diff --git a/rag2_0/demo/intent_recognition_example.py b/rag2_0/demo/intent_recognition_example.py index 3e896b8..8ccc587 100644 --- a/rag2_0/demo/intent_recognition_example.py +++ b/rag2_0/demo/intent_recognition_example.py @@ -17,6 +17,7 @@ import concurrent.futures from tqdm import tqdm import time import sys +import argparse from typing import List, Dict # 加载环境变量 load_dotenv() @@ -176,6 +177,7 @@ def save_results_to_excel(results, output_file, is_final=False): # 示例查询 examples_query = """那储能软件如何操作""" +examples_query = """博微软件如何新建工程啊""" conversation_context="" chat_history=[ { @@ -199,34 +201,68 @@ previous_slots={ "software_version": None, "operation_steps": None } + +def parse_arguments(): + """解析命令行参数""" + parser = argparse.ArgumentParser(description='意图识别和问题改写工具') + + # 添加数据文件路径参数 + parser.add_argument('--input', '-i', type=str, + help='输入Excel文件路径，包含待处理的提问数据(第一列)') + parser.add_argument('--output', '-o', type=str, + help='输出Excel文件路径，用于保存处理结果') + + # 添加LLM相关参数 + parser.add_argument('--model', '-m', type=str, + help='LLM模型名称，默认使用环境变量中的配置') + parser.add_argument('--api_base', '-a', type=str, + help='API基础URL，默认使用环境变量中的配置') + + # 添加处理相关参数 + parser.add_argument('--max_workers', '-w', type=int, default=20, + help='并发处理的最大线程数，默认为20') + parser.add_argument('--debug', '-d', action='store_true', + help='启用调试模式，使用示例查询而非从文件读取') + parser.add_argument('--query', '-q', type=str, + help='在调试模式下使用的查询字符串') + + return parser.parse_args() + def main(): """ 意图识别和问题改写示例 """ - # 从环境变量中获取配置 + # 解析命令行参数 + args = parse_arguments() + + # 从环境变量中获取配置，命令行参数优先 api_key = os.getenv("OPENAI_API_KEY") - base_url = os.getenv("OPENAI_API_BASE") - model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo") + base_url = args.api_base if args.api_base else os.getenv("OPENAI_API_BASE") + model_name = args.model if args.model else os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo") # 初始化意图识别器 recognizer = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name) # 读取提问数据 current_dir = os.path.dirname(os.path.abspath(__file__)) - data_file = os.path.join(current_dir, "..", "..", "data", "excel", "200条点踩数据测试.xlsx") - output_file = os.path.join(current_dir, "..", "..", "data", "excel", "200条点踩数据测试_槽位填充结果.xlsx") + data_file = args.input if args.input else os.path.join(current_dir, "..", "..", "data", "excel", "历史提问数据(dislike)_提问明确.xlsx") + output_file = args.output if args.output else os.path.join(current_dir, "..", "..", "data", "excel", "历史提问数据(dislike)_槽位(分类)填充结果.xlsx") + + # 检测是否为调试模式 + is_debug = args.debug or (hasattr(sys, 'gettrace') and sys.gettrace() is not None) - # 检测是否为调试模式，调试模式下使用examples_query，否则从Excel读取 - is_debug = hasattr(sys, 'gettrace') and sys.gettrace() is not None - # is_debug = False if is_debug: - examples = examples_query.strip().split("\n") + # 如果提供了查询参数，使用它；否则使用默认示例 + if args.query: + examples = [args.query] + else: + examples = examples_query.strip().split("\n") else: examples = load_questions_from_excel(data_file) if not is_debug: - max_workers = 20 # 减少并发数以避免API限制 + max_workers = args.max_workers logging.info(f"共有 {len(examples)} 个问题需要处理，使用 {max_workers} 个并发线程") # 创建一个与输入顺序相同的结果列表 @@ -262,8 +298,8 @@ def main(): for idx, query in enumerate(examples): if query.strip() == "": continue - process_query(recognizer, query, conversation_context, chat_history, previous_slots) - # print(json.dumps(process_query(recognizer, query), ensure_ascii=False, indent=2)) + # process_query(recognizer, query, conversation_context, chat_history, previous_slots) + print(json.dumps(process_query(recognizer, query), ensure_ascii=False, indent=2)) def setup_logging(): # 配置日志输出到控制台 diff --git a/rag2_0/dify/dify_tool.py b/rag2_0/dify/dify_tool.py index a5b61e1..009e33d 100644 --- a/rag2_0/dify/dify_tool.py +++ b/rag2_0/dify/dify_tool.py @@ -318,7 +318,7 @@ content: "{content}" except Exception as e: return -1 - def get_retrieve_info(self, query: str, outputs: dict) -> tuple: + def get_retrieve_info(self, query: str, outputs: dict, reranker_sorce_info:list) -> tuple: """ 获取检索信息并计算分数 @@ -333,20 +333,21 @@ content: "{content}" min_score = 10 total_score = 0 valid_scores = 0 - retrieve_content = [] + retrieve_title = [] # 使用线程池并发计算分数 with ThreadPoolExecutor() as executor: # 创建任务列表 future_to_content = {} - for result in outputs["result"]: - content = result["content"].strip() + for result in outputs: + content = result["segment_content"].strip() + segment_id = result["segment_id"].strip() future = executor.submit(self.calculate_score, query=query, content=content) - future_to_content[future] = content + future_to_content[future] = (content, segment_id) # 收集结果 for future in as_completed(future_to_content): - content = future_to_content[future] + content, segment_id = future_to_content[future] score = future.result() content_title = content.split("\n")[0] @@ -357,10 +358,11 @@ content: "{content}" valid_scores += 1 if content_title: - retrieve_content.append(content_title + f"--相关性得分({score}分)") + current_score = next((cur_source_info["score"] for cur_source_info in reranker_sorce_info if cur_source_info["segment_id"] == segment_id), None) + retrieve_title.append(content_title + f"--LLM得分({score}分)--重排得分({current_score:.2f}分)") avg_score = total_score / valid_scores if valid_scores > 0 else 0 - return retrieve_content, max_score, min_score, avg_score + return retrieve_title, max_score, min_score, avg_score class NewWorkflowChat(BaseWorkflowChat): @@ -395,7 +397,6 @@ class NewWorkflowChat(BaseWorkflowChat): "新问题分类": workflow_info["问题分类"], "槽点信息": workflow_info["槽点信息"], "新检索词条": workflow_info["检索词条"], - "检索内容": workflow_info["检索内容"], "message_id":message_id } @@ -421,14 +422,23 @@ class NewWorkflowChat(BaseWorkflowChat): vertical_classification = "" sub_classification = "" slot_info = "" - + reranker_sorce=[] try: + # 先取出重排得分 message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id) for workflow_node in message_info["workflow_node_executions_info"]: - if workflow_node["title"] == "知识检索结果后处理": - outputs = json.loads(workflow_node["outputs"]) - retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs) - retrieve_content = outputs["result"] + if workflow_node["title"] == "软件知识检索聚合": + retrieve_outputs = json.loads(workflow_node["inputs"])["result"] + reranker_sorce = [{"score":result["metadata"]["score"], "segment_id":result["metadata"]["segment_id"]} for result in retrieve_outputs] + + + for workflow_node in message_info["workflow_node_executions_info"]: + if workflow_node["title"] == "软件知识检索聚合": + retrieve_outputs = json.loads(workflow_node["inputs"])["result"] + reranker_sorce = [{"score":result["metadata"]["score"], "segment_id":result["metadata"]["segment_id"]} for