添加 A_Memorix 插件 v2.0.0（包含运行时与文档）
引入 A_Memorix 插件 v2.0.0：新增大量运行时组件、存储/模式更新、检索能力提升、管理工具、导入/调优工作流以及相关文档。关键新增内容包括：lifecycle_orchestrator、SDKMemoryKernel/运行时初始化器、新的存储层与 metadata_store 变更（SCHEMA_VERSION v8）、检索增强（双路径检索、图关系召回、稀疏 BM25），以及多种工具服务（episode/person_profile/relation/segmentation/tuning/search execution）。同时新增 Web 导入/摘要导入器及大量维护脚本。还更新了插件清单、embedding API 适配器、plugin.py、requirements/pyproject，以及主入口文件，使新插件接入项目。该变更为 2.0.0 版本发布做好准备，实现统一的 SDK Tool 接口并扩展整体运行能力。
This commit is contained in:
DawnARC
@@ -1,8 +1,16 @@
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"""SDK runtime exports for A_Memorix."""
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from .search_runtime_initializer import (
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SearchRuntimeBundle,
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SearchRuntimeInitializer,
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build_search_runtime,
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)
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from .sdk_memory_kernel import KernelSearchRequest, SDKMemoryKernel
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__all__ = [
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"SearchRuntimeBundle",
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"SearchRuntimeInitializer",
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"build_search_runtime",
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"KernelSearchRequest",
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"SDKMemoryKernel",
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]
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268
plugins/A_memorix/core/runtime/lifecycle_orchestrator.py
Normal file
268
plugins/A_memorix/core/runtime/lifecycle_orchestrator.py
Normal file
@@ -0,0 +1,268 @@
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"""Lifecycle bootstrap/teardown helpers extracted from plugin.py."""
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from __future__ import annotations
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import asyncio
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from pathlib import Path
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from typing import Any
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from src.common.logger import get_logger
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from ..embedding import create_embedding_api_adapter
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from ..retrieval import SparseBM25Config, SparseBM25Index
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from ..storage import (
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GraphStore,
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MetadataStore,
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QuantizationType,
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SparseMatrixFormat,
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VectorStore,
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)
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from ..utils.runtime_self_check import ensure_runtime_self_check
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from ..utils.relation_write_service import RelationWriteService
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logger = get_logger("A_Memorix.LifecycleOrchestrator")
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async def ensure_initialized(plugin: Any) -> None:
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if plugin._initialized:
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plugin._runtime_ready = plugin._check_storage_ready()
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return
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async with plugin._init_lock:
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if plugin._initialized:
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plugin._runtime_ready = plugin._check_storage_ready()
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return
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logger.info("A_Memorix 插件正在异步初始化存储组件...")
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plugin._validate_runtime_config()
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await initialize_storage_async(plugin)
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report = await ensure_runtime_self_check(plugin, force=True)
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if not bool(report.get("ok", False)):
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logger.error(
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"A_Memorix runtime self-check failed: "
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f"{report.get('message', 'unknown')}; "
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"建议执行 python plugins/A_memorix/scripts/runtime_self_check.py --json"
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)
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if plugin.graph_store and plugin.metadata_store:
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relation_count = plugin.metadata_store.count_relations()
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if relation_count > 0 and not plugin.graph_store.has_edge_hash_map():
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raise RuntimeError(
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"检测到 relations 数据存在但 edge-hash-map 为空。"
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" 请先执行 scripts/release_vnext_migrate.py migrate。"
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)
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plugin._initialized = True
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plugin._runtime_ready = plugin._check_storage_ready()
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plugin._update_plugin_config()
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logger.info("A_Memorix 插件异步初始化成功")
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def start_background_tasks(plugin: Any) -> None:
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"""Start background tasks idempotently."""
