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

scripts/info_extraction.py

@@ -25,7 +25,7 @@ from rich.progress import (
     TextColumn,
 )
 from raw_data_preprocessor import RAW_DATA_PATH, load_raw_data
-from src.config.config import global_config
+from src.config.config import global_config, model_config
 from src.llm_models.utils_model import LLMRequest
 from dotenv import load_dotenv
 
@@ -96,11 +96,11 @@ open_ie_doc_lock = Lock()
 shutdown_event = Event()
 
 lpmm_entity_extract_llm = LLMRequest(
-    model=global_config.model.lpmm_entity_extract,
+    model_set=model_config.model_task_config.lpmm_entity_extract,
     request_type="lpmm.entity_extract"
 )
 lpmm_rdf_build_llm = LLMRequest(
-    model=global_config.model.lpmm_rdf_build,
+    model_set=model_config.model_task_config.lpmm_rdf_build,
     request_type="lpmm.rdf_build"
 )
 def process_single_text(pg_hash, raw_data):

src/chat/knowledge/embedding_store.py

@@ -3,6 +3,7 @@ import json
 import os
 import math
 import asyncio
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Dict, List, Tuple
 
 import numpy as np
@@ -26,12 +27,20 @@ from rich.progress import (
     SpinnerColumn,
     TextColumn,
 )
-from src.manager.local_store_manager import local_storage
 from src.chat.utils.utils import get_embedding
 from src.config.config import global_config
 
 
 install(extra_lines=3)
+
+# 多线程embedding配置常量
+DEFAULT_MAX_WORKERS = 10  # 默认最大线程数
+DEFAULT_CHUNK_SIZE = 10   # 默认每个线程处理的数据块大小
+MIN_CHUNK_SIZE = 1        # 最小分块大小
+MAX_CHUNK_SIZE = 50       # 最大分块大小
+MIN_WORKERS = 1           # 最小线程数
+MAX_WORKERS = 20          # 最大线程数
+
 ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
 EMBEDDING_DATA_DIR = os.path.join(ROOT_PATH, "data", "embedding")
 EMBEDDING_DATA_DIR_STR = str(EMBEDDING_DATA_DIR).replace("\\", "/")
@@ -87,13 +96,23 @@ class EmbeddingStoreItem:
 
 
 class EmbeddingStore:
-    def __init__(self, namespace: str, dir_path: str):
+    def __init__(self, namespace: str, dir_path: str, max_workers: int = DEFAULT_MAX_WORKERS, chunk_size: int = DEFAULT_CHUNK_SIZE):
         self.namespace = namespace
         self.dir = dir_path
         self.embedding_file_path = f"{dir_path}/{namespace}.parquet"
         self.index_file_path = f"{dir_path}/{namespace}.index"
         self.idx2hash_file_path = dir_path + "/" + namespace + "_i2h.json"
 
+        # 多线程配置参数验证和设置
+        self.max_workers = max(MIN_WORKERS, min(MAX_WORKERS, max_workers))
+        self.chunk_size = max(MIN_CHUNK_SIZE, min(MAX_CHUNK_SIZE, chunk_size))
+        
+        # 如果配置值被调整，记录日志
+        if self.max_workers != max_workers:
+            logger.warning(f"max_workers 已从 {max_workers} 调整为 {self.max_workers} (范围: {MIN_WORKERS}-{MAX_WORKERS})")
+        if self.chunk_size != chunk_size:
+            logger.warning(f"chunk_size 已从 {chunk_size} 调整为 {self.chunk_size} (范围: {MIN_CHUNK_SIZE}-{MAX_CHUNK_SIZE})")
+
         self.store = {}
 
         self.faiss_index = None
@@ -125,17 +144,135 @@ class EmbeddingStore:
                 return []
             return result
 
