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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
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webjoin111
pre-commit-ci[bot]
parent
922d092650
commit
52f7dbdedf
@@ -9,25 +9,26 @@ from zhenxun.services.ai.capabilities import (
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CombinedCapability,
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)
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from zhenxun.services.ai.context.memory.builder import MemoryBuilder
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from zhenxun.services.ai.context.memory.engine import MemoryReader, MemoryWriter
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from zhenxun.services.ai.context.memory.engine import SessionMemoryContext
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from zhenxun.services.ai.context.memory.models import MemoryConfig
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from zhenxun.services.ai.context.memory.types import SessionMetadata
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from zhenxun.services.ai.core.messages import LLMMessage, TextPart
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from zhenxun.services.ai.core.options import GenerationConfig
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from zhenxun.services.ai.core.options import BaseOutputDefinition, GenerationConfig
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from zhenxun.services.ai.core.protocols.tool import ToolExecutable
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from zhenxun.services.ai.core.templates import PromptTemplate
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from zhenxun.services.ai.flow.agent.capabilities import (
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OutputValidationCapability,
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TaskTrackingCapability,
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)
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from zhenxun.services.ai.flow.agent.models import Persona
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from zhenxun.services.ai.run import RunContext
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from zhenxun.services.ai.run import AgentTask, RunContext
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from zhenxun.services.ai.run.di import DependencyInjector
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from zhenxun.services.ai.tools.engine.registry import (
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ToolCollection,
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tool_provider_manager,
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)
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from zhenxun.services.ai.tools.models import ResolvedToolPayload
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from zhenxun.services.ai.utils.scope import ScopeSelector
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from zhenxun.services.ai.utils.runtime import ContextUtils
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from zhenxun.utils.pydantic_compat import model_copy
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@@ -36,7 +37,8 @@ class AgentProfileResolver:
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@staticmethod
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def resolve_memory(
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agent_memory_config: MemoryConfig, override_memory: Any | None
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agent_memory_config: MemoryConfig,
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override_memory: bool | MemoryConfig | MemoryBuilder | None,
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) -> MemoryConfig:
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"""
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解析并合并 Memory 记忆域的配置。
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@@ -86,11 +88,11 @@ class CapabilityBuilder:
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async def build_for_run(
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agent_name: str,
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namespace: str,
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output_type: Any | None,
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output_type: type[Any] | BaseOutputDefinition | None,
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raw_schema: dict | None,
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agent_guardrails: list,
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task_guardrails: list,
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task_obj: Any | None,
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task_obj: AgentTask | None,
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agent_capabilities: list,
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profile_capabilities: list | None,
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context: RunContext,
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@@ -160,11 +162,11 @@ class ContextBuilder:
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@staticmethod
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async def build_prompts(
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instruction: str | PromptTemplate,
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system_prompts: list[Any],
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system_prompts: list[Callable],
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run_context: RunContext,
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run_scoped_cap: CombinedCapability,
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persona: Persona | None = None,
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) -> tuple[str, list[Any]]:
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) -> tuple[str, list[LLMMessage]]:
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"""
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解析、合并并渲染 Agent 的系统提示词和上下文记忆。
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包含对依赖参数的动态注入和 Jinja 模板渲染。
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@@ -291,9 +293,9 @@ class ToolBuilder:
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@staticmethod
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async def resolve_tools(
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tool_definitions: list[Any],
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toolset_funcs: list[Any],
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system_tools: list[Any],
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tool_definitions: list[ToolExecutable | Callable | dict[str, Any] | str],
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toolset_funcs: list[Callable],
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system_tools: list[ToolExecutable | Callable | dict[str, Any] | str],
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namespace: str,
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run_context: RunContext,
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run_scoped_cap: CombinedCapability,
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@@ -359,7 +361,7 @@ class ToolBuilder:
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@staticmethod
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async def prepare_effective_tools(
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effective_tools: list[Any],
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effective_tools: list[ToolExecutable],
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context: RunContext,
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tool_filters: list[Callable],
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run_scoped_cap: CombinedCapability,
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@@ -407,11 +409,15 @@ class ToolBuilder:
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final_defs_map = {d.name.lower(): d for d in current_tool_defs if d}
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final_effective_tools_list = []
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seen_names = set()
