Jaroslav Beneš
a5fa982ae3
MAX_ROUNDS = 1 was wrong, and wrong in a way worth writing down. The loop already ends the moment a round comes back with no tool calls -- that is the model saying it has what it needs, and it is the termination condition every agentic harness uses. A round limit was never a schedule; it exists to catch the case where the model never says so. One is low enough to stop being a ceiling and start being a schedule: it overrode the model's judgement on every single turn. And it broke something concrete. Several built-ins are two-step pairs -- knowledge_get and notes_get read a document "by the id a search returned" -- so one round left the library searchable and not readable. That is not an edge case, it is the library working at half depth, and I understated it as "cannot search the web and then read a result" when the change went in. It is a setting now, under General, default 5, with 0 meaning no ceiling. The loop and the harness both read settings_store.chat_rounds, so the model is never told a budget that is not its own; tools.MAX_ROUNDS is the fallback for callers with no session and a test pins the two equal. core.rounds goes back to naming the number, and vanishes entirely when there is no ceiling rather than promising zero rounds. The other half of "let it decide how long to go": an agent reply that ends while its plan still has open tasks is asked once to carry on. Only against a plan, because that is the one thing there is to be objectively wrong about -- a model with no plan that says it has finished is believed, and arguing with it would be guessing. At most twice in a row, with the count reset the moment it calls a tool again, so the bound is on consecutive stops rather than on stops in total. Never in Plan mode and never past plan_submit, which ends the turn on purpose. Giving up is recorded as an event rather than left silent. The model's own words go back with the nudge, which turned up a real bug on the way: ReasoningSplitter holds back a few characters against a <think> tag split across chunks, so round_text at the end of a round was missing its tail. That text is echoed as an assistant turn for tool rounds too, so a model has been occasionally asked to continue from a transcript where it trailed off mid-sentence. Flushed per round now. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
1447 lines
61 KiB
Python
1447 lines
61 KiB
Python
"""Background reply generation.
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Generation used to be driven by the SSE request: the browser opening the stream
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was what produced the tokens, so navigating away cancelled the reply mid-
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sentence. Here it runs as its own task instead, and the SSE endpoint merely
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*follows* it. Closing the page, opening another chat, or starting a new one
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leaves the answer being written; coming back attaches to it and immediately
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receives everything produced so far.
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The registry is in-process, which is right for the single-worker deployment
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this ships with. Several workers would need the state in the database or a
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broker, because the request that follows a generation would not necessarily
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land in the process running it.
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"""
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from __future__ import annotations
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import asyncio
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import contextlib
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import json
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import logging
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import time
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import uuid
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from dataclasses import dataclass, field, replace
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from datetime import UTC, datetime, timedelta
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from sqlalchemy import select
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from lembas.db.models import KIND_AGENT, ROLE_ASSISTANT, ROLE_USER, Chat, Message, User
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from lembas.db.session import session_scope
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from lembas.services import chat as chat_service
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from lembas.services import compaction as compaction_service
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from lembas.services import interaction, settings_store, tokens, tool_labels
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from lembas.services import metrics as metrics_service
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from lembas.services import prompts as prompts_service
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from lembas.services import tools as tools_service
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from lembas.services.agent import policy as agent_policy
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from lembas.services.llm.openai_client import (
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LLMError,
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chunk_usage,
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delta_reasoning,
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delta_text,
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delta_tool_calls,
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stream_chat,
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)
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from lembas.services.reasoning import REASONING, ReasoningSplitter
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from lembas.services.tools import ToolOutcome
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log = logging.getLogger(__name__)
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# How often the partial answer is offered to followers. Markdown is re-rendered
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# whole each time -- a list or a code fence is only correct once its context
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# exists -- so this trades a little work for formatting that appears as the
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# model writes. 100ms is below the threshold where the eye reads it as stepping.
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RENDER_INTERVAL = 0.1
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# Finished generations linger so a follower attaching at the last moment still
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# gets the final frames, then are pruned.
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KEEP_FINISHED = timedelta(minutes=5)
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# What "no ceiling" resolves to. A setting of 0 means an administrator does not
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# want a round limit, but a loop needs *some* stop or a model stuck calling one
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# cheap tool runs until the process does. This is high enough never to be
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# reached by anything but that.
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MAX_TOOL_ROUNDS = 200
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# How many times in a row a reply that stopped with plan tasks outstanding may
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# be told to carry on. Two, so a model that genuinely has nothing left to do can
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# say so and be believed rather than argued with indefinitely.
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MAX_NUDGES = 2
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@dataclass
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class Generation:
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"""The live state of one reply being written."""
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chat_id: str
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message_id: str
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content: list[str] = field(default_factory=list)
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reasoning: list[str] = field(default_factory=list)
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reasoning_ms: int = 0
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# One entry per tool call made while producing this reply, in order. Shown
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# live as the model works and kept on the message afterwards.
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tool_events: list[dict] = field(default_factory=list)
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# --- What it cost --------------------------------------------------------
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# Prompt and completion are summed across tool rounds: what the reply cost.
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# context_tokens is overwritten each round with that round's prompt plus
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# completion, because a three-round reply pays for its prompt three times
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# but only ever occupies the window once.
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prompt_tokens: int = 0
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completion_tokens: int = 0
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context_tokens: int = 0
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context_limit: int = 0
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# Filled from the assembled request before the first chunk, so a follower
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# has a percentage to show while the reply is still being written -- real
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# usage only arrives in a single chunk at the very end.
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prompt_estimate: int = 0
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rounds: int = 0
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# time.monotonic() at the start. A field rather than a local in `_run`
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# because `_follow` is a different function that sees only this object, and
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# without it there is nothing to compute a live tokens/second against.
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started_at: float = 0.0
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elapsed_ms: int = 0
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error: str = ""
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stopped: bool = False
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done: bool = False
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# Bumped on every change. Followers compare against it rather than being
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# woken individually: with a 100ms cadence a short poll is simpler than
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# future bookkeeping, and cannot drop a wakeup.
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version: int = 0
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# What the reply is doing when it is not producing tokens. Shown in the
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# streaming bubble, because a silent multi-second pause before the first
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# token is what a hang looks like.
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status: str = ""
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# Number of browsers currently watching. Decides whether a finished reply
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# counts as unread.
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followers: int = 0
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finished_at: datetime | None = None
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cancel: bool = False
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# Set while the reply is stopped waiting for a person -- an approval, or a
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# question the model asked. None at every other moment. Read by `_follow`,
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# which sends the card, and by `request_stop`, which resolves it: `cancel`
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# is otherwise only ever read between streamed chunks, and there are no
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# chunks while this is set.
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pending: interaction.Interruption | None = None
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# Seconds spent waiting for a person, cumulative. Taken off the wall-clock
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# budget so that thinking time is the model's and not the reader's.
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waited: float = 0.0
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# How much tool output this reply has handed back, against the agent budget.
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# A model that fills its own context with build logs has no room left to
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# answer with.