+    def _get_embeddings_batch_threaded(self, strs: List[str], chunk_size: int = 10, max_workers: int = 10, progress_callback=None) -> List[Tuple[str, List[float]]]:
+        """使用多线程批量获取嵌入向量
+        
+        Args:
+            strs: 要获取嵌入的字符串列表
+            chunk_size: 每个线程处理的数据块大小
+            max_workers: 最大线程数
+            progress_callback: 进度回调函数，接收一个参数表示完成的数量
+            
+        Returns:
+            包含(原始字符串, 嵌入向量)的元组列表，保持与输入顺序一致
+        """
+        if not strs:
+            return []
+            
+        # 分块
+        chunks = []
+        for i in range(0, len(strs), chunk_size):
+            chunk = strs[i:i + chunk_size]
+            chunks.append((i, chunk))  # 保存起始索引以维持顺序
+        
+        # 结果存储，使用字典按索引存储以保证顺序
+        results = {}
+        
+        def process_chunk(chunk_data):
+            """处理单个数据块的函数"""
+            start_idx, chunk_strs = chunk_data
+            chunk_results = []
+            
+            # 为每个线程创建独立的LLMRequest实例
+            from src.llm_models.utils_model import LLMRequest
+            from src.config.config import model_config
+            
+            try:
+                # 创建线程专用的LLM实例
+                llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
+                
+                for i, s in enumerate(chunk_strs):
+                    try:
+                        # 直接使用异步函数
+                        embedding = asyncio.run(llm.get_embedding(s))
+                        if embedding and len(embedding) > 0:
+                            chunk_results.append((start_idx + i, s, embedding[0]))  # embedding[0] 是实际的向量
+                        else:
+                            logger.error(f"获取嵌入失败: {s}")
+                            chunk_results.append((start_idx + i, s, []))
+                        
+                        # 每完成一个嵌入立即更新进度
+                        if progress_callback:
+                            progress_callback(1)
+                            
+                    except Exception as e:
+                        logger.error(f"获取嵌入时发生异常: {s}, 错误: {e}")
+                        chunk_results.append((start_idx + i, s, []))
+                        
+                        # 即使失败也要更新进度
+                        if progress_callback:
+                            progress_callback(1)
+                        
+            except Exception as e:
+                logger.error(f"创建LLM实例失败: {e}")
+                # 如果创建LLM实例失败，返回空结果
+                for i, s in enumerate(chunk_strs):
+                    chunk_results.append((start_idx + i, s, []))
+                    # 即使失败也要更新进度
+                    if progress_callback:
+                        progress_callback(1)
+                    
+            return chunk_results
+        
+        # 使用线程池处理
+        with ThreadPoolExecutor(max_workers=max_workers) as executor:
+            # 提交所有任务
+            future_to_chunk = {executor.submit(process_chunk, chunk): chunk for chunk in chunks}
+            
+            # 收集结果（进度已在process_chunk中实时更新）
+            for future in as_completed(future_to_chunk):
+                try:
+                    chunk_results = future.result()
+                    for idx, s, embedding in chunk_results:
+                        results[idx] = (s, embedding)
+                except Exception as e:
+                    chunk = future_to_chunk[future]
+                    logger.error(f"处理数据块时发生异常: {chunk}, 错误: {e}")
+                    # 为失败的块添加空结果
+                    start_idx, chunk_strs = chunk
+                    for i, s in enumerate(chunk_strs):
+                        results[start_idx + i] = (s, [])
+        
+        # 按原始顺序返回结果
+        ordered_results = []
+        for i in range(len(strs)):
+            if i in results:
+                ordered_results.append(results[i])
+            else:
+                # 防止遗漏
+                ordered_results.append((strs[i], []))
+                
+        return ordered_results
+
     def get_test_file_path(self):
         return EMBEDDING_TEST_FILE
 
