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1 Commits
v4.7.2
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feat/agent
| Author | SHA1 | Date | |
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944b81ce09 |
@@ -24,6 +24,7 @@ from astrbot.core.db import BaseDatabase
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from astrbot.core.db.migration.migra_45_to_46 import migrate_45_to_46
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from astrbot.core.db.migration.migra_45_to_46 import migrate_45_to_46
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from astrbot.core.db.migration.migra_webchat_session import migrate_webchat_session
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from astrbot.core.db.migration.migra_webchat_session import migrate_webchat_session
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from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
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from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
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from astrbot.core.memory.memory_manager import MemoryManager
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from astrbot.core.persona_mgr import PersonaManager
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from astrbot.core.persona_mgr import PersonaManager
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from astrbot.core.pipeline.scheduler import PipelineContext, PipelineScheduler
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from astrbot.core.pipeline.scheduler import PipelineContext, PipelineScheduler
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from astrbot.core.platform.manager import PlatformManager
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from astrbot.core.platform.manager import PlatformManager
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@@ -136,6 +137,8 @@ class AstrBotCoreLifecycle:
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# 初始化知识库管理器
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# 初始化知识库管理器
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self.kb_manager = KnowledgeBaseManager(self.provider_manager)
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self.kb_manager = KnowledgeBaseManager(self.provider_manager)
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# 初始化记忆管理器
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self.memory_manager = MemoryManager()
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# 初始化提供给插件的上下文
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# 初始化提供给插件的上下文
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self.star_context = Context(
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self.star_context = Context(
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@@ -149,6 +152,7 @@ class AstrBotCoreLifecycle:
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self.persona_mgr,
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self.persona_mgr,
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self.astrbot_config_mgr,
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self.astrbot_config_mgr,
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self.kb_manager,
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self.kb_manager,
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self.memory_manager,
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)
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)
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# 初始化插件管理器
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# 初始化插件管理器
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@@ -1,11 +1,20 @@
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import abc
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import abc
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from dataclasses import dataclass
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from dataclasses import dataclass
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from typing import TypedDict
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@dataclass
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@dataclass
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class Result:
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class Result:
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class ResultData(TypedDict):
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id: str
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doc_id: str
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text: str
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metadata: str
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created_at: int
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updated_at: int
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similarity: float
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similarity: float
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data: dict
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data: ResultData | dict
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class BaseVecDB:
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class BaseVecDB:
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822
astrbot/core/memory/DESIGN.excalidraw
Normal file
822
astrbot/core/memory/DESIGN.excalidraw
Normal file
@@ -0,0 +1,822 @@
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"type": "excalidraw",
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|
||||||
|
"opacity": 100,
|
||||||
|
"groupIds": [],
|
||||||
|
"frameId": null,
|
||||||
|
"index": "aO",
|
||||||
|
"roundness": null,
|
||||||
|
"seed": 1670509249,
|
||||||
|
"version": 186,
|
||||||
|
"versionNonce": 1964540737,
|
||||||
|
"isDeleted": false,
|
||||||
|
"boundElements": [],
|
||||||
|
"updated": 1763704060295,
|
||||||
|
"link": null,
|
||||||
|
"locked": false,
|
||||||
|
"text": "chunk2",
|
||||||
|
"fontSize": 20,
|
||||||
|
"fontFamily": 5,
|
||||||
|
"textAlign": "left",
|
||||||
|
"verticalAlign": "top",
|
||||||
|
"containerId": null,
|
||||||
|
"originalText": "chunk2",
|
||||||
|
"autoResize": true,
|
||||||
|
"lineHeight": 1.25
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "LHKMRdSowgcl2LsKacxTz",
|
||||||
|
"type": "text",
|
||||||
|
"x": 484.9493410573871,
|
||||||
|
"y": 292.45619471187945,
|
||||||
|
"width": 273.579833984375,
|
||||||
|
"height": 50,
|
||||||
|
"angle": 0,
|
||||||
|
"strokeColor": "#1e1e1e",
|
||||||
|
"backgroundColor": "transparent",
|
||||||
|
"fillStyle": "solid",
|
||||||
|
"strokeWidth": 2,
|
||||||
|
"strokeStyle": "solid",
|
||||||
|
"roughness": 1,
|
||||||
|
"opacity": 100,
|
||||||
|
"groupIds": [],
|
||||||
|
"frameId": null,
|
||||||
|
"index": "aP",
|
||||||
|
"roundness": null,
|
||||||
|
"seed": 945666991,
|
||||||
|
"version": 104,
|
||||||
|
"versionNonce": 1512137505,
|
||||||
|
"isDeleted": false,
|
||||||
|
"boundElements": null,
|
||||||
|
"updated": 1763704096016,
|
||||||
|
"link": null,
|
||||||
|
"locked": false,
|
||||||
|
"text": "RANKED By DECAY SCORE,\nTOP K",
|
||||||
|
"fontSize": 20,
|
||||||
|
"fontFamily": 5,
|
||||||
|
"textAlign": "left",
|
||||||
|
"verticalAlign": "top",
|
||||||
|
"containerId": null,
|
||||||
|
"originalText": "RANKED By DECAY SCORE,\nTOP K",
|
||||||
|
"autoResize": true,
|
||||||
|
"lineHeight": 1.25
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"appState": {
|
||||||
|
"gridSize": 20,
|
||||||
|
"gridStep": 5,
|
||||||
|
"gridModeEnabled": false,
|
||||||
|
"viewBackgroundColor": "#ffffff"
|
||||||
|
},
|
||||||
|
"files": {}
|
||||||
|
}
|
||||||
76
astrbot/core/memory/_README.md
Normal file
76
astrbot/core/memory/_README.md
Normal file
@@ -0,0 +1,76 @@
|
|||||||
|
## Decay Score
|
||||||
|
|
||||||
|
记忆衰减分数定义为：
|
||||||
|
|
||||||
|
\[
|
||||||
|
\text{decay\_score}
|
||||||
|
= \alpha \cdot e^{-\lambda \cdot \Delta t \cdot \beta}
|
||||||
|
|
||||||
|
+ (1-\alpha)\cdot (1 - e^{-\gamma \cdot c})
|
||||||
|
\]
|
||||||
|
|
||||||
|
其中：
|
||||||
|
|
||||||
|
+ \(\Delta t\)：自上次检索以来经过的时间（天），由 `last_retrieval_at` 计算；
|
||||||
|
+ \(c\)：检索次数，对应字段 `retrieval_count`；
|
||||||
|
+ \(\alpha\)：控制时间衰减和检索次数影响的权重；
|
||||||
|
+ \(\gamma\)：控制检索次数影响的速率；
|
||||||
|
+ \(\lambda\)：控制时间衰减的速率；
|
||||||
|
+ \(\beta\)：时间衰减调节因子；
|
||||||
|
|
||||||
|
\[
|
||||||
|
\beta = \frac{1}{1 + a \cdot c}
|
||||||
|
\]
|
||||||
|
|
||||||
|
+ \(a\)：控制检索次数对时间衰减影响的权重。
|
||||||
|
|
||||||
|
## ADD MEMORY
|
||||||
|
|
||||||
|
+ LLM 通过 `astr_add_memory` 工具调用，传入记忆内容和记忆类型。
|
||||||
|
+ 生成 `mem_id = uuid4()`。
|
||||||
|
+ 从上下文中获取 `owner_id = unified_message_origin`。
|
||||||
|
|
||||||
|
步骤：
|
||||||
|
|
||||||
|
1. 使用 VecDB 以新记忆内容为 query，检索前 20 条相似记忆。
|
||||||
|
2. 从中取相似度最高的前 5 条：
|
||||||
|
+ 若相似度超过“合并阈值”（如 `sim >= merge_threshold`）：
|
||||||
|
+ 将该条记忆视为同一记忆，使用 LLM 将旧内容与新内容合并；
|
||||||
|
+ 在同一个 `mem_id` 上更新 MemoryDB 和 VecDB（UPDATE，而非新建）。
|
||||||
|
+ 否则：
|
||||||
|
+ 作为全新的记忆插入：
|
||||||
|
+ 写入 VecDB（metadata 中包含 `mem_id`, `owner_id`）；
|
||||||
|
+ 写入 MemoryDB 的 `memory_chunks` 表，初始化：
|
||||||
|
+ `created_at = now`
|
||||||
|
+ `last_retrieval_at = now`
|
||||||
|
+ `retrieval_count = 1` 等。
|
||||||
|
3. 对 VecDB 返回的前 20 条记忆，如果相似度高于某个“赫布阈值”（`hebb_threshold`），则：
|
||||||
|
+ `retrieval_count += 1`
|
||||||
|
+ `last_retrieval_at = now`
|
||||||
|
|
||||||
|
这一步体现了赫布学习：与新记忆共同被激活的旧记忆会获得一次强化。
|
||||||
|
|
||||||
|
## QUERY MEMORY (STATIC)
|
||||||
|
|
||||||
|
+ LLM 通过 `astr_query_memory` 工具调用，无参数。
|
||||||
|
|
||||||
|
步骤：
|
||||||
|
|
||||||
|
1. 从 MemoryDB 的 `memory_chunks` 表中查询当前用户所有活跃记忆：
|
||||||
|
+ `SELECT * FROM memory_chunks WHERE owner_id = ? AND is_active = 1`
|
||||||
|
2. 对每条记忆，根据 `last_retrieval_at` 和 `retrieval_count` 计算对应的 `decay_score`。
|
||||||
|
3. 按 `decay_score` 从高到低排序，返回前 `top_k` 条记忆内容给 LLM。
|
||||||
|
4. 对返回的这 `top_k` 条记忆：
|
||||||
|
+ `retrieval_count += 1`
|
||||||
|
+ `last_retrieval_at = now`
|
||||||
|
|
||||||
|
## QUERY MEMORY (DYNAMIC)（暂不实现）
|
||||||
|
|
||||||
|
+ LLM 提供查询内容作为语义 query。
|
||||||
|
+ 使用 VecDB 检索与该 query 最相似的前 `N` 条记忆（`N > top_k`）。
|
||||||
|
+ 根据 `mem_id` 从 `memory_chunks` 中加载对应记录。
|
||||||
|
+ 对这批候选记忆计算：
|
||||||
|
+ 语义相似度（来自 VecDB）
|
||||||
|
+ `decay_score`
|
||||||
|
+ 最终排序分数（例如 `w1 * sim + w2 * decay_score`）
|
||||||
|
+ 按最终排序分数从高到低返回前 `top_k` 条记忆内容，并更新它们的 `retrieval_count` 和 `last_retrieval_at`。
|
||||||
63
astrbot/core/memory/entities.py
Normal file
63
astrbot/core/memory/entities.py
Normal file
@@ -0,0 +1,63 @@
|
|||||||
|
import uuid
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from sqlmodel import Field, MetaData, SQLModel
|
||||||
|
|
||||||
|
MEMORY_TYPE_IMPORTANCE = {"persona": 1.3, "fact": 1.0, "ephemeral": 0.8}
|
||||||
|
|
||||||
|
|
||||||
|
class BaseMemoryModel(SQLModel, table=False):
|
||||||
|
metadata = MetaData()
|
||||||
|
|
||||||
|
|
||||||
|
class MemoryChunk(BaseMemoryModel, table=True):
|
||||||
|
"""A chunk of memory stored in the system."""