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if not hasattr(plugin, "_episode_generation_task"):
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plugin._episode_generation_task = None
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if (
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plugin.get_config("summarization.enabled", True)
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and plugin.get_config("schedule.enabled", True)
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and (plugin._scheduled_import_task is None or plugin._scheduled_import_task.done())
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):
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plugin._scheduled_import_task = asyncio.create_task(plugin._scheduled_import_loop())
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if (
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plugin.get_config("advanced.enable_auto_save", True)
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and (plugin._auto_save_task is None or plugin._auto_save_task.done())
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):
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plugin._auto_save_task = asyncio.create_task(plugin._auto_save_loop())
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if (
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plugin.get_config("person_profile.enabled", True)
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and (plugin._person_profile_refresh_task is None or plugin._person_profile_refresh_task.done())
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):
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plugin._person_profile_refresh_task = asyncio.create_task(plugin._person_profile_refresh_loop())
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if plugin._memory_maintenance_task is None or plugin._memory_maintenance_task.done():
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plugin._memory_maintenance_task = asyncio.create_task(plugin._memory_maintenance_loop())
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rv_cfg = plugin.get_config("retrieval.relation_vectorization", {}) or {}
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if isinstance(rv_cfg, dict):
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rv_enabled = bool(rv_cfg.get("enabled", False))
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rv_backfill = bool(rv_cfg.get("backfill_enabled", False))
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else:
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rv_enabled = False
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rv_backfill = False
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if rv_enabled and rv_backfill and (
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plugin._relation_vector_backfill_task is None or plugin._relation_vector_backfill_task.done()
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):
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plugin._relation_vector_backfill_task = asyncio.create_task(plugin._relation_vector_backfill_loop())
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episode_task = getattr(plugin, "_episode_generation_task", None)
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episode_loop = getattr(plugin, "_episode_generation_loop", None)
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if (
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callable(episode_loop)
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and bool(plugin.get_config("episode.enabled", True))
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and bool(plugin.get_config("episode.generation_enabled", True))
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and (episode_task is None or episode_task.done())
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):
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plugin._episode_generation_task = asyncio.create_task(episode_loop())
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async def cancel_background_tasks(plugin: Any) -> None:
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"""Cancel all background tasks and wait for cleanup."""
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tasks = [
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("scheduled_import", plugin._scheduled_import_task),
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("auto_save", plugin._auto_save_task),
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("person_profile_refresh", plugin._person_profile_refresh_task),
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("memory_maintenance", plugin._memory_maintenance_task),
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("relation_vector_backfill", plugin._relation_vector_backfill_task),
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("episode_generation", getattr(plugin, "_episode_generation_task", None)),
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]
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for _, task in tasks:
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if task and not task.done():
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task.cancel()
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for name, task in tasks:
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if not task:
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continue
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try:
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await task
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except asyncio.CancelledError:
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pass
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except Exception as e:
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logger.warning(f"后台任务 {name} 退出异常: {e}")
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plugin._scheduled_import_task = None
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plugin._auto_save_task = None
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plugin._person_profile_refresh_task = None
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plugin._memory_maintenance_task = None
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plugin._relation_vector_backfill_task = None
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plugin._episode_generation_task = None
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async def initialize_storage_async(plugin: Any) -> None:
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"""Initialize storage components asynchronously."""