     def save_embedding_test_vectors(self):
-        """保存测试字符串的嵌入到本地"""
+        """保存测试字符串的嵌入到本地（使用多线程优化）"""
+        logger.info("开始保存测试字符串的嵌入向量...")
+        
+        # 使用多线程批量获取测试字符串的嵌入
+        embedding_results = self._get_embeddings_batch_threaded(
+            EMBEDDING_TEST_STRINGS,
+            chunk_size=min(self.chunk_size, len(EMBEDDING_TEST_STRINGS)),
+            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS))
+        )
+        
+        # 构建测试向量字典
         test_vectors = {}
-        for idx, s in enumerate(EMBEDDING_TEST_STRINGS):
+        for idx, (s, embedding) in enumerate(embedding_results):
-            test_vectors[str(idx)] = self._get_embedding(s)
+            if embedding:
+                test_vectors[str(idx)] = embedding
+            else:
+                logger.error(f"获取测试字符串嵌入失败: {s}")
+                # 使用原始单线程方法作为后备
+                test_vectors[str(idx)] = self._get_embedding(s)
+        
         with open(self.get_test_file_path(), "w", encoding="utf-8") as f:
             json.dump(test_vectors, f, ensure_ascii=False, indent=2)
         
+        logger.info("测试字符串嵌入向量保存完成")
+
     def load_embedding_test_vectors(self):
         """加载本地保存的测试字符串嵌入"""
         path = self.get_test_file_path()
@@ -145,29 +282,64 @@ class EmbeddingStore:
             return json.load(f)
 
     def check_embedding_model_consistency(self):
-        """校验当前模型与本地嵌入模型是否一致"""
+        """校验当前模型与本地嵌入模型是否一致（使用多线程优化）"""
         local_vectors = self.load_embedding_test_vectors()
         if local_vectors is None:
             logger.warning("未检测到本地嵌入模型测试文件，将保存当前模型的测试嵌入。")
             self.save_embedding_test_vectors()
             return True
-        for idx, s in enumerate(EMBEDDING_TEST_STRINGS):
+            
-            local_emb = local_vectors.get(str(idx))
+        # 检查本地向量完整性
-            if local_emb is None:
+        for idx in range(len(EMBEDDING_TEST_STRINGS)):
+            if local_vectors.get(str(idx)) is None:
                 logger.warning("本地嵌入模型测试文件缺失部分测试字符串，将重新保存。")
                 self.save_embedding_test_vectors()
                 return True
-            new_emb = self._get_embedding(s)
+        
+        logger.info("开始检验嵌入模型一致性...")
+        
+        # 使用多线程批量获取当前模型的嵌入
+        embedding_results = self._get_embeddings_batch_threaded(
+            EMBEDDING_TEST_STRINGS,
+            chunk_size=min(self.chunk_size, len(EMBEDDING_TEST_STRINGS)),
+            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS))
+        )
+        
+        # 检查一致性
+        for idx, (s, new_emb) in enumerate(embedding_results):
+            local_emb = local_vectors.get(str(idx))
+            if not new_emb:
+                logger.error(f"获取测试字符串嵌入失败: {s}")
+                return False
+                
             sim = cosine_similarity(local_emb, new_emb)
             if sim < EMBEDDING_SIM_THRESHOLD:
-                logger.error("嵌入模型一致性校验失败")
+                logger.error(f"嵌入模型一致性校验失败，字符串: {s}, 相似度: {sim:.4f}")
                 return False
+                
         logger.info("嵌入模型一致性校验通过。")
         return True
 
     def batch_insert_strs(self, strs: List[str], times: int) -> None:
-        """向库中存入字符串"""
+        """向库中存入字符串（使用多线程优化）"""
+        if not strs:
+            return
+            
         total = len(strs)
+        
+        # 过滤已存在的字符串
+        new_strs = []
+        for s in strs:
+            item_hash = self.namespace + "-" + get_sha256(s)
+            if item_hash not in self.store:
+                new_strs.append(s)
+        
+        if not new_strs:
+            logger.info(f"所有字符串已存在于{self.namespace}嵌入库中，跳过处理")
+            return
+            
+        logger.info(f"需要处理 {len(new_strs)}/{total} 个新字符串")
+        
         with Progress(
             SpinnerColumn(),
             TextColumn("[progress.description]{task.description}"),
@@ -181,19 +353,38 @@ class EmbeddingStore:
             transient=False,
         ) as progress:
             task = progress.add_task(f"存入嵌入库：({times}/{TOTAL_EMBEDDING_TIMES})", total=total)
-            for s in strs:
-                # 计算hash去重
-                item_hash = self.namespace + "-" + get_sha256(s)
-                if item_hash in self.store:
-                    progress.update(task, advance=1)
-                    continue
             
