Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

src/chat/focus_chat/heartflow_prompt_builder.py

@@ -4,17 +4,13 @@ from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
 from src.chat.person_info.relationship_manager import relationship_manager
-from src.chat.utils.utils import get_embedding
 import time
-from typing import Union, Optional
+from typing import Optional
 from src.chat.utils.utils import get_recent_group_speaker
 from src.manager.mood_manager import mood_manager
 from src.chat.memory_system.Hippocampus import HippocampusManager
 from src.chat.knowledge.knowledge_lib import qa_manager
 import random
-import json
-import math
-from src.common.database.database_model import Knowledges
 
 
 logger = get_logger("prompt")
@@ -263,129 +259,6 @@ class PromptBuilder:
 
         return prompt
 
-    async def get_prompt_info_old(self, message: str, threshold: float):
-        start_time = time.time()
-        related_info = ""
-        logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
-        # 1. 先从LLM获取主题，类似于记忆系统的做法
-        topics = []
-
-        # 如果无法提取到主题，直接使用整个消息
-        if not topics:
-            logger.info("未能提取到任何主题，使用整个消息进行查询")
-            embedding = await get_embedding(message, request_type="prompt_build")
-            if not embedding:
-                logger.error("获取消息嵌入向量失败")
-                return ""
-
-            related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
-            logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
-            return related_info
-
-        # 2. 对每个主题进行知识库查询
-        logger.info(f"开始处理{len(topics)}个主题的知识库查询")
-
-        # 优化：批量获取嵌入向量，减少API调用
-        embeddings = {}
-        topics_batch = [topic for topic in topics if len(topic) > 0]
-        if message:  # 确保消息非空
-            topics_batch.append(message)
-
-        # 批量获取嵌入向量
-        embed_start_time = time.time()
-        for text in topics_batch:
-            if not text or len(text.strip()) == 0:
-                continue
-
-            try:
-                embedding = await get_embedding(text, request_type="prompt_build")
-                if embedding:
-                    embeddings[text] = embedding
-                else:
-                    logger.warning(f"获取'{text}'的嵌入向量失败")
-            except Exception as e:
-                logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
-
-        logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
-
-        if not embeddings:
-            logger.error("所有嵌入向量获取失败")
-            return ""
-
-        # 3. 对每个主题进行知识库查询
-        all_results = []
-        query_start_time = time.time()
-
-        # 首先添加原始消息的查询结果
-        if message in embeddings:
-            original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
-            if original_results:
-                for result in original_results:
-                    result["topic"] = "原始消息"
-                all_results.extend(original_results)
-                logger.info(f"原始消息查询到{len(original_results)}条结果")
-
-        # 然后添加每个主题的查询结果
-        for topic in topics:
-            if not topic or topic not in embeddings:
-                continue
-
-            try:
-                topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
-                if topic_results:
-                    # 添加主题标记
-                    for result in topic_results:
-                        result["topic"] = topic
-                    all_results.extend(topic_results)
-                    logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
-            except Exception as e:
-                logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
-
-        logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
-
-        # 4. 去重和过滤
-        process_start_time = time.time()
-        unique_contents = set()
-        filtered_results = []
-        for result in all_results:
-            content = result["content"]
-            if content not in unique_contents:
-                unique_contents.add(content)
-                filtered_results.append(result)
-
-        # 5. 按相似度排序
-        filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
-
-        # 6. 限制总数量（最多10条）
-        filtered_results = filtered_results[:10]
-        logger.info(
-            f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
-        )
-
-        # 7. 格式化输出
-        if filtered_results:
-            format_start_time = time.time()
-            grouped_results = {}
-            for result in filtered_results:
-                topic = result["topic"]
-                if topic not in grouped_results:
-                    grouped_results[topic] = []
-                grouped_results[topic].append(result)
-
-            # 按主题组织输出
-            for topic, results in grouped_results.items():
-                related_info += f"【主题: {topic}】\n"
-                for _i, result in enumerate(results, 1):
-                    _similarity = result["similarity"]
-                    content = result["content"].strip()
-                    related_info += f"{content}\n"
-                related_info += "\n"
-
-            logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
-
-        logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
-        return related_info
-
     async def get_prompt_info(self, message: str, threshold: float):
         related_info = ""
         start_time = time.time()
@@ -405,93 +278,11 @@ class PromptBuilder:
                 logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
                 return related_info
             else:
-                logger.debug("从LPMM知识库获取知识失败，使用旧版数据库进行检索")
-                knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
-                related_info += knowledge_from_old
-                logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
-                return related_info
+                logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...")
+                return "未检索到知识"
         except Exception as e:
             logger.error(f"获取知识库内容时发生异常: {str(e)}")
-            try:
-                knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
-                related_info += knowledge_from_old
-                logger.debug(
-                    f"异常后使用旧版数据库获取知识，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}"
-                )
-                return related_info
-            except Exception as e2:
-                logger.error(f"使用旧版数据库获取知识时也发生异常: {str(e2)}")
-                return ""
-
-    @staticmethod
-    def get_info_from_db(
-        query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
-    ) -> Union[str, list]:
-        if not query_embedding:
-            return "" if not return_raw else []
-
-        results_with_similarity = []
-        try:
-            # Fetch all knowledge entries
-            # This might be inefficient for very large databases.
-            # Consider strategies like FAISS or other vector search libraries if performance becomes an issue.
-            all_knowledges = Knowledges.select()
-
-            if not all_knowledges:
-                return [] if return_raw else ""
-
-            query_embedding_magnitude = math.sqrt(sum(x * x for x in query_embedding))
-            if query_embedding_magnitude == 0:  # Avoid division by zero
-                return "" if not return_raw else []
-
-            for knowledge_item in all_knowledges:
-                try:
-                    db_embedding_str = knowledge_item.embedding
-                    db_embedding = json.loads(db_embedding_str)
-
-                    if len(db_embedding) != len(query_embedding):
-                        logger.warning(
-                            f"Embedding length mismatch for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}. Skipping."
-                        )
-                        continue
-
-                    # Calculate Cosine Similarity
-                    dot_product = sum(q * d for q, d in zip(query_embedding, db_embedding))
-                    db_embedding_magnitude = math.sqrt(sum(x * x for x in db_embedding))
-
-                    if db_embedding_magnitude == 0:  # Avoid division by zero
-                        similarity = 0.0
-                    else:
-                        similarity = dot_product / (query_embedding_magnitude * db_embedding_magnitude)
-
-                    if similarity >= threshold:
-                        results_with_similarity.append({"content": knowledge_item.content, "similarity": similarity})
-                except json.JSONDecodeError:
-                    logger.error(
-                        f"Failed to parse embedding for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}"
-                    )
-                except Exception as e:
-                    logger.error(f"Error processing knowledge item: {e}")
-
-            # Sort by similarity in descending order
-            results_with_similarity.sort(key=lambda x: x["similarity"], reverse=True)
-
-            # Limit results
-            limited_results = results_with_similarity[:limit]
-
-            logger.debug(f"知识库查询结果数量 (after Peewee processing): {len(limited_results)}")
-
-            if not limited_results:
-                return "" if not return_raw else []
-
-            if return_raw:
-                return limited_results
-            else:
-                return "\n".join(str(result["content"]) for result in limited_results)
-
-        except Exception as e:
-            logger.error(f"Error querying Knowledges with Peewee: {e}")
-            return "" if not return_raw else []
+            return "未检索到知识"
 
 
 init_prompt()