是单例
                pass
        # 返回 SSE 响应
        return EventSourceResponse(
            event_generator(),
            headers={
                "Cache-Control": "no-cache",
                "Connection": "keep-alive"
            }
        )
    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

# 路由

@test_router.post("/agent/chat", response_model=TestForm)
async def chat_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
    根据场景获取智能体反馈
    """

    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        # 验证参数

        # 从字典中获取input
        input_data = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
  
        # stream 流式执行
        output = await test_agent_client.handle_query(trace_id  , task_prompt_info, input_data, context, param.config)
        # 直接执行
        server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))
    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="agent/chat")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="agent/chat")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))


@test_router.post("/agent/stream", response_class=Response)
async def chat_agent_stream(param: TestForm,
                     trace_id: str = Depends(get_operation_id)):
    """
    根据场景获取智能体反馈 (SSE流式响应)
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")

       
        # 提取参数
        input_data = param.input
        context = param.context
        header_info = {
          
        }
        task_prompt_info = {"task_prompt": ""}
          # 如果business_scene为None，则使用大模型进行意图识别

        server_logger.info(trace_id=trace_id, msg=f"{param}")
        # 创建 SSE 流式响应
        async def event_generator():
            try:
                # 流式处理查询
                async for chunk in test_agent_client.handle_query_stream(
                        trace_id=trace_id,
                        config_param=param.config,
                        task_prompt_info=task_prompt_info,
                        input_query=input_data,
                        context=context,
                        header_info=header_info
                ):
                    server_logger.debug(trace_id=trace_id, msg=f"{chunk}")
                    # 发送数据块
                    yield {
                        "event": "message",
                        "data": json.dumps({
                            "code": 0,
                            "output": chunk,
                            "completed": False,
                            "trace_id": trace_id,
                        }, ensure_ascii=False)
                    }
                # 获取缓存数据
                result_data = {}
                # 发送结束事件
                yield {
                    "event": "message_end",
                    "data": json.dumps({
                        "completed": True,
                        "message": json.dumps(result_data, ensure_ascii=False),
                        "code": 0,
                        "trace_id": trace_id,
                    }, ensure_ascii=False),
                }
            except Exception as e:
                # 错误处理
                yield {
                    "event": "error",
                    "data": json.dumps({
                        "trace_id": trace_id,
                        "message": str(e),
                        "code": 1
                    }, ensure_ascii=False)
                }
            finally:
                # 不需要关闭客户端，因为它是单例
                pass

        # 返回 SSE 响应
        return EventSourceResponse(
            event_generator(),
            headers={
                "Cache-Control": "no-cache",
                "Connection": "keep-alive"
            }
        )

    except Exception as err:
        # 初始错误处理
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="agent/stream")
        return JSONResponse(
            return_json(code=1, msg=f"{err}", trace_id=trace_id),
            status_code=500
        )



@test_router.post("/graph/stream", response_class=Response)
async def chat_graph_stream(param: TestForm,
                     trace_id: str = Depends(get_operation_id)):
    """
        根据场景获取智能体反馈 (SSE流式响应)
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        # request_param = {
        #     "input": param.input,
        #     "config": param.config,
        #     "context": param.context
        # }
        # 创建 SSE 流式响应 
        async def event_generator():
                try:
                    # 流式处理查询
                    async for chunk in test_workflow_graph.handle_query_stream(
                            param=param,
                            trace_id=trace_id,
                    ):
                        server_logger.debug(trace_id=trace_id, msg=f"{chunk}")
                        # 发送数据块
                        yield {
                            "event": "message",
                            "data": json.dumps({
                                "code": 0,
                                "output": chunk,
                                "completed": False,
                                "trace_id": trace_id,
                                "dataType": "text"
                            }, ensure_ascii=False)
                        }

                    # 发送结束事件
                    yield {
                        "event": "message_end",
                        "data": json.dumps({
                            "completed": True,
                            "message": "Stream completed",
                            "code": 0,
                            "trace_id": trace_id,
                            "dataType": "text"
                        }, ensure_ascii=False),
                    }
                except Exception as e:
                    # 错误处理
                    yield {
                        "event": "error",
                        "data": json.dumps({
                            "trace_id": trace_id,
                            "msg": str(e),
                            "code": 1,
                            "dataType": "text"
                        }, ensure_ascii=False)
                    }
                finally:
                    # 不需要关闭客户端，因为它是单例
                    pass