-                # 获取embedding
+            # 首先更新已存在项的进度
-                embedding = self._get_embedding(s)
+            already_processed = total - len(new_strs)
+            if already_processed > 0:
+                progress.update(task, advance=already_processed)
             
-                # 存入
+            if new_strs:
-                self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
+                # 使用实例配置的参数，智能调整分块和线程数
-                progress.update(task, advance=1)
+                optimal_chunk_size = max(MIN_CHUNK_SIZE, min(self.chunk_size, len(new_strs) // self.max_workers if self.max_workers > 0 else self.chunk_size))
+                optimal_max_workers = min(self.max_workers, max(MIN_WORKERS, len(new_strs) // optimal_chunk_size if optimal_chunk_size > 0 else 1))
+                
+                logger.debug(f"使用多线程处理: chunk_size={optimal_chunk_size}, max_workers={optimal_max_workers}")
+                
+                # 定义进度更新回调函数
+                def update_progress(count):
+                    progress.update(task, advance=count)
+                
+                # 批量获取嵌入，并实时更新进度
+                embedding_results = self._get_embeddings_batch_threaded(
+                    new_strs, 
+                    chunk_size=optimal_chunk_size, 
+                    max_workers=optimal_max_workers,
+                    progress_callback=update_progress
+                )
+                
+                # 存入结果（不再需要在这里更新进度，因为已经在回调中更新了）
+                for s, embedding in embedding_results:
+                    item_hash = self.namespace + "-" + get_sha256(s)
+                    if embedding:  # 只有成功获取到嵌入才存入
+                        self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
+                    else:
+                        logger.warning(f"跳过存储失败的嵌入: {s[:50]}...")
 
     def save_to_file(self) -> None:
         """保存到文件"""
@@ -316,31 +507,37 @@ class EmbeddingStore:
 
 
 class EmbeddingManager:
-    def __init__(self):
+    def __init__(self, max_workers: int = DEFAULT_MAX_WORKERS, chunk_size: int = DEFAULT_CHUNK_SIZE):
+        """
+        初始化EmbeddingManager
+        
+        Args:
+            max_workers: 最大线程数
+            chunk_size: 每个线程处理的数据块大小
+        """
         self.paragraphs_embedding_store = EmbeddingStore(
-            local_storage["pg_namespace"],  # type: ignore
+            "paragraph",  # type: ignore
             EMBEDDING_DATA_DIR_STR,
+            max_workers=max_workers,
+            chunk_size=chunk_size,
         )
         self.entities_embedding_store = EmbeddingStore(
-            local_storage["pg_namespace"],  # type: ignore
+            "entity",  # type: ignore
             EMBEDDING_DATA_DIR_STR,
+            max_workers=max_workers,
+            chunk_size=chunk_size,
         )
         self.relation_embedding_store = EmbeddingStore(
-            local_storage["pg_namespace"],  # type: ignore
+            "relation",  # type: ignore
             EMBEDDING_DATA_DIR_STR,
+            max_workers=max_workers,
+            chunk_size=chunk_size,
         )
         self.stored_pg_hashes = set()
 
     def check_all_embedding_model_consistency(self):
         """对所有嵌入库做模型一致性校验"""
-        for store in [
+        return self.paragraphs_embedding_store.check_embedding_model_consistency()
-            self.paragraphs_embedding_store,
-            self.entities_embedding_store,
-            self.relation_embedding_store,
-        ]:
-            if not store.check_embedding_model_consistency():
-                return False
-        return True
 
     def _store_pg_into_embedding(self, raw_paragraphs: Dict[str, str]):
         """将段落编码存入Embedding库"""

src/chat/knowledge/knowledge_lib.py

@@ -6,7 +6,6 @@ from src.chat.knowledge.qa_manager import QAManager
 from src.chat.knowledge.kg_manager import KGManager
 from src.chat.knowledge.global_logger import logger
 from src.config.config import global_config as bot_global_config
-from src.manager.local_store_manager import local_storage
 import os
 
