44e41bbb5a
- 移除 MinIOBackend 和复合存储后端，统一使用 StateBackend - 依赖 LangGraph checkpointer 自动恢复 state（attachments, files） - 前端适配 StateBackend 字典格式文件结构 - 优化 @提及 文件搜索逻辑，支持路径匹配
238 lines
8.8 KiB
Python
238 lines
8.8 KiB
Python
from __future__ import annotations
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import importlib.util
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import os
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import tomllib as tomli
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from abc import abstractmethod
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from pathlib import Path
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from langgraph.checkpoint.memory import InMemorySaver
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from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver, aiosqlite
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from langgraph.graph.state import CompiledStateGraph
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from src import config as sys_config
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from src.agents.common.context import BaseContext
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from src.utils import logger
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class BaseAgent:
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"""
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定义一个基础 Agent 供 各类 graph 继承
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"""
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name = "base_agent"
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description = "base_agent"
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capabilities: list[str] = [] # 智能体能力列表，如 ["file_upload", "web_search"] 等
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context_schema: type[BaseContext] = BaseContext # 智能体上下文 schema
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def __init__(self, **kwargs):
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self.graph = None # will be covered by get_graph
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self.checkpointer = None
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self._async_conn = None
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self.workdir = Path(sys_config.save_dir) / "agents" / self.module_name
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self.workdir.mkdir(parents=True, exist_ok=True)
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self._metadata_cache = None # Cache for metadata to avoid repeated file reads
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@property
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def module_name(self) -> str:
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"""Get the module name of the agent class."""
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return self.__class__.__module__.split(".")[-2]
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@property
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def id(self) -> str:
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"""Get the agent's class name."""
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return self.__class__.__name__
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async def get_info(self):
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# Load metadata from file
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metadata = self.load_metadata()
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# Merge metadata with class attributes, metadata takes precedence
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return {
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"id": self.id,
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"name": metadata.get("name", getattr(self, "name", "Unknown")),
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"description": metadata.get("description", getattr(self, "description", "Unknown")),
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"examples": metadata.get("examples", []),
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"configurable_items": self.context_schema.get_configurable_items(),
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"has_checkpointer": await self.check_checkpointer(),
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"capabilities": getattr(self, "capabilities", []), # 智能体能力列表
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}
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async def get_config(self):
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return self.context_schema.from_file(module_name=self.module_name)
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async def stream_values(self, messages: list[str], input_context=None, **kwargs):
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graph = await self.get_graph()
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context = self.context_schema()
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agent_config = (input_context or {}).get("agent_config")
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if isinstance(agent_config, dict):
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context.update(agent_config)
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context.update(input_context or {})
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for event in graph.astream({"messages": messages}, stream_mode="values", context=context):
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yield event["messages"]
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async def stream_messages(self, messages: list[str], input_context=None, **kwargs):
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graph = await self.get_graph()
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context = self.context_schema()
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agent_config = (input_context or {}).get("agent_config")
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if isinstance(agent_config, dict):
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context.update(agent_config)
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context.update(input_context or {})
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logger.debug(f"stream_messages: {context}")
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# 构建配置：LangGraph 会自动从 checkpointer 恢复 state
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input_config = {
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"configurable": {"thread_id": context.thread_id, "user_id": context.user_id},
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"recursion_limit": 300,
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}
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async for msg, metadata in graph.astream(
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{"messages": messages},
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stream_mode="messages",
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context=context,
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config=input_config,
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):
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yield msg, metadata
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async def invoke_messages(self, messages: list[str], input_context=None, **kwargs):
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graph = await self.get_graph()
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context = self.context_schema()
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agent_config = (input_context or {}).get("agent_config")
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if isinstance(agent_config, dict):
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context.update(agent_config)
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context.update(input_context or {})
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logger.debug(f"invoke_messages: {context}")
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# 构建配置
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input_config = {
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"configurable": {"thread_id": context.thread_id, "user_id": context.user_id},
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"recursion_limit": 100,
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}
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msg = await graph.ainvoke(
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{"messages": messages},