|
||||||
|
|
||||||
|
__tablename__ = "memory_chunks" # type: ignore
|
||||||
|
|
||||||
|
id: int | None = Field(
|
||||||
|
primary_key=True,
|
||||||
|
sa_column_kwargs={"autoincrement": True},
|
||||||
|
default=None,
|
||||||
|
)
|
||||||
|
mem_id: str = Field(
|
||||||
|
max_length=36,
|
||||||
|
nullable=False,
|
||||||
|
unique=True,
|
||||||
|
default_factory=lambda: str(uuid.uuid4()),
|
||||||
|
index=True,
|
||||||
|
)
|
||||||
|
fact: str = Field(nullable=False)
|
||||||
|
"""The factual content of the memory chunk."""
|
||||||
|
owner_id: str = Field(max_length=255, nullable=False, index=True)
|
||||||
|
"""The identifier of the owner (user) of the memory chunk."""
|
||||||
|
created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
|
||||||
|
"""The timestamp when the memory chunk was created."""
|
||||||
|
last_retrieval_at: datetime = Field(
|
||||||
|
default_factory=lambda: datetime.now(timezone.utc)
|
||||||
|
)
|
||||||
|
"""The timestamp when the memory chunk was last retrieved."""
|
||||||
|
retrieval_count: int = Field(default=1, nullable=False)
|
||||||
|
"""The number of times the memory chunk has been retrieved."""
|
||||||
|
memory_type: str = Field(max_length=20, nullable=False, default="fact")
|
||||||
|
"""The type of memory (e.g., 'persona', 'fact', 'ephemeral')."""
|
||||||
|
is_active: bool = Field(default=True, nullable=False)
|
||||||
|
"""Whether the memory chunk is active."""
|
||||||
|
|
||||||
|
def compute_decay_score(self, current_time: datetime) -> float:
|
||||||
|
"""Compute the decay score of the memory chunk based on time and retrievals."""
|
||||||
|
# Constants for the decay formula
|
||||||
|
alpha = 0.5
|
||||||
|
gamma = 0.1
|
||||||
|
lambda_ = 0.05
|
||||||
|
a = 0.1
|
||||||
|
|
||||||
|
# Calculate delta_t in days
|
||||||
|
delta_t = (current_time - self.last_retrieval_at).total_seconds() / 86400
|
||||||
|
c = self.retrieval_count
|
||||||
|
beta = 1 / (1 + a * c)
|
||||||
|
decay_score = alpha * np.exp(-lambda_ * delta_t * beta) + (1 - alpha) * (
|
||||||
|
1 - np.exp(-gamma * c)
|
||||||
|
)
|
||||||
|
return decay_score * MEMORY_TYPE_IMPORTANCE.get(self.memory_type, 1.0)
|
||||||
174
astrbot/core/memory/mem_db_sqlite.py
Normal file
174
astrbot/core/memory/mem_db_sqlite.py
Normal file
@@ -0,0 +1,174 @@
|
|||||||
|
from contextlib import asynccontextmanager
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
from sqlalchemy import select, text, update
|
||||||
|
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
|
||||||
|
from sqlmodel import col
|
||||||
|
|
||||||
|
from astrbot.core import logger
|
||||||
|
|
||||||
|
from .entities import BaseMemoryModel, MemoryChunk
|
||||||
|
|
||||||
|
|
||||||
|
class MemoryDatabase:
|
||||||
|
def __init__(self, db_path: str = "data/astr_memory/memory.db") -> None:
|
||||||
|
"""Initialize memory database
|
||||||
|
|
||||||
|
Args:
|
||||||
|
db_path: Database file path, default is data/astr_memory/memory.db
|
||||||
|
|
||||||
|
"""
|
||||||
|
self.db_path = db_path
|
||||||
|
self.DATABASE_URL = f"sqlite+aiosqlite:///{db_path}"
|
||||||
|
self.inited = False
|
||||||
|
|
||||||
|
# Ensure directory exists
|
||||||
|
Path(db_path).parent.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
# Create async engine
|
||||||
|
self.engine = create_async_engine(
|
||||||
|
self.DATABASE_URL,
|
||||||
|
echo=False,
|
||||||
|
pool_pre_ping=True,
|
||||||
|
pool_recycle=3600,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Create session factory
|
||||||
|
self.async_session = async_sessionmaker(
|
||||||
|
self.engine,
|
||||||
|
class_=AsyncSession,
|
||||||
|
expire_on_commit=False,
|
||||||
|
)
|
||||||
|
|
||||||
|
@asynccontextmanager
|
||||||
|
async def get_db(self):
|
||||||
|
"""Get database session
|
||||||
|
|
||||||
|
Usage:
|
||||||
|
async with mem_db.get_db() as session:
|
||||||
|
# Perform database operations
|
||||||
|
result = await session.execute(stmt)
|
||||||
|
"""
|
||||||
|
async with self.async_session() as session:
|
||||||
|
yield session
|
||||||
|
|
||||||
|
async def initialize(self) -> None:
|
||||||
|
"""Initialize database, create tables and configure SQLite parameters"""
|
||||||
|
async with self.engine.begin() as conn:
|
||||||
|
# Create all memory related tables
|
||||||
|
await conn.run_sync(BaseMemoryModel.metadata.create_all)
|
||||||
|
|
||||||
|
# Configure SQLite performance optimization parameters
|
||||||
|
await conn.execute(text("PRAGMA journal_mode=WAL"))
|
||||||
|
await conn.execute(text("PRAGMA synchronous=NORMAL"))
|
||||||
|
await conn.execute(text("PRAGMA cache_size=20000"))
|
||||||
|
await conn.execute(text("PRAGMA temp_store=MEMORY"))
|
||||||
|
await conn.execute(text("PRAGMA mmap_size=134217728"))
|
||||||
|
await conn.execute(text("PRAGMA optimize"))
|
||||||
|
await conn.commit()
|
||||||
|
|
||||||
|
await self._create_indexes()
|
||||||
|
self.inited = True
|
||||||
|
logger.info(f"Memory database initialized: {self.db_path}")
|
||||||
|
|
||||||
|
async def _create_indexes(self) -> None:
|
||||||
|
"""Create indexes for memory_chunks table"""
|
||||||
|
async with self.get_db() as session:
|
||||||
|
async with session.begin():
|
||||||
|
# Create memory chunks table indexes
|
||||||
|
await session.execute(
|
||||||
|
text(
|
||||||
|
"CREATE INDEX IF NOT EXISTS idx_mem_mem_id "
|
||||||
|
"ON memory_chunks(mem_id)",
|
||||||
|
),
|
||||||
|
)
|
||||||
|
await session.execute(
|
||||||
|
text(
|
||||||
|
"CREATE INDEX IF NOT EXISTS idx_mem_owner_id "
|
||||||
|
"ON memory_chunks(owner_id)",
|
||||||
|
),
|
||||||
|
)
|
||||||
|
await session.execute(
|
||||||
|
text(
|
||||||
|
"CREATE INDEX IF NOT EXISTS idx_mem_owner_active "
|
||||||
|
"ON memory_chunks(owner_id, is_active)",
|
||||||
|
),
|
||||||
|
)
|
||||||
|
await session.commit()
|
||||||
|
|
||||||
|
async def close(self) -> None:
|
||||||
|
"""Close database connection"""
|
||||||
|
await self.engine.dispose()
|
||||||
|
logger.info(f"Memory database closed: {self.db_path}")
|
||||||
|
|
||||||
|
async def insert_memory(self, memory: MemoryChunk) -> MemoryChunk:
|
||||||
|
"""Insert a new memory chunk"""
|
||||||
|
async with self.get_db() as session:
|
||||||
|
session.add(memory)
|
||||||
|
await session.commit()
|
||||||
|
await session.refresh(memory)
|
||||||
|
return memory
|
||||||
|
|
||||||
|
async def get_memory_by_id(self, mem_id: str) -> MemoryChunk | None:
|
||||||
|
"""Get memory chunk by mem_id"""
|
||||||
|
async with self.get_db() as session:
|
||||||
|
stmt = select(MemoryChunk).where(col(MemoryChunk.mem_id) == mem_id)
|
||||||
|
result = await session.execute(stmt)
|
||||||
|
return result.scalar_one_or_none()
|
||||||
|
|
||||||
|
async def update_memory(self, memory: MemoryChunk) -> MemoryChunk:
|
||||||
|
"""Update an existing memory chunk"""
|
||||||
|
async with self.get_db() as session:
|
||||||
|
session.add(memory)
|
||||||
|
await session.commit()
|
||||||
|
await session.refresh(memory)
|
||||||
|
return memory
|
||||||
|
|
||||||
|
async def get_active_memories(self, owner_id: str) -> list[MemoryChunk]:
|
||||||
|
"""Get all active memories for a user"""
|
||||||
|
async with self.get_db() as session:
|
||||||
|
stmt = select(MemoryChunk).where(
|
||||||
|
col(MemoryChunk.owner_id) == owner_id,
|
||||||
|
col(MemoryChunk.is_active) == True, # noqa: E712
|
||||||
|
)
|
||||||
|
result = await session.execute(stmt)
|
||||||
|
return list(result.scalars().all())
|
||||||
|
|
||||||
|
async def update_retrieval_stats(
|
||||||
|
self,
|
||||||
|
mem_ids: list[str],
|
||||||
|
current_time: datetime | None = None,
|
||||||
|
) -> None:
|
||||||
|
"""Update retrieval statistics for multiple memories"""
|
||||||
|
if not mem_ids:
|
||||||
|
return
|
||||||
|
|
||||||
|
if current_time is None:
|
||||||
|
current_time = datetime.now(timezone.utc)
|
||||||
|
|
||||||
|
async with self.get_db() as session:
|
||||||
|
async with session.begin():
|
||||||
|
stmt = (
|
||||||
|
update(MemoryChunk)
|
||||||
|
.where(col(MemoryChunk.mem_id).in_(mem_ids))
|
||||||
|
.values(
|
||||||
|
retrieval_count=MemoryChunk.retrieval_count + 1,
|
||||||
|
last_retrieval_at=current_time,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
await session.execute(stmt)
|
||||||
|
await session.commit()
|
||||||
|
|
||||||
|
async def deactivate_memory(self, mem_id: str) -> bool:
|
||||||
|
"""Deactivate a memory chunk"""
|
||||||
|
async with self.get_db() as session:
|
||||||
|
async with session.begin():
|
||||||
|
stmt = (
|
||||||
|
update(MemoryChunk)
|
||||||
|
.where(col(MemoryChunk.mem_id) == mem_id)
|
||||||
|
.values(is_active=False)
|
||||||
|
)
|
||||||
|
result = await session.execute(stmt)
|
||||||
|
await session.commit()
|
||||||
|
return result.rowcount > 0 if result.rowcount else False # type: ignore
|
||||||
281
astrbot/core/memory/memory_manager.py
Normal file
281
astrbot/core/memory/memory_manager.py
Normal file
@@ -0,0 +1,281 @@
|
|||||||
|
import json
|
||||||
|
import uuid
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
from astrbot.core import logger
|
||||||
|
from astrbot.core.db.vec_db.faiss_impl import FaissVecDB
|
||||||
|
from astrbot.core.provider.provider import EmbeddingProvider
|
||||||
|
from astrbot.core.provider.provider import Provider as LLMProvider
|
||||||
|
|
||||||
|
from .entities import MemoryChunk
|
||||||
|
from .mem_db_sqlite import MemoryDatabase
|
||||||
|
|
||||||
|
MERGE_THRESHOLD = 0.85
|
||||||
|
"""Similarity threshold for merging memories"""
|
||||||
|
HEBB_THRESHOLD = 0.70
|
||||||
|
"""Similarity threshold for Hebbian learning reinforcement"""
|
||||||
|
MERGE_SYSTEM_PROMPT = """You are a memory consolidation assistant. Your task is to merge two related memory entries into a single, comprehensive memory.
|
||||||
|
|
||||||
|
Input format:
|
||||||
|
- Old memory: [existing memory content]
|
||||||
|
- New memory: [new memory content to be integrated]
|
||||||
|
|
||||||
|
Your output should be a single, concise memory that combines the essential information from both entries. Preserve specific details, update outdated information, and eliminate redundancy. Output only the merged memory content without any explanations or meta-commentary."""
|
||||||
|
|
||||||
|
|
||||||
|
class MemoryManager:
|
||||||
|
"""Manager for user long-term memory storage and retrieval"""
|
||||||
|
|
||||||
|
def __init__(self, memory_root_dir: str = "data/astr_memory"):
|
||||||
|
self.memory_root_dir = Path(memory_root_dir)
|
||||||
|
self.memory_root_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
self.mem_db: MemoryDatabase | None = None
|
||||||
|
self.vec_db: FaissVecDB | None = None
|
||||||
|
|
||||||
|
self._initialized = False
|
||||||
|
|
||||||
|
async def initialize(
|
||||||
|
self,
|
||||||
|
embedding_provider: EmbeddingProvider,
|
||||||
|
merge_llm_provider: LLMProvider,
|
||||||
|
):
|
||||||
|
"""Initialize memory database and vector database"""
|
||||||
|
# Initialize MemoryDB
|
||||||
|
db_path = self.memory_root_dir / "memory.db"
|
||||||
|
self.mem_db = MemoryDatabase(db_path.as_posix())
|
||||||
|
await self.mem_db.initialize()
|
||||||
|
|
||||||
|
self.embedding_provider = embedding_provider
|
||||||
|
self.merge_llm_provider = merge_llm_provider
|
||||||
|
|
||||||
|
# Initialize VecDB
|
||||||
|
doc_store_path = self.memory_root_dir / "doc.db"
|
||||||
|
index_store_path = self.memory_root_dir / "index.faiss"
|
||||||
|
self.vec_db = FaissVecDB(
|
||||||
|
doc_store_path=doc_store_path.as_posix(),
|
||||||
|
index_store_path=index_store_path.as_posix(),
|
||||||
|
embedding_provider=self.embedding_provider,
|
||||||
|
)
|
||||||
|
await self.vec_db.initialize()
|
||||||
|
|
||||||
|
logger.info("Memory manager initialized")
|
||||||
|
self._initialized = True
|
||||||
|
|
||||||
|
async def terminate(self):
|
||||||
|
"""Close all database connections"""
|
||||||
|
if self.vec_db:
|
||||||
|
await self.vec_db.close()
|
||||||
|
if self.mem_db:
|
||||||
|
await self.mem_db.close()
|
||||||
|
|
||||||
|
async def add_memory(
|
||||||
|
self,
|
||||||
|
fact: str,
|
||||||
|
owner_id: str,
|
||||||
|
memory_type: str = "fact",
|
||||||
|
) -> MemoryChunk:
|
||||||
|
"""Add a new memory with similarity check and merge logic
|
||||||
|
|
||||||
|
Implements the ADD MEMORY workflow from _README.md:
|
||||||
|
1. Search for similar memories using VecDB
|
||||||
|
2. If similarity >= merge_threshold, merge with existing memory
|
||||||
|
3. Otherwise, create new memory
|
||||||
|
4. Apply Hebbian learning to similar memories (similarity >= hebb_threshold)
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fact: Memory content
|
||||||
|
owner_id: User identifier
|
||||||
|
memory_type: Memory type ('persona', 'fact', 'ephemeral')
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The created or updated MemoryChunk
|
||||||
|
|
||||||
|
"""
|
||||||
|
if not self.vec_db or not self.mem_db:
|
||||||
|
raise RuntimeError("Memory manager not initialized")
|
||||||
|
|
||||||
|
current_time = datetime.now(timezone.utc)
|
||||||
|
|
||||||
|
# Step 1: Search for similar memories
|
||||||
|
similar_results = await self.vec_db.retrieve(
|
||||||
|
query=fact,
|
||||||
|
k=20,
|
||||||
|
fetch_k=50,
|
||||||
|
metadata_filters={"owner_id": owner_id},
|
||||||
|
)
|
||||||
|
|
||||||
|
# Step 2: Check if we should merge with existing memories (top 3 similar ones)
|
||||||
|
merge_candidates = [
|
||||||
|
r for r in similar_results[:3] if r.similarity >= MERGE_THRESHOLD
|
||||||
|
]
|
||||||
|
|
||||||
|
if merge_candidates:
|
||||||
|
# Get all candidate memories from database
|
||||||
|
candidate_memories: list[tuple[str, MemoryChunk]] = []
|
||||||
|
for candidate in merge_candidates:
|
||||||
|
mem_id = json.loads(candidate.data["metadata"])["mem_id"]
|
||||||
|
memory = await self.mem_db.get_memory_by_id(mem_id)
|
||||||
|
if memory:
|
||||||
|
candidate_memories.append((mem_id, memory))
|
||||||
|
|
||||||
|
if candidate_memories:
|
||||||
|
# Use the most similar memory as the base
|
||||||
|
base_mem_id, base_memory = candidate_memories[0]
|
||||||
|
|
||||||
|
# Collect all facts to merge (existing candidates + new fact)
|