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output_bytes: int = 0
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# A plan proposed in Plan mode, or one being kept current while it is
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# carried out. See services/plans.py for the shape. Written onto the
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# message, so the Execute button sends exactly what was proposed rather than
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# something parsed back out of prose.
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plan: dict | None = None
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# Whether that plan came from `plan_submit`, which ends the turn, rather
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# than from `plan_update`, which does not. Both write `plan` so that
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# `_persist` stays one writer with one rule; only this decides whether the
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# tools are withdrawn for a final round.
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plan_final: bool = False
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# The queue, seen from the reply's side. `drained` says this reply's ending
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# handed the next waiting prompt to a fresh one; `injected_ids` names the
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# prompts taken into *this* reply between two rounds of tool calls. Both are
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# read only by `_follow`, which turns them into bubbles on the `done` frame
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# -- the one frame that reaches a browser after a reply is over.
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drained: bool = False
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injected_ids: list[str] = field(default_factory=list)
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# How many times *in a row* this reply has ended with plan tasks still open
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# and been told to carry on. Reset the moment it calls a tool again, so the
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# count is of consecutive stops rather than of stops in total.
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nudges: int = 0
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def touch(self) -> None:
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self.version += 1
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@property
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def text(self) -> str:
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return "".join(self.content)
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@property
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def thinking(self) -> str:
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return "".join(self.reasoning)
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_RUNNING: dict[str, Generation] = {}
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_TASKS: dict[str, asyncio.Task] = {}
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def get(message_id: str) -> Generation | None:
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return _RUNNING.get(message_id)
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def request_stop(message_id: str) -> bool:
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"""Ask a running generation to stop. Returns whether one was found."""
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generation = _RUNNING.get(message_id)
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if generation is None or generation.done:
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return False
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generation.cancel = True
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# A paused reply produces no chunks, and the chunk loop is the only place
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# `cancel` is ever read -- so without this, Stop does nothing at all while
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# an approval card is on screen. Resolving the pause is the wakeup; `_run`
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# then takes its ordinary stopped path rather than needing a second branch.
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if generation.pending is not None:
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generation.pending.resolve(interaction.CANCELLED)
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return True
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def answer(
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chat_id: str,
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interaction_id: str,
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*,
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verdict: str = "",
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answers: dict[str, str] | None = None,
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) -> bool:
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"""Resolve whichever running reply is parked on this interruption.
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A linear scan of the registry: it holds one entry per reply in flight, and
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this runs at human speed. Scoped to the chat because the caller has already
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checked that this reader owns *that* chat, and an id alone would not.
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"""
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for generation in _RUNNING.values():
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pending = generation.pending
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if generation.chat_id != chat_id or pending is None or pending.id != interaction_id:
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continue
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outcome = verdict if verdict in _VERDICTS else interaction.ANSWER
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return pending.resolve(outcome, answers=answers)
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return False
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def running_for(chat_id: str) -> Generation | None:
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"""The reply being written in this chat, if there is one.
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A linear scan for the reason `answer` gives above: one entry per reply in
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flight, consulted at human speed. `_prune` first, because a finished
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generation lingers `KEEP_FINISHED` so that late followers still get the
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final frames -- and without the sweep those five minutes would look like a
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chat that is permanently busy, and queue everything typed into it.
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"""
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_prune()
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for generation in _RUNNING.values():
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if generation.chat_id == chat_id and not generation.done:
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return generation
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return None
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_VERDICTS = (interaction.ALLOW, interaction.ALLOW_ALWAYS, interaction.DENY)
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def _prune() -> None:
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cutoff = datetime.now(UTC) - KEEP_FINISHED
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now = time.monotonic()
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for message_id, generation in list(_RUNNING.items()):
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# A paused reply is deliberately not `done` -- a page reload has to be
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# able to reattach to it. Its timeout is what stops it lingering, and
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# this is the belt to that pair of braces: a deadline long past means
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# the timeout did not fire, and a task parked forever is worse than one
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# that gives up.
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pending = generation.pending
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if pending is not None and now > pending.expires_at + KEEP_FINISHED.total_seconds():
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log.warning("resolving a stuck interaction on message %s", message_id)
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pending.resolve(interaction.EXPIRED)
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if generation.done and generation.finished_at and generation.finished_at < cutoff:
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_RUNNING.pop(message_id, None)
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_TASKS.pop(message_id, None)
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def ensure(chat_id: str, message_id: str) -> Generation:
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"""Start generating this reply if it is not already under way.
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Idempotent, because more than one thing can ask for it: the route that
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created the message, and any page load that finds the message unfinished.
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`_prune` runs first, not after the lookup. Below it, a stale entry could
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never expire: the early return is the only path a repeated id takes, so the
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sweep was unreachable for exactly the message that needed it.
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"""
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_prune()
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existing = _RUNNING.get(message_id)
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if existing is not None:
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return existing
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generation = Generation(chat_id=chat_id, message_id=message_id)
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_RUNNING[message_id] = generation
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_TASKS[message_id] = asyncio.create_task(_run(generation))
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return generation
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def restart(chat_id: str, message_id: str) -> Generation:
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"""Produce this reply again, discarding any finished attempt at it.
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`ensure` is idempotent on purpose, and that is load-bearing: a page load
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finding an unfinished reply must attach to it rather than start a second
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one, and `_follow` calls it too. Regeneration is the one caller that means
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the opposite.
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It is also the one caller that reuses an existing Message row -- blanked and
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marked incomplete -- rather than creating a new one. The finished Generation
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for that id is still in the registry, because finished ones linger
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KEEP_FINISHED so a follower arriving at the last moment still gets the final
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frames. `ensure` handed that one straight back: no request was made,
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`_follow` replayed the previous answer, and the `done` frame re-rendered a
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streaming shell because the row said incomplete. That was the reconnect loop,
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and the Send button stuck on Stop.
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"""
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previous = _RUNNING.pop(message_id, None)
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task = _TASKS.pop(message_id, None)
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if previous is not None and not previous.done:
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previous.cancel = True
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if task is not None:
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task.cancel()
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return ensure(chat_id, message_id)
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async def shutdown() -> None:
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"""Stop every running generation, keeping what each has produced."""
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for task in list(_TASKS.values()):
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task.cancel()
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for task in list(_TASKS.values()):
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with contextlib.suppress(asyncio.CancelledError, Exception):
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await task
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async def _run(generation: Generation) -> None:
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"""Produce one reply, then persist it. Never raises into the task.
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A reply is not necessarily one request. When tools are offered and the
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model asks to use one, the loop below runs it, appends the result to the
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conversation and asks again -- up to tools_service.MAX_ROUNDS times, after
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which the model has to answer with what it has. Text produced before a tool
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call is kept, so a model that narrates what it is about to look up does not
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lose that when the results come back.
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"""
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splitter = ReasoningSplitter()
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started = time.monotonic()
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generation.started_at = started
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reasoning_started: float | None = None
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question = ""
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endpoint = model_id = None
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needs_title = False
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title_prompt = ""
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# Bound before the try, because the finally clears the credential on it and
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# a chat that has been deleted returns before it would otherwise be set.
