LQAgentPlatform/utils_test/RAG_Test/test_rag_pipeline.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
RAG链路独立测试工具
用于快速调通和验证参数合规性检查的RAG检索+LLM审查功能

核心功能:
1. rag_enhanced_check() - 完整的RAG检索逻辑
2. check_parameter_compliance() - 参数合规性检查(与原链路完全一致)

使用方法:
    python test_rag_pipeline.py
"""

import sys
import os
import json
import time
import asyncio
from typing import Dict, Any
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from core.construction_review.component.infrastructure.milvus import MilvusConfig, MilvusManager
from core.construction_review.component.infrastructure.parent_tool import (
    enhance_with_parent_docs,
    extract_first_result
)
from core.construction_review.component.reviewers.base_reviewer import BaseReviewer, ReviewResult
from foundation.ai.rag.retrieval.entities_enhance import entity_enhance
from foundation.ai.rag.retrieval.query_rewrite import query_rewrite_manager
from foundation.ai.agent.generate.model_generate import generate_model_client
from core.construction_review.component.reviewers.utils.prompt_loader import prompt_loader
from foundation.observability.logger.loggering import review_logger as logger


# ============================================================================
# 简化的BaseReviewer类 - 用于调用LLM审查
# ============================================================================

class SimpleReviewer(BaseReviewer):
    """
    简化的审查器 - 继承BaseReviewer,用于调用LLM审查
    """

    def __init__(self):
        """初始化简化的审查器"""
        super().__init__()
        self.model_client = generate_model_client
        self.prompt_loader = prompt_loader


# 全局审查器实例
simple_reviewer = SimpleReviewer()


# ============================================================================
# 核心RAG链路函数
# ============================================================================

def rag_enhanced_check(milvus_manager, unit_content: dict) -> dict:
    """
    RAG增强检查 - 完整链路

    流程:
    1. 查询提取 (query_rewrite_manager.query_extract)
    2. 实体增强检索 (entity_enhance.entities_enhance_retrieval)
    3. 父文档增强 (enhance_with_parent_docs)
    4. 提取第一个结果 (extract_first_result)

    Args:
        milvus_manager: MilvusManager实例
        unit_content: 包含content字段的字典,格式: {"content": "待检索的文本内容"}

    Returns:
        dict: RAG检索结果,包含:
            - vector_search: 向量检索结果列表
            - retrieval_status: 检索状态
            - file_name: 参考文件名
            - text_content: 参考文本内容
            - metadata: 元数据信息
    """
    # 创建数据流跟踪字典
    pipeline_data = {
        "stage": "rag_enhanced_check",
        "timestamp": time.time(),
        "steps": {}
    }

    query_content = unit_content['content']
    logger.info(f"[RAG增强] 开始处理, 内容长度: {len(query_content)}")

    # Step 1: 查询提取
    logger.info("=" * 80)
    logger.info("Step 1: 查询提取")
    logger.info("=" * 80)

    logger.info(f"开始查询提取, 输入内容长度: {len(query_content)}")
    logger.info(f"输入内容预览: {query_content[:200]}...")

    # 执行查询提取
    query_pairs = query_rewrite_manager.query_extract(query_content)

    logger.info(f"[RAG增强] 提取到 {len(query_pairs)} 个查询对")

    # 打印查询对详情
    for idx, query_pair in enumerate(query_pairs):
        logger.info(f"  查询对 {idx+1}: {query_pair}")

    # 保存Step 1的输入输出
    pipeline_data["steps"]["1_query_extract"] = {
        "input": {
            "content_length": len(query_content),
            "content_full": query_content,
            "content_preview": query_content[:200]
        },
        "output": {
            "query_pairs_count": len(query_pairs),
            "query_pairs": [str(qp) for qp in query_pairs],  # 转为字符串列表便于查看
            "extraction_timestamp": time.time()
        }
    }

    # Step 2: 实体增强检索
    logger.info("=" * 80)
    logger.info("Step 2: 实体增强检索")
    logger.info("=" * 80)

    logger.info(f"开始实体增强检索, 输入查询对数量: {len(query_pairs)}")

    # 保存输入
    entity_enhance_input = {
        "query_pairs": [str(qp) for qp in query_pairs],
        "query_pairs_count": len(query_pairs)
    }

    # 详细记录每个查询对的处理过程
    entity_enhance_process_details = []

    # 手动展开实体增强检索的每个步骤,便于记录数据流
    import asyncio

    def run_async(coro):
        """在合适的环境中运行异步函数"""
        try:
            loop = asyncio.get_running_loop()
            import concurrent.futures
            with concurrent.futures.ThreadPoolExecutor() as executor:
                future = executor.submit(asyncio.run, coro)
                return future.result()
        except RuntimeError:
            return asyncio.run(coro)