||||||
|
all_facts = [mem.fact for _, mem in candidate_memories] + [fact]
|
||||||
|
merged_fact = await self._merge_multiple_memories(all_facts)
|
||||||
|
|
||||||
|
# Update the base memory
|
||||||
|
base_memory.fact = merged_fact
|
||||||
|
base_memory.last_retrieval_at = current_time
|
||||||
|
base_memory.retrieval_count += 1
|
||||||
|
updated_memory = await self.mem_db.update_memory(base_memory)
|
||||||
|
|
||||||
|
# Update VecDB for base memory
|
||||||
|
await self.vec_db.delete(base_mem_id)
|
||||||
|
await self.vec_db.insert(
|
||||||
|
content=merged_fact,
|
||||||
|
metadata={
|
||||||
|
"mem_id": base_mem_id,
|
||||||
|
"owner_id": owner_id,
|
||||||
|
"memory_type": memory_type,
|
||||||
|
},
|
||||||
|
id=base_mem_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Deactivate and remove other merged memories
|
||||||
|
for mem_id, _ in candidate_memories[1:]:
|
||||||
|
await self.mem_db.deactivate_memory(mem_id)
|
||||||
|
await self.vec_db.delete(mem_id)
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"Merged {len(candidate_memories)} memories into {base_mem_id} for user {owner_id}"
|
||||||
|
)
|
||||||
|
return updated_memory
|
||||||
|
|
||||||
|
# Step 3: Create new memory
|
||||||
|
mem_id = str(uuid.uuid4())
|
||||||
|
new_memory = MemoryChunk(
|
||||||
|
mem_id=mem_id,
|
||||||
|
fact=fact,
|
||||||
|
owner_id=owner_id,
|
||||||
|
memory_type=memory_type,
|
||||||
|
created_at=current_time,
|
||||||
|
last_retrieval_at=current_time,
|
||||||
|
retrieval_count=1,
|
||||||
|
is_active=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Insert into MemoryDB
|
||||||
|
created_memory = await self.mem_db.insert_memory(new_memory)
|
||||||
|
|
||||||
|
# Insert into VecDB
|
||||||
|
await self.vec_db.insert(
|
||||||
|
content=fact,
|
||||||
|
metadata={
|
||||||
|
"mem_id": mem_id,
|
||||||
|
"owner_id": owner_id,
|
||||||
|
"memory_type": memory_type,
|
||||||
|
},
|
||||||
|
id=mem_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Step 4: Apply Hebbian learning to similar memories
|
||||||
|
hebb_mem_ids = [
|
||||||
|
json.loads(r.data["metadata"])["mem_id"]
|
||||||
|
for r in similar_results
|
||||||
|
if r.similarity >= HEBB_THRESHOLD
|
||||||
|
]
|
||||||
|
if hebb_mem_ids:
|
||||||
|
await self.mem_db.update_retrieval_stats(hebb_mem_ids, current_time)
|
||||||
|
logger.debug(
|
||||||
|
f"Applied Hebbian learning to {len(hebb_mem_ids)} memories for user {owner_id}",
|
||||||
|
)
|
||||||
|
|
||||||
|
logger.info(f"Created new memory {mem_id} for user {owner_id}")
|
||||||
|
return created_memory
|
||||||
|
|
||||||
|
async def query_memory(
|
||||||
|
self,
|
||||||
|
owner_id: str,
|
||||||
|
top_k: int = 5,
|
||||||
|
) -> list[MemoryChunk]:
|
||||||
|
"""Query user's memories using static retrieval with decay score ranking
|
||||||
|
|
||||||
|
Implements the QUERY MEMORY (STATIC) workflow from _README.md:
|
||||||
|
1. Get all active memories for user from MemoryDB
|
||||||
|
2. Compute decay_score for each memory
|
||||||
|
3. Sort by decay_score and return top_k
|
||||||
|
4. Update retrieval statistics for returned memories
|
||||||
|
|
||||||
|
Args:
|
||||||
|
owner_id: User identifier
|
||||||
|
top_k: Number of memories to return
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List of top_k MemoryChunk sorted by decay score
|
||||||
|
"""
|
||||||
|
if not self.mem_db:
|
||||||
|
raise RuntimeError("Memory manager not initialized")
|
||||||
|
|
||||||
|
current_time = datetime.now(timezone.utc)
|
||||||
|
|
||||||
|
# Step 1: Get all active memories for user
|
||||||
|
all_memories = await self.mem_db.get_active_memories(owner_id)
|
||||||
|
|
||||||
|
if not all_memories:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# Step 2-3: Compute decay scores and sort
|
||||||
|
memories_with_scores = [
|
||||||
|
(mem, mem.compute_decay_score(current_time)) for mem in all_memories
|
||||||
|
]
|
||||||
|
memories_with_scores.sort(key=lambda x: x[1], reverse=True)
|
||||||
|
|
||||||
|
# Get top_k memories
|
||||||
|
top_memories = [mem for mem, _ in memories_with_scores[:top_k]]
|
||||||
|
|
||||||
|
# Step 4: Update retrieval statistics
|
||||||
|
mem_ids = [mem.mem_id for mem in top_memories]
|
||||||
|
await self.mem_db.update_retrieval_stats(mem_ids, current_time)
|
||||||
|
|
||||||
|
logger.debug(f"Retrieved {len(top_memories)} memories for user {owner_id}")
|
||||||
|
return top_memories
|
||||||
|
|
||||||
|
async def _merge_multiple_memories(self, facts: list[str]) -> str:
|
||||||
|
"""Merge multiple memory facts using LLM in one call
|
||||||
|
|
||||||
|
Args:
|
||||||
|
facts: List of memory facts to merge
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Merged memory content
|
||||||
|
"""
|
||||||
|
if not self.merge_llm_provider:
|
||||||
|
return " ".join(facts)
|
||||||
|
|
||||||
|
if len(facts) == 1:
|
||||||
|
return facts[0]
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Format all facts as a numbered list
|
||||||
|
facts_list = "\n".join(f"{i + 1}. {fact}" for i, fact in enumerate(facts))
|
||||||
|
user_prompt = (
|
||||||
|
f"Please merge the following {len(facts)} related memory entries "
|
||||||
|
"into a single, comprehensive memory:"
|
||||||
|
f"\n{facts_list}\n\nOutput only the merged memory content."
|
||||||
|
)
|
||||||
|
response = await self.merge_llm_provider.text_chat(
|
||||||
|
prompt=user_prompt,
|
||||||
|
system_prompt=MERGE_SYSTEM_PROMPT,
|
||||||
|
)
|
||||||
|
|
||||||
|
merged_content = response.completion_text.strip()
|
||||||
|
return merged_content if merged_content else " ".join(facts)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Failed to merge memories with LLM: {e}, using fallback")
|
||||||
|
return " ".join(facts)
|
||||||
156
astrbot/core/memory/tools.py
Normal file
156
astrbot/core/memory/tools.py
Normal file
@@ -0,0 +1,156 @@
|
|||||||
|
from pydantic import Field
|
||||||
|
from pydantic.dataclasses import dataclass
|
||||||
|
|
||||||
|
from astrbot.core.agent.tool import FunctionTool, ToolExecResult
|
||||||
|
from astrbot.core.astr_agent_context import AstrAgentContext, ContextWrapper
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class AddMemory(FunctionTool[AstrAgentContext]):
|
||||||
|
"""Tool for adding memories to user's long-term memory storage"""
|
||||||
|
|
||||||
|
name: str = "astr_add_memory"
|
||||||
|
description: str = (
|
||||||
|
"Add a new memory to the user's long-term memory storage. "
|
||||||
|
"Use this tool only when the user explicitly asks you to remember something, "
|
||||||
|
"or when they share stable preferences, identity, or long-term goals that will be useful in future interactions."
|
||||||
|
)
|
||||||
|
parameters: dict = Field(
|
||||||
|
default_factory=lambda: {
|
||||||
|
"type": "object",
|
||||||
|
"properties": {
|
||||||
|
"fact": {
|
||||||
|
"type": "string",
|
||||||
|
"description": (
|
||||||
|
"The concrete memory content to store, such as a user preference, "
|
||||||
|
"identity detail, long-term goal, or stable profile fact."