result in retrieve_outputs] + elif workflow_node["title"] == "提取处理后的知识": + outputs = json.loads(workflow_node["outputs"])["knowledge_list"] + retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs, reranker_sorce_info=reranker_sorce) elif workflow_node["title"] == "问题优化结果解析": outputs = json.loads(workflow_node["outputs"]) rewrite_query = outputs["optimize_query"] @@ -439,11 +449,18 @@ class NewWorkflowChat(BaseWorkflowChat): slot_info = json.dumps(json_result["slot_filling"], ensure_ascii=False, indent=2) except Exception as e: raise e - + + retrieve_content = "" + if len(reranker_sorce)==0: + retrieve_content="未检索知识库" + elif len(reranker_sorce) > 0 and len(retrieve_title)==0: + retrieve_content = "知识与提问不相关，被丢弃" + else: + retrieve_content = "\n".join(retrieve_title) + return { "问题改写": rewrite_query, - "检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库", - "检索内容": retrieve_content, + "检索词条": retrieve_content, "问题分类": f"{vertical_classification} - {sub_classification}", "槽点信息": slot_info, @@ -479,7 +496,6 @@ class OldWorkFlowChat(BaseWorkflowChat): "旧流程答案": answer, "旧问题改写": workflow_info["问题改写"], "旧检索词条": workflow_info["检索词条"], - "检索内容": workflow_info["检索内容"], "message_id":message_id } @@ -519,7 +535,6 @@ class OldWorkFlowChat(BaseWorkflowChat): return { "问题改写": rewrite_query, "检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库", - "检索内容": retrieve_content, } if __name__ == "__main__": diff --git a/rag2_0/dify/test_dify_chatapi.py b/rag2_0/dify/test_dify_chatapi.py index 24daabf..0075faa 100755 --- a/rag2_0/dify/test_dify_chatapi.py +++ b/rag2_0/dify/test_dify_chatapi.py @@ -411,7 +411,6 @@ content: "{content}" return { "问题改写": rewrite_query, "检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库", - "检索内容": retrieve_content, "问题分类": f"{vertical_classification} - {sub_classification}", "槽点信息": slot_info } @@ -451,7 +450,6 @@ content: "{content}" return { "问题改写": rewrite_query, "检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库", - "检索内容": retrieve_content, } def get_retrieve_title_similarity(self, old_retrieve_content:list[dict], new_retrieve_content:list[dict]) -> str: @@ -589,9 +587,7 @@ content: "{content}" if judge_result is None: judge_result = "" - - # retrieve_title_score = self.get_retrieve_title_similarity(old_retrieve_content=old_workflow_info["检索内容"], new_retrieve_content=new_workflow_info["检索内容"]) - + # 返回结果 return { "问题": query, diff --git a/rag2_0/dify/test_workorder.py b/rag2_0/dify/test_workorder.py new file mode 100644 index 0000000..3f1333e --- /dev/null +++ b/rag2_0/dify/test_workorder.py @@ -0,0 +1,101 @@ +import pandas as pd +import random +import math + +work_order_excel="data/excel/6万工单记录.xlsx" + +soft_row_data={ + "博微配网计价通D3":{"基本功能":[], "高级功能":[]}, + "储能C1软件":{"基本功能":[], "高级功能":[]}, + "西藏计价通Z1":{"基本功能":[], "高级功能":[]}, + "技改检修工程计价通T1":{"基本功能":[], "高级功能":[]}, + "检修清单计价通T1":{"基本功能":[], "高级功能":[]}, + "电力建设计价通软件":{"基本功能":[], "高级功能":[]}, +} + +df = pd.read_excel(work_order_excel) + +for idx, row in df.iterrows(): + if pd.isna(row["产品线"]): + continue + + if "博微配网计价通D3" in row["产品线"]: + soft_row_data["博微配网计价通D3"][row["问题类型"]].append((idx, row)) + elif "博微电力建设计价通软件" in row["产品线"]: + soft_row_data["电力建设计价通软件"][row["问题类型"]].append((idx, row)) + elif "新能源系列" in row["产品线"] and "博微新型储能电站建设计价通C1软件" in