 INVALID_ENTITY = [
@@ -21,9 +20,6 @@ INVALID_ENTITY = [
     "她们",
     "它们",
 ]
-PG_NAMESPACE = "paragraph"
-ENT_NAMESPACE = "entity"
-REL_NAMESPACE = "relation"
 
 RAG_GRAPH_NAMESPACE = "rag-graph"
 RAG_ENT_CNT_NAMESPACE = "rag-ent-cnt"
@@ -34,54 +30,6 @@ ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..",
 DATA_PATH = os.path.join(ROOT_PATH, "data")
 
 
-def _initialize_knowledge_local_storage():
-    """
-    初始化知识库相关的本地存储配置
-    使用字典批量设置，避免重复的if判断
-    """
-    # 定义所有需要初始化的配置项
-    default_configs = {
-        # 路径配置
-        "root_path": ROOT_PATH,
-        "data_path": f"{ROOT_PATH}/data",
-        # 实体和命名空间配置
-        "lpmm_invalid_entity": INVALID_ENTITY,
-        "pg_namespace": PG_NAMESPACE,
-        "ent_namespace": ENT_NAMESPACE,
-        "rel_namespace": REL_NAMESPACE,
-        # RAG相关命名空间配置
-        "rag_graph_namespace": RAG_GRAPH_NAMESPACE,
-        "rag_ent_cnt_namespace": RAG_ENT_CNT_NAMESPACE,
-        "rag_pg_hash_namespace": RAG_PG_HASH_NAMESPACE,
-    }
-
-    # 日志级别映射：重要配置用info，其他用debug
-    important_configs = {"root_path", "data_path"}
-
-    # 批量设置配置项
-    initialized_count = 0
-    for key, default_value in default_configs.items():
-        if local_storage[key] is None:
-            local_storage[key] = default_value
-
-            # 根据重要性选择日志级别
-            if key in important_configs:
-                logger.info(f"设置{key}: {default_value}")
-            else:
-                logger.debug(f"设置{key}: {default_value}")
-
-            initialized_count += 1
-
-    if initialized_count > 0:
-        logger.info(f"知识库本地存储初始化完成，共设置 {initialized_count} 项配置")
-    else:
-        logger.debug("知识库本地存储配置已存在，跳过初始化")
-
-
-# 初始化本地存储路径
-# sourcery skip: dict-comprehension
-_initialize_knowledge_local_storage()
-
 qa_manager = None
 inspire_manager = None
 
@@ -120,7 +68,7 @@ if bot_global_config.lpmm_knowledge.enable:
 
     # 数据比对：Embedding库与KG的段落hash集合
     for pg_hash in kg_manager.stored_paragraph_hashes:
-        key = f"{PG_NAMESPACE}-{pg_hash}"
+        key = f"paragraph-{pg_hash}"
         if key not in embed_manager.stored_pg_hashes:
             logger.warning(f"KG中存在Embedding库中不存在的段落：{key}")
 

src/chat/utils/utils_voice.py

@@ -15,7 +15,7 @@ async def get_voice_text(voice_base64: str) -> str:
         logger.warning("语音识别未启用，无法处理语音消息")
         return "[语音]"
     try:
-        _llm = LLMRequest(model_set=model_config.model_task_config.voice, request_type="voice")
+        _llm = LLMRequest(model_set=model_config.model_task_config.voice, request_type="audio")
         text = await _llm.generate_response_for_voice(voice_base64)
         if text is None:
             logger.warning("未能生成语音文本")

src/llm_models/utils_model.py

@@ -277,7 +277,7 @@ class LLMRequest:
                         extra_params=model_info.extra_params,
                     )
                 elif request_type == RequestType.AUDIO:
-                    assert message_list is not None, "message_list cannot be None for audio requests"
+                    assert audio_base64 is not None, "audio_base64 cannot be None for audio requests"
                     return await client.get_audio_transcriptions(
                         model_info=model_info,
                         audio_base64=audio_base64,