|
||||||
|
),
|
||||||
|
},
|
||||||
|
"memory_type": {
|
||||||
|
"type": "string",
|
||||||
|
"enum": ["persona", "fact", "ephemeral"],
|
||||||
|
"description": (
|
||||||
|
"The relative importance of this memory. "
|
||||||
|
"Use 'persona' for core identity or highly impactful information, "
|
||||||
|
"'fact' for normal long-term preferences, "
|
||||||
|
"and 'ephemeral' for minor or tentative facts."
|
||||||
|
),
|
||||||
|
},
|
||||||
|
},
|
||||||
|
"required": ["fact", "memory_type"],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
async def call(
|
||||||
|
self, context: ContextWrapper[AstrAgentContext], **kwargs
|
||||||
|
) -> ToolExecResult:
|
||||||
|
"""Add a memory to long-term storage
|
||||||
|
|
||||||
|
Args:
|
||||||
|
context: Agent context
|
||||||
|
**kwargs: Must contain 'fact' and 'memory_type'
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
ToolExecResult with success message
|
||||||
|
|
||||||
|
"""
|
||||||
|
mm = context.context.context.memory_manager
|
||||||
|
fact = kwargs.get("fact")
|
||||||
|
memory_type = kwargs.get("memory_type", "fact")
|
||||||
|
|
||||||
|
if not fact:
|
||||||
|
return "Missing required parameter: fact"
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Get owner_id from context
|
||||||
|
owner_id = context.context.event.unified_msg_origin
|
||||||
|
|
||||||
|
# Add memory using memory manager
|
||||||
|
memory = await mm.add_memory(
|
||||||
|
fact=fact,
|
||||||
|
owner_id=owner_id,
|
||||||
|
memory_type=memory_type,
|
||||||
|
)
|
||||||
|
|
||||||
|
return f"Memory added successfully (ID: {memory.mem_id})"
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return f"Failed to add memory: {str(e)}"
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class QueryMemory(FunctionTool[AstrAgentContext]):
|
||||||
|
"""Tool for querying user's long-term memories"""
|
||||||
|
|
||||||
|
name: str = "astr_query_memory"
|
||||||
|
description: str = (
|
||||||
|
"Query the user's long-term memory storage and return the most relevant memories. "
|
||||||
|
"Use this tool when you need user-specific context, preferences, or past facts "
|
||||||
|
"that are not explicitly present in the current conversation."
|
||||||
|
)
|
||||||
|
parameters: dict = Field(
|
||||||
|
default_factory=lambda: {
|
||||||
|
"type": "object",
|
||||||
|
"properties": {
|
||||||
|
"top_k": {
|
||||||
|
"type": "integer",
|
||||||
|
"description": (
|
||||||
|
"Maximum number of memories to retrieve after retention-based ranking. "
|
||||||
|
"Typically between 3 and 10."
|
||||||
|
),
|
||||||
|
"default": 5,
|
||||||
|
"minimum": 1,
|
||||||
|
"maximum": 20,
|
||||||
|
},
|
||||||
|
},
|
||||||
|
"required": [],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
async def call(
|
||||||
|
self, context: ContextWrapper[AstrAgentContext], **kwargs
|
||||||
|
) -> ToolExecResult:
|
||||||
|
"""Query memories from long-term storage
|
||||||
|
|
||||||
|
Args:
|
||||||
|
context: Agent context
|
||||||
|
**kwargs: Optional 'top_k' parameter
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
ToolExecResult with formatted memory list
|
||||||
|
|
||||||
|
"""
|
||||||
|
mm = context.context.context.memory_manager
|
||||||
|
top_k = kwargs.get("top_k", 5)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Get owner_id from context
|
||||||
|
owner_id = context.context.event.unified_msg_origin
|
||||||
|
|
||||||
|
# Query memories using memory manager
|
||||||
|
memories = await mm.query_memory(
|
||||||
|
owner_id=owner_id,
|
||||||
|
top_k=top_k,
|
||||||
|
)
|
||||||
|
|
||||||
|
if not memories:
|
||||||
|
return "No memories found for this user."
|
||||||
|
|
||||||
|
# Format memories for output
|
||||||
|
formatted_memories = []
|
||||||
|
for i, mem in enumerate(memories, 1):
|
||||||
|
formatted_memories.append(
|
||||||
|
f"{i}. [{mem.memory_type.upper()}] {mem.fact} "
|
||||||
|
f"(retrieved {mem.retrieval_count} times, "
|
||||||
|
f"last: {mem.last_retrieval_at.strftime('%Y-%m-%d')})"
|
||||||
|
)
|
||||||
|
|
||||||
|
result_text = "Retrieved memories:\n" + "\n".join(formatted_memories)
|
||||||
|
return result_text
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return f"Failed to query memories: {str(e)}"
|
||||||
|
|
||||||
|
|
||||||
|
ADD_MEMORY_TOOL = AddMemory()
|
||||||
|
QUERY_MEMORY_TOOL = QueryMemory()
|
||||||
@@ -30,6 +30,7 @@ from ....astr_agent_context import AgentContextWrapper
|
|||||||
from ....astr_agent_hooks import MAIN_AGENT_HOOKS
|
from ....astr_agent_hooks import MAIN_AGENT_HOOKS
|
||||||
from ....astr_agent_run_util import AgentRunner, run_agent
|
from ....astr_agent_run_util import AgentRunner, run_agent
|
||||||
from ....astr_agent_tool_exec import FunctionToolExecutor
|
from ....astr_agent_tool_exec import FunctionToolExecutor
|
||||||
|
from ....memory.tools import ADD_MEMORY_TOOL, QUERY_MEMORY_TOOL
|
||||||
from ...context import PipelineContext, call_event_hook
|
from ...context import PipelineContext, call_event_hook
|
||||||
from ..stage import Stage
|
from ..stage import Stage
|
||||||
from ..utils import KNOWLEDGE_BASE_QUERY_TOOL, retrieve_knowledge_base
|
from ..utils import KNOWLEDGE_BASE_QUERY_TOOL, retrieve_knowledge_base
|
||||||
@@ -124,6 +125,15 @@ class LLMRequestSubStage(Stage):
|
|||||||
req.func_tool = ToolSet()
|
req.func_tool = ToolSet()
|
||||||
req.func_tool.add_tool(KNOWLEDGE_BASE_QUERY_TOOL)
|
req.func_tool.add_tool(KNOWLEDGE_BASE_QUERY_TOOL)
|
||||||
|
|
||||||
|
async def _apply_memory(self, req: ProviderRequest):
|
||||||
|
mm = self.ctx.plugin_manager.context.memory_manager
|
||||||
|
if not mm or not mm._initialized:
|
||||||
|
return
|
||||||
|
if req.func_tool is None:
|
||||||
|
req.func_tool = ToolSet()
|
||||||
|
req.func_tool.add_tool(ADD_MEMORY_TOOL)
|
||||||
|
req.func_tool.add_tool(QUERY_MEMORY_TOOL)
|
||||||
|
|
||||||
def _truncate_contexts(
|
def _truncate_contexts(
|
||||||
self,
|
self,
|
||||||
contexts: list[dict],
|
contexts: list[dict],
|
||||||
@@ -377,6 +387,9 @@ class LLMRequestSubStage(Stage):
|
|||||||
# apply knowledge base feature
|
# apply knowledge base feature
|
||||||
await self._apply_kb(event, req)
|
await self._apply_kb(event, req)
|
||||||
|
|
||||||
|
# apply memory feature
|
||||||
|
await self._apply_memory(req)
|
||||||
|
|
||||||
# fix contexts json str
|
# fix contexts json str
|
||||||
if isinstance(req.contexts, str):
|
if isinstance(req.contexts, str):
|
||||||
req.contexts = json.loads(req.contexts)
|
req.contexts = json.loads(req.contexts)
|
||||||
|
|||||||
@@ -14,6 +14,7 @@ from astrbot.core.config.astrbot_config import AstrBotConfig
|
|||||||
from astrbot.core.conversation_mgr import ConversationManager
|
from astrbot.core.conversation_mgr import ConversationManager
|
||||||
from astrbot.core.db import BaseDatabase
|
from astrbot.core.db import BaseDatabase
|
||||||
from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
|
from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
|
||||||
|
from astrbot.core.memory.memory_manager import MemoryManager
|
||||||
from astrbot.core.message.message_event_result import MessageChain
|
from astrbot.core.message.message_event_result import MessageChain
|
||||||
from astrbot.core.persona_mgr import PersonaManager
|
from astrbot.core.persona_mgr import PersonaManager