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tool_context = None
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try:
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# Before the request is assembled, so build_request is called once and
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# what goes out is the compacted conversation -- there is no second
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# assembly path. Here rather than in post_message because that route's
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# whole contract is to return immediately, and a three-second
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# summarisation in front of it would break exactly that.
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await _maybe_compact(generation)
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# Before the session opens, for the same reason compaction is: the
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# listing is an SSH round trip, and holding a database session across
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# one to save opening a second is the wrong trade. `build_request`
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# below reads whatever this left in the cache and never fetches.
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await _warm_project(generation)
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with session_scope() as db:
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chat = db.get(Chat, generation.chat_id)
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message = db.get(Message, generation.message_id)
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if chat is None or message is None:
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generation.error = "That chat no longer exists."
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return
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endpoint, model_id = chat_service.resolve_endpoint(db, chat)
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owner = db.get(User, chat.user_id)
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# Read while the session is open: everything below outlives it.
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# Resolved once, so that what the loop is allowed to *run* is the
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# same set the endpoint was *offered* -- not whatever happens to
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# exist by the time a call comes back.
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toolset = tools_service.resolve_tools(db, chat, owner)
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offered = toolset.schemas
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payload = chat_service.build_request(
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db, chat, upto=message, tools=offered, user=owner
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)
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question = _question_from(payload)
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needs_title = not chat.title_generated
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# Read here, with the rest, because titling happens after this
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# session has closed and must not open another one.
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title_prompt = prompts_service.resolve(db, "task.title")
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tool_context = tools_service.context_for(db, owner, chat, tools=toolset)
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model = chat_service.model_for(db, chat)
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generation.context_limit = model.context_length if model is not None else 0
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# Kept for `_inject`, which builds a user turn after this session
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# has closed. A turn taken in mid-reply has to be shaped exactly as
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# the same words typed a moment later would have been -- images to a
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# vision model, a plain string to anything else, or the endpoint
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# rejects the whole request.
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vision = chat_service.model_supports(db, chat, "vision")
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chat_rounds = settings_store.chat_rounds(db)
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nudge_enabled = bool(settings_store.agents(db).get("nudge_unfinished"))
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generation.prompt_estimate = tokens.estimate_request(payload)
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limits = tool_context.agent.limits if tool_context.agent else None
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# A ceiling, not a schedule -- the loop below ends the moment a round
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# produces no tool calls, which is the model saying it is done. Zero
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# means an ordinary chat has no ceiling either; `steps` is already a
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# runaway backstop rather than a budget, so an agent chat is bounded by
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# tokens and the clock instead.
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budget = limits.steps if limits else (chat_rounds or MAX_TOOL_ROUNDS)
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for round_number in range(budget + 1):
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generation.rounds = round_number + 1
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# Checked between rounds, never mid-stream: cutting a reply off in
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# the middle of a sentence to enforce a budget produces garbage, and
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# Stop already covers the mid-stream case. Time spent waiting for a
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# person is subtracted -- somebody who thinks for ten minutes about
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# one command should not thereby spend the whole allowance.
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if limits is not None and round_number:
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spent = (time.monotonic() - started) - generation.waited
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if spent > limits.wall_seconds:
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_gave_up(generation, f"after {spent / 60:.0f} minutes")
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break
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if generation.output_bytes > limits.output_bytes:
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_gave_up(generation, "with too much output to read")
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break
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written = _written(generation)
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if limits.completion_tokens and written > limits.completion_tokens:
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_gave_up(generation, f"after writing about {written:,} tokens")
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break
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accumulator = tools_service.ToolCallAccumulator()
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# Text the model produced in *this* round, needed separately from
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# generation.content when echoing the assistant turn back.
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round_text: list[str] = []
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async for chunk in stream_chat(endpoint, payload):
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counts = chunk_usage(chunk)
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if counts is not None:
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generation.prompt_tokens += counts.get("prompt_tokens", 0)
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generation.completion_tokens += counts.get("completion_tokens", 0)
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# Overwritten, not summed: this round's prompt already
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# contains every earlier round.
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generation.context_tokens = counts.get("prompt_tokens", 0) + counts.get(
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"completion_tokens", 0
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)
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generation.touch()
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|
|
thought = delta_reasoning(chunk)
|
|
if thought:
|
|
if reasoning_started is None:
|
|
reasoning_started = time.monotonic()
|
|
generation.reasoning.append(thought)
|
|
generation.touch()
|
|
|
|
if offered:
|
|
fragments = delta_tool_calls(chunk)
|
|
if fragments:
|
|
accumulator.feed(fragments)
|
|
|
|
text = delta_text(chunk)
|
|
if text:
|
|
for kind, piece in splitter.feed(text):
|
|
if kind == REASONING:
|
|
if reasoning_started is None:
|
|
reasoning_started = time.monotonic()
|
|
generation.reasoning.append(piece)
|
|
else:
|
|
if reasoning_started is not None and not generation.reasoning_ms:
|
|
generation.reasoning_ms = int(
|
|
(time.monotonic() - reasoning_started) * 1000
|
|
)
|
|
generation.content.append(piece)
|
|
round_text.append(piece)
|
|
generation.touch()
|
|
|
|
if generation.cancel:
|
|
generation.stopped = True
|
|
break
|
|
|
|
# Let followers and other tasks run between chunks.
|
|
await asyncio.sleep(0)
|
|
|
|
# The round is over, so anything the splitter is still holding back
|
|
# against a `<think>` tag split across chunks is not a tag. Flushed
|
|
# here rather than only after the loop, because `round_text` is
|
|
# echoed back as an assistant turn -- for a tool round and for a
|
|
# nudge alike -- and a turn missing its last few words is a turn the
|
|
# model is asked to continue from having apparently trailed off.
|
|
for kind, piece in splitter.flush():
|
|
if kind == REASONING:
|
|
generation.reasoning.append(piece)
|
|
else:
|
|
generation.content.append(piece)
|
|
round_text.append(piece)
|
|
|
|
calls = accumulator.calls
|
|
if generation.stopped or not calls:
|
|
# The model says it is done. Believe it -- unless this is an
|
|
# agent chat whose plan still has work in it, in which case ask
|
|
# once. `_nudge` returns the turn to send, or None.
|
|
added = _nudge(
|
|
generation,
|
|
tool_context,
|
|
enabled=nudge_enabled,
|
|
stopped=generation.stopped,
|
|
round_number=round_number,
|
|
budget=budget,
|
|
)
|
|
if added is None:
|
|
break
|
|
# Its own words go back with the nudge. Without the assistant
|
|
# turn the model is asked to carry on from a transcript in which
|
|
# it never spoke, and repeats itself.
|
|
said = "".join(round_text).strip()
|
|
messages = [*payload["messages"]]
|
|
if said:
|
|
messages.append({"role": "assistant", "content": said})
|
|
payload = {**payload, "messages": [*messages, added]}
|
|
continue
|
|
|
|
# Something was called, so whatever it said it had finished, it had
|
|
# not. The count is of *consecutive* stops.
|
|
generation.nudges = 0
|
|
|
|
if round_number == budget:
|
|
# Out of rounds with the model still asking for tools. Recorded
|
|
# rather than silently dropped: an answer that stops here needs
|
|
# to be explicable.
|
|
#
|
|
# `budget`, not `MAX_ROUNDS`. The loop is sized by the budget on
|
|
# the line above and the message below has always reported it,
|
|
# but the comparison was against the global 3 -- so an agent
|
|
# chat allowed forty steps stopped after three and said it had
|
|
# taken forty. Two numbers, one of them wrong, in code whose
|
|
# whole job is to say what happened.
|
|
howmany = "one round" if budget == 1 else f"{budget} rounds"
|
|
generation.tool_events.append(
|
|
{
|
|
"name": calls[0]["name"],
|
|
"status": "error",
|
|
"error": (
|
|
f"Stopped after {howmany} of tool calls without an answer."
