更新
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import os
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import sys
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import logging
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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current_file = os.path.splitext(os.path.basename(__file__))[0]
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from datetime import datetime
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# 获取当前时间，格式化为字符串
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now_str = datetime.now().strftime("%Y%m%d%H%M%S")
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log_filename = f"test_code1{now_str}.log"
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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handlers=[
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logging.FileHandler(os.path.join("logs", log_filename), encoding="utf-8"),
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logging.StreamHandler()
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],
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)
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logger = logging.getLogger(current_file)
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import logging
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def setup_logger(logger_name):
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"""
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设置指定名称的logger，将其级别设置为WARNING并禁用传播
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:param logger_name: logger的名称
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"""
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.WARNING) # 设置httpcore及其子模块的级别
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logger.propagate = False # 可选：禁用传播（防止被根logger处理）
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return logger
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logger_names = ["httpx", "openai", "langsmith.client", "neo4j", "urllib3", "httpcore"]
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for name in logger_names:
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setup_logger(name)
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import json
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import os
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from src.dialog_manager import DialogManager
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from src.multi_llm_client import MultiAPIKeyChatOpenAI
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from src.code_executor import CodeExecutor
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from src.neo4j_raw_retriever import Neo4jRawRetriever
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from src.prompt_manager import PromptManager
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import yaml
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from src.config import Config
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from src.document_loader import load_file
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from src.embedding_client import EmbeddingClient
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from src.project import ProjectBuilder, ProjectToolkit
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from src.project_implementation import ProjectToolkitNeo4j
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success_count = 0
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fail_count = 0
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questions = []
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error_list = []
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success_results = []
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fail_results = []
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def main():
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global fail_count, success_count, questions, error_list
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config = Config()
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business_structure = load_file(config.business_object_structure_path)
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bowei_api_docs = load_file(config.bowei_api_docs_path)
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llm_client = MultiAPIKeyChatOpenAI(config.openai)
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llm_client_coder = MultiAPIKeyChatOpenAI(config.openai_coder)
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prompt_manager = PromptManager()
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neo4j_conf = config.neo4j_conf
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embedding_conf = config.embedding
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embedding_client = EmbeddingClient(embedding_conf)
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# 创建Neo4j检索器
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knowledge_retriever = Neo4jRawRetriever(neo4j_conf)
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ProjectBuilder.register(ProjectToolkitNeo4j, knowledge_retriever.driver)
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code_executor = CodeExecutor(prompt_manager.prompts, llm_client_coder, config.max_retries)
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dialog_manager = DialogManager(
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llm_client,
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business_structure,
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bowei_api_docs,
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code_executor,
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knowledge_retriever,
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prompt_manager,
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)
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# 加载 zhibiao.jsonl
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zhibiao_data = []
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with open('./data/zhibiao.jsonl', 'r', encoding='utf-8') as f:
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for line in f:
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zhibiao_data.append(json.loads(line))
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# 提取指标映射关系并批量执行
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for item in zhibiao_data:
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query = item['query']
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name = item['name']
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selected_knowledge = item['result']
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logger.info(f"指标名称 {name} 问题: {query} 开始生成代码")
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result = dialog_manager.generated_code(query, selected_knowledge)
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if isinstance(result, dict) and result.get('status', False):
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code = result['data']
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success_count += 1
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success_results.append({
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"name": name,
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"query": query,
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"code": code,
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})
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else:
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questions.append(query)
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fail_count += 1
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error_msg = result.get('message', '调用 generated_code 返回空结果') if isinstance(result, dict) else f"问题 {query} {selected_knowledge} 调用 execute_generated_code 返回空结果"
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error_list.append(error_msg)
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fail_results.append({
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"name": name,
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"query": query,
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"messages": error_msg,
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"selected_knowledge": selected_knowledge,
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})
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#print(result)
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if __name__ == "__main__":
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main()
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total = success_count + fail_count
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if total > 0:
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success_rate = success_count / total
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fail_rate = fail_count / total
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else:
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success_rate = fail_rate = 0
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print(f"问题总数: {total}")
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print(f"成功比例: {success_rate * 100:.2f}%")
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print(f"失败比例: {fail_rate * 100:.2f}%")
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print("错误列表:")
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for error in error_list:
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print(error)
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# 保存成功结果到 jsonl 文件
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success_filename = f'./data/code.jsonl'
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with open(success_filename, 'w', encoding='utf-8') as f:
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for item in success_results:
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f.write(json.dumps(item, ensure_ascii=False) + '\n')
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# 保存失败结果到 jsonl 文件
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fail_filename = f'./data/fail_{now_str}.jsonl'
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with open(fail_filename, 'w', encoding='utf-8') as f:
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for item in fail_results:
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f.write(json.dumps(item, ensure_ascii=False) + '\n')
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@@ -0,0 +1,184 @@
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# tests/test_userinteraction.py
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import os
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import sys
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import json
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import logging
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import os
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from datetime import datetime
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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current_file = os.path.splitext(os.path.basename(__file__))[0]
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now_str = datetime.now().strftime("%Y%m%d%H%M%S")
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log_filename = f"{current_file}_{now_str}.log"
