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♻️ refactor(core): 重构 AI 编排框架与记忆及 RAG 子系统 (#2149)
* ♻️ 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>
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webjoin111
pre-commit-ci[bot]
webjoin111
pre-commit-ci[bot]
parent
922d092650
commit
52f7dbdedf
@@ -1,8 +1,9 @@
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from collections.abc import Sequence
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from typing import Any, cast
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from typing import cast
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from zhenxun.services.ai.core.engine.context_renderer import ContextConverter
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from zhenxun.services.ai.core.messages import AgentMessage
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from zhenxun.services.ai.run.context import RunContext
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from zhenxun.services.ai.utils.logger import log_memory as logger
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from zhenxun.utils.pydantic_compat import model_copy
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@@ -14,144 +15,37 @@ from .models import MemoryConfig
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from .types import SessionMetadata
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class MemoryReader:
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class SessionMemoryContext:
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"""
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记忆读取器 (Memory Reader)。
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负责从数据库中提取短期上下文历史，召回长期的背景知识，并执行自动压缩。
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会话记忆门面。
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封装了当前会话记忆的读写操作、上下文压缩管线以及入库清洗中间件。
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"""
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def __init__(
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self, session_meta: SessionMetadata, memory_config: MemoryConfig | None
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self,
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session_meta: SessionMetadata,
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memory_config: MemoryConfig | None,
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context: RunContext,
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):
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"""
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初始化记忆读取器。
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初始化会话记忆门面。
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参数:
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session_meta: 会话元数据，包含 Namespace 与作用域映射等上下文信息。
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memory_config: 记忆系统的配置对象，控制长期、短期及槽位记忆的启用与逻辑。
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memory_config: 记忆系统的配置对象，控制长期、短期记忆的启用与逻辑。
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context: 必填的运行时上下文环境 (RunContext)，供中间件进行依赖注入。
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"""
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self.session_meta = session_meta
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self.memory_config = memory_config
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self.context = context
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async def get_long_term_context(self, user_input: str) -> str:
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"""
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基于用户输入召回长期记忆（RAG），返回格式化后的背景提示词。
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"""
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if (
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not self.memory_config
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or not self.memory_config.long_term.enable
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or not user_input
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):
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return ""
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policy = self.memory_config.long_term.auto_recall
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should_recall = False
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if isinstance(policy, bool):
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should_recall = policy
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elif callable(policy):
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import inspect
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try:
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res = policy(user_input, self.session_meta)
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if inspect.isawaitable(res):
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should_recall = await res
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else:
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should_recall = bool(res)
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except Exception as e:
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logger.error(f"[MemoryReader] 自定义 auto_recall 函数执行失败: {e}")
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should_recall = False
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if not should_recall:
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return ""
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ltm_scope = memory_manager.get_long_term_memory(
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self.memory_config,
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namespace=self.session_meta.selector.namespace or "global",
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)
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if not ltm_scope:
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return ""
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matches = await ltm_scope.recall(session=self.session_meta, query=user_input)
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if matches:
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logger.debug(f"🧠 [MemoryReader] 长期记忆召回详情 (Query: '{user_input}'):")
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for i, m in enumerate(matches):
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logger.debug(
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f" [{i + 1}] 得分: {m.score:.4f} | 内容: {m.record.content}"
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)
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threshold = self.memory_config.long_term.recall_threshold
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valid_matches = [m for m in matches if m.score >= threshold]
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if not valid_matches:
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logger.debug("🧠 [MemoryReader] 召回的记忆均未达到相关性阈值，已丢弃。")
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return ""
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fact_str = "\n".join(f"- {m.record.content}" for m in valid_matches)
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logger.debug(
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f"🧠 [MemoryReader]"
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f"成功截取并注入 {len(valid_matches)} 条高价值长期记忆。"
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)
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return f"[系统补充：有关用户的长期记忆设定]\n{fact_str}"
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return ""
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async def get_slots_context(self) -> str:
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"""
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读取并组装核心槽位记忆 (Memory Slots)，返回 XML 格式字符串供大模型使用。
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"""
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if not self.memory_config or not self.memory_config.slots.enable:
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return ""
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slot_ctx = memory_manager.get_slot_context(
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self.memory_config,
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namespace=self.session_meta.selector.namespace or "global",
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)
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if not slot_ctx:
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return ""
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if self.memory_config.slots.default_slots:
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for default_slot in self.memory_config.slots.default_slots:
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existing = await slot_ctx.get_slot(
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self.session_meta, default_slot.label
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)
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if not existing:
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await slot_ctx.set_slot(self.session_meta, default_slot)
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slots = await slot_ctx.list_pinned_slots(self.session_meta)
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if not slots:
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return ""
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show_scope = False
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if (
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self.memory_config
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and self.memory_config.slots.scopes
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and len(self.memory_config.slots.scopes) > 1
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):
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show_scope = True
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xml_parts = ["<memory_slots>"]
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for slot in slots:
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if show_scope:
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semantic_name = self.session_meta.scope_name_mapping.get(
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slot.scope, "未知"
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)
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xml_parts.append(
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f' <slot name="{slot.label}" scope="{semantic_name}">\n'
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f" {slot.content}\n"
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" </slot>"
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)
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else:
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xml_parts.append(
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f' <slot name="{slot.label}">\n {slot.content}\n </slot>'
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)
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xml_parts.append("</memory_slots>")
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return "\n".join(xml_parts)
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async def get_short_term_context(
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async def read(
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self,
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model_name: str,
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override_history: Sequence[AgentMessage] | None = None,
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) -> list[AgentMessage]:
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"""
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拉取短期对话历史，并执行 Token 压缩。
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拉取短期对话历史，并执行 Token 压缩与管线修剪。
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"""
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current_history: list[AgentMessage] = []
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if override_history is not None:
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@@ -187,43 +81,18 @@ class MemoryReader:
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if changed:
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await chat_context.set_messages(self.session_meta, new_history)
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logger.info(
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"💾 [MemoryReader] 压缩截断完毕，已同步覆写数据库。"
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"💾 [SessionMemory] 压缩截断完毕，已同步覆写数据库。"
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f"压缩后条数: {len(new_history)}"
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)
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current_history = cast(list[AgentMessage], new_history)
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return current_history
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class MemoryWriter:
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"""
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记忆写入器 (Memory Writer)。
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负责将对话增量安全地写入数据库。
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"""
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def __init__(
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self,
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session_meta: SessionMetadata,
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memory_config: MemoryConfig | None,
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context: Any = None,
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):
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"""
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初始化记忆写入器。
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参数:
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session_meta: 会话元数据，包含 Namespace 与作用域映射等上下文信息。
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memory_config: 记忆系统的配置对象，控制记忆存入的逻辑。
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context: 运行时上下文环境，作为可选参数传入，供中间件使用，默认 None。
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"""
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self.session_meta = session_meta
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self.memory_config = memory_config
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self.context = context
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async def save_new_messages(
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async def write(
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self,
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new_messages: Sequence[AgentMessage],
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):
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"""将新产生的对话增量保存到数据库"""
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) -> None:
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"""将新产生的对话增量，经过入库中间件清洗后保存到数据库"""
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if not new_messages:
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return
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