|
|
),
|
|
}
|
|
)
|
|
generation.touch()
|
|
break
|
|
|
|
messages = [
|
|
*payload["messages"],
|
|
tools_service.assistant_turn(calls, "".join(round_text)),
|
|
]
|
|
|
|
# Decided before anything runs, never during. A round's calls run
|
|
# together under a semaphore, and four people-shaped pauses inside
|
|
# that gather would queue behind each other invisibly -- see
|
|
# services/interaction.py.
|
|
decided, allowed = await _authorise(generation, tool_context, calls)
|
|
if generation.stopped:
|
|
break
|
|
|
|
generation.status = _tool_status(calls)
|
|
generation.touch()
|
|
try:
|
|
outcomes = await _run_calls(
|
|
tool_context, calls, decided=decided, allowed=allowed
|
|
)
|
|
finally:
|
|
generation.status = ""
|
|
generation.touch()
|
|
|
|
for call, outcome in zip(calls, outcomes, strict=True):
|
|
generation.tool_events.append(outcome.event)
|
|
generation.output_bytes += len(outcome.content)
|
|
messages.append(tools_service.tool_turn(call, outcome.content))
|
|
if outcome.event.get("plan"):
|
|
generation.plan = outcome.event["plan"]
|
|
# Only `plan_submit` sets this. `plan_update` writes the
|
|
# same key -- so `_persist` stays one writer with one
|
|
# rule -- but is bookkeeping mid-work and must not end the
|
|
# reply, or the turn would stop dead every time a task was
|
|
# ticked off.
|
|
if outcome.event.get("plan_final"):
|
|
generation.plan_final = True
|
|
generation.touch()
|
|
|
|
# Something typed while this reply was working. Taken in here, at a
|
|
# round boundary, rather than made to wait for the whole reply: an
|
|
# agent that has just finished one loop and is about to start
|
|
# another is exactly when "actually, do it the other way" is worth
|
|
# having.
|
|
#
|
|
# Only while there is a round left to answer in. Injecting into the
|
|
# last one would deliver the prompt into a reply that then runs out
|
|
# of budget without addressing it -- and it is marked delivered, so
|
|
# nothing would ever send it again. Below that line it waits for
|
|
# `_drain`, which always gives it a reply of its own.
|
|
if round_number + 1 < budget and (
|
|
added := _inject(generation, generation.chat_id, vision)
|
|
):
|
|
messages.append(added)
|
|
|
|
payload = {**payload, "messages": messages}
|
|
|
|
# A plan ends the turn. One more request so the model can say what
|
|
# it proposed and why -- a bubble containing only a card reads as
|
|
# though it had nothing to add -- but with the tools withdrawn, so
|
|
# "one more round" cannot become three rounds of it changing its
|
|
# mind about a plan the reader is being asked to approve.
|
|
if generation.plan_final:
|
|
offered = []
|
|
payload.pop("tools", None)
|
|
|
|
for kind, piece in splitter.flush():
|
|
(generation.reasoning if kind == REASONING else generation.content).append(piece)
|
|
generation.touch()
|
|
|
|
except LLMError as exc:
|
|
generation.error = exc.message
|
|
log.info("generation failed for chat %s: %s", generation.chat_id, exc.message)
|
|
except asyncio.CancelledError:
|
|
# Shutdown, not a reader navigating away -- that no longer reaches here.
|
|
generation.stopped = True
|
|
raise
|
|
except Exception: # noqa: BLE001 - a task that dies silently is worse
|
|
generation.error = "Something went wrong while generating this reply."
|
|
log.exception("unexpected generation failure for chat %s", generation.chat_id)
|
|
finally:
|
|
if reasoning_started is not None and not generation.reasoning_ms:
|
|
generation.reasoning_ms = int((time.monotonic() - reasoning_started) * 1000)
|
|
|
|
generation.elapsed_ms = int((time.monotonic() - started) * 1000)
|
|
if not generation.completion_tokens:
|
|
# The endpoint reported nothing, so fall back to the estimate. Marked
|
|
# as such everywhere it is shown -- four characters to a token is
|
|
# wrong enough on code and CJK to be worth saying out loud.
|
|
generation.completion_tokens = tokens.estimate(
|
|
generation.text + generation.thinking
|
|
)
|
|
generation.prompt_tokens = generation.prompt_estimate
|
|
generation.context_tokens = generation.prompt_tokens + generation.completion_tokens
|
|
|
|
# Naming the chat is a second, short completion, so it has to happen
|
|
# here rather than in the synchronous persist step below. Best-effort:
|
|
# a chat title is never worth surfacing an error for.
|
|
title = ""
|
|
if needs_title and question:
|
|
if generation.error or endpoint is None:
|
|
title = chat_service.fallback_title(question)
|
|
else:
|
|
with contextlib.suppress(Exception):
|
|
title = await chat_service.generate_title(
|
|
endpoint,
|
|
model_id,
|
|
question,
|
|
generation.text,
|
|
template=title_prompt,
|
|
)
|
|
title = title or chat_service.fallback_title(question)
|
|
|
|
# The decrypted SSH credential dies with the reply rather than with the
|
|
# object holding it. A finished Generation lingers KEEP_FINISHED so a
|
|
# follower arriving at the last moment still gets the final frames, and
|
|
# a private key should not sit in memory for five minutes waiting on
|
|
# that.
|
|
if tool_context is not None and getattr(tool_context, "agent", None) is not None:
|
|
tool_context.agent.clear()
|
|
|
|
# Written *before* `done`, because `_follow` breaks out of its loop the
|
|
# moment it sees that flag and immediately re-renders the bubble from
|
|
# the row. The other order left a window in which the finished frame
|
|
# showed the previous turn's stored values.
|
|
_persist(generation, title, time.monotonic() - started)
|
|
# After the row is authoritative and before `done`, for the same reason
|
|
# `_persist` is: `_follow` breaks the instant it sees that flag, and the
|
|
# frame it then sends is the one that has to carry the next turn's
|
|
# bubbles. There is no push channel that outlives a single reply.
|
|
_drain(generation)
|
|
generation.done = True
|
|
generation.finished_at = datetime.now(UTC)
|
|
generation.touch()
|
|
|
|
|
|
# How long a reply will wait for a directory listing before starting without
|
|
# one. Short on purpose: the listing is a convenience and the reply is the
|
|
# thing somebody is waiting for. A walk that outruns this keeps going in the
|
|
# background and the next turn has it.
|
|
INDEX_WAIT = 6.0
|
|
|
|
|
|
async def _warm_project(generation: Generation) -> None:
|
|
"""Fill this chat's project caches: the directory listing, and AGENTS.md.