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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handlers=[
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logging.FileHandler(os.path.join("logs", log_filename), encoding="utf-8"),
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logging.StreamHandler()
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],
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)
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logger = logging.getLogger(current_file)
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def setup_logger(logger_name):
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"""
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设置指定名称的logger，将其级别设置为WARNING并禁用传播
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:param logger_name: logger的名称
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"""
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.WARNING) # 设置httpcore及其子模块的级别
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logger.propagate = False # 可选：禁用传播（防止被根logger处理）
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return logger
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logger_names = ["httpx", "openai", "langsmith.client", "neo4j", "urllib3", "httpcore"]
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for name in logger_names:
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setup_logger(name)
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from src.config import Config
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from src.document_loader import load_file
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from src.multi_llm_client import MultiAPIKeyChatOpenAI
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from src.user_interaction import UserInteraction
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import json
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from src.dialog_manager import DialogManager
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from src.multi_llm_client import MultiAPIKeyChatOpenAI
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from src.code_executor import CodeExecutor
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from src.neo4j_raw_retriever import Neo4jRawRetriever
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from src.prompt_manager import PromptManager
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import yaml
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from src.config import Config
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from src.document_loader import load_file
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from src.embedding_client import EmbeddingClient
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from src.project import ProjectBuilder, ProjectToolkit
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from src.project_implementation import ProjectToolkitNeo4j
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success_count = 0
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fail_count = 0
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questions = []
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error_list = []
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success_list = []
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def main():
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global success_count, fail_count, questions, error_list, success_list
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config = Config()
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business_structure = load_file(config.business_object_structure_path)
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bowei_api_docs = load_file(config.bowei_api_docs_path)
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llm_client = MultiAPIKeyChatOpenAI(config.openai)
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user_interaction = UserInteraction(llm_client.llm, business_structure)
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llm_client_coder = MultiAPIKeyChatOpenAI(config.openai_coder)
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prompt_manager = PromptManager()
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neo4j_conf = config.neo4j_conf
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embedding_conf = config.embedding
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embedding_client = EmbeddingClient(embedding_conf)
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# 创建Neo4j检索器
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knowledge_retriever = Neo4jRawRetriever(neo4j_conf)
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ProjectBuilder.register(ProjectToolkitNeo4j, knowledge_retriever.driver)
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code_executor = CodeExecutor(prompt_manager.prompts, llm_client_coder, config.max_retries)
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dialog_manager = DialogManager(
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llm_client,
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business_structure,
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bowei_api_docs,
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code_executor,
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knowledge_retriever,
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prompt_manager,
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)
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# 读取 zhibiao.json
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zhibiao_path = os.path.join(os.path.dirname(__file__), "../data/zhibiao.json")
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with open(zhibiao_path, "r", encoding="utf-8") as f:
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zhibiao_data = json.load(f)
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isTest = True
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isTest = False
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if isTest:
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zhibiao_data = [
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{
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"指标名称": "杆塔总基数",
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"指标描述": {
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"指标映射": "从【架空输电线路本体工程/附件安装工程】项目划分中获取名称属于【'合计'】的费用",
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"映射规则": "YX2-1~7"
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},
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"code": "",
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"单位": "基",
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"单价类型": None,
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"序号": "1",
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"提取方式": None,
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"指标类型": "工程量指标",
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"数据来源": "定额数量"
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}
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]
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for idx, item in enumerate(zhibiao_data):
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name = item.get("指标名称", "")
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datasource = item.get("数据来源", "")
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if datasource in ("报表指标", "指标库"):
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logger.info(f"跳过索引 {idx}，数据来源为 {datasource}")
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continue
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query = item.get("指标描述", {}).get("指标映射", "")
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if not query:
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logger.warning(f"索引 {idx} 缺少指标映射，跳过")
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continue
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try:
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# 调用用户交互理解接口（同步调用）
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result = user_interaction.understand(query)
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if not result:
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logger.error(f"问题: {query} 没有找到符合要求的数据")
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fail_count += 1
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error_list.append(f"指标名称 {name} 问题 {query} 调用 understand 返回空结果")
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continue
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# 这里示例只打印理解结果，你可以根据业务逻辑替换为后续处理
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logger.info(
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f"指标名称 {name} 问题: {query} 理解结果: "
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f"{[{'name': r.get('name'), 'constraints': r.get('constraints')} for r in result]}"
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)
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success_list.append({
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"name": name,
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"query": query,
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"result": [{'name': r.get('name'), 'constraints': r.get('constraints')} for r in result]
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})
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success_count += 1
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except Exception as e:
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logger.error(f"指标名称 {name} 问题: {query} 处理异常: {e}")
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fail_count += 1
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error_list.append(f"指标名称 {name} 问题 {query} 异常: {e}")
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total = success_count + fail_count
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success_rate = (success_count / total) * 100 if total > 0 else 0
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fail_rate = (fail_count / total) * 100 if total > 0 else 0
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print(f"问题总数: {total}")
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print(f"成功比例: {success_rate:.2f}%)")
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print(f"失败比例: {fail_rate:.2f}%)")
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print("错误列表:")
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for error in error_list:
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print(error)
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# 将成功内容保存为 jsonl 文件
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success_jsonl_path = os.path.join(os.path.dirname(__file__), f"../data/zhibiao.jsonl")
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with open(success_jsonl_path, "w", encoding="utf-8") as f:
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for item in success_list:
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f.write(json.dumps(item, ensure_ascii=False) + "\n")
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if __name__ == "__main__":
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main()
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