row["产品名称"]: + soft_row_data["储能C1软件"][row["问题类型"]].append((idx, row)) + elif "博微西藏计价通Z1" in row["产品线"]: + soft_row_data["西藏计价通Z1"][row["问题类型"]].append((idx, row)) + elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-概预算" in row["产品名称"]: + soft_row_data["技改检修工程计价通T1"][row["问题类型"]].append((idx, row)) + elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-清单" in row["产品名称"]: + soft_row_data["检修清单计价通T1"][row["问题类型"]].append((idx, row)) + +# 计算每个软件和功能类型的数据量 +total_count = 0 +counts = {} +for software, types in soft_row_data.items(): + counts[software] = {} + for type_name, rows in types.items(): + counts[software][type_name] = len(rows) + total_count += len(rows) + +print(f"原始数据总量: {total_count}条") +for software, types in counts.items(): + print(f"{software}: 基本功能 {types['基本功能']}条, 高级功能 {types['高级功能']}条") + +# 计算均衡提取的数量 +total_target = 2000 +categories_count = sum(len(types) for types in soft_row_data.values()) +per_category_target = math.ceil(total_target / categories_count) + +# 均衡提取数据 +balanced_data = [] +extracted_counts = {} +extracted_indices = set() # 使用集合存储已提取数据的索引 + +for software, types in soft_row_data.items(): + extracted_counts[software] = {} + + for type_name, rows in types.items(): + # 如果数据量不足，全部提取；否则随机抽取目标数量 + if len(rows) <= per_category_target: + extracted = rows + else: + extracted = random.sample(rows, per_category_target) + + extracted_counts[software][type_name] = len(extracted) + for idx, row in extracted: + extracted_indices.add(idx) # 记录已提取数据的索引 + balanced_data.append(row) + +# 数据量不足2000时，从剩余数据中补充 +remaining_target = total_target - len(balanced_data) +if remaining_target > 0: + # 收集所有未被选中的数据 + remaining_data = [] + for software, types in soft_row_data.items(): + for type_name, rows in types.items(): + # 添加未被选中的数据 + for idx, row in rows: + if idx not in extracted_indices: + remaining_data.append(row) + + # 如果剩余数据足够，随机抽取补充 + if len(remaining_data) >= remaining_target: + additional_data = random.sample(remaining_data, remaining_target) + else: + additional_data = remaining_data + + balanced_data.extend(additional_data) + +# 输出结果 +print(f"\n均衡提取后数据总量: {len(balanced_data)}条") +for software, types in extracted_counts.items(): + print(f"{software}: 基本功能 {types['基本功能']}条, 高级功能 {types['高级功能']}条") + +# 将均衡提取的数据转换为DataFrame并保存 +balanced_df = pd.DataFrame(balanced_data) +balanced_df.to_excel("data/excel/均衡提取2000条工单.xlsx", index=False) +print(f"\n已将均衡提取的{len(balanced_data)}条数据保存至'data/excel/均衡提取2000条工单.xlsx'") \ No newline at end of file diff --git a/rag2_0/intent_recognition/DataModels.py b/rag2_0/intent_recognition/DataModels.py index 7a25fcb..f29a351 100644 --- a/rag2_0/intent_recognition/DataModels.py +++ b/rag2_0/intent_recognition/DataModels.py @@ -160,7 +160,7 @@ class SoftwareFunctionSlots(SlotBase): self.project_type="单工程" missing_slots = {} if not self.software_name: - missing_slots["software_name"] = f"{SoftwareFunctionSlots.model_fields['software_name'].description}，可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}" + missing_slots["software_name"] = f"{SoftwareFunctionSlots.model_fields['software_name'].description}，支持的软件：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}" if not self.function_name: missing_slots["function_name"] = SoftwareFunctionSlots.model_fields["function_name"].description if not self.operation: @@ -181,7 +181,7 @@ class SoftwareTroubleShootingSlots(SlotBase): """检查必填槽位是否都存在""" missing_slots = {} if not self.software_name: - missing_slots["software_name"] = f"{SoftwareTroubleShootingSlots.model_fields['software_name'].description}，可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}" + missing_slots["software_name"] = f"{SoftwareTroubleShootingSlots.model_fields['software_name'].description}，支持的软件：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}" if not self.function_name: missing_slots["function_name"] = SoftwareTroubleShootingSlots.model_fields["function_name"].description if not self.error_message: @@ -191,7 +191,7 @@ class SoftwareTroubleShootingSlots(SlotBase): # 2. 业务问题 # 2.1 专业咨询 class ProfessionalConsultingSlots(SlotBase): - scene_subject: str = Field(default="", description="场景主体") + scene_subject: str = Field(default="", description="业务主体。即询问的业务对象(规范、标准、费用等)") business_scene: str = Field(default="", description="业务场景描述") software_name: Optional[str] = Field(default="", description="软件名称") @@ -266,7 +266,6 @@ class InstallationDownloadSlots(SlotBase): missing_slots = {} if not self.software_name and not self.file_name: missing_slots["software_name"] = f"{InstallationDownloadSlots.model_fields['software_name'].description}," - f"可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}" missing_slots["file_name"] = InstallationDownloadSlots.model_fields["file_name"].description if not self.operation_stage: missing_slots["operation_stage"] = InstallationDownloadSlots.model_fields["operation_stage"].description diff --git a/rag2_0/intent_recognition/IntentRecognition.py b/rag2_0/intent_recognition/IntentRecognition.py index 5920a4e..03329bf 100644 --- a/rag2_0/intent_recognition/IntentRecognition.py +++ b/rag2_0/intent_recognition/IntentRecognition.py @@ -304,8 +304,8 @@ class IntentRecognizer: rewrite_start_time = time.time() # 准备问题改写提示 - # terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms] - terms_dict = [term.model_dump() for term in keywords.terms] + terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms] + # terms_dict = [term.model_dump() for term in keywords.terms] keywords_str = json.dumps(terms_dict, ensure_ascii=False) query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite) # formatted_prompt = query_rewrite_prompt.format(query=query, @@ -401,27 +401,27 @@ class IntentRecognizer: ) # 步骤3: 进行意图识别和槽位填充 - result = self._process_intent_and_slot(rewrite.rewrite, conversation_context, chat_history, previous_slots) - result.update({"keywords": keywords_terms.model_dump(), - "rewrite": rewrite.model_dump(), - "query_keys": query_keys}) - return result - # # 步骤3: 进行意图分类 - # classification = self._classify_intent(query) + # result = self._process_intent_and_slot(rewrite.rewrite, conversation_context, chat_history, previous_slots) + # result.update({"keywords": keywords_terms.model_dump(), + # "rewrite": rewrite.model_dump(), + # "query_keys": query_keys}) + # return result + # 步骤3: 进行意图分类 + classification = self._classify_intent(rewrite.rewrite, conversation_context, chat_history, previous_slots) - # # 步骤4: 进行槽位填充 - # # 如果是有效分类，进行槽位填充 - # slot_filling_result = {} - # if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]: - # slot_filling_result = self._fill_slots(rewrite.rewrite, classification) + # 步骤4: 