|
|
|
|
Never raises and never blocks for long. `harness` reads both caches
|
|
synchronously while assembling the system message, so something has to fill
|
|
them, and this is the one place in a reply's life that is both asynchronous
|
|
and already doing network work. One function for both because it already
|
|
resolves the chat, the owner and the context, and doing that twice would be
|
|
two sessions for nothing.
|
|
|
|
The first reply in a brand-new chat on a big tree may start before the walk
|
|
finishes. That is deliberate: the fragments carrying them vanish when they
|
|
are empty rather than appearing as headings with nothing under them, and by
|
|
the following turn they are there.
|
|
|
|
**The skip is per cache.** It used to be one early return on the listing
|
|
being present, and bolting a second cache on behind that would have meant
|
|
the new one was silently never warmed on any chat that had a listing --
|
|
which is to say, on every chat after the first reply.
|
|
"""
|
|
from lembas.services.agent import index as index_service
|
|
from lembas.services.agent import instructions as instructions_service
|
|
from lembas.services.agent import session as agent_session
|
|
|
|
try:
|
|
with session_scope() as db:
|
|
values = settings_store.agents(db)
|
|
chat = db.get(Chat, generation.chat_id)
|
|
if chat is None or chat.kind != KIND_AGENT:
|
|
return
|
|
owner = db.get(User, chat.user_id)
|
|
context = agent_session.resolve(db, chat, owner)
|
|
profile_id = chat.ssh_profile_id or ""
|
|
if context is None or not profile_id:
|
|
return
|
|
|
|
where = (profile_id, context.project_dir)
|
|
jobs = []
|
|
if values.get("index_enabled") and index_service.cached(*where) is None:
|
|
jobs.append(
|
|
index_service.ensure(context.executor(), profile_id, context.project_dir)
|
|
)
|
|
if values.get("instructions_enabled") and instructions_service.cached(*where) is None:
|
|
jobs.append(
|
|
instructions_service.ensure(
|
|
context.executor(),
|
|
profile_id,
|
|
context.project_dir,
|
|
budget=int(values.get("instructions_chars") or 0),
|
|
)
|
|
)
|
|
if not jobs:
|
|
return
|
|
|
|
await asyncio.wait_for(asyncio.gather(*jobs), timeout=INDEX_WAIT)
|
|
except TimeoutError:
|
|
log.debug("index for chat %s outran its wait; carrying on", generation.chat_id)
|
|
except Exception as exc: # noqa: BLE001 - a missing listing is not a failed reply
|
|
log.info("could not warm the index for chat %s: %s", generation.chat_id, exc)
|
|
|
|
|
|
async def _maybe_compact(generation: Generation) -> None:
|
|
"""Summarise the earlier turns if the window is about to be full.
|
|
|
|
Never raises. A failed compaction logs and sends the uncompacted request,
|
|
which either works or fails upstream with a message that says what actually
|
|
happened -- refusing to answer because the summariser was unavailable would
|
|
be a worse trade.
|
|
|
|
The awaited call is deliberately outside any session, the same shape titling
|
|
uses: read everything needed, close, ask, reopen to write.
|
|
"""
|
|
try:
|
|
with session_scope() as db:
|
|
chat = db.get(Chat, generation.chat_id)
|
|
message = db.get(Message, generation.message_id)
|
|
if chat is None or message is None:
|
|
return
|
|
|
|
pending = _pending_text(db, message)
|
|
if not compaction_service.should_compact(db, chat, pending=pending):
|
|
return
|
|
|
|
template = prompts_service.resolve(db, "task.compact")
|
|
upto = compaction_service.last_complete(db, chat)
|
|
if not template.strip() or upto is None:
|
|
return
|
|
|
|
endpoint, model_id = chat_service.resolve_endpoint(db, chat)
|
|
transcript = compaction_service.transcript(db, chat, upto=upto)
|
|
previous = compaction_service.previous_summary_block(chat)
|
|
upto_id = upto.id
|
|
|
|
generation.status = "Summarising earlier messages…"
|
|
generation.touch()
|
|
|
|
summary = await chat_service.summarise_for_compaction(
|
|
endpoint,
|
|
model_id,
|
|
transcript=transcript,
|
|
previous_summary=previous,
|
|
template=template,
|
|
)
|
|
if not summary:
|
|
return
|
|
|
|
with session_scope() as db:
|
|
chat = db.get(Chat, generation.chat_id)
|
|
upto = db.get(Message, upto_id)
|
|
if chat is None or upto is None:
|
|
return
|
|
compaction_service.apply(chat, summary=summary, upto=upto)
|
|
db.commit()
|
|
log.info("chat %s compacted automatically through %s", chat.id, upto_id)
|
|
except Exception: # noqa: BLE001 - the reply matters more than the tidy-up
|
|
log.exception("automatic compaction failed for chat %s", generation.chat_id)
|
|
finally:
|
|
generation.status = ""
|
|
generation.touch()
|
|
|
|
|
|
# How many of a round's tool calls may be in flight at once. A bound rather
|
|
# than none: a model that asks for eight would otherwise open eight sockets and
|
|
# eight database sessions at the same moment.
|
|
MAX_PARALLEL_TOOLS = 4
|
|
|
|
|
|
def _gave_up(generation, why: str) -> None:
|
|
"""Stop, and leave something in the transcript saying why.
|
|
|
|
A reply that simply stopped would look like the model losing interest. The
|
|
event is the same shape the out-of-rounds branch uses, so it renders with
|
|
everything else.
|
|
"""
|
|
generation.tool_events.append(
|
|
{
|
|
"name": "budget",
|
|
"kind": "agent",
|
|
"status": "error",
|
|
"results": [],
|
|
"error": f"Stopped {why}. Ask again to carry on from here.",
|
|
}
|
|
)
|
|
generation.touch()
|
|
|
|
|
|
def _nudge(
|
|
generation: Generation,
|
|
context,
|
|
*,
|
|
enabled: bool,
|
|
stopped: bool,
|
|
round_number: int,
|
|
budget: int,
|
|
) -> dict | None:
|
|
"""The turn telling an agent to carry on, or None to let the reply end.
|
|
|
|
A model that stops with work outstanding is the failure `core.keep_working`
|
|
is worded against, and prompting is the cheaper half of the fix. This is the
|
|
other half, and it only fires where there is something objective to check
|
|
against: an open task on the chat's own plan. Without a plan there is
|
|
nothing to be wrong about, so nothing happens -- a model that has genuinely
|
|
finished must be able to say so and be believed.
