ruxia
238 lines
9.5 KiB
Python
238 lines
9.5 KiB
Python
import gradio as gr
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from graph.graph_rag import GraphRAG
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import traceback
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import time
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import sys
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import json
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# 添加意图识别和槽位提取相关导入
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from manager.manager_intention import ParserIntentJson
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from utils.utils import extract_names_from_json
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from chains_lab.unified_chain import UnifiedNLUChain
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# 初始化GraphRAG系统
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print("正在初始化GraphRAG系统...")
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rag = GraphRAG(
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neo4j_uri="bolt://10.1.6.34:7687",
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neo4j_auth=("neo4j", "password")
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)
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print("GraphRAG系统初始化完成")
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# 初始化意图识别和槽位提取系统
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print("正在初始化意图识别和槽位提取系统...")
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parser = ParserIntentJson("./data/dynamic_structure/intent_json.json")
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suffix_file_path = "./data/booway_knowledge_base/keywords_kg/suffix_keywords.json"
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suffix_fields = extract_names_from_json(suffix_file_path)
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suffix_fields.extend(['gec5', 'bczc2', 'xzwb2', 'BPQ', 'BPY'])
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unified_nlu_chain = UnifiedNLUChain(suffix_fields, parser)
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print("意图识别和槽位提取系统初始化完成")
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def process_query(query):
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"""处理用户查询并返回结果"""
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if not query.strip():
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return "请输入您的问题"
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try:
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print(f"正在处理查询: {query}")
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# 步骤0: 意图识别和槽位提取
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print("步骤0: 开始意图识别和槽位提取...")
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sys.stdout.flush()
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start_time = time.time()
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nlu_result = unified_nlu_chain.invoke({"query": query})
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# 直接使用nlu_result中的full_response，它已经是一个字典
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nlu_response = nlu_result["full_response"]
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print(f"意图识别和槽位提取完成，耗时: {time.time() - start_time:.2f}秒")
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# 检查nlu_response是否已经是字典
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try:
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# 如果nlu_response是字符串，则解析为字典
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if isinstance(nlu_response, str):
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nlu_data = json.loads(nlu_response)
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else:
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# 如果已经是字典，直接使用
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nlu_data = nlu_response
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print(f"识别到的意图: {nlu_data.get('意图', {})}")
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# 提取功能需求槽位
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functionality = None
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slots = {}
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# 从二级意图中提取功能需求
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if '意图' in nlu_data and '二级意图' in nlu_data['意图']:
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second_intent = nlu_data['意图']['二级意图']
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if 'slot_lv2' in second_intent:
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slot_lv2 = second_intent['slot_lv2']
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functionality = slot_lv2.get('functionality_required')
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# 收集所有槽位
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for slot_name, slot_value in slot_lv2.items():
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if slot_value and slot_value != '未知':
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slots[slot_name] = slot_value
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print(f"提取的功能需求: {functionality}")
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print(f"提取的所有槽位: {slots}")
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# 如果没有提取到功能需求，使用原始查询
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if not functionality:
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search_query = query
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print("未提取到功能需求，使用原始查询进行检索")
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else:
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search_query = functionality
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print(f"使用提取的功能需求进行检索: {search_query}")
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# 保存提取的槽位信息
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extracted_slots = slots
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except (json.JSONDecodeError, TypeError) as e:
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print(f"解析NLU结果失败: {str(e)}，使用原始查询进行检索")
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print(f"NLU响应类型: {type(nlu_response)}")
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search_query = query
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extracted_slots = {}
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nlu_data = {"意图": {"一级意图": {"name": "未知"}, "二级意图": {"name": "未知"}}}
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# 添加详细的步骤日志
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print("步骤1: 开始检索相关信息...")
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sys.stdout.flush() # 确保日志立即显示
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# 在process_query函数中修改检索部分
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start_time = time.time()
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# 将槽位信息传递给检索器
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retrieved_info = rag.retriever.retrieve(search_query, top_k=5, slots=extracted_slots)
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print(f"检索完成，耗时: {time.time() - start_time:.2f}秒")
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print(f"检索到 {len(retrieved_info)} 条相关信息")
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print("步骤2: 开始生成回答...")