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data_dir_str = plugin.get_config("storage.data_dir", "./data")
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if data_dir_str.startswith("."):
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plugin_dir = Path(__file__).resolve().parents[2]
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data_dir = (plugin_dir / data_dir_str).resolve()
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else:
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data_dir = Path(data_dir_str)
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logger.info(f"A_Memorix 数据存储路径: {data_dir}")
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data_dir.mkdir(parents=True, exist_ok=True)
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plugin.embedding_manager = create_embedding_api_adapter(
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batch_size=plugin.get_config("embedding.batch_size", 32),
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max_concurrent=plugin.get_config("embedding.max_concurrent", 5),
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default_dimension=plugin.get_config("embedding.dimension", 1024),
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model_name=plugin.get_config("embedding.model_name", "auto"),
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retry_config=plugin.get_config("embedding.retry", {}),
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)
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logger.info("嵌入 API 适配器初始化完成")
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try:
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detected_dimension = await plugin.embedding_manager._detect_dimension()
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logger.info(f"嵌入维度检测成功: {detected_dimension}")
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except Exception as e:
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logger.warning(f"嵌入维度检测失败: {e}，使用默认值")
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detected_dimension = plugin.embedding_manager.default_dimension
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quantization_str = plugin.get_config("embedding.quantization_type", "int8")
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if str(quantization_str or "").strip().lower() != "int8":
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raise ValueError("embedding.quantization_type 在 vNext 仅允许 int8(SQ8)。")
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quantization_type = QuantizationType.INT8
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plugin.vector_store = VectorStore(
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dimension=detected_dimension,
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quantization_type=quantization_type,
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data_dir=data_dir / "vectors",
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)
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plugin.vector_store.min_train_threshold = plugin.get_config("embedding.min_train_threshold", 40)
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logger.info(
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"向量存储初始化完成（"
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f"维度: {detected_dimension}, "
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f"训练阈值: {plugin.vector_store.min_train_threshold}）"
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)
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matrix_format_str = plugin.get_config("graph.sparse_matrix_format", "csr")
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matrix_format_map = {
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"csr": SparseMatrixFormat.CSR,
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"csc": SparseMatrixFormat.CSC,
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}
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matrix_format = matrix_format_map.get(matrix_format_str, SparseMatrixFormat.CSR)
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plugin.graph_store = GraphStore(
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matrix_format=matrix_format,
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data_dir=data_dir / "graph",
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)
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logger.info("图存储初始化完成")
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plugin.metadata_store = MetadataStore(data_dir=data_dir / "metadata")
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plugin.metadata_store.connect()
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logger.info("元数据存储初始化完成")
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plugin.relation_write_service = RelationWriteService(
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metadata_store=plugin.metadata_store,
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graph_store=plugin.graph_store,
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vector_store=plugin.vector_store,
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embedding_manager=plugin.embedding_manager,
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)
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logger.info("关系写入服务初始化完成")
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sparse_cfg_raw = plugin.get_config("retrieval.sparse", {}) or {}