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for t_exec in effective_tools:
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t_name = getattr(t_exec, "name", "unknown")
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if t_name.lower() in final_defs_map:
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t_name_lower = t_name.lower()
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if t_name_lower in final_defs_map and t_name_lower not in seen_names:
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seen_names.add(t_name_lower)
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cloned_tool = copy.copy(t_exec)
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cloned_tool._dynamic_def = final_defs_map[t_name.lower()]
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setattr(cloned_tool, "_dynamic_def", final_defs_map[t_name_lower])
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final_effective_tools_list.append(cloned_tool)
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return ToolCollection(final_effective_tools_list)
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@@ -425,7 +431,7 @@ class SessionBuilder:
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namespace: str,
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agent_name: str,
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effective_memory: MemoryConfig,
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) -> tuple[Any, Any, Any]:
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) -> tuple[SessionMetadata, SessionMemoryContext]:
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"""
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根据当前用户、群组和平台标识，动态隔离前缀，计算并构建会话与记忆存储的读写门面。
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@@ -436,78 +442,21 @@ class SessionBuilder:
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effective_memory: 运行时最终生效的 MemoryConfig 配置对象。
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返回：
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tuple[Any, Any, Any]: 包含 (SessionMetadata 会话元数据, MemoryReader 记忆读取器, MemoryWriter 记忆写入器) 的元组。
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tuple[SessionMetadata, SessionMemoryContext]: 包含 (会话元数据, 会话记忆门面上下文) 的元组。
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""" # noqa: E501
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bot_id = None
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bot_inst = context.get_bot()
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if bot_inst and hasattr(bot_inst, "self_id"):
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bot_id = str(bot_inst.self_id)
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selector = ScopeSelector(
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user_id=context.get_user_id(),
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group_id=context.get_group_id(),
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platform=context.get_platform(),
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bot_id=bot_id,
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namespace=namespace,
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agent_name=agent_name,
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)
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all_scopes = {"/"}
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scope_name_mapping = {}
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target_builder = None
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if effective_memory.short_term and effective_memory.short_term.isolation:
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sel = effective_memory.short_term.isolation.resolve(
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deps=context.deps,
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prefix="",
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default_namespace=namespace,
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default_agent=agent_name,
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)
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all_scopes.add(sel.scope_prefix)
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target_builder = effective_memory.short_term.isolation
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for config_part in [effective_memory.slots, effective_memory.long_term]:
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if config_part and hasattr(config_part, "scopes") and config_part.scopes:
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for name, builder in config_part.scopes.items():
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sel = builder.resolve(
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deps=context.deps,
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prefix="",
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default_namespace=namespace,
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default_agent=agent_name,
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)
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all_scopes.add(sel.scope_prefix)
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scope_name_mapping[sel.scope_prefix] = name
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parts = selector.get_scope_parts()
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for i in range(len(parts)):
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all_scopes.add("/" + "/".join(parts[: i + 1]))
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accessible_scopes = sorted(all_scopes, key=lambda x: len(x.split("/")))
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short_term_builder = (
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effective_memory.short_term.isolation
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if effective_memory.short_term
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else effective_memory.base_isolation
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)
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short_term_selector = short_term_builder.resolve(
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deps=context.deps,
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prefix="",
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default_namespace=namespace,
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default_agent=agent_name,
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session_metadata = ContextUtils.build_session_meta(
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context=context, target_builder=target_builder, custom_namespace=namespace
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)
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context.session.session_meta = session_metadata
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session_metadata = SessionMetadata(
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session_id=short_term_selector.scope_prefix,
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selector=selector,
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scope_prefix=selector.scope_prefix,
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accessible_scopes=accessible_scopes,
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scope_name_mapping=scope_name_mapping,
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)
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reader = MemoryReader(
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session_meta=session_metadata, memory_config=effective_memory
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)
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writer = MemoryWriter(
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memory_context = SessionMemoryContext(
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session_meta=session_metadata,
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memory_config=effective_memory,
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context=context,
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)
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return session_metadata, reader, writer
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return session_metadata, memory_context
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