378 lines
15 KiB
Python
378 lines
15 KiB
Python
from dataclasses import dataclass
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import json
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import os
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import math
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from typing import Dict, List, Tuple
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import numpy as np
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import pandas as pd
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# import tqdm
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import faiss
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from .llm_client import LLMClient
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from .lpmmconfig import ENT_NAMESPACE, PG_NAMESPACE, REL_NAMESPACE, global_config
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from .utils.hash import get_sha256
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from .global_logger import logger
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from rich.traceback import install
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from rich.progress import (
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Progress,
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BarColumn,
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TimeElapsedColumn,
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TimeRemainingColumn,
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TaskProgressColumn,
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MofNCompleteColumn,
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SpinnerColumn,
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TextColumn,
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)
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install(extra_lines=3)
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ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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EMBEDDING_DATA_DIR = (
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os.path.join(ROOT_PATH, "data", "embedding")
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if global_config["persistence"]["embedding_data_dir"] is None
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else os.path.join(ROOT_PATH, global_config["persistence"]["embedding_data_dir"])
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)
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EMBEDDING_DATA_DIR_STR = str(EMBEDDING_DATA_DIR).replace("\\", "/")
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TOTAL_EMBEDDING_TIMES = 3 # 统计嵌入次数
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# 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
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# 这些字符串的嵌入结果应该是固定的，不能随时间变化
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EMBEDDING_TEST_STRINGS = [
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"阿卡伊真的太好玩了，神秘性感大女同等着你",
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"你怎么知道我arc12.64了",
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"我是蕾缪乐小姐的狗",
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"关注Oct谢谢喵",
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"不是w6我不草",
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"关注千石可乐谢谢喵",
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"来玩CLANNAD，AIR，樱之诗，樱之刻谢谢喵",
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"关注墨梓柒谢谢喵",
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"Ciallo~",
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"来玩巧克甜恋谢谢喵",
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"水印",
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"我也在纠结晚饭，铁锅炒鸡听着就香！",
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"test你妈喵",
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]
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EMBEDDING_TEST_FILE = os.path.join(ROOT_PATH, "data", "embedding_model_test.json")
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EMBEDDING_SIM_THRESHOLD = 0.99
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def cosine_similarity(a, b):
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# 计算余弦相似度
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dot = sum(x * y for x, y in zip(a, b, strict=False))
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norm_a = math.sqrt(sum(x * x for x in a))
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norm_b = math.sqrt(sum(x * x for x in b))
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if norm_a == 0 or norm_b == 0:
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return 0.0
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return dot / (norm_a * norm_b)
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@dataclass
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class EmbeddingStoreItem:
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"""嵌入库中的项"""
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def __init__(self, item_hash: str, embedding: List[float], content: str):
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self.hash = item_hash
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self.embedding = embedding
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self.str = content
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def to_dict(self) -> dict:
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"""转为dict"""
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return {
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"hash": self.hash,
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"embedding": self.embedding,
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"str": self.str,
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}
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class EmbeddingStore:
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def __init__(self, llm_client: LLMClient, namespace: str, dir_path: str):
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self.namespace = namespace
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self.llm_client = llm_client
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self.dir = dir_path
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self.embedding_file_path = dir_path + "/" + namespace + ".parquet"
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self.index_file_path = dir_path + "/" + namespace + ".index"
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self.idx2hash_file_path = dir_path + "/" + namespace + "_i2h.json"
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self.store = dict()
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self.faiss_index = None
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self.idx2hash = None
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def _get_embedding(self, s: str) -> List[float]:
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return self.llm_client.send_embedding_request(global_config["embedding"]["model"], s)
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def get_test_file_path(self):
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return EMBEDDING_TEST_FILE
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def save_embedding_test_vectors(self):
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"""保存测试字符串的嵌入到本地"""
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test_vectors = {}
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for idx, s in enumerate(EMBEDDING_TEST_STRINGS):
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test_vectors[str(idx)] = self._get_embedding(s)
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with open(self.get_test_file_path(), "w", encoding="utf-8") as f:
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json.dump(test_vectors, f, ensure_ascii=False, indent=2)
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def load_embedding_test_vectors(self):
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"""加载本地保存的测试字符串嵌入"""
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path = self.get_test_file_path()
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if not os.path.exists(path):
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return None