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if not isinstance(sparse_cfg_raw, dict):
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sparse_cfg_raw = {}
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try:
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sparse_cfg = SparseBM25Config(**sparse_cfg_raw)
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except Exception as e:
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logger.warning(f"sparse 配置非法，回退默认配置: {e}")
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sparse_cfg = SparseBM25Config()
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plugin.sparse_index = SparseBM25Index(
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metadata_store=plugin.metadata_store,
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config=sparse_cfg,
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)
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logger.info(
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"稀疏检索组件初始化完成: "
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f"enabled={sparse_cfg.enabled}, "
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f"lazy_load={sparse_cfg.lazy_load}, "
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f"mode={sparse_cfg.mode}, "
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f"tokenizer={sparse_cfg.tokenizer_mode}"
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)
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if sparse_cfg.enabled and not sparse_cfg.lazy_load:
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plugin.sparse_index.ensure_loaded()
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if plugin.vector_store.has_data():
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try:
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plugin.vector_store.load()
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logger.info(f"向量数据已加载，共 {plugin.vector_store.num_vectors} 个向量")
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except Exception as e:
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logger.warning(f"加载向量数据失败: {e}")
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try:
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warmup_summary = plugin.vector_store.warmup_index(force_train=True)
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if warmup_summary.get("ok"):
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logger.info(
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"向量索引预热完成: "
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f"trained={warmup_summary.get('trained')}, "
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f"index_ntotal={warmup_summary.get('index_ntotal')}, "
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f"fallback_ntotal={warmup_summary.get('fallback_ntotal')}, "
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f"bin_count={warmup_summary.get('bin_count')}, "
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f"duration_ms={float(warmup_summary.get('duration_ms', 0.0)):.2f}"
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)
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else:
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logger.warning(
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"向量索引预热失败，继续启用 sparse 降级路径: "
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f"{warmup_summary.get('error', 'unknown')}"
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)
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except Exception as e:
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logger.warning(f"向量索引预热异常，继续启用 sparse 降级路径: {e}")
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if plugin.graph_store.has_data():
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try:
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plugin.graph_store.load()
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logger.info(f"图数据已加载，共 {plugin.graph_store.num_nodes} 个节点")
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except Exception as e:
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logger.warning(f"加载图数据失败: {e}")
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logger.info(f"知识库数据目录: {data_dir}")
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File diff suppressed because it is too large
Load Diff
240
plugins/A_memorix/core/runtime/search_runtime_initializer.py
Normal file
240
plugins/A_memorix/core/runtime/search_runtime_initializer.py
Normal file
@@ -0,0 +1,240 @@
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"""Shared runtime initializer for Action/Tool/Command retrieval components."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any, Dict, Optional
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from src.common.logger import get_logger
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from ..retrieval import (
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DualPathRetriever,
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DualPathRetrieverConfig,
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DynamicThresholdFilter,
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FusionConfig,
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GraphRelationRecallConfig,
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RelationIntentConfig,