|
|
|
|
Every "no" is a plain None:
|
|
|
|
* the setting is off, or the reply was stopped, or it errored;
|
|
* this is not an agent chat, or is one in Plan mode -- `plan_submit` ends
|
|
the turn deliberately and nudging past it would be arguing with the whole
|
|
point of the mode;
|
|
* there is no plan, or every task on it is done or dropped;
|
|
* there is no round left to carry on in, or it has already been asked
|
|
MAX_NUDGES times in a row.
|
|
|
|
The last one is recorded rather than silent. A reply that stopped twice with
|
|
work outstanding is worth being able to see afterwards.
|
|
"""
|
|
agent = getattr(context, "agent", None)
|
|
if not enabled or stopped or generation.error or agent is None:
|
|
return None
|
|
if agent.mode == agent_policy.MODE_PLAN or generation.plan_final:
|
|
return None
|
|
|
|
plan = generation.plan if generation.plan is not None else agent.plan
|
|
open_tasks = [
|
|
task
|
|
for phase in (plan or {}).get("phases", [])
|
|
for task in phase.get("tasks", [])
|
|
if task.get("status") not in ("done", "dropped")
|
|
]
|
|
if not open_tasks:
|
|
return None
|
|
if round_number >= budget:
|
|
return None
|
|
|
|
if generation.nudges >= MAX_NUDGES:
|
|
generation.tool_events.append(
|
|
{
|
|
"name": "plan_update",
|
|
"kind": "plan",
|
|
"status": "error",
|
|
"error": (
|
|
f"Stopped with {len(open_tasks)} task(s) still open on the "
|
|
f"plan, after being asked twice to carry on."
|
|
),
|
|
"results": [],
|
|
}
|
|
)
|
|
generation.touch()
|
|
return None
|
|
|
|
generation.nudges += 1
|
|
remaining = "\n".join(f"- {task['id']} {task['text']}" for task in open_tasks[:8])
|
|
# A user turn, and phrased as the reader would phrase it. Everything else
|
|
# this codebase injects is quoted and attributed because it came out of a
|
|
# file or a machine; this is the application speaking on the reader's behalf
|
|
# about the reader's own plan, which is the one case where that is honest.
|
|
return {
|
|
"role": "user",
|
|
"content": (
|
|
"The plan still has work in it:\n"
|
|
f"{remaining}\n\n"
|
|
"Carry on with the next one. If something here cannot be done, or is "
|
|
"no longer worth doing, mark it dropped with plan_update and say why "
|
|
"— do not leave it open and stop."
|
|
),
|
|
}
|
|
|
|
|
|
def _written(generation: Generation) -> int:
|
|
"""How much this reply has written so far, in tokens, reported or estimated.
|
|
|
|
Both, because neither alone is enough. `completion_tokens` is only populated
|
|
when the endpoint sends a usage block, and a good half of the ones this
|
|
talks to -- llama.cpp, Ollama and friends -- never do; the fallback estimate
|
|
is otherwise computed once, in `_run`'s `finally:`, long after the loop that
|
|
needs it. A ceiling reading only the reported figure would work on OpenAI
|
|
and silently do nothing everywhere else, which is the worst kind of limit:
|
|
one that looks configured.
|
|
|
|
Reasoning counts. It was generated and it was paid for, even though it is
|
|
deliberately never replayed as context.
|
|
"""
|
|
return max(
|
|
generation.completion_tokens,
|
|
tokens.estimate(generation.text + generation.thinking),
|
|
)
|
|
|
|
|
|
def _tool_status(calls: list[dict]) -> str:
|
|
"""What to show while tools run.
|
|
|
|
A remote tool -- an HTTP endpoint, an MCP server -- can take seconds with
|
|
nothing streaming, and a silent pause is exactly what a hang looks like.
|
|
"""
|
|
if len(calls) == 1:
|
|
return f"Running {tool_labels.label_for(calls[0]['name'])}…"
|
|
return f"Running {len(calls)} tools…"
|
|
|
|
|
|
def _arguments_of(call: dict) -> dict:
|
|
try:
|
|
args = json.loads(call["arguments"] or "{}")
|
|
except json.JSONDecodeError:
|
|
return {}
|
|
return args if isinstance(args, dict) else {}
|
|
|
|
|
|
def _describe(name: str, args: dict) -> tuple[str, str]:
|
|
"""What an approval card says about one call: a title, and the detail.
|
|
|
|
The detail is the thing being agreed to -- the command line, the path -- and
|
|
is shown verbatim and escaped. A summary that paraphrased it would be a card
|
|
approving something other than what runs.
|
|
|
|
Delegated to services/tool_labels.py, which the transcript and the status
|
|
line read too. This used to be a hand-written if-chain and was the fourth
|
|
place with its own wording for the same tool.
|
|
"""
|
|
return tool_labels.describe(name, args)
|
|
|
|
|
|
def _approvals(context, calls: list[dict]) -> list[interaction.Item]:
|
|
"""The calls in this round that a person has to allow before they run.
|
|
|
|
Only in an agent chat: `context.agent` is None everywhere else, and an
|
|
ordinary conversation behaves exactly as it did. Within one, *every* call
|
|
goes through the table, including the built-in ones -- `notes_edit` writes,
|
|
and Plan mode meaning "look but do not touch" has to mean that too.
|
|
"""
|
|
agent = getattr(context, "agent", None)
|
|
if agent is None:
|
|
return []
|
|
|
|
book = context.tools if context.tools is not None else tools_service.REGISTRY
|
|
items: list[interaction.Item] = []
|
|
|
|
for index, call in enumerate(calls):
|
|
tool = book.get(call["name"])
|
|
if tool is None or tool.risk == tools_service.RISK_ASK:
|
|
continue # unknown names are refused by run_tool; questions are their own card
|
|
|
|
args = _arguments_of(call)
|
|
command = str(args.get("command") or "") if call["name"] == "shell_run" else ""
|
|
decision = agent_policy.decide(
|
|
mode=agent.mode,
|
|
risk=tool.risk,
|
|
tool_name=call["name"],
|
|
command=command,
|
|
allow=agent.allow,
|
|
deny=agent.deny,
|
|
)
|
|
if decision.verdict == agent_policy.ALLOW:
|
|
continue
|
|
|
|
title, detail = _describe(call["name"], args)
|
|
items.append(
|
|
interaction.Item(
|
|
index=index,
|
|
key=f"a{index}",
|
|
kind=interaction.KIND_APPROVAL,
|
|
tool_name=call["name"],
|
|
title=f"{title} on {agent.label}",
|
|
detail=detail,
|
|
reason=decision.reason,
|
|
)
|
|
)
|
|
return items
|
|
|
|
|
|
def _ask_items(context, calls: list[dict]) -> list[interaction.Item]:
|
|
"""Which of this round's calls need a person, and what to show about each.