    # 导入retrieval_manager
    from foundation.ai.rag.retrieval.retrieval import retrieval_manager

    bfp_result_lists = []

    # 遍历每个查询对进行处理
    for idx, query_pair in enumerate(query_pairs):
        logger.info(f"\n{'='*60}")
        logger.info(f"处理查询对 {idx+1}/{len(query_pairs)}")
        logger.info(f"{'='*60}")

        # 提取查询对的各个字段
        entity = query_pair['entity']
        search_keywords = query_pair['search_keywords']
        background = query_pair['background']

        logger.info(f"  实体(entity): {entity}")
        logger.info(f"  搜索关键词(search_keywords): {search_keywords}")
        logger.info(f"  背景(background): {background}")

        # 记录当前查询对的输入
        current_query_detail = {
            "index": idx + 1,
            "input": {
                "entity": entity,
                "search_keywords": search_keywords,
                "background": background
            },
            "steps": {}
        }

        # Step 2.1: 实体召回 (entity_recall)
        logger.info(f"  Step 2.1: 实体召回 (recall_top_k=5, max_results=5)")

        entity_list = run_async(retrieval_manager.entity_recall(
            entity,
            search_keywords,
            recall_top_k=5,
            max_results=5
        ))

        logger.info(f"  ✅ 实体召回完成, 召回实体数量: {len(entity_list) if entity_list else 0}")

        # 记录实体召回结果
        current_query_detail["steps"]["2_1_entity_recall"] = {
            "input": {
                "entity": entity,
                "search_keywords": search_keywords,
                "recall_top_k": 5,
                "max_results": 5
            },
            "output": {
                "entity_list": entity_list,
                "entity_count": len(entity_list) if entity_list else 0
            }
        }

        # Step 2.2: BFP召回 (async_bfp_recall)
        logger.info(f"  Step 2.2: BFP召回 (top_k=3)")

        bfp_result = run_async(retrieval_manager.async_bfp_recall(
            entity_list,
            background,
            top_k=3
        ))

        logger.info(f"  ✅ BFP召回完成, BFP结果数量: {len(bfp_result) if bfp_result else 0}")
        logger.info(f"  bfp_result: {bfp_result}")

        # 记录BFP召回结果
        current_query_detail["steps"]["2_2_bfp_recall"] = {
            "input": {
                "entity_list": entity_list,
                "background": background,
                "top_k": 3
            },
            "output": {
                "bfp_result": bfp_result,
                "bfp_result_count": len(bfp_result) if bfp_result else 0
            }
        }

        bfp_result_lists.append(bfp_result)

        entity_enhance_process_details.append(current_query_detail)

        logger.info(f"✅ 查询对 {idx+1} 处理完成")

    logger.info(f"\n{'='*80}")
    logger.info(f"实体增强检索全部完成")
    logger.info(f"总查询对数: {len(query_pairs)}")
    logger.info(f"总BFP结果数: {len(bfp_result_lists)}")
    logger.info(f"{'='*80}")

    # 保存Step 2的详细输出
    pipeline_data["steps"]["2_entity_enhance_retrieval"] = {
        "input": entity_enhance_input,
        "output": {
            "results_count": len(bfp_result_lists),
            "results": bfp_result_lists,
            "process_details": entity_enhance_process_details  # 每个查询对的详细处理过程
        },
        "timestamp": time.time()
    }

    # Step 3: 检查检索结果
    if not bfp_result_lists:
        logger.warning("[RAG增强] 实体检索未返回结果")

        # 保存最终数据流
        os.makedirs(r"temp\entity_bfp_recall", exist_ok=True)
        with open(rf"temp\entity_bfp_recall\rag_pipeline_data.json", "w", encoding='utf-8') as f:
            json.dump(pipeline_data, f, ensure_ascii=False, indent=4)

        return {
            'vector_search': [],
            'retrieval_status': 'no_results',
            'file_name': '',
            'text_content': '',
            'metadata': {}
        }

    logger.info(f"[RAG增强] 实体检索返回 {len(bfp_result_lists)} 个结果")

    # Step 4: 父文档增强 (使用独立工具函数)
    logger.info("=" * 80)
    logger.info("Step 3: 父文档增强")
    logger.info("=" * 80)

    try:
        enhancement_result = enhance_with_parent_docs(milvus_manager, bfp_result_lists)
        enhanced_results = enhancement_result['enhanced_results']
        enhanced_count = enhancement_result['enhanced_count']
        parent_docs = enhancement_result['parent_docs']