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RetrievalStrategy,
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SparseBM25Config,
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ThresholdConfig,
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ThresholdMethod,
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)
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_logger = get_logger("A_Memorix.SearchRuntimeInitializer")
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_REQUIRED_COMPONENT_KEYS = (
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"vector_store",
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"graph_store",
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"metadata_store",
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"embedding_manager",
|
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)
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def _get_config_value(config: Optional[dict], key: str, default: Any = None) -> Any:
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if not isinstance(config, dict):
|
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return default
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current: Any = config
|
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for part in key.split("."):
|
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if isinstance(current, dict) and part in current:
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current = current[part]
|
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else:
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return default
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return current
|
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|
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|
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def _safe_dict(value: Any) -> Dict[str, Any]:
|
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return value if isinstance(value, dict) else {}
|
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def _resolve_debug_enabled(plugin_config: Optional[dict]) -> bool:
|
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advanced = _get_config_value(plugin_config, "advanced", {})
|
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if isinstance(advanced, dict):
|
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return bool(advanced.get("debug", False))
|
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return bool(_get_config_value(plugin_config, "debug", False))
|
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|
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|
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@dataclass
|
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class SearchRuntimeBundle:
|
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"""Resolved runtime components and initialized retriever/filter."""
|
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|
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vector_store: Optional[Any] = None
|
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graph_store: Optional[Any] = None
|
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metadata_store: Optional[Any] = None
|
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embedding_manager: Optional[Any] = None
|
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sparse_index: Optional[Any] = None
|
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retriever: Optional[DualPathRetriever] = None
|
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threshold_filter: Optional[DynamicThresholdFilter] = None
|
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error: str = ""
|
||||
|
||||
@property
|
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def ready(self) -> bool:
|
||||
return (
|
||||
self.retriever is not None
|
||||
and self.vector_store is not None
|
||||
and self.graph_store is not None
|
||||
and self.metadata_store is not None
|
||||
and self.embedding_manager is not None
|
||||
)
|
||||
|
||||
|
||||
def _resolve_runtime_components(plugin_config: Optional[dict]) -> SearchRuntimeBundle:
|
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bundle = SearchRuntimeBundle(
|
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vector_store=_get_config_value(plugin_config, "vector_store"),
|
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graph_store=_get_config_value(plugin_config, "graph_store"),
|
||||
metadata_store=_get_config_value(plugin_config, "metadata_store"),
|
||||
embedding_manager=_get_config_value(plugin_config, "embedding_manager"),
|
||||
sparse_index=_get_config_value(plugin_config, "sparse_index"),
|
||||
)
|
||||
|
||||
missing_required = any(
|
||||
getattr(bundle, key) is None for key in _REQUIRED_COMPONENT_KEYS
|
||||
)
|
||||
if not missing_required:
|
||||
return bundle
|
||||
|
||||
try:
|
||||
from ...plugin import AMemorixPlugin
|
||||
|
||||
instances = AMemorixPlugin.get_storage_instances()
|
||||
except Exception:
|
||||
instances = {}
|
||||
|
||||
if not isinstance(instances, dict) or not instances:
|
||||
return bundle
|
||||
|
||||
if bundle.vector_store is None:
|
||||
bundle.vector_store = instances.get("vector_store")
|
||||
if bundle.graph_store is None:
|
||||
bundle.graph_store = instances.get("graph_store")
|
||||
if bundle.metadata_store is None:
|
||||
bundle.metadata_store = instances.get("metadata_store")
