from dataclasses import dataclass import json import os import math from typing import Dict, List, Tuple import numpy as np import pandas as pd # import tqdm import faiss from .llm_client import LLMClient from .lpmmconfig import ENT_NAMESPACE, PG_NAMESPACE, REL_NAMESPACE, global_config from .utils.hash import get_sha256 from .global_logger import logger from rich.traceback import install from rich.progress import ( Progress, BarColumn, TimeElapsedColumn, TimeRemainingColumn, TaskProgressColumn, MofNCompleteColumn, SpinnerColumn, TextColumn, ) install(extra_lines=3) ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..")) EMBEDDING_DATA_DIR = ( os.path.join(ROOT_PATH, "data", "embedding") if global_config["persistence"]["embedding_data_dir"] is None else os.path.join(ROOT_PATH, global_config["persistence"]["embedding_data_dir"]) ) EMBEDDING_DATA_DIR_STR = str(EMBEDDING_DATA_DIR).replace("\\", "/") TOTAL_EMBEDDING_TIMES = 3 # 统计嵌入次数 # 嵌入模型测试字符串，测试模型一致性，来自开发群的聊天记录 # 这些字符串的嵌入结果应该是固定的，不能随时间变化 EMBEDDING_TEST_STRINGS = [ "阿卡伊真的太好玩了，神秘性感大女同等着你", "你怎么知道我arc12.64了", "我是蕾缪乐小姐的狗", "关注Oct谢谢喵", "不是w6我不草", "关注千石可乐谢谢喵", "来玩CLANNAD，AIR，樱之诗，樱之刻谢谢喵", "关注墨梓柒谢谢喵", "Ciallo~", "来玩巧克甜恋谢谢喵", "水印", "我也在纠结晚饭，铁锅炒鸡听着就香！", "test你妈喵", ] EMBEDDING_TEST_FILE = os.path.join(ROOT_PATH, "data", "embedding_model_test.json") EMBEDDING_SIM_THRESHOLD = 0.99 def cosine_similarity(a, b): # 计算余弦相似度 dot = sum(x * y for x, y in zip(a, b, strict=False)) norm_a = math.sqrt(sum(x * x for x in a)) norm_b = math.sqrt(sum(x * x for x in b)) if norm_a == 0 or norm_b == 0: return 0.0 return dot / (norm_a * norm_b) @dataclass class EmbeddingStoreItem: """嵌入库中的项""" def __init__(self, item_hash: str, embedding: List[float], content: str): self.hash = item_hash self.embedding = embedding self.str = content def to_dict(self) -> dict: """转为dict""" return { "hash": self.hash, "embedding": self.embedding, "str": self.str, } class EmbeddingStore: def __init__(self, llm_client: LLMClient, namespace: str, dir_path: str): self.namespace = namespace self.llm_client = llm_client self.dir = dir_path self.embedding_file_path = dir_path + "/" + namespace + ".parquet" self.index_file_path = dir_path + "/" + namespace + ".index" self.idx2hash_file_path = dir_path + "/" + namespace + "_i2h.json" self.store = dict() self.faiss_index = None self.idx2hash = None def _get_embedding(self, s: str) -> List[float]: return self.llm_client.send_embedding_request(global_config["embedding"]["model"], s) 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""" test_vectors = {} for idx, s in enumerate(EMBEDDING_TEST_STRINGS): 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) 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() if not os.path.exists(path): return None with open(path, "r", encoding="utf-8") as f: 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""" 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)) if local_emb 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) 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") 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""" total = len(strs) with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), BarColumn(), TaskProgressColumn(), MofNCompleteColumn(), "•", TimeElapsedColumn(), "<", TimeRemainingColumn(), 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 embedding = self._get_embedding(s) # 存入 self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s) progress.update(task, advance=1) def save_to_file(self) -> None: """保存到文件""" data = [] logger.info(f"正在保存{self.namespace}嵌入库到文件{self.embedding_file_path}") for item in self.store.values(): data.append(item.to_dict()) data_frame = pd.DataFrame(data) if not os.path.exists(self.dir): os.makedirs(self.dir, exist_ok=True) if not os.path.exists(self.embedding_file_path): open(self.embedding_file_path, "w").close() data_frame.to_parquet(self.embedding_file_path, engine="pyarrow", index=False) logger.info(f"{self.namespace}嵌入库保存成功") if self.faiss_index is not None and self.idx2hash is not None: logger.info(f"正在保存{self.namespace}嵌入库的FaissIndex到文件{self.index_file_path}") faiss.write_index(self.faiss_index, self.index_file_path) logger.info(f"{self.namespace}嵌入库的FaissIndex保存成功") logger.info(f"正在保存{self.namespace}嵌入库的idx2hash映射到文件{self.idx2hash_file_path}") with open(self.idx2hash_file_path, "w", encoding="utf-8") as f: f.write(json.dumps(self.idx2hash, ensure_ascii=False, indent=4)) logger.info(f"{self.namespace}嵌入库的idx2hash映射保存成功") def load_from_file(self) -> None: """从文件中加载""" if not os.path.exists(self.embedding_file_path): raise Exception(f"文件{self.embedding_file_path}不存在") logger.info("正在加载嵌入库...") logger.debug(f"正在从文件{self.embedding_file_path}中加载{self.namespace}嵌入库") data_frame = pd.read_parquet(self.embedding_file_path, engine="pyarrow") total = len(data_frame) with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), BarColumn(), TaskProgressColumn(), MofNCompleteColumn(), "•", TimeElapsedColumn(), "<", TimeRemainingColumn(), transient=False, ) as progress: task = progress.add_task("加载嵌入库", total=total) for _, row in data_frame.iterrows(): self.store[row["hash"]] = EmbeddingStoreItem(row["hash"], row["embedding"], row["str"]) progress.update(task, advance=1) logger.info(f"{self.namespace}嵌入库加载成功") try: if os.path.exists(self.index_file_path): logger.info(f"正在加载{self.namespace}嵌入库的FaissIndex...") logger.debug(f"正在从文件{self.index_file_path}中加载{self.namespace}嵌入库的FaissIndex") self.faiss_index = faiss.read_index(self.index_file_path) logger.info(f"{self.namespace}嵌入库的FaissIndex加载成功") else: raise Exception(f"文件{self.index_file_path}不存在") if os.path.exists(self.idx2hash_file_path): logger.info(f"正在加载{self.namespace}嵌入库的idx2hash映射...") logger.debug(f"正在从文件{self.idx2hash_file_path}中加载{self.namespace}嵌入库的idx2hash映射") with open(self.idx2hash_file_path, "r") as f: self.idx2hash = json.load(f) logger.info(f"{self.namespace}嵌入库的idx2hash映射加载成功") else: raise Exception(f"文件{self.idx2hash_file_path}不存在") except Exception as e: logger.error(f"加载{self.namespace}嵌入库的FaissIndex时发生错误：{e}") logger.warning("正在重建Faiss索引") self.build_faiss_index() logger.info(f"{self.namespace}嵌入库的FaissIndex重建成功") self.save_to_file() def build_faiss_index(self) -> None: """重新构建Faiss索引，以余弦相似度为度量""" # 获取所有的embedding array = [] self.idx2hash = dict() for key in self.store: array.append(self.store[key].embedding) self.idx2hash[str(len(array) - 1)] = key embeddings = np.array(array, dtype=np.float32) # L2归一化 faiss.normalize_L2(embeddings) # 构建索引 self.faiss_index = faiss.IndexFlatIP(global_config["embedding"]["dimension"]) self.faiss_index.add(embeddings) def search_top_k(self, query: List[float], k: int) -> List[Tuple[str, float]]: """搜索最相似的k个项，以余弦相似度为度量 Args: query: 查询的embedding k: 返回的最相似的k个项 Returns: result: 最相似的k个项的(hash, 余弦相似度)列表 """ if self.faiss_index is None: logger.debug("FaissIndex尚未构建,返回None") return None if self.idx2hash is None: logger.warning("idx2hash尚未构建,返回None") return None # L2归一化 faiss.normalize_L2(np.array([query], dtype=np.float32)) # 搜索 distances, indices = self.faiss_index.search(np.array([query]), k) # 整理结果 indices = list(indices.flatten()) distances = list(distances.flatten()) result = [ (self.idx2hash[str(int(idx))], float(sim)) for (idx, sim) in zip(indices, distances, strict=False) if idx in range(len(self.idx2hash)) ] return result class EmbeddingManager: def __init__(self, llm_client: LLMClient): self.paragraphs_embedding_store = EmbeddingStore( llm_client, PG_NAMESPACE, EMBEDDING_DATA_DIR_STR, ) self.entities_embedding_store = EmbeddingStore( llm_client, ENT_NAMESPACE, EMBEDDING_DATA_DIR_STR, ) self.relation_embedding_store = EmbeddingStore( llm_client, REL_NAMESPACE, EMBEDDING_DATA_DIR_STR, ) 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 [ 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""" self.paragraphs_embedding_store.batch_insert_strs(list(raw_paragraphs.values()), times=1) def _store_ent_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]): """将实体编码存入Embedding库""" entities = set() for triple_list in triple_list_data.values(): for triple in triple_list: entities.add(triple[0]) entities.add(triple[2]) self.entities_embedding_store.batch_insert_strs(list(entities), times=2) def _store_rel_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]): """将关系编码存入Embedding库""" graph_triples = [] # a list of unique relation triple (in tuple) from all chunks for triples in triple_list_data.values(): graph_triples.extend([tuple(t) for t in triples]) graph_triples = list(set(graph_triples)) self.relation_embedding_store.batch_insert_strs([str(triple) for triple in graph_triples], times=3) def load_from_file(self): """从文件加载""" self.paragraphs_embedding_store.load_from_file() self.entities_embedding_store.load_from_file() self.relation_embedding_store.load_from_file() # 从段落库中获取已存储的hash self.stored_pg_hashes = set(self.paragraphs_embedding_store.store.keys()) def store_new_data_set( self, raw_paragraphs: Dict[str, str], triple_list_data: Dict[str, List[List[str]]], ): if not self.check_all_embedding_model_consistency(): raise Exception("嵌入模型与本地存储不一致，请检查模型设置或清空嵌入库后重试。") """存储新的数据集""" self._store_pg_into_embedding(raw_paragraphs) self._store_ent_into_embedding(triple_list_data) self._store_rel_into_embedding(triple_list_data) self.stored_pg_hashes.update(raw_paragraphs.keys()) def save_to_file(self): """保存到文件""" self.paragraphs_embedding_store.save_to_file() self.entities_embedding_store.save_to_file() self.relation_embedding_store.save_to_file() def rebuild_faiss_index(self): """重建Faiss索引（请在添加新数据后调用）""" self.paragraphs_embedding_store.build_faiss_index() self.entities_embedding_store.build_faiss_index() self.relation_embedding_store.build_faiss_index()