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context=context,
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config=input_config,
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)
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return msg
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async def check_checkpointer(self):
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app = await self.get_graph()
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if not hasattr(app, "checkpointer") or app.checkpointer is None:
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logger.warning(f"智能体 {self.name} 的 Graph 未配置 checkpointer，无法获取历史记录")
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return False
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return True
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async def get_history(self, user_id, thread_id) -> list[dict]:
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"""获取历史消息"""
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try:
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app = await self.get_graph()
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if not await self.check_checkpointer():
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return []
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config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
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state = await app.aget_state(config)
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result = []
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if state:
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messages = state.values.get("messages", [])
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for msg in messages:
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if hasattr(msg, "model_dump"):
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msg_dict = msg.model_dump() # 转换成字典
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else:
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msg_dict = dict(msg) if hasattr(msg, "__dict__") else {"content": str(msg)}
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result.append(msg_dict)
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return result
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except Exception as e:
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logger.error(f"获取智能体 {self.name} 历史消息出错: {e}")
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return []
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def reload_graph(self):
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"""重置 graph 缓存，强制下次调用 get_graph 时重新构建"""
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self.graph = None
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logger.info(f"{self.name} graph 缓存已清空，将在下次调用时重新构建")
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@abstractmethod
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async def get_graph(self, **kwargs) -> CompiledStateGraph:
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"""
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获取并编译对话图实例。
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必须确保在编译时设置 checkpointer，否则将无法获取历史记录。
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例如: graph = workflow.compile(checkpointer=sqlite_checkpointer)
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"""
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pass
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async def _get_checkpointer(self):
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if self.checkpointer is not None:
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return self.checkpointer
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# 创建数据库连接并确保设置 checkpointer
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checkpointer = None
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try:
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checkpointer = AsyncSqliteSaver(await self.get_async_conn())
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except Exception as e:
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logger.error(f"构建 Graph 设置 checkpointer 时出错: {e}, 尝试使用内存存储")
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checkpointer = InMemorySaver()
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self.checkpointer = checkpointer
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return self.checkpointer
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async def get_async_conn(self) -> aiosqlite.Connection:
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"""获取异步数据库连接"""
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if self._async_conn is not None:
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return self._async_conn
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conn = await aiosqlite.connect(os.path.join(self.workdir, "aio_history.db"))
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# Patch: langgraph's AsyncSqliteSaver expects is_alive() method which aiosqlite may not have
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if not hasattr(conn, "is_alive"):
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conn.is_alive = lambda: True
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self._async_conn = conn
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return self._async_conn
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async def get_aio_memory(self) -> AsyncSqliteSaver:
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"""获取异步存储实例"""
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return AsyncSqliteSaver(await self.get_async_conn())
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def load_metadata(self) -> dict:
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"""Load metadata from metadata.toml file in the agent's source directory."""
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if self._metadata_cache is not None:
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return self._metadata_cache
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# Try to find metadata.toml in the agent's source directory
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try:
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# Get the agent's source file directory
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agent_module = self.__class__.__module__
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# Use importlib to get the module's file path
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spec = importlib.util.find_spec(agent_module)
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if spec and spec.origin:
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agent_file = Path(spec.origin)
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agent_dir = agent_file.parent
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else:
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# Fallback: construct path from module name
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module_path = agent_module.replace(".", "/")
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agent_file = Path(f"src/{module_path}.py")
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agent_dir = agent_file.parent
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metadata_file = agent_dir / "metadata.toml"
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if metadata_file.exists():
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with open(metadata_file, "rb") as f:
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metadata = tomli.load(f)
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self._metadata_cache = metadata
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logger.debug(f"Loaded metadata from {metadata_file}")
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return metadata
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else:
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logger.debug(f"No metadata.toml found for {self.module_name} at {metadata_file}")
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self._metadata_cache = {}
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return {}
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except Exception as e:
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logger.error(f"Error loading metadata for {self.module_name}: {e}")
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self._metadata_cache = {}
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return {}
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