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sys.stdout.flush()
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start_time = time.time()
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# 将意图和槽位信息也传递给生成器
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response = rag.generator.generate_response(query, retrieved_info, nlu_data)
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print(f"生成完成，耗时: {time.time() - start_time:.2f}秒")
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# 构建响应
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result = {
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"query": query,
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"nlu_data": nlu_data,
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"retrieved_info": retrieved_info,
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"response": response
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}
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print("步骤3: 构建最终响应...")
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final_response = f"### 回答\n{result['response']}\n\n"
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# 添加意图和槽位信息
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final_response += "### 意图识别和槽位提取\n"
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if '意图' in nlu_data:
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if '一级意图' in nlu_data['意图']:
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final_response += f"一级意图: {nlu_data['意图']['一级意图'].get('name', '未知')}\n"
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if '二级意图' in nlu_data['意图']:
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final_response += f"二级意图: {nlu_data['意图']['二级意图'].get('name', '未知')}\n"
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# 添加槽位信息
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final_response += "槽位:\n"
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if '二级意图' in nlu_data['意图'] and 'slot_lv2' in nlu_data['意图']['二级意图']:
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for slot_name, slot_value in nlu_data['意图']['二级意图']['slot_lv2'].items():
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if slot_value and slot_value != '未知':
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final_response += f"- {slot_name}: {slot_value}\n"
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else:
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final_response += "未识别到意图\n"
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final_response += "\n"
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# 添加检索到的信息 - 修改部分
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final_response += "### 检索到的相关信息\n"
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for i, info in enumerate(result['retrieved_info'], 1):
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similarity = info.get('similarity', 'N/A')
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if isinstance(similarity, float):
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similarity = f"{similarity:.2f}"
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# 获取节点详细信息
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node = info.get('node', {})
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# 获取节点类型
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node_type = "未知类型"
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if "labels" in node and node["labels"]:
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if isinstance(node["labels"], list) and len(node["labels"]) > 0:
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node_type = node["labels"][0]
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elif isinstance(node["labels"], str):
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node_type = node["labels"]
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# 获取节点名称
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name = node.get("original_name", "") or node.get("display_name", "") or node.get("name", "未知名称")
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# 获取节点描述
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description = node.get("描述", "无描述")
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# 获取节点路径（如果有）
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path = node.get("path", "")
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if not path and "path_to_root" in node:
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if isinstance(node["path_to_root"], list):
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path = " > ".join(node["path_to_root"])
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else:
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path = str(node["path_to_root"])
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# 构建显示信息
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node_info = f"{i}. 【{node_type}】{name} (相似度: {similarity})\n"
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if path:
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node_info += f" 路径: {path}\n"
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if description:
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# 如果描述太长，截断显示
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if len(description) > 200:
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node_info += f" 描述: {description[:200]}...\n"
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else:
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node_info += f" 描述: {description}\n"
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# 添加原始文本表示
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node_info += f" 文本表示: {info['text']}\n"
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final_response += node_info + "\n"
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print("处理完成，返回结果")
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return final_response
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except Exception as e:
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error_msg = f"处理查询时出错: {str(e)}\n\n"
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error_msg += traceback.format_exc()
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print(error_msg)
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return f"### 错误\n```\n{error_msg}\n```"
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# 创建Gradio界面
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with gr.Blocks(title="知识图谱问答系统") as demo:
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gr.Markdown("基于知识图谱的检索增强生成(RAG)系统")
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with gr.Row():
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with gr.Column():
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query_input = gr.Textbox(
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label="请输入您的问题",
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placeholder="例如：配网D3软件的工程量计算功能是什么？",
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lines=1,
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# 可以增加高度，但保持单行行为
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scale=2,
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# 可以设置最小高度
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min_width=400
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)
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# 更新提示文本
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gr.Markdown("*按回车键直接提交问题*")
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submit_btn = gr.Button("提交")
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with gr.Column():
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output = gr.Markdown(label="回答")
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# 设置回车键触发提交
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query_input.submit(fn=process_query, inputs=query_input, outputs=output)
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# 保留原有的按钮点击提交
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submit_btn.click(fn=process_query, inputs=query_input, outputs=output)
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# 启动应用
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if __name__ == "__main__":
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try:
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print("正在启动Gradio界面...")
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demo.launch(server_name="0.0.0.0",
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server_port=7860,
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share=False,
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debug=True
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)
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finally:
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print("正在关闭资源...")
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rag.close() |