|
||||
if bundle.embedding_manager is None:
|
||||
bundle.embedding_manager = instances.get("embedding_manager")
|
||||
if bundle.sparse_index is None:
|
||||
bundle.sparse_index = instances.get("sparse_index")
|
||||
return bundle
|
||||
|
||||
|
||||
def build_search_runtime(
|
||||
plugin_config: Optional[dict],
|
||||
logger_obj: Optional[Any],
|
||||
owner_tag: str,
|
||||
*,
|
||||
log_prefix: str = "",
|
||||
) -> SearchRuntimeBundle:
|
||||
"""Build retriever + threshold filter with unified fallback/config parsing."""
|
||||
|
||||
log = logger_obj or _logger
|
||||
owner = str(owner_tag or "runtime").strip().lower() or "runtime"
|
||||
prefix = str(log_prefix or "").strip()
|
||||
prefix_text = f"{prefix} " if prefix else ""
|
||||
|
||||
runtime = _resolve_runtime_components(plugin_config)
|
||||
if any(getattr(runtime, key) is None for key in _REQUIRED_COMPONENT_KEYS):
|
||||
runtime.error = "存储组件未完全初始化"
|
||||
log.warning(f"{prefix_text}[{owner}] 存储组件未完全初始化，无法使用检索功能")
|
||||
return runtime
|
||||
|
||||
sparse_cfg_raw = _safe_dict(_get_config_value(plugin_config, "retrieval.sparse", {}) or {})
|
||||
fusion_cfg_raw = _safe_dict(_get_config_value(plugin_config, "retrieval.fusion", {}) or {})
|
||||
relation_intent_cfg_raw = _safe_dict(
|
||||
_get_config_value(plugin_config, "retrieval.search.relation_intent", {}) or {}
|
||||
)
|
||||
graph_recall_cfg_raw = _safe_dict(
|
||||
_get_config_value(plugin_config, "retrieval.search.graph_recall", {}) or {}
|
||||
)
|
||||
|
||||
try:
|
||||
sparse_cfg = SparseBM25Config(**sparse_cfg_raw)
|
||||
except Exception as e:
|
||||
log.warning(f"{prefix_text}[{owner}] sparse 配置非法，回退默认: {e}")
|
||||
sparse_cfg = SparseBM25Config()
|
||||
|
||||
try:
|
||||
fusion_cfg = FusionConfig(**fusion_cfg_raw)
|
||||
except Exception as e:
|
||||
log.warning(f"{prefix_text}[{owner}] fusion 配置非法，回退默认: {e}")
|
||||
fusion_cfg = FusionConfig()
|
||||
|
||||
try:
|
||||
relation_intent_cfg = RelationIntentConfig(**relation_intent_cfg_raw)
|
||||
except Exception as e:
|
||||
log.warning(f"{prefix_text}[{owner}] relation_intent 配置非法，回退默认: {e}")
|
||||
relation_intent_cfg = RelationIntentConfig()
|
||||
|
||||
try:
|
||||
graph_recall_cfg = GraphRelationRecallConfig(**graph_recall_cfg_raw)
|
||||
except Exception as e:
|
||||
log.warning(f"{prefix_text}[{owner}] graph_recall 配置非法，回退默认: {e}")
|
||||
graph_recall_cfg = GraphRelationRecallConfig()
|
||||
|
||||
try:
|
||||
config = DualPathRetrieverConfig(
|
||||
top_k_paragraphs=_get_config_value(plugin_config, "retrieval.top_k_paragraphs", 20),
|
||||
top_k_relations=_get_config_value(plugin_config, "retrieval.top_k_relations", 10),
|
||||
top_k_final=_get_config_value(plugin_config, "retrieval.top_k_final", 10),
|
||||
alpha=_get_config_value(plugin_config, "retrieval.alpha", 0.5),
|
||||
enable_ppr=_get_config_value(plugin_config, "retrieval.enable_ppr", True),
|
||||
ppr_alpha=_get_config_value(plugin_config, "retrieval.ppr_alpha", 0.85),
|
||||
ppr_timeout_seconds=_get_config_value(
|
||||
plugin_config, "retrieval.ppr_timeout_seconds", 1.5
|
||||
),
|
||||
ppr_concurrency_limit=_get_config_value(
|
||||
plugin_config, "retrieval.ppr_concurrency_limit", 4
|
||||
),
|
||||
enable_parallel=_get_config_value(plugin_config, "retrieval.enable_parallel", True),
|
||||
retrieval_strategy=RetrievalStrategy.DUAL_PATH,
|
||||
debug=_resolve_debug_enabled(plugin_config),
|
||||
sparse=sparse_cfg,
|
||||
fusion=fusion_cfg,
|
||||
relation_intent=relation_intent_cfg,
|
||||
graph_recall=graph_recall_cfg,
|
||||
)
|
||||
|
||||
runtime.retriever = DualPathRetriever(
|
||||
vector_store=runtime.vector_store,
|
||||
graph_store=runtime.graph_store,
|
||||
metadata_store=runtime.metadata_store,
|
||||
embedding_manager=runtime.embedding_manager,
|
||||
sparse_index=runtime.sparse_index,
|
||||
config=config,
|
||||
)
|
||||
|
||||
threshold_config = ThresholdConfig(
|
||||
method=ThresholdMethod.ADAPTIVE,
|
||||
min_threshold=_get_config_value(plugin_config, "threshold.min_threshold", 0.3),
|
||||
max_threshold=_get_config_value(plugin_config, "threshold.max_threshold", 0.95),
|
||||
percentile=_get_config_value(plugin_config, "threshold.percentile", 75.0),
|
||||
std_multiplier=_get_config_value(plugin_config, "threshold.std_multiplier", 1.5),
|
||||
min_results=_get_config_value(plugin_config, "threshold.min_results", 3),
|
||||
enable_auto_adjust=_get_config_value(plugin_config, "threshold.enable_auto_adjust", True),
|
||||
)
|
||||
runtime.threshold_filter = DynamicThresholdFilter(threshold_config)
|
||||
runtime.error = ""
|
||||
log.info(f"{prefix_text}[{owner}] 检索运行时初始化完成")
|
||||
except Exception as e:
|
||||
runtime.retriever = None
|
||||
runtime.threshold_filter = None
|
||||
runtime.error = str(e)
|
||||
log.error(f"{prefix_text}[{owner}] 检索运行时初始化失败: {e}")
|
||||
|
||||
return runtime
|
||||
|
||||
|
||||
class SearchRuntimeInitializer:
|
||||
"""Compatibility wrapper around the function style initializer."""
|
||||
|
||||
@staticmethod
|
||||
def build_search_runtime(
|
||||
plugin_config: Optional[dict],
|
||||
logger_obj: Optional[Any],
|
||||
owner_tag: str,
|
||||
*,
|
||||
log_prefix: str = "",
|
||||
) -> SearchRuntimeBundle:
|
||||
return build_search_runtime(
|
||||
plugin_config=plugin_config,
|
||||
logger_obj=logger_obj,
|
||||
owner_tag=owner_tag,
|
||||
log_prefix=log_prefix,
|
||||
)
|
||||
Reference in New Issue
Block a user