        # 保存Step 3输出
        pipeline_data["steps"]["3_parent_doc_enhancement"] = {
            "input": {
                "bfp_results_count": len(bfp_result_lists)
            },
            "output": {
                "enhanced_count": enhanced_count,
                "parent_docs_count": len(parent_docs),
                "parent_docs": parent_docs,
                "enhanced_results": enhanced_results
            }
        }

        # 保存增强后的结果
        os.makedirs(r"temp\entity_bfp_recall", exist_ok=True)
        with open(rf"temp\entity_bfp_recall\enhance_with_parent_docs.json", "w", encoding='utf-8') as f:
            json.dump(enhanced_results, f, ensure_ascii=False, indent=4)

        logger.info(f"[RAG增强] 成功增强 {enhanced_count} 个结果")
        logger.info(f"[RAG增强] 使用了 {len(parent_docs)} 个父文档")

        # 打印父文档信息
        for idx, parent_doc in enumerate(parent_docs):
            logger.info(f"  父文档 {idx+1}: {parent_doc.get('file_name', 'unknown')}")

    except Exception as e:
        logger.error(f"[RAG增强] 父文档增强失败: {e}", exc_info=True)

        # 保存错误信息
        pipeline_data["steps"]["3_parent_doc_enhancement"] = {
            "input": {
                "bfp_results_count": len(bfp_result_lists)
            },
            "output": {
                "error": str(e),
                "error_type": type(e).__name__
            }
        }

        # 失败时使用原始结果
        enhanced_results = bfp_result_lists
        parent_docs = []

    # Step 5: 提取第一个结果返回 (使用增强后的结果)
    logger.info("=" * 80)
    logger.info("Step 4: 提取第一个结果")
    logger.info("=" * 80)

    final_result = extract_first_result(enhanced_results)

    # 保存Step 4输出
    pipeline_data["steps"]["4_extract_first_result"] = {
        "input": {
            "enhanced_results_count": len(enhanced_results)
        },
        "output": {
            "final_result": final_result
        }
    }

    # 保存最终结果用于调试
    with open(rf"temp\entity_bfp_recall\extract_first_result.json", "w", encoding='utf-8') as f:
        json.dump(final_result, f, ensure_ascii=False, indent=4)

    # 保存完整数据流
    pipeline_data["final_result"] = final_result
    os.makedirs(r"temp\entity_bfp_recall", exist_ok=True)
    with open(rf"temp\entity_bfp_recall\rag_pipeline_data.json", "w", encoding='utf-8') as f:
        json.dump(pipeline_data, f, ensure_ascii=False, indent=4)

    logger.info(f"[RAG增强] 最终提取结果文件名: {final_result.get('file_name', '无')}")
    logger.info(f"[RAG增强] 最终提取结果内容长度: {len(final_result.get('text_content', ''))}")
    logger.info(f"[RAG增强] 完整数据流已保存到: temp/entity_bfp_recall/rag_pipeline_data.json")

    return final_result


# ============================================================================
# 参数合规性检查函数 (与原链路完全一致)
# ============================================================================

async def check_parameter_compliance(trace_id_idx: str, review_content: str, review_references: str,
                                    reference_source: str, review_location_label: str, state: str, stage_name: str) -> Dict[str, Any]:
    """
    参数合规性检查 - 实体概念/工程术语知识库
    (与原链路完全一致的方法签名和实现)

    Args:
        trace_id_idx: 追踪ID索引
        review_content: 审查内容
        review_references: 审查参考信息
        reference_source: 参考来源
        review_location_label: 审查位置标签
        state: 状态字典
        stage_name: 阶段名称

    Returns:
        Dict[str, Any]: 参数合规性检查结果
    """
    # 从原链路导入Stage枚举
    from core.construction_review.component.ai_review_engine import Stage

    reviewer_type = Stage.TECHNICAL.value['reviewer_type']
    prompt_name = Stage.TECHNICAL.value['parameter']
    trace_id = prompt_name + trace_id_idx

    # 直接调用原链路的review方法
    return await simple_reviewer.review("parameter_compliance_check", trace_id, reviewer_type, prompt_name, review_content, review_references,
                                       reference_source, review_location_label, state, stage_name, timeout=45)