        # 返回 SSE 响应
        return EventSourceResponse(
            event_generator(),
            headers={
                "Cache-Control": "no-cache",
                "Connection": "keep-alive"
            }
        )
        
    except Exception as err:
        # 初始错误处理
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="graph/stream")
        return JSONResponse(
            return_json(code=1, msg=f"{err}", trace_id=trace_id),
            status_code=500
        )




@test_router.post("/data/governance", response_model=TestForm)
async def generate_chat_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        生成类模型
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        output = test_generate_model_client.get_model_data_governance_invoke(trace_id , task_prompt_info, 
                                                                                 input_query, context)
        # 直接执行
        server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))



@test_router.post("/data/pdf/governance", response_model=TestForm)
async def pdf_governance_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        生成类模型
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        #file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        file_directory= "test/pdf_files"
         # 初始化知识问答处理
        pdf_processor = PDFProcessor(directory=file_directory)
        file_data = pdf_processor.process_pdfs_group()
        server_logger.info(trace_id=trace_id, msg=f"【result】: {file_data}", log_type="agent/chat")
        output = None
        #output = test_generate_model_client.get_model_data_governance_invoke(trace_id , task_prompt_info, input_query, context)
        # 直接执行
        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="generate/stream")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    


@test_router.post("/data/bfp/governance", response_model=TestForm)
async def bfp_governance_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档切分处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        #file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        file_directory= "test/bfp_files"
         # 初始化知识问答处理
        pdf_processor = BfpPDFProcessor(directory=file_directory)
        file_data = pdf_processor.process_pdfs_group()
        server_logger.info(trace_id=trace_id, msg=f"【result】: ", log_type="bfp/governance")
        output = None
        

        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))


@test_router.post("/data/embedding/test", response_model=TestForm)
async def embedding_test_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        embedding模型测试
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        text = input_query
         # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        from foundation.models.silicon_flow import SiliconFlowAPI
        base_api_platform = SiliconFlowAPI()
        embedding = base_api_platform.get_embeddings([text])[0]
        embed_dim = len(embedding)
        server_logger.info(trace_id=trace_id, msg=f"【result】: {embed_dim}")

        output = f"embed_dim={embed_dim},embedding:{embedding}"
        #output = test_generate_model_client.get_model_data_governance_invoke(trace_id , task_prompt_info, input_query, context)
        # 直接执行
        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="embedding")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="embedding")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="embedding")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))





@test_router.post("/data/pgvector/test", response_model=TestForm)
async def pgvector_test_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        pg_vector 向量检索测试 
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        output = "success"
         # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
         # 初始化数据库管理器
        # 1、原始测试
        # db_manager = PGVectorManager(client)
        # db_manager.db_test(query_text=input_query)

        # 2、抽象测试
        pg_vector_db = PGVectorDB(base_api_platform=client)
        output = pg_vector_db.similarity_cosine_search(param={"table_name": "test_documents"}, query_text=input_query)

        
        # 直接执行
        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    




@test_router.post("/data/milvus/test", response_model=TestForm)
async def pgvector_test_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        milvus 向量检索测试 
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        output = "success"
         # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
         # 初始化数据库管理器
        # 1、原始测试
        db_manager = MilvusVectorManager(client)
        db_manager.db_test(query_text=input_query)

        # 2、抽象测试
        # pg_vector_db = MilvusVectorManager(base_api_platform=client)
        # output = pg_vector_db.similarity_cosine_search(param={"table_name": "test_documents"}, query_text=input_query)

        
        # 直接执行
        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    



@test_router.post("/data/bfp/pdf_md", response_model=TestForm)
async def bfp_md_indb_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档切分处理 和 入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        #file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        #file_directory= "test/bfp_files"
        file_directory= "test/test"
        output_directory = "test/bfp_md_files"
         # 初始化知识问答处理
        pdf_processor = BfpPDFMineruMdProcessor(directory=file_directory , output_directory=output_directory)
        pdf_processor.process_tqdm_pdfs_group()

        server_logger.info(trace_id=trace_id, msg=f"【result】: ", log_type="bfp/pdf_md")
        output = None

        

        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/pdf_md")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/pdf_md")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    




@test_router.post("/data/bfp/indb", response_model=TestForm)
async def bfp_indb_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档切分处理 和 入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        #file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        file_directory= "test/bfp_files"
         # 初始化知识问答处理
        pdf_processor = BfpPDFProcessor(directory=file_directory)
        file_data_list , total_chunks = pdf_processor.get_pdfs_group_data()
        server_logger.info(trace_id=trace_id, msg=f"【result】: ", log_type="bfp/governance")
        output = None
        
        # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
        # 抽象测试
        pg_vector_db = PGVectorDB(base_api_platform=client)

        for file_data in file_data_list:
            #file_data = file_data[0:5]
            # 数据标准化处理
            documents = pg_vector_db.document_standard(file_data)
            pg_vector_db.add_tqdm_batch_documents(param={"table_name": "tv_basis_of_preparation"}, documents=documents)


        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/governance")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    



@test_router.post("/data/bfp/batch/indb", response_model=TestForm)
async def bfp_batch_indb_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档 批量切分和入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
        # 抽象测试
        pg_vector_db = PGVectorDB(base_api_platform=client)
        #file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        file_directory= "test/bfp_files"
         # 初始化知识问答处理
        pdf_processor = BfpPDFProcessor(directory=file_directory, base_vector=pg_vector_db)
        pdf_processor.process_tqdm_pdfs_group(key_name="table_name")
        server_logger.info(trace_id=trace_id, msg=f"【result】: ", log_type="bfp/batch/indb")
        output = "success"
        
        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/batch/indb")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/batch/indb")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    



@test_router.post("/data/bfp/search", response_model=TestForm)
async def bfp_search_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档切分处理 和 入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        top_k = int(session_id)
        
        output = None
        # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
        # 抽象测试
        pg_vector_db = PGVectorDB(base_api_platform=client)
        output = pg_vector_db.retriever(param={"table_name": "tv_basis_of_preparation"}, query_text=input_query , top_k=top_k)

        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/search")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/search")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    





@test_router.post("/data/bfp/milvus/batch/indb", response_model=TestForm)
async def bfp_batch_indb_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档 批量切分和入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
        # 抽象测试
        vector_db = MilvusVectorManager(base_api_platform=client)
        #file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        file_directory= "test/bfp_files"
         # 初始化知识问答处理
        pdf_processor = BfpPDFProcessor(directory=file_directory, base_vector=vector_db)
        pdf_processor.process_tqdm_pdfs_group(key_name="collection_name")
        server_logger.info(trace_id=trace_id, msg=f"【result】: ", log_type="bfp/milvus/batch/indb")
        output = "success"
        
        #server_logger.debug(trace_id=trace_id, msg=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/milvus/batch/indb")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/milvus/batch/indb")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))
    



@test_router.post("/data/bfp/milvus/search", response_model=TestForm)
async def bfp_search_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档切分处理 和 入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        print(trace_id)
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        task_prompt_info = {"task_prompt": ""}
        top_k = int(session_id)
        
        output = None
        # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
        # 抽象测试
        vector_db = MilvusVectorManager(base_api_platform=client)
        output = vector_db.retriever(param={"collection_name": "tv_basis_of_preparation"}, query_text=input_query , top_k=top_k)

        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/milvus/search")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/milvus/search")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))


@test_router.post("/data/bfp/md/milvus/batch/indb", response_model=TestForm)
async def bfp_md_batch_indb_endpoint(
        param: TestForm,
        trace_id: str = Depends(get_operation_id)):
    """
        编制依据文档 批量切分和入库处理
    """
    try:
        server_logger.info(trace_id=trace_id, msg=f"{param}")
        # 从字典中获取input
        input_query = param.input
        session_id = param.config.session_id
        context = param.context
        header_info = {
        }
        # 初始化客户端（需提前设置环境变量 SILICONFLOW_API_KEY）
        client = SiliconFlowAPI()
        # 抽象测试
        vector_db = MilvusVectorManager(base_api_platform=client)
        # file_directory= "I:/wangxun_dev_workspace/lq_workspace/LQDataGovernance/test/pdf_files"
        file_directory = "test/bfp_md_files"
        # 初始化知识问答处理
        processor = BfpMarkdownProcessor(directory=file_directory, base_vector=vector_db)
        processor.process_tqdm_pdfs_group(key_name="collection_name")
        server_logger.info(trace_id=trace_id, msg=f"【result】: ", log_type="bfp/md/milvus/batch/indb")
        output = "success"

        # server_logger.debug(trace_id=trace_id, ms g=f"【result】: {output}", log_type="agent/chat")
        # 返回字典格式的响应
        return JSONResponse(
            return_json(data={"output": output}, data_type="text", trace_id=trace_id))

    except ValueError as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/md/milvus/batch/indb")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))

    except Exception as err:
        handler_err(server_logger, trace_id=trace_id, err=err, err_name="bfp/md/milvus/batch/indb")
        return JSONResponse(return_json(code=100500, msg=f"{err}", trace_id=trace_id))