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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def check_embedding_model_consistency(self):
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"""校验当前模型与本地嵌入模型是否一致"""
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local_vectors = self.load_embedding_test_vectors()
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if local_vectors is None:
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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。")
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self.save_embedding_test_vectors()
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return True
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for idx, s in enumerate(EMBEDDING_TEST_STRINGS):
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local_emb = local_vectors.get(str(idx))
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if local_emb is None:
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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。")
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self.save_embedding_test_vectors()
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return True
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new_emb = self._get_embedding(s)
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sim = cosine_similarity(local_emb, new_emb)
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if sim < EMBEDDING_SIM_THRESHOLD:
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logger.error("嵌入模型一致性校验失败")
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return False
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logger.info("嵌入模型一致性校验通过。")
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return True
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def batch_insert_strs(self, strs: List[str], times: int) -> None:
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"""向库中存入字符串"""
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total = len(strs)
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TaskProgressColumn(),
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MofNCompleteColumn(),
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"•",
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TimeElapsedColumn(),
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"<",
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TimeRemainingColumn(),
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transient=False,
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) as progress:
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task = progress.add_task(f"存入嵌入库：({times}/{TOTAL_EMBEDDING_TIMES})", total=total)
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for s in strs:
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# 计算hash去重
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item_hash = self.namespace + "-" + get_sha256(s)
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if item_hash in self.store:
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progress.update(task, advance=1)
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continue
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# 获取embedding
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embedding = self._get_embedding(s)
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# 存入
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self.store[item_hash] = EmbeddingStoreItem(item_hash, embedding, s)
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progress.update(task, advance=1)
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def save_to_file(self) -> None:
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"""保存到文件"""
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data = []
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logger.info(f"正在保存{self.namespace}嵌入库到文件{self.embedding_file_path}")
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for item in self.store.values():
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data.append(item.to_dict())
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data_frame = pd.DataFrame(data)
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if not os.path.exists(self.dir):
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os.makedirs(self.dir, exist_ok=True)
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if not os.path.exists(self.embedding_file_path):
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open(self.embedding_file_path, "w").close()
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data_frame.to_parquet(self.embedding_file_path, engine="pyarrow", index=False)
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logger.info(f"{self.namespace}嵌入库保存成功")
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if self.faiss_index is not None and self.idx2hash is not None:
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logger.info(f"正在保存{self.namespace}嵌入库的FaissIndex到文件{self.index_file_path}")
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faiss.write_index(self.faiss_index, self.index_file_path)
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logger.info(f"{self.namespace}嵌入库的FaissIndex保存成功")
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logger.info(f"正在保存{self.namespace}嵌入库的idx2hash映射到文件{self.idx2hash_file_path}")
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with open(self.idx2hash_file_path, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.idx2hash, ensure_ascii=False, indent=4))
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logger.info(f"{self.namespace}嵌入库的idx2hash映射保存成功")
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def load_from_file(self) -> None:
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"""从文件中加载"""
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if not os.path.exists(self.embedding_file_path):
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raise Exception(f"文件{self.embedding_file_path}不存在")
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logger.info("正在加载嵌入库...")
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logger.debug(f"正在从文件{self.embedding_file_path}中加载{self.namespace}嵌入库")
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data_frame = pd.read_parquet(self.embedding_file_path, engine="pyarrow")
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total = len(data_frame)
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TaskProgressColumn(),
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MofNCompleteColumn(),
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"•",
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TimeElapsedColumn(),
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"<",
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TimeRemainingColumn(),
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transient=False,
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) as progress:
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task = progress.add_task("加载嵌入库", total=total)
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for _, row in data_frame.iterrows():
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self.store[row["hash"]] = EmbeddingStoreItem(row["hash"], row["embedding"], row["str"])
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progress.update(task, advance=1)
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logger.info(f"{self.namespace}嵌入库加载成功")
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try:
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if os.path.exists(self.index_file_path):
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logger.info(f"正在加载{self.namespace}嵌入库的FaissIndex...")
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logger.debug(f"正在从文件{self.index_file_path}中加载{self.namespace}嵌入库的FaissIndex")
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self.faiss_index = faiss.read_index(self.index_file_path)
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logger.info(f"{self.namespace}嵌入库的FaissIndex加载成功")
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else:
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raise Exception(f"文件{self.index_file_path}不存在")
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if os.path.exists(self.idx2hash_file_path):
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logger.info(f"正在加载{self.namespace}嵌入库的idx2hash映射...")
