mirror of
https://github.com/bytedance/deer-flow.git
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Lawrance_YXLiao
2eeb597985
* fix(runs): expose active progress counters * fix(runs): avoid delayed progress flush on completion * fix(runs): tighten progress snapshot semantics * fix(runs): preserve omitted progress fields * chore(runs): remove duplicate journal initialization
129 lines
3.5 KiB
Python
129 lines
3.5 KiB
Python
"""Abstract interface for run metadata storage.
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RunManager depends on this interface. Implementations:
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- MemoryRunStore: in-memory dict (development, tests)
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- Future: RunRepository backed by SQLAlchemy ORM
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All methods accept an optional user_id for user isolation.
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When user_id is None, no user filtering is applied (single-user mode).
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"""
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from __future__ import annotations
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import abc
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from typing import Any
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class RunStore(abc.ABC):
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@abc.abstractmethod
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async def put(
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self,
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run_id: str,
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*,
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thread_id: str,
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assistant_id: str | None = None,
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user_id: str | None = None,
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model_name: str | None = None,
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status: str = "pending",
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multitask_strategy: str = "reject",
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metadata: dict[str, Any] | None = None,
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kwargs: dict[str, Any] | None = None,
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error: str | None = None,
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created_at: str | None = None,
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) -> None:
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pass
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@abc.abstractmethod
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async def get(
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self,
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run_id: str,
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*,
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user_id: str | None = None,
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) -> dict[str, Any] | None:
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pass
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@abc.abstractmethod
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async def list_by_thread(
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self,
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thread_id: str,
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*,
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user_id: str | None = None,
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limit: int = 100,
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) -> list[dict[str, Any]]:
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pass
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@abc.abstractmethod
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async def update_status(
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self,
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run_id: str,
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status: str,
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*,
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error: str | None = None,
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) -> None:
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pass
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@abc.abstractmethod
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async def delete(self, run_id: str) -> None:
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pass
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@abc.abstractmethod
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async def update_model_name(
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self,
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run_id: str,
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model_name: str | None,
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) -> None:
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"""Update the model_name field for an existing run."""
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pass
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@abc.abstractmethod
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async def update_run_completion(
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self,
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run_id: str,
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*,
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status: str,
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total_input_tokens: int = 0,
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total_output_tokens: int = 0,
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total_tokens: int = 0,
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llm_call_count: int = 0,
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lead_agent_tokens: int = 0,
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subagent_tokens: int = 0,
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middleware_tokens: int = 0,
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message_count: int = 0,
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last_ai_message: str | None = None,
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first_human_message: str | None = None,
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error: str | None = None,
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) -> None:
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pass
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async def update_run_progress(
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self,
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run_id: str,
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*,
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total_input_tokens: int | None = None,
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total_output_tokens: int | None = None,
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total_tokens: int | None = None,
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llm_call_count: int | None = None,
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lead_agent_tokens: int | None = None,
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subagent_tokens: int | None = None,
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middleware_tokens: int | None = None,
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message_count: int | None = None,
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last_ai_message: str | None = None,
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first_human_message: str | None = None,
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) -> None:
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"""Persist a best-effort running snapshot without changing run status."""
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return None
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@abc.abstractmethod
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async def list_pending(self, *, before: str | None = None) -> list[dict[str, Any]]:
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pass
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@abc.abstractmethod
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async def aggregate_tokens_by_thread(self, thread_id: str, *, include_active: bool = False) -> dict[str, Any]:
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"""Aggregate token usage for completed runs in a thread.
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Returns a dict with keys: total_tokens, total_input_tokens,
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total_output_tokens, total_runs, by_model (model_name → {tokens, runs}),
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by_caller ({lead_agent, subagent, middleware}).
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"""
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pass
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