|
|
|
|
Looked up through `context.tools`, the map of what was actually offered --
|
|
the same authority `run_tool` uses. A name that is not in it is left alone
|
|
here and refused there, so an unknown tool cannot smuggle itself past by
|
|
being unclassifiable.
|
|
"""
|
|
book = context.tools if context.tools is not None else tools_service.REGISTRY
|
|
items: list[interaction.Item] = []
|
|
|
|
for index, call in enumerate(calls):
|
|
tool = book.get(call["name"])
|
|
if tool is None or tool.risk != tools_service.RISK_ASK:
|
|
continue
|
|
args = _arguments_of(call)
|
|
|
|
for asked in _questions_in(args):
|
|
options = [str(o).strip() for o in (asked.get("options") or []) if str(o).strip()]
|
|
items.append(
|
|
interaction.Item(
|
|
index=index,
|
|
key=f"q{len(items)}",
|
|
kind=interaction.KIND_QUESTION,
|
|
tool_name=call["name"],
|
|
title=str(asked.get("question") or "").strip() or "A question for you",
|
|
options=tuple(options[: interaction.MAX_OPTIONS]),
|
|
)
|
|
)
|
|
return items
|
|
|
|
|
|
def _questions_in(args: dict) -> list[dict]:
|
|
"""The questions in one `ask_user` call, however it was spelled.
|
|
|
|
The schema asks for a list of objects, and a capable model sends that. A
|
|
small one sends a bare `question` string, or a list of plain strings, or
|
|
one object where a list belonged -- all of which mean something obvious, so
|
|
they are read rather than refused. Getting this wrong costs a whole round
|
|
trip and produces a card saying "A question for you" and nothing else.
|
|
"""
|
|
raw = args.get("questions")
|
|
if raw is None:
|
|
raw = args.get("question")
|
|
if raw is None:
|
|
return []
|
|
if isinstance(raw, str | dict):
|
|
raw = [raw]
|
|
if not isinstance(raw, list):
|
|
return []
|
|
|
|
out: list[dict] = []
|
|
for entry in raw[: interaction.MAX_QUESTIONS]:
|
|
if isinstance(entry, str) and entry.strip():
|
|
# A bare string, possibly alongside a sibling `options` that was
|
|
# meant to go with it -- which only makes sense for a lone question.
|
|
out.append({"question": entry, "options": args.get("options") if len(raw) == 1 else []})
|
|
elif isinstance(entry, dict) and str(entry.get("question") or "").strip():
|
|
out.append(entry)
|
|
return out
|
|
|
|
|
|
async def _authorise(
|
|
generation, context, calls: list[dict]
|
|
) -> tuple[dict[int, ToolOutcome], set[int]]:
|
|
"""Which of this round's calls may run, and what the others answer instead.
|
|
|
|
Returns outcomes keyed by the call's index. Every index the caller does not
|
|
find here is cleared to run; every index it does find is answered without
|
|
the runner being reached at all. That is what keeps
|
|
`zip(calls, outcomes, strict=True)` aligned -- an endpoint matching on
|
|
`tool_call_id` pairs the wrong content with the right id otherwise.
|
|
|
|
Also returns the indices a person explicitly allowed, so the runners can be
|
|
told. They re-check the mode as a backstop and would otherwise refuse the
|
|
very thing that was just approved -- the mode says "ask", and asking is what
|
|
happened.
|
|
"""
|
|
questions = _ask_items(context, calls)
|
|
approvals = _approvals(context, calls)
|
|
items = [*approvals, *questions]
|
|
if not items:
|
|
return {}, set()
|
|
|
|
timeout = float(context.interaction_timeout or 900)
|
|
pause = interaction.build(uuid.uuid4().hex, items, timeout=timeout)
|
|
generation.status = interaction.summarise(pause.items)
|
|
reply = await interaction.wait_for(generation, pause, timeout=timeout)
|
|
generation.status = ""
|
|
|
|
if reply.ended:
|
|
generation.stopped = True
|
|
return {}, set()
|
|
|
|
decided: dict[int, ToolOutcome] = {}
|
|
allowed: set[int] = set()
|
|
|
|
# An approval that came back as a refusal answers its call without the
|
|
# runner being reached; one that came back allowed is simply left out, which
|
|
# is how `_run_calls` is told to go ahead.
|
|
for item in approvals:
|
|
if reply.permitted:
|
|
allowed.add(item.index)
|
|
continue
|
|
decided[item.index] = _not_allowed(item, reply)
|
|
|
|
# Questions are grouped back by call, because one `ask_user` call may have
|
|
# carried several and the endpoint expects exactly one tool turn per call.
|
|
grouped: dict[int, list[interaction.Item]] = {}
|
|
for item in questions:
|
|
grouped.setdefault(item.index, []).append(item)
|
|
for index, asked in grouped.items():
|
|
decided[index] = _answered(asked, reply)
|
|
|
|
return decided, allowed
|
|
|
|
|
|
def _not_allowed(item: interaction.Item, reply: interaction.Reply) -> ToolOutcome:
|
|
"""What the model is told when a person declined, or never answered.
|
|
|
|
Told plainly, and told to stop rather than to try again: a model that reads
|
|
"not allowed" as "not allowed *that way*" will spend the rest of the reply
|
|
looking for a way round, which is the opposite of what the refusal meant.
|
|
"""
|
|
event = {
|
|
"name": item.tool_name,
|
|
"kind": "agent",
|
|
"label": item.title,
|
|
"query": item.detail,
|
|
"results": [],
|
|
}
|
|
if reply.outcome == interaction.EXPIRED:
|
|
return ToolOutcome(
|
|
"Nobody answered, so this was not run. Stop and say what you were "
|
|
"about to do and why.",
|
|
{**event, "status": "error", "error": "Not answered."},
|
|
)
|
|
return ToolOutcome(
|
|
"They declined this. Do not try it another way — say what you were "
|
|
"going to do and ask what they would prefer.",
|
|
{**event, "status": "error", "error": "Declined."},
|
|
)
|
|
|
|
|
|
def _answered(items: list[interaction.Item], reply: interaction.Reply) -> ToolOutcome:
|
|
"""What one `ask_user` call gets back, however many questions it put."""