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logger.debug(f"正在从文件{self.idx2hash_file_path}中加载{self.namespace}嵌入库的idx2hash映射")
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with open(self.idx2hash_file_path, "r") as f:
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self.idx2hash = json.load(f)
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logger.info(f"{self.namespace}嵌入库的idx2hash映射加载成功")
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else:
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raise Exception(f"文件{self.idx2hash_file_path}不存在")
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except Exception as e:
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logger.error(f"加载{self.namespace}嵌入库的FaissIndex时发生错误：{e}")
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logger.warning("正在重建Faiss索引")
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self.build_faiss_index()
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logger.info(f"{self.namespace}嵌入库的FaissIndex重建成功")
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self.save_to_file()
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def build_faiss_index(self) -> None:
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"""重新构建Faiss索引，以余弦相似度为度量"""
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# 获取所有的embedding
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array = []
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self.idx2hash = dict()
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for key in self.store:
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array.append(self.store[key].embedding)
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self.idx2hash[str(len(array) - 1)] = key
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embeddings = np.array(array, dtype=np.float32)
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# L2归一化
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faiss.normalize_L2(embeddings)
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# 构建索引
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self.faiss_index = faiss.IndexFlatIP(global_config["embedding"]["dimension"])
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self.faiss_index.add(embeddings)
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def search_top_k(self, query: List[float], k: int) -> List[Tuple[str, float]]:
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"""搜索最相似的k个项，以余弦相似度为度量
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Args:
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query: 查询的embedding
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k: 返回的最相似的k个项
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Returns:
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result: 最相似的k个项的(hash, 余弦相似度)列表
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"""
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if self.faiss_index is None:
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logger.debug("FaissIndex尚未构建,返回None")
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return None
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if self.idx2hash is None:
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logger.warning("idx2hash尚未构建,返回None")
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return None
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# L2归一化
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faiss.normalize_L2(np.array([query], dtype=np.float32))
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# 搜索
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distances, indices = self.faiss_index.search(np.array([query]), k)
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# 整理结果
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indices = list(indices.flatten())
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distances = list(distances.flatten())
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result = [
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(self.idx2hash[str(int(idx))], float(sim))
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for (idx, sim) in zip(indices, distances, strict=False)
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if idx in range(len(self.idx2hash))
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]
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return result
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class EmbeddingManager:
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def __init__(self, llm_client: LLMClient):
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self.paragraphs_embedding_store = EmbeddingStore(
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llm_client,
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PG_NAMESPACE,
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EMBEDDING_DATA_DIR_STR,
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)
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self.entities_embedding_store = EmbeddingStore(
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llm_client,
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ENT_NAMESPACE,
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EMBEDDING_DATA_DIR_STR,
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)
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self.relation_embedding_store = EmbeddingStore(
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llm_client,
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REL_NAMESPACE,
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EMBEDDING_DATA_DIR_STR,
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)
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self.stored_pg_hashes = set()
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def check_all_embedding_model_consistency(self):
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"""对所有嵌入库做模型一致性校验"""
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for store in [
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self.paragraphs_embedding_store,
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self.entities_embedding_store,
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self.relation_embedding_store,
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]:
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if not store.check_embedding_model_consistency():
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return False
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return True
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def _store_pg_into_embedding(self, raw_paragraphs: Dict[str, str]):
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"""将段落编码存入Embedding库"""
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self.paragraphs_embedding_store.batch_insert_strs(list(raw_paragraphs.values()), times=1)
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def _store_ent_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]):
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"""将实体编码存入Embedding库"""
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entities = set()
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for triple_list in triple_list_data.values():
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for triple in triple_list:
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entities.add(triple[0])
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entities.add(triple[2])
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self.entities_embedding_store.batch_insert_strs(list(entities), times=2)
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def _store_rel_into_embedding(self, triple_list_data: Dict[str, List[List[str]]]):
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"""将关系编码存入Embedding库"""
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graph_triples = [] # a list of unique relation triple (in tuple) from all chunks
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for triples in triple_list_data.values():
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graph_triples.extend([tuple(t) for t in triples])
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graph_triples = list(set(graph_triples))
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self.relation_embedding_store.batch_insert_strs([str(triple) for triple in graph_triples], times=3)
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def load_from_file(self):
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"""从文件加载"""
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self.paragraphs_embedding_store.load_from_file()
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self.entities_embedding_store.load_from_file()
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self.relation_embedding_store.load_from_file()
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# 从段落库中获取已存储的hash
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self.stored_pg_hashes = set(self.paragraphs_embedding_store.store.keys())
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def store_new_data_set(
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self,
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raw_paragraphs: Dict[str, str],
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triple_list_data: Dict[str, List[List[str]]],
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):
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if not self.check_all_embedding_model_consistency():
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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。")
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"""存储新的数据集"""
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self._store_pg_into_embedding(raw_paragraphs)
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self._store_ent_into_embedding(triple_list_data)
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self._store_rel_into_embedding(triple_list_data)
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self.stored_pg_hashes.update(raw_paragraphs.keys())
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def save_to_file(self):
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"""保存到文件"""
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self.paragraphs_embedding_store.save_to_file()
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self.entities_embedding_store.save_to_file()
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self.relation_embedding_store.save_to_file()
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def rebuild_faiss_index(self):
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"""重建Faiss索引（请在添加新数据后调用）"""
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self.paragraphs_embedding_store.build_faiss_index()
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self.entities_embedding_store.build_faiss_index()
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self.relation_embedding_store.build_faiss_index()
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