# ============================================================================
# 主测试函数
# ============================================================================

async def main():
    """
    主测试函数 - 测试参数合规性检查的完整流程

    流程:
    1. 初始化Milvus Manager
    2. 准备测试内容
    3. 调用RAG获取参考信息
    4. 调用参数合规性检查(与原链路一致)
    5. 保存完整数据流
    """
    print("\n" + "=" * 80)
    print("RAG链路独立测试工具 - 参数合规性检查".center(80))
    print("=" * 80 + "\n")

    # 初始化Milvus Manager
    print("📌 初始化Milvus Manager...")
    logger.info("初始化Milvus Manager...")
    try:
        milvus_manager = MilvusManager(MilvusConfig())
        print("✅ Milvus Manager 初始化成功\n")
    except Exception as e:
        print(f"❌ Milvus Manager 初始化失败: {e}")
        logger.error(f"Milvus Manager 初始化失败: {e}", exc_info=True)
        return

    # 测试内容
    test_content = """主要部件说明
1、主梁总成
主梁总成由主梁和导梁构成。主梁单节长12m,共7节,每节重10.87t,主梁为主要承载受力构件,其上弦杆上方设有轨道供纵移桁车走行,实现预制梁的纵向移动;下弦设有反滚轮行走轨道,作为导梁纵移、前中支腿移动纵行轨道。导梁长18m,主要是为降低过孔挠度和承受中支腿移动荷载,起安全引导、辅助过孔作用。主梁、导梁为三角桁架构件单元,采用销轴连接,前、后端各设置横联构架。

图4-1 主梁总成图
注意事项:
(1)更换上、下弦销轴时,应优先向设备供应方购买符合要求的备件。自行更换时,材料性能必须优于设计零件性能,并按规定进行热处理,否则可能造成人员、设备事故。
(2)销轴不得弯曲受力,不得用销轴作为锤砸工具,不得任意放置及焊接"""

    unit_content = {"content": test_content}

    print(f"📝 测试内容长度: {len(test_content)} 字符")
    print(f"📝 测试内容预览:\n{test_content[:200]}...\n")

    # 创建数据流跟踪字典
    pipeline_data = {
        "stage": "parameter_compliance_check_full_pipeline",
        "timestamp": time.time(),
        "steps": {}
    }

    # Step 1: RAG增强检索
    print("=" * 80)
    print("【Step 1】RAG增强检索".center(80))
    print("=" * 80)

    logger.info("=" * 80)
    logger.info("Step 1: RAG增强检索")
    logger.info("=" * 80)

    start_time = time.time()

    rag_result = rag_enhanced_check(milvus_manager, unit_content)
    review_references = rag_result.get('text_content', '')
    reference_source = rag_result.get('file_name', '')

    # 保存Step 1数据
    pipeline_data["steps"]["1_rag_retrieval"] = {
        "input": {
            "unit_content": unit_content
        },
        "output": {
            "rag_result": rag_result,
            "review_references_length": len(review_references),
            "reference_source": reference_source
        },
        "execution_time": time.time() - start_time
    }

    if not review_references:
        logger.warning("RAG检索未返回参考信息,将继续使用空参考进行审查")
        print("⚠️  RAG检索未返回参考信息\n")
    else:
        print(f"✅ RAG检索成功")
        print(f"   参考来源: {reference_source}")
        print(f"   参考内容长度: {len(review_references)} 字符\n")

    # Step 2: 调用参数合规性检查 (使用原链路的方法)
    print("=" * 80)
    print("【Step 2】参数合规性检查 (LLM审查)".center(80))
    print("=" * 80)

    logger.info("=" * 80)
    logger.info("Step 2: 参数合规性检查")
    logger.info("=" * 80)

    trace_id_idx = "_test_001"
    review_location_label = "测试文档-第1章"
    state = None
    stage_name = "test_stage"

    logger.info(f"开始调用参数合规性检查")
    logger.info(f"  - trace_id_idx: {trace_id_idx}")
    logger.info(f"  - review_content长度: {len(test_content)}")
    logger.info(f"  - review_references长度: {len(review_references)}")
    logger.info(f"  - reference_source: {reference_source}")

    # 保存Step 2输入
    pipeline_data["steps"]["2_parameter_compliance_check"] = {
        "input": {
            "trace_id_idx": trace_id_idx,
            "review_content_length": len(test_content),
            "review_content_preview": test_content[:200],
            "review_references_length": len(review_references),
            "review_references_preview": review_references[:200] if review_references else "",
            "reference_source": reference_source,
            "review_location_label": review_location_label,
            "stage_name": stage_name
        },
        "output": {}
    }

    start_time = time.time()

    try:
        # 调用与原链路完全一致的方法
        result = await check_parameter_compliance(
            trace_id_idx=trace_id_idx,
            review_content=test_content,
            review_references=review_references,
            reference_source=reference_source,
            review_location_label=review_location_label,
            state=state,
            stage_name=stage_name
        )

        elapsed_time = time.time() - start_time

        # 保存Step 2输出
        pipeline_data["steps"]["2_parameter_compliance_check"]["output"] = {
            "success": result.success,
            "execution_time": result.execution_time,
            "error_message": result.error_message,
            "details": result.details
        }