|
||||||
from astrbot.core.platform import Platform
|
from astrbot.core.platform import Platform
|
||||||
@@ -65,6 +66,7 @@ class Context:
|
|||||||
persona_manager: PersonaManager,
|
persona_manager: PersonaManager,
|
||||||
astrbot_config_mgr: AstrBotConfigManager,
|
astrbot_config_mgr: AstrBotConfigManager,
|
||||||
knowledge_base_manager: KnowledgeBaseManager,
|
knowledge_base_manager: KnowledgeBaseManager,
|
||||||
|
memory_manager: MemoryManager,
|
||||||
):
|
):
|
||||||
self._event_queue = event_queue
|
self._event_queue = event_queue
|
||||||
"""事件队列。消息平台通过事件队列传递消息事件。"""
|
"""事件队列。消息平台通过事件队列传递消息事件。"""
|
||||||
@@ -79,6 +81,7 @@ class Context:
|
|||||||
self.persona_manager = persona_manager
|
self.persona_manager = persona_manager
|
||||||
self.astrbot_config_mgr = astrbot_config_mgr
|
self.astrbot_config_mgr = astrbot_config_mgr
|
||||||
self.kb_manager = knowledge_base_manager
|
self.kb_manager = knowledge_base_manager
|
||||||
|
self.memory_manager = memory_manager
|
||||||
|
|
||||||
async def llm_generate(
|
async def llm_generate(
|
||||||
self,
|
self,
|
||||||
|
|||||||
@@ -5,6 +5,7 @@ from .conversation import ConversationRoute
|
|||||||
from .file import FileRoute
|
from .file import FileRoute
|
||||||
from .knowledge_base import KnowledgeBaseRoute
|
from .knowledge_base import KnowledgeBaseRoute
|
||||||
from .log import LogRoute
|
from .log import LogRoute
|
||||||
|
from .memory import MemoryRoute
|
||||||
from .persona import PersonaRoute
|
from .persona import PersonaRoute
|
||||||
from .plugin import PluginRoute
|
from .plugin import PluginRoute
|
||||||
from .session_management import SessionManagementRoute
|
from .session_management import SessionManagementRoute
|
||||||
@@ -21,6 +22,7 @@ __all__ = [
|
|||||||
"FileRoute",
|
"FileRoute",
|
||||||
"KnowledgeBaseRoute",
|
"KnowledgeBaseRoute",
|
||||||
"LogRoute",
|
"LogRoute",
|
||||||
|
"MemoryRoute",
|
||||||
"PersonaRoute",
|
"PersonaRoute",
|
||||||
"PluginRoute",
|
"PluginRoute",
|
||||||
"SessionManagementRoute",
|
"SessionManagementRoute",
|
||||||
|
|||||||
174
astrbot/dashboard/routes/memory.py
Normal file
174
astrbot/dashboard/routes/memory.py
Normal file
@@ -0,0 +1,174 @@
|
|||||||
|
"""Memory management API routes"""
|
||||||
|
|
||||||
|
from quart import jsonify, request
|
||||||
|
|
||||||
|
from astrbot.core import logger
|
||||||
|
from astrbot.core.core_lifecycle import AstrBotCoreLifecycle
|
||||||
|
from astrbot.core.db import BaseDatabase
|
||||||
|
|
||||||
|
from .route import Response, Route, RouteContext
|
||||||
|
|
||||||
|
|
||||||
|
class MemoryRoute(Route):
|
||||||
|
"""Memory management routes"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
context: RouteContext,
|
||||||
|
db: BaseDatabase,
|
||||||
|
core_lifecycle: AstrBotCoreLifecycle,
|
||||||
|
):
|
||||||
|
super().__init__(context)
|
||||||
|
self.db = db
|
||||||
|
self.core_lifecycle = core_lifecycle
|
||||||
|
self.memory_manager = core_lifecycle.memory_manager
|
||||||
|
self.provider_manager = core_lifecycle.provider_manager
|
||||||
|
self.routes = [
|
||||||
|
("/memory/status", ("GET", self.get_status)),
|
||||||
|
("/memory/initialize", ("POST", self.initialize)),
|
||||||
|
("/memory/update_merge_llm", ("POST", self.update_merge_llm)),
|
||||||
|
]
|
||||||
|
self.register_routes()
|
||||||
|
|
||||||
|
async def get_status(self):
|
||||||
|
"""Get memory system status"""
|
||||||
|
try:
|
||||||
|
is_initialized = self.memory_manager._initialized
|
||||||
|
|
||||||
|
status_data = {
|
||||||
|
"initialized": is_initialized,
|
||||||
|
"embedding_provider_id": None,
|
||||||
|
"merge_llm_provider_id": None,
|
||||||
|
}
|
||||||
|
|
||||||
|
if is_initialized:
|
||||||
|
# Get embedding provider info
|
||||||
|
if self.memory_manager.embedding_provider:
|
||||||
|
status_data["embedding_provider_id"] = (
|
||||||
|
self.memory_manager.embedding_provider.provider_config["id"]
|
||||||
|
)
|
||||||
|
# Get merge LLM provider info
|
||||||
|
if self.memory_manager.merge_llm_provider:
|
||||||
|
status_data["merge_llm_provider_id"] = (
|
||||||
|
self.memory_manager.merge_llm_provider.provider_config["id"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return jsonify(Response().ok(status_data).__dict__)
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Failed to get memory status: {e}")
|
||||||
|
return jsonify(Response().error(str(e)).__dict__)
|
||||||
|
|
||||||
|
async def initialize(self):
|
||||||
|
"""Initialize memory system with embedding and merge LLM providers"""
|
||||||
|
try:
|
||||||
|
data = await request.get_json()
|
||||||
|
embedding_provider_id = data.get("embedding_provider_id")
|
||||||
|
merge_llm_provider_id = data.get("merge_llm_provider_id")
|
||||||
|
|
||||||
|
if not embedding_provider_id or not merge_llm_provider_id:
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.error(
|
||||||
|
"embedding_provider_id and merge_llm_provider_id are required"
|
||||||
|
)
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Check if already initialized
|
||||||
|
if self.memory_manager._initialized:
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.error(
|
||||||
|
"Memory system already initialized. Embedding provider cannot be changed.",
|
||||||
|
)
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Get providers
|
||||||
|
embedding_provider = await self.provider_manager.get_provider_by_id(
|
||||||
|
embedding_provider_id,
|
||||||
|
)
|
||||||
|
merge_llm_provider = await self.provider_manager.get_provider_by_id(
|
||||||
|
merge_llm_provider_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
if not embedding_provider:
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.error(f"Embedding provider {embedding_provider_id} not found")
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
if not merge_llm_provider:
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.error(f"Merge LLM provider {merge_llm_provider_id} not found")
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Initialize memory manager
|
||||||
|
await self.memory_manager.initialize(
|
||||||
|
embedding_provider=embedding_provider,
|
||||||
|
merge_llm_provider=merge_llm_provider,
|
||||||
|
)
|
||||||
|
|
||||||
|
logger.info(
|
||||||
|
f"Memory system initialized with embedding: {embedding_provider_id}, "
|
||||||
|
f"merge LLM: {merge_llm_provider_id}",
|
||||||
|
)
|
||||||
|
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.ok({"message": "Memory system initialized successfully"})
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Failed to initialize memory system: {e}")
|
||||||
|
return jsonify(Response().error(str(e)).__dict__)
|
||||||
|
|
||||||
|
async def update_merge_llm(self):
|
||||||
|
"""Update merge LLM provider (only allowed after initialization)"""
|
||||||
|
try:
|
||||||
|
data = await request.get_json()
|
||||||
|
merge_llm_provider_id = data.get("merge_llm_provider_id")
|
||||||
|
|
||||||
|
if not merge_llm_provider_id:
|
||||||
|
return jsonify(
|
||||||
|
Response().error("merge_llm_provider_id is required").__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Check if initialized
|
||||||
|
if not self.memory_manager._initialized:
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.error("Memory system not initialized. Please initialize first.")
