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* ♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 - 【重构】重构 `BaseRunnable` 并引入统一的 `RunIntent` 意图载体，规范 Agent、Team 和 Workflow 的执行流 - 【解耦】将中期记忆槽和长期向量记忆从 `MemoryConfig` 中解耦，转为独立的能力组件与工具箱进行管理 - 【记忆】移除 `MemoryReader` 和 `MemoryWriter`，统一封装为 `SessionMemoryContext` 会话记忆门面 - 【RAG】重构检索器与存储后端接口，统一采用 `QueryRequest` 进行多维度联合检索，并引入 `InMemoryScorer` 提升打分性能 - 【事件】优化 `EventBus` 异步事件分发机制，引入队列机制确保事件按序处理，避免并发竞态问题 - 【依赖注入】移除 `memory` 注入项，优化 `DependencyInjector` 的签名解析缓存以提升性能 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
from typing import Any
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import numpy as np
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from zhenxun.services.ai.context.rag.models import BaseRecord, SearchResult
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from zhenxun.services.ai.utils.logger import log_rag as logger
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def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
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"""使用 numpy 计算两组向量的余弦相似度"""
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if not vec1 or not vec2 or len(vec1) != len(vec2):
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return 0.0
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v1, v2 = np.array(vec1), np.array(vec2)
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norm1, norm2 = np.linalg.norm(v1), np.linalg.norm(v2)
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if norm1 == 0 or norm2 == 0:
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return 0.0
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return float(np.dot(v1, v2) / (norm1 * norm2))
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def normalize_vector(vec: list[float] | np.ndarray) -> np.ndarray:
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"""将一维向量转化为 float32 数组并进行 L2 归一化"""
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v = np.array(vec, dtype=np.float32)
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norm = np.linalg.norm(v)
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if norm == 0:
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return v
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return v / norm
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def normalize_matrix(mat: np.ndarray) -> np.ndarray:
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"""将二维矩阵的每一行进行 L2 归一化"""
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norms = np.linalg.norm(mat, axis=1, keepdims=True)
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norms[norms == 0] = 1.0
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return mat / norms
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def normalize_query_text(query: Any) -> str:
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"""辅助函数：提取各种输入形式（如字符串、平台Message对象）的纯文本用于检索"""
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if isinstance(query, str):
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return query
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if hasattr(query, "extract_plain_text"):
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return query.extract_plain_text()
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return str(query) if query is not None else ""
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class InMemoryScorer:
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"""提供内存级别的纯 Python 向量打分与 BM25 稀疏打分工具类"""
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@staticmethod
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def calculate_sparse_scores(
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query: str, candidate_records: list[BaseRecord]
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) -> list[SearchResult]:
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import jieba
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tokens = set(jieba.lcut_for_search(query.lower()))
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if not tokens:
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return [SearchResult(record=r, score=0.1) for r in candidate_records]
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results = []
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for r in candidate_records:
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content_lower = r.content.lower()
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matched_count = sum(1 for t in tokens if t in content_lower)
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score = matched_count / len(tokens) if tokens else 0.1
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results.append(SearchResult(record=r, score=score))
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return results
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@staticmethod
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def calculate_dense_scores(
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query_embedding: list[float], candidate_records: list[BaseRecord]
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) -> list[SearchResult]:
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if not candidate_records or not query_embedding:
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return []
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q_vec = normalize_vector(query_embedding)
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vec_records = [r for r in candidate_records if r.embedding]
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missing_records = [r for r in candidate_records if not r.embedding]
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results = []
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if vec_records:
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try:
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raw_mat = np.array([r.embedding for r in vec_records], dtype=np.float32)
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norm_mat = normalize_matrix(raw_mat)
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scores = norm_mat @ q_vec
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for r, score in zip(vec_records, scores):
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results.append(SearchResult(record=r, score=float(score)))
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except ValueError as e:
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logger.warning(
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"⚠️ 维度不匹配，已安全跳过向量检索(降级)。原因: " + str(e)
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)
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for r in missing_records:
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results.append(SearchResult(record=r, score=0.1))
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return results
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