        # 保存完整数据流
        pipeline_data["final_result"] = {
            "success": result.success,
            "execution_time": result.execution_time,
            "error_message": result.error_message,
            "details": result.details
        }

        os.makedirs(r"temp\entity_bfp_recall", exist_ok=True)
        with open(rf"temp\entity_bfp_recall\parameter_compliance_full_pipeline.json", "w", encoding='utf-8') as f:
            json.dump(pipeline_data, f, ensure_ascii=False, indent=4)

        logger.info(f"✅ 参数合规性检查完成, 总耗时: {elapsed_time:.2f}秒")
        logger.info(f"📁 完整数据流已保存到: temp/entity_bfp_recall/parameter_compliance_full_pipeline.json")

    except Exception as e:
        error_msg = f"参数合规性检查失败: {str(e)}"
        logger.error(error_msg, exc_info=True)

        # 保存错误信息
        pipeline_data["steps"]["2_parameter_compliance_check"]["output"] = {
            "error": error_msg,
            "error_type": type(e).__name__,
            "traceback": str(e)
        }
        pipeline_data["error"] = {
            "error_message": error_msg,
            "error_type": type(e).__name__
        }

        os.makedirs(r"temp\entity_bfp_recall", exist_ok=True)
        with open(rf"temp\entity_bfp_recall\parameter_compliance_full_pipeline.json", "w", encoding='utf-8') as f:
            json.dump(pipeline_data, f, ensure_ascii=False, indent=4)

        print(f"❌ 参数合规性检查失败: {error_msg}\n")
        return

    # 输出测试结果
    print("\n" + "=" * 80)
    print("测试结果".center(80))
    print("=" * 80)

    status_icon = "✅" if result.success else "❌"
    print(f"\n{status_icon} 参数合规性检查")
    print(f"   执行时间: {result.execution_time:.2f}秒")

    if result.success:
        print(f"   审查成功!")
        print(f"   详细信息: {result.details.get('name', 'N/A')}")

        # 如果有RAG参考信息,打印出来
        if 'rag_reference_source' in result.details:
            print(f"\n   📚 RAG参考信息:")
            print(f"      参考来源: {result.details['rag_reference_source']}")
            print(f"      参考内容长度: {len(result.details.get('rag_review_references', ''))} 字符")

        # 打印审查响应(截取前500字符)
        response = result.details.get('response', '')
        if response:
            print(f"\n   📋 审查响应 (前500字符):")
            print(f"      {response[:500]}...")
    else:
        print(f"   错误信息: {result.error_message}")

    # 输出文件位置
    print("\n" + "=" * 80)
    print("详细结果已保存到:".center(80))
    print("  📁 temp/entity_bfp_recall/rag_pipeline_data.json - RAG检索完整数据流")
    print("  📁 temp/entity_bfp_recall/enhance_with_parent_docs.json - 父文档增强结果")
    print("  📁 temp/entity_bfp_recall/extract_first_result.json - 最终提取结果")
    print("  📁 temp/entity_bfp_recall/parameter_compliance_full_pipeline.json - 参数检查完整数据流")
    print("=" * 80 + "\n")

    print("✅ 测试完成!")

    # 保存测试结果摘要
    os.makedirs(r"temp\entity_bfp_recall", exist_ok=True)
    test_summary = {
        "test_type": "parameter_compliance",
        "check_display_name": "参数合规性检查",
        "timestamp": time.time(),
        "result": {
            'success': result.success,
            'execution_time': result.execution_time,
            'error_message': result.error_message,
            'details_summary': {
                'name': result.details.get('name'),
                'has_rag_reference': 'rag_reference_source' in result.details,
                'response_length': len(result.details.get('response', '')),
                'response_preview': result.details.get('response', '')[:200]
            }
        }
    }
    with open(rf"temp\entity_bfp_recall\test_summary.json", "w", encoding='utf-8') as f:
        json.dump(test_summary, f, ensure_ascii=False, indent=4)

    return result


if __name__ == "__main__":
    # 运行异步主函数
    asyncio.run(main())