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Get new merge LLM provider
|
||||||
|
merge_llm_provider = await self.provider_manager.get_provider_by_id(
|
||||||
|
merge_llm_provider_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
if not merge_llm_provider:
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.error(f"Merge LLM provider {merge_llm_provider_id} not found")
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Update merge LLM provider
|
||||||
|
self.memory_manager.merge_llm_provider = merge_llm_provider
|
||||||
|
|
||||||
|
logger.info(f"Updated merge LLM provider to: {merge_llm_provider_id}")
|
||||||
|
|
||||||
|
return jsonify(
|
||||||
|
Response()
|
||||||
|
.ok({"message": "Merge LLM provider updated successfully"})
|
||||||
|
.__dict__,
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Failed to update merge LLM provider: {e}")
|
||||||
|
return jsonify(Response().error(str(e)).__dict__)
|
||||||
@@ -79,6 +79,7 @@ class AstrBotDashboard:
|
|||||||
self.persona_route = PersonaRoute(self.context, db, core_lifecycle)
|
self.persona_route = PersonaRoute(self.context, db, core_lifecycle)
|
||||||
self.t2i_route = T2iRoute(self.context, core_lifecycle)
|
self.t2i_route = T2iRoute(self.context, core_lifecycle)
|
||||||
self.kb_route = KnowledgeBaseRoute(self.context, core_lifecycle)
|
self.kb_route = KnowledgeBaseRoute(self.context, core_lifecycle)
|
||||||
|
self.memory_route = MemoryRoute(self.context, db, core_lifecycle)
|
||||||
|
|
||||||
self.app.add_url_rule(
|
self.app.add_url_rule(
|
||||||
"/api/plug/<path:subpath>",
|
"/api/plug/<path:subpath>",
|
||||||
|
|||||||
@@ -293,4 +293,3 @@ function handleSidebarMouseLeave() {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
</style>
|
</style>
|
||||||
|
|
||||||
|
|||||||
@@ -301,4 +301,3 @@ export function useMessages(
|
|||||||
toggleStreaming
|
toggleStreaming
|
||||||
};
|
};
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@@ -12,6 +12,7 @@
|
|||||||
"console": "Console",
|
"console": "Console",
|
||||||
"alkaid": "Alkaid Lab",
|
"alkaid": "Alkaid Lab",
|
||||||
"knowledgeBase": "Knowledge Base",
|
"knowledgeBase": "Knowledge Base",
|
||||||
|
"memory": "Long-term Memory",
|
||||||
"about": "About",
|
"about": "About",
|
||||||
"settings": "Settings",
|
"settings": "Settings",
|
||||||
"documentation": "Documentation",
|
"documentation": "Documentation",
|
||||||
|
|||||||
@@ -12,6 +12,7 @@
|
|||||||
"console": "控制台",
|
"console": "控制台",
|
||||||
"alkaid": "Alkaid",
|
"alkaid": "Alkaid",
|
||||||
"knowledgeBase": "知识库",
|
"knowledgeBase": "知识库",
|
||||||
|
"memory": "长期记忆",
|
||||||
"about": "关于",
|
"about": "关于",
|
||||||
"settings": "设置",
|
"settings": "设置",
|
||||||
"documentation": "官方文档",
|
"documentation": "官方文档",
|
||||||
|
|||||||
@@ -48,6 +48,11 @@ const sidebarItem: menu[] = [
|
|||||||
icon: 'mdi-book-open-variant',
|
icon: 'mdi-book-open-variant',
|
||||||
to: '/knowledge-base',
|
to: '/knowledge-base',
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
title: 'core.navigation.memory',
|
||||||
|
icon: 'mdi-brain',
|
||||||
|
to: '/memory',
|
||||||
|
},
|
||||||
{
|
{
|
||||||
title: 'core.navigation.chat',
|
title: 'core.navigation.chat',
|
||||||
icon: 'mdi-chat',
|
icon: 'mdi-chat',
|
||||||
|
|||||||
@@ -90,6 +90,11 @@ const MainRoutes = {
|
|||||||
}
|
}
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
name: 'Memory',
|
||||||
|
path: '/memory',
|
||||||
|
component: () => import('@/views/MemoryPage.vue')
|
||||||
|
},
|
||||||
|
|
||||||
// 旧版本的知识库路由
|
// 旧版本的知识库路由
|
||||||
{
|
{
|
||||||
|
|||||||
358
dashboard/src/views/MemoryPage.vue
Normal file
358
dashboard/src/views/MemoryPage.vue
Normal file
@@ -0,0 +1,358 @@
|
|||||||
|
<template>
|
||||||
|
<div class="memory-page">
|
||||||
|
<v-container fluid class="pa-0">
|
||||||
|
<!-- 页面标题 -->
|
||||||
|
<v-row class="d-flex justify-space-between align-center px-4 py-3 pb-8">
|
||||||
|
<div>
|
||||||
|
<h1 class="text-h1 font-weight-bold mb-2">
|
||||||
|
<v-icon color="black" class="me-2">mdi-brain</v-icon>{{ t('core.navigation.memory') }}
|
||||||
|
</h1>
|
||||||
|
<p class="text-subtitle-1 text-medium-emphasis mb-4">
|
||||||
|
管理长期记忆系统的配置
|
||||||
|
</p>
|
||||||
|
</div>
|
||||||
|
</v-row>
|
||||||
|
|
||||||
|
<!-- 加载状态 -->
|
||||||
|
<v-row v-if="loading">
|
||||||
|
<v-col cols="12">
|
||||||
|
<v-card>
|
||||||
|
<v-card-text class="text-center">
|
||||||
|
<v-progress-circular indeterminate color="primary"></v-progress-circular>
|
||||||
|
</v-card-text>
|
||||||
|
</v-card>
|
||||||
|
</v-col>
|
||||||
|
</v-row>
|
||||||
|
|
||||||
|
<!-- 主内容 -->
|
||||||
|
<v-row v-else>
|
||||||
|
<v-col cols="12" md="8" lg="6">
|
||||||
|
<v-card rounded="lg">
|
||||||
|
<v-card-title class="d-flex align-center">
|
||||||
|
<v-icon class="mr-2">mdi-cog</v-icon>
|
||||||
|
记忆系统配置
|
||||||
|
</v-card-title>
|
||||||
|
<v-divider></v-divider>
|
||||||
|
|
||||||
|
<v-card-text>
|
||||||
|
<!-- 状态显示 -->
|
||||||
|
<v-alert
|
||||||
|
:type="memoryStatus.initialized ? 'success' : 'info'"
|
||||||
|
variant="tonal"
|
||||||
|
class="mb-4"
|
||||||
|
>
|
||||||
|
<div class="d-flex align-center">
|
||||||
|
<v-icon class="mr-2">
|
||||||
|
{{ memoryStatus.initialized ? 'mdi-check-circle' : 'mdi-information' }}
|
||||||
|
</v-icon>
|
||||||
|
<div>
|
||||||
|
<strong>状态：</strong>
|
||||||
|
{{ memoryStatus.initialized ? '已初始化' : '未初始化' }}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</v-alert>
|
||||||
|
|
||||||
|
<!-- 未初始化时显示初始化表单 -->
|
||||||
|
<div v-if="!memoryStatus.initialized">
|
||||||
|
<v-form @submit.prevent="initializeMemory">
|
||||||
|
<v-select
|
||||||
|
v-model="selectedEmbeddingProvider"
|
||||||
|
:items="embeddingProviders"
|
||||||
|
item-title="text"
|
||||||
|
item-value="value"
|
||||||
|
label="Embedding 模型 *"
|
||||||
|
hint="用于生成向量表示，初始化后不可更改"
|
||||||
|
persistent-hint
|
||||||
|
class="mb-4"
|
||||||
|
required
|
||||||
|
:disabled="initializing"
|
||||||
|
></v-select>
|
||||||
|
|
||||||
|
<v-select
|
||||||
|
v-model="selectedMergeLLM"
|
||||||
|
:items="llmProviders"
|
||||||
|
item-title="text"
|
||||||
|
item-value="value"
|
||||||
|
label="合并 LLM *"
|
||||||
|
hint="用于合并相似记忆，可在初始化后更改"
|
||||||
|
persistent-hint
|
||||||
|
class="mb-4"
|
||||||
|
required
|
||||||
|
:disabled="initializing"
|
||||||
|
></v-select>
|
||||||
|
|
||||||
|
<v-btn
|
||||||
|
type="submit"
|
||||||
|
color="primary"
|
||||||
|
:loading="initializing"
|
||||||
|
:disabled="!selectedEmbeddingProvider || !selectedMergeLLM"
|
||||||
|
block
|
||||||
|
size="large"
|
||||||
|
>
|
||||||
|
初始化记忆系统
|
||||||
|
</v-btn>
|
||||||
|
</v-form>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
<!-- 已初始化时显示配置信息 -->
|
||||||
|
<div v-else>
|
||||||
|
<v-list>
|
||||||
|
<v-list-item>
|
||||||
|
<template v-slot:prepend>
|
||||||
|
<v-icon>mdi-vector-triangle</v-icon>
|
||||||
|
</template>
|
||||||
|
<v-list-item-title>Embedding 模型</v-list-item-title>
|
||||||
|
<v-list-item-subtitle>
|
||||||
|
{{ getProviderName(memoryStatus.embedding_provider_id) }}
|
||||||
|
</v-list-item-subtitle>
|
||||||
|
</v-list-item>
|
||||||
|
|
||||||
|
<v-divider class="my-2"></v-divider>
|
||||||
|
|
||||||
|
<v-list-item>
|
||||||
|
<template v-slot:prepend>
|
||||||
|
<v-icon>mdi-robot</v-icon>
|
||||||
|
</template>
|
||||||
|
<v-list-item-title>合并 LLM</v-list-item-title>
|
||||||
|
<v-list-item-subtitle>
|
||||||
|
{{ getProviderName(memoryStatus.merge_llm_provider_id) }}
|
||||||
|
</v-list-item-subtitle>
|
||||||
|
</v-list-item>
|
||||||
|
</v-list>
|
||||||
|
|
||||||
|
<v-divider class="my-4"></v-divider>
|
||||||
|
|
||||||
|
<v-form @submit.prevent="updateMergeLLM">
|
||||||
|
<v-select
|
||||||
|
v-model="newMergeLLM"
|
||||||
|
:items="llmProviders"
|
||||||
|
item-title="text"
|
||||||
|
item-value="value"
|
||||||
|
label="更新合并 LLM"
|
||||||
|
hint="可以更换用于合并记忆的 LLM"