进行槽位填充 + # 如果是有效分类，进行槽位填充 + slot_filling_result = {} + if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]: + slot_filling_result = self._fill_slots(rewrite.rewrite, classification, conversation_context, chat_history, previous_slots) - # return { - # "classification": classification.model_dump(), - # "keywords": keywords_terms.model_dump(), - # "rewrite": rewrite.model_dump(), - # "query_keys": query_keys, - # "slot_filling": slot_filling_result - # } + return { + "classification": classification.model_dump(), + "keywords": keywords_terms.model_dump(), + "rewrite": rewrite.model_dump(), + "query_keys": query_keys, + "slot_filling": slot_filling_result + } def _fill_slots(self, query: str, classification: Classification, conversation_context: str = "", diff --git a/rag2_0/intent_recognition/Multi_PromptTemplates.py b/rag2_0/intent_recognition/Multi_PromptTemplates.py index 5d99b0f..092e975 100644 --- a/rag2_0/intent_recognition/Multi_PromptTemplates.py +++ b/rag2_0/intent_recognition/Multi_PromptTemplates.py @@ -127,11 +127,13 @@ query_rewrite_prompt_pro_old=""" query_rewrite_prompt_pro=""" # 电力造价问答优化工程师（精简版） **角色**：基于历史对话和术语库重构问题，提升知识库检索准确率。 +最高准则：保持问题核心意图，但允许在指代消除、背景继承下添加隐含功能词。但重构后的问题，所有引入的主体背景等均要来源于历史对话、聊天背景或术语库，不得凭空捏造未提及的内容。 ## 核心原则 -1. 语义保真 → 保持问题核心意图 -2. 术语规范 → 同义词转标准词并【】标记 -3. 背景继承 → 补充历史对话的隐含信息 +1. **指代消除 → 当指示代词（"那"/"这"）出现时，强制继承历史对话的最新核心主题（如功能或任务），并应用到当前主体。** +2. 背景继承 → 补充历史对话和聊天背景中的隐含信息（包括主题和功能）。 +4. 术语规范 → 同义词转标准词并【】标记。提问中的同义词(synonymous)替换为标准词(name) +5. 语义保真 → 保持问题核心意图，但允许在指代消除、背景继承下添加隐含功能词。 ## 处理流程 ### 一、输入解析 @@ -155,37 +157,30 @@ query_rewrite_prompt_pro=""" ### 二、重构决策树 ```mermaid graph TD - A[输入问题] --> B{{匹配关键词或上下文？}} - B -- 是 --> C[执行重构] - B -- 否 --> D[直接输出原始问题] - C --> E[补充缺失背景] - E --> F[同义词替换+【】标记] - F --> G[保留原生专业术语] + A[输入问题] --> B{{包含指示代词？}} + B -- 是 --> C[提取历史最新主题] + C --> D{{主题是否明确？}} + D -- 是 --> E[继承主题到当前问题] + E --> F[执行重构] + D -- 否 --> F + F --> G[补充缺失背景] + G --> H[同义词替换+【】标记] + H --> I[保留原生专业术语] + B -- 否 --> I ``` ### 三、重构优先级 -1. **背景补充** - - 历史对话中确定的背景信息需要保留（例："这软件"→"【配网工程计价通D3软件】"） - -2. **术语处理** - - 同义词转标准词 → 将提问中的同义词(synonymous)替换为标准词(name) - - 存在即标记 → 【计算式】 - -3. **结构优化** - - 保持原问题的5W2H特征,确保问题意图不发生改变。 - - 明确指代关系（"该功能"→"【批量导入】功能"） +1. **指代消除 → 当指示代词出现时，优先继承历史对话的核心主题（如功能词），并替换当前问题的动词部分。** +2. 背景继承 → 历史对话中确定的背景信息需要保留。 +3. 术语处理 → 同义词转标准词 + 【】标记。 +4. 同义词转标准词 → 将提问中的同义词(synonymous)替换为标准词(name) +4. 结构优化 → 保持原问题的5W2H特征，指代消除、背景继承下允许微调意图。 ## 输出规范 {output_format} -## 典型案例 -| 场景 | 输入问题 | 输出结果 | -|---------------------|-----------------------------------|------------------------------------------| -| 强上下文关联 | “怎么升级旧版工程” | {{"rewrite":"【西藏Z1】如何执行【老版本定额升级】？"}} | -| 弱术语匹配 | “界面文字太小怎么办” | 原样输出 | -| 代词+背景继承 | “这个定额如何导入” | {{"rewrite":"【山东定额】如何执行【批量导入定额】？"}}| - ## 质量自检 +- [] **主题是否合理继承？**（当有代词时，历史主题必须注入） - [] 核心诉求是否保留？ - [] 背景信息是否合理补充？ - [] 术语标记是否完整【】？ diff --git a/rag2_0/tool/APIKeyManager.py b/rag2_0/tool/APIKeyManager.py index 670d8c3..cffc222 100644 --- a/rag2_0/tool/APIKeyManager.py +++ b/rag2_0/tool/APIKeyManager.py @@ -19,7 +19,6 @@ import requests API_KEY_LIST=[ "sk-kvgfuqeqvpmfsccykyoohheshclcrtvjlnewratvrjpkpbkc", -"sk-zhnbqnpuumuuvegnvbgoggxafpukbzchpgrugpkobiwkzsar", "sk-kzhxlqvqcxlnbdgnpalqnzumkmspepkttkgbophnkqanainw", "sk-bzttugqtlskrvguvhckwamdssvgmgnrqpsialpdbskfsyyak", "sk-tovmogiablsoeabwgqyvevpcfichyjpuzqdymmvksspdrtqt",