feat(memory-graph): 完成 Phase 2 - 记忆构建与工具接口
Phase 2 实现内容: - 时间解析器 (utils/time_parser.py): 支持自然语言时间表达式 - 记忆提取器 (core/extractor.py): 参数验证和标准化 - 记忆构建器 (core/builder.py): 自动构造记忆子图，支持节点去重和关联 - 嵌入生成器 (utils/embeddings.py): API 优先策略，降低本地负载 - LLM 工具接口 (tools/memory_tools.py): create_memory, link_memories, search_memories 关键修复: - VectorStore: 支持 ChromaDB 列表元数据的 JSON 序列化 - 测试数据同步: 确保向量存储和图存储数据一致性 测试结果: 时间解析器: 6/6 通过 记忆提取器: 3 个测试用例通过 记忆构建器: 构建记忆子图成功 端到端流程: 成功创建 3 条记忆 记忆关联: 建立因果关系成功 记忆搜索: 语义搜索返回正确结果 工具 Schema: 3 个工具定义完整 下一步: Phase 3 - 管理层实现
This commit is contained in:
Windpicker-owo
299
src/memory_graph/utils/embeddings.py
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299
src/memory_graph/utils/embeddings.py
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"""
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嵌入向量生成器：优先使用配置的 embedding API，sentence-transformers 作为备选
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"""
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from __future__ import annotations
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import asyncio
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from functools import lru_cache
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from typing import List, Optional
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import numpy as np
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from src.common.logger import get_logger
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logger = get_logger(__name__)
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class EmbeddingGenerator:
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"""
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嵌入向量生成器
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策略：
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1. 优先使用配置的 embedding API（通过 LLMRequest）
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2. 如果 API 不可用，回退到本地 sentence-transformers
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3. 如果 sentence-transformers 未安装，使用随机向量（仅测试）
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优点：
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- 降低本地运算负载
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- 即使未安装 sentence-transformers 也可正常运行
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- 保持与现有系统的一致性
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"""
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def __init__(
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self,
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use_api: bool = True,
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fallback_model_name: str = "paraphrase-multilingual-MiniLM-L12-v2",
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):
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"""
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初始化嵌入生成器
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Args:
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use_api: 是否优先使用 API（默认 True）
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fallback_model_name: 回退本地模型名称
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"""
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self.use_api = use_api
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self.fallback_model_name = fallback_model_name
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# API 相关
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self._llm_request = None
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self._api_available = False
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self._api_dimension = None
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# 本地模型相关
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self._local_model = None
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self._local_model_loaded = False
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async def _initialize_api(self):
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"""初始化 embedding API"""
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if self._api_available:
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return
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try:
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from src.config.config import model_config
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from src.llm_models.utils_model import LLMRequest
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embedding_config = model_config.model_task_config.embedding
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self._llm_request = LLMRequest(
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model_set=embedding_config,
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request_type="memory_graph.embedding"
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)
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# 获取嵌入维度
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if hasattr(embedding_config, "embedding_dimension") and embedding_config.embedding_dimension:
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self._api_dimension = embedding_config.embedding_dimension
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self._api_available = True
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logger.info(f"✅ Embedding API 初始化成功 (维度: {self._api_dimension})")
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except Exception as e:
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logger.warning(f"⚠️ Embedding API 初始化失败: {e}")
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self._api_available = False
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def _load_local_model(self):
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"""延迟加载本地模型"""
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if not self._local_model_loaded:
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try:
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from sentence_transformers import SentenceTransformer
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logger.info(f"📦 加载本地嵌入模型: {self.fallback_model_name}")
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self._local_model = SentenceTransformer(self.fallback_model_name)
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self._local_model_loaded = True
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logger.info("✅ 本地嵌入模型加载成功")
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except ImportError:
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logger.warning(
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"⚠️ sentence-transformers 未安装，将使用随机向量（仅测试用）\n"
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" 安装方法: pip install sentence-transformers"
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)
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self._local_model_loaded = False
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except Exception as e:
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logger.warning(f"⚠️ 本地模型加载失败: {e}")
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self._local_model_loaded = False
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async def generate(self, text: str) -> np.ndarray:
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"""
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生成单个文本的嵌入向量
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策略：
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1. 优先使用 API
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2. API 失败则使用本地模型
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3. 本地模型不可用则使用随机向量
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Args:
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text: 输入文本
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Returns:
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嵌入向量
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"""
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if not text or not text.strip():
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logger.warning("输入文本为空，返回零向量")
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dim = self._get_dimension()
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return np.zeros(dim, dtype=np.float32)
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try:
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# 策略 1: 使用 API
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if self.use_api:
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embedding = await self._generate_with_api(text)
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if embedding is not None:
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return embedding
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# 策略 2: 使用本地模型
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embedding = await self._generate_with_local_model(text)
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if embedding is not None:
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return embedding
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# 策略 3: 随机向量（仅测试）
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logger.warning(f"⚠️ 所有嵌入策略失败，使用随机向量: {text[:30]}...")