|
||||||
|
persistent-hint
|
||||||
|
class="mb-4"
|
||||||
|
:disabled="updating"
|
||||||
|
></v-select>
|
||||||
|
|
||||||
|
<v-btn
|
||||||
|
type="submit"
|
||||||
|
color="primary"
|
||||||
|
:loading="updating"
|
||||||
|
:disabled="!newMergeLLM || newMergeLLM === memoryStatus.merge_llm_provider_id"
|
||||||
|
block
|
||||||
|
variant="tonal"
|
||||||
|
>
|
||||||
|
更新合并 LLM
|
||||||
|
</v-btn>
|
||||||
|
</v-form>
|
||||||
|
</div>
|
||||||
|
</v-card-text>
|
||||||
|
</v-card>
|
||||||
|
</v-col>
|
||||||
|
|
||||||
|
<!-- 说明卡片 -->
|
||||||
|
<v-col cols="12" md="4" lg="6">
|
||||||
|
<v-card rounded="lg">
|
||||||
|
<v-card-title class="d-flex align-center">
|
||||||
|
<v-icon class="mr-2">mdi-information</v-icon>
|
||||||
|
说明
|
||||||
|
</v-card-title>
|
||||||
|
<v-divider></v-divider>
|
||||||
|
<v-card-text>
|
||||||
|
<v-list density="compact">
|
||||||
|
<v-list-item>
|
||||||
|
<v-list-item-title class="text-wrap">
|
||||||
|
<strong>Embedding 模型：</strong>用于将文本转换为向量，支持语义相似度搜索。
|
||||||
|
<v-chip size="x-small" color="warning" class="ml-2">不可更改</v-chip>
|
||||||
|
</v-list-item-title>
|
||||||
|
</v-list-item>
|
||||||
|
<v-list-item>
|
||||||
|
<v-list-item-title class="text-wrap">
|
||||||
|
<strong>合并 LLM：</strong>当检测到相似记忆时，使用此模型合并为一条记忆。
|
||||||
|
<v-chip size="x-small" color="success" class="ml-2">可更改</v-chip>
|
||||||
|
</v-list-item-title>
|
||||||
|
</v-list-item>
|
||||||
|
<v-list-item>
|
||||||
|
<v-list-item-title class="text-wrap">
|
||||||
|
<strong>注意：</strong>Embedding 模型一旦选择后无法更改，请谨慎选择。
|
||||||
|
</v-list-item-title>
|
||||||
|
</v-list-item>
|
||||||
|
</v-list>
|
||||||
|
</v-card-text>
|
||||||
|
</v-card>
|
||||||
|
</v-col>
|
||||||
|
</v-row>
|
||||||
|
</v-container>
|
||||||
|
|
||||||
|
<!-- 提示框 -->
|
||||||
|
<v-snackbar v-model="snackbar.show" :color="snackbar.color" :timeout="3000">
|
||||||
|
{{ snackbar.message }}
|
||||||
|
</v-snackbar>
|
||||||
|
</div>
|
||||||
|
</template>
|
||||||
|
|
||||||
|
<script setup lang="ts">
|
||||||
|
import { ref, onMounted } from 'vue';
|
||||||
|
import axios from 'axios';
|
||||||
|
import { useI18n } from '@/i18n/composables';
|
||||||
|
|
||||||
|
const { t } = useI18n();
|
||||||
|
|
||||||
|
interface MemoryStatus {
|
||||||
|
initialized: boolean;
|
||||||
|
embedding_provider_id: string | null;
|
||||||
|
merge_llm_provider_id: string | null;
|
||||||
|
}
|
||||||
|
|
||||||
|
interface Provider {
|
||||||
|
value: string;
|
||||||
|
text: string;
|
||||||
|
}
|
||||||
|
|
||||||
|
const loading = ref(true);
|
||||||
|
const initializing = ref(false);
|
||||||
|
const updating = ref(false);
|
||||||
|
|
||||||
|
const memoryStatus = ref<MemoryStatus>({
|
||||||
|
initialized: false,
|
||||||
|
embedding_provider_id: null,
|
||||||
|
merge_llm_provider_id: null,
|
||||||
|
});
|
||||||
|
|
||||||
|
const embeddingProviders = ref<Provider[]>([]);
|
||||||
|
const llmProviders = ref<Provider[]>([]);
|
||||||
|
|
||||||
|
const selectedEmbeddingProvider = ref<string>('');
|
||||||
|
const selectedMergeLLM = ref<string>('');
|
||||||
|
const newMergeLLM = ref<string>('');
|
||||||
|
|
||||||
|
const snackbar = ref({
|
||||||
|
show: false,
|
||||||
|
message: '',
|
||||||
|
color: 'success',
|
||||||
|
});
|
||||||
|
|
||||||
|
const showMessage = (message: string, color: string = 'success') => {
|
||||||
|
snackbar.value.message = message;
|
||||||
|
snackbar.value.color = color;
|
||||||
|
snackbar.value.show = true;
|
||||||
|
};
|
||||||
|
|
||||||
|
const getProviderName = (providerId: string | null): string => {
|
||||||
|
if (!providerId) return '未设置';
|
||||||
|
const embedding = embeddingProviders.value.find(p => p.value === providerId);
|
||||||
|
const llm = llmProviders.value.find(p => p.value === providerId);
|
||||||
|
return embedding?.text || llm?.text || providerId;
|
||||||
|
};
|
||||||
|
|
||||||
|
const loadProviders = async () => {
|
||||||
|
try {
|
||||||
|
// Load embedding providers
|
||||||
|
const embeddingResponse = await axios.get('/api/config/provider/list', {
|
||||||
|
params: { provider_type: 'embedding' }
|
||||||
|
});
|
||||||
|
if (embeddingResponse.data.status === 'ok') {
|
||||||
|
embeddingProviders.value = (embeddingResponse.data.data || []).map((p: any) => ({
|
||||||
|
value: p.id,
|
||||||
|
text: `${p.embedding_model} (${p.id})`,
|
||||||
|
}));
|
||||||
|
}
|
||||||
|
|
||||||
|
// Load LLM providers
|
||||||
|
const llmResponse = await axios.get('/api/config/provider/list', {
|
||||||
|
params: { provider_type: 'chat_completion' }
|
||||||
|
});
|
||||||
|
if (llmResponse.data.status === 'ok') {
|
||||||
|
llmProviders.value = (llmResponse.data.data || []).map((p: any) => ({
|
||||||
|
value: p.id,
|
||||||
|
text: `${p?.model_config?.model} (${p.id})`,
|
||||||
|
}));
|
||||||
|
}
|
||||||
|
} catch (error) {
|
||||||
|
console.error('Failed to load providers:', error);
|
||||||
|
showMessage('加载提供商列表失败', 'error');
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
const loadStatus = async () => {
|
||||||
|
try {
|
||||||
|
const response = await axios.get('/api/memory/status');
|
||||||
|
if (response.data.status === 'ok') {
|
||||||
|
memoryStatus.value = response.data.data;
|
||||||
|
if (memoryStatus.value.merge_llm_provider_id) {
|
||||||
|
newMergeLLM.value = memoryStatus.value.merge_llm_provider_id;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
} catch (error) {
|
||||||
|
console.error('Failed to load memory status:', error);
|
||||||
|
showMessage('加载记忆系统状态失败', 'error');
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
const initializeMemory = async () => {
|
||||||
|
if (!selectedEmbeddingProvider.value || !selectedMergeLLM.value) {
|
||||||
|
showMessage('请选择 Embedding 模型和合并 LLM', 'warning');
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
initializing.value = true;
|
||||||
|
try {
|
||||||
|
const response = await axios.post('/api/memory/initialize', {
|
||||||
|
embedding_provider_id: selectedEmbeddingProvider.value,
|
||||||
|
merge_llm_provider_id: selectedMergeLLM.value,
|
||||||
|
});
|
||||||
|
|
||||||
|
if (response.data.status === 'ok') {
|
||||||
|
showMessage('记忆系统初始化成功', 'success');
|
||||||
|
await loadStatus();
|
||||||
|
} else {
|
||||||
|
showMessage(response.data.message || '初始化失败', 'error');
|
||||||
|
}
|
||||||
|
} catch (error: any) {
|
||||||
|
console.error('Failed to initialize memory:', error);
|
||||||
|
showMessage(error.response?.data?.message || '初始化失败', 'error');
|
||||||
|
} finally {
|
||||||
|
initializing.value = false;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
const updateMergeLLM = async () => {
|
||||||
|
if (!newMergeLLM.value) {
|
||||||
|
showMessage('请选择新的合并 LLM', 'warning');
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
|
||||||
|
updating.value = true;
|
||||||
|
try {
|
||||||
|
const response = await axios.post('/api/memory/update_merge_llm', {
|
||||||
|
merge_llm_provider_id: newMergeLLM.value,
|
||||||
|
});
|
||||||
|
|
||||||
|
if (response.data.status === 'ok') {
|
||||||
|
showMessage('合并 LLM 更新成功', 'success');
|
||||||
|
await loadStatus();
|
||||||
|
} else {
|
||||||
|
showMessage(response.data.message || '更新失败', 'error');
|
||||||
|
}
|
||||||
|
} catch (error: any) {
|
||||||
|
console.error('Failed to update merge LLM:', error);
|
||||||
|
showMessage(error.response?.data?.message || '更新失败', 'error');
|
||||||
|
} finally {
|
||||||
|
updating.value = false;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
onMounted(async () => {
|
||||||
|
loading.value = true;
|
||||||
|
await Promise.all([loadProviders(), loadStatus()]);
|
||||||
|
loading.value = false;
|
||||||
|
});
|
||||||
|
</script>
|
||||||
|
|
||||||
|
<style scoped>
|
||||||
|
.memory-page {
|
||||||
|
min-height: 100vh;
|
||||||
|
padding: 8px;
|
||||||
|
}
|
||||||
|
</style>
|
||||||
Reference in New Issue
Block a user