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dim = self._get_dimension()
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return np.random.rand(dim).astype(np.float32)
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except Exception as e:
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logger.error(f"❌ 嵌入生成失败: {e}", exc_info=True)
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dim = self._get_dimension()
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return np.random.rand(dim).astype(np.float32)
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async def _generate_with_api(self, text: str) -> Optional[np.ndarray]:
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"""使用 API 生成嵌入"""
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try:
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# 初始化 API
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if not self._api_available:
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await self._initialize_api()
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if not self._api_available or not self._llm_request:
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return None
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# 调用 API
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embedding_list, model_name = await self._llm_request.get_embedding(text)
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if embedding_list and len(embedding_list) > 0:
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embedding = np.array(embedding_list, dtype=np.float32)
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logger.debug(f"🌐 API 生成嵌入: {text[:30]}... -> {len(embedding)}维 (模型: {model_name})")
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return embedding
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return None
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except Exception as e:
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logger.debug(f"API 嵌入生成失败: {e}")
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return None
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async def _generate_with_local_model(self, text: str) -> Optional[np.ndarray]:
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"""使用本地模型生成嵌入"""
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try:
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# 加载本地模型
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if not self._local_model_loaded:
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self._load_local_model()
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if not self._local_model_loaded or not self._local_model:
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return None
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# 在线程池中运行
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loop = asyncio.get_event_loop()
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embedding = await loop.run_in_executor(None, self._encode_single_local, text)
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logger.debug(f"💻 本地生成嵌入: {text[:30]}... -> {len(embedding)}维")
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return embedding
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except Exception as e:
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logger.debug(f"本地模型嵌入生成失败: {e}")
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return None
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def _encode_single_local(self, text: str) -> np.ndarray:
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"""同步编码单个文本（本地模型）"""
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if self._local_model is None:
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raise RuntimeError("本地模型未加载")
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embedding = self._local_model.encode(text, convert_to_numpy=True) # type: ignore
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return embedding.astype(np.float32)
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def _get_dimension(self) -> int:
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"""获取嵌入维度"""
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# 优先使用 API 维度
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if self._api_dimension:
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return self._api_dimension
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# 其次使用本地模型维度
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if self._local_model_loaded and self._local_model:
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try:
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return self._local_model.get_sentence_embedding_dimension()
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except:
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pass
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# 默认 384（sentence-transformers 常用维度）
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return 384
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async def generate_batch(self, texts: List[str]) -> List[np.ndarray]:
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"""
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批量生成嵌入向量
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Args:
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texts: 文本列表
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Returns:
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嵌入向量列表
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"""
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if not texts:
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return []
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try:
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# 过滤空文本
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valid_texts = [t for t in texts if t and t.strip()]
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if not valid_texts:
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logger.warning("所有文本为空，返回零向量列表")
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dim = self._get_dimension()
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return [np.zeros(dim, dtype=np.float32) for _ in texts]
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# 使用 API 批量生成（如果可用）
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if self.use_api:
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results = await self._generate_batch_with_api(valid_texts)
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if results:
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return results
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# 回退到逐个生成
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results = []
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for text in valid_texts:
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embedding = await self.generate(text)
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results.append(embedding)
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logger.info(f"✅ 批量生成嵌入: {len(texts)} 个文本")
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return results
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except Exception as e:
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logger.error(f"❌ 批量嵌入生成失败: {e}", exc_info=True)
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dim = self._get_dimension()
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return [np.random.rand(dim).astype(np.float32) for _ in texts]
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async def _generate_batch_with_api(self, texts: List[str]) -> Optional[List[np.ndarray]]:
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"""使用 API 批量生成"""
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try:
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# 对于大多数 API，批量调用就是多次单独调用
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# 这里保持简单，逐个调用
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results = []
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for text in texts:
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embedding = await self._generate_with_api(text)
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if embedding is None:
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return None # 如果任何一个失败，返回 None 触发回退
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results.append(embedding)
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return results
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except Exception as e:
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logger.debug(f"API 批量生成失败: {e}")
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return None
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def get_embedding_dimension(self) -> int:
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"""获取嵌入向量维度"""
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return self._get_dimension()
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# 全局单例
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_global_generator: Optional[EmbeddingGenerator] = None
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def get_embedding_generator(
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use_api: bool = True,
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fallback_model_name: str = "paraphrase-multilingual-MiniLM-L12-v2",
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) -> EmbeddingGenerator:
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"""
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获取全局嵌入生成器单例
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Args:
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use_api: 是否优先使用 API
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fallback_model_name: 回退本地模型名称
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Returns:
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EmbeddingGenerator 实例
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"""
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global _global_generator
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if _global_generator is None:
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_global_generator = EmbeddingGenerator(
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use_api=use_api,
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fallback_model_name=fallback_model_name
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)
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return _global_generator
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