|
|
event = {
|
|
"name": items[0].tool_name,
|
|
"kind": "ask",
|
|
"label": "Asked you",
|
|
"query": "; ".join(item.title for item in items),
|
|
"results": [],
|
|
}
|
|
|
|
if reply.outcome == interaction.EXPIRED:
|
|
return ToolOutcome(
|
|
"They did not answer. Carry on as best you can without it, or say "
|
|
"what you still need.",
|
|
{**event, "status": "error", "error": "No answer.", "text": ""},
|
|
)
|
|
|
|
answered = [(item, reply.answer_to(item)) for item in items]
|
|
given = [(item, text) for item, text in answered if text]
|
|
if not given:
|
|
return ToolOutcome(
|
|
"They closed the question without answering.",
|
|
{**event, "status": "error", "error": "No answer.", "text": ""},
|
|
)
|
|
|
|
# Each answer is quoted next to the question it belongs to. With four
|
|
# questions on one card, a bare list of answers would leave the model
|
|
# matching them up by position and sometimes getting it wrong.
|
|
lines = [f"{item.title}\n → {text}" for item, text in given]
|
|
skipped = [item for item, text in answered if not text]
|
|
if skipped:
|
|
lines.append(
|
|
"They left unanswered: " + "; ".join(item.title for item in skipped)
|
|
)
|
|
|
|
body = "\n\n".join(lines)
|
|
return ToolOutcome(f"They answered:\n\n{body}", {**event, "status": "ok", "text": body})
|
|
|
|
|
|
async def _run_calls(
|
|
context,
|
|
calls: list[dict],
|
|
*,
|
|
decided: dict[int, ToolOutcome] | None = None,
|
|
allowed: set[int] | None = None,
|
|
) -> list:
|
|
"""Run one round's calls together, results in call order.
|
|
|
|
Sequential was right when every tool was a local database read. A remote one
|
|
is latency-bound, and three two-second calls in a row are six seconds of a
|
|
reply looking hung -- while the model has already been told it may ask for
|
|
several at once.
|
|
|
|
The results are indexed rather than appended as they finish, because each
|
|
tool turn has to line up with the assistant turn's `tool_calls`: an endpoint
|
|
matching on `tool_call_id` would otherwise pair the right id with the wrong
|
|
content the moment two calls came back out of order.
|
|
|
|
Safe to run together because `run_tool` never raises, so no failure cancels
|
|
its siblings, and each runner opens its own `session_scope()` against a
|
|
database in WAL mode with a busy timeout.
|
|
"""
|
|
limit = asyncio.Semaphore(MAX_PARALLEL_TOOLS)
|
|
|
|
async def one(index: int, call: dict):
|
|
# Already answered by a person, or refused before it got here. It still
|
|
# occupies its index, because the tool turns have to line up.
|
|
if decided and index in decided:
|
|
return decided[index]
|
|
|
|
# A per-call copy for anything a person allowed, so the runner's own
|
|
# check does not undo their decision. A copy rather than a flag on the
|
|
# shared context, because a round runs its calls together and only some
|
|
# of them were approved.
|
|
ctx = context
|
|
if allowed and index in allowed and getattr(context, "agent", None) is not None:
|
|
ctx = replace(context, agent=context.agent.as_approved())
|
|
|
|
async with limit:
|
|
return await tools_service.run_tool(ctx, call["name"], call["arguments"])
|
|
|
|
return list(await asyncio.gather(*(one(i, c) for i, c in enumerate(calls))))
|
|
|
|
|
|
def _pending_text(db, message: Message) -> str:
|
|
"""The user turn this reply is answering, for the size estimate."""
|
|
previous = db.scalars(
|
|
select(Message)
|
|
.where(Message.chat_id == message.chat_id, Message.created_at < message.created_at)
|
|
.order_by(Message.created_at.desc())
|
|
.limit(1)
|
|
).first()
|
|
return previous.content if previous is not None else ""
|
|
|
|
|
|
def _question_from(payload: dict) -> str:
|
|
"""The last thing the user said, for auto-titling."""
|
|
for entry in reversed(payload.get("messages", [])):
|
|
if entry.get("role") != ROLE_USER:
|
|
continue
|
|
content = entry.get("content")
|
|
if isinstance(content, str):
|
|
return content
|
|
if isinstance(content, list):
|
|
return " ".join(
|
|
part.get("text", "")
|
|
for part in content
|
|
if isinstance(part, dict) and part.get("type") == "text"
|
|
).strip()
|
|
return ""
|
|
|
|
|
|
def _next_waiting(db, chat_id: str) -> Message | None:
|
|
"""The oldest prompt in this chat that has not been sent."""
|
|
return db.scalars(
|
|
select(Message)
|
|
.where(
|
|
Message.chat_id == chat_id,
|
|
Message.role == ROLE_USER,
|
|
Message.queued.is_(True),
|
|
)
|
|
.order_by(Message.created_at)
|
|
.limit(1)
|
|
).first()
|
|
|
|
|
|
def _drain(generation: Generation) -> None:
|
|
"""Hand the next waiting prompt to a reply of its own, if there is one.
|
|
|
|
Exactly one, not all of them. Draining the lot would put two consecutive
|
|
user turns into the next request, which several local chat templates refuse
|
|
outright -- `build_messages` already goes to some trouble over that around
|
|
the compaction lead. "One after another" is also what was asked for: the
|
|
second waiting prompt is drained by the reply the first one starts, and so
|
|
on down the chain.
|
|
|
|
Three refusals, and none of them is a special case:
|
|
|
|
- **Superseded.** The same test `_persist` makes, for the same reason: a
|
|
regeneration cancels its predecessor and the predecessor's `finally:`
|
|
still runs. Without this, regenerating would drain the queue *and* leave
|
|
a third generation running.
|
|
- **Stopped.** Stop means stop, and the queue stays visible and
|
|
undelivered with Send now beside it. This is also what makes shutdown
|
|
safe -- cancellation sets `stopped`, so a restart never fires off a reply
|
|
with nobody watching.
|
|
- **Errored.** The endpoint has just failed. Feeding the next prompt into it
|
|
produces a second failure and spends somebody's words to do it.
|
|
"""
|
|
owner = _RUNNING.get(generation.message_id)
|
|
if owner is not None and owner is not generation:
|
|
return
|
|
if generation.stopped or generation.error:
|
|
return
|
|
|
|
try:
|
|
with session_scope() as db:
|
|
chat = db.get(Chat, generation.chat_id)
|
|
if chat is None:
|
|
return
|
|
waiting = _next_waiting(db, chat.id)
|
|
if waiting is None:
|
|
return
|
|
|
|
waiting.queued = False
|
|
assistant = chat_service.create_message(
|
|
db, chat, ROLE_ASSISTANT, "", complete_=False, model_id=chat.model_id
|
|
)
|
|
chat_id, assistant_id = chat.id, assistant.id
|
|
except Exception: # noqa: BLE001 - the reply is over either way
|
|
log.exception("could not drain the queue for chat %s", generation.chat_id)
|
|
return
|
|
|
|
# Outside the session: this starts a task, and a task is not something to
|
|
# hold a database session open across.
|
|
ensure(chat_id, assistant_id)
|
|
generation.drained = True
|
|
|
|
|
|
def _inject(generation: Generation, chat_id: str, vision: bool) -> dict | None:
|
|
"""Take the oldest waiting prompt into this reply, between two rounds.
|
|
|
|
Marked delivered and committed *before* the request goes out, so this is
|
|
at-most-once. A crash in between loses the turn, which is recoverable --
|
|
the words are still in the transcript with Send now beside them. The other
|
|
way round would ask the same question twice and let an agent act on it
|
|
twice, which is not.
|
|
|
|
Sent verbatim, in the user role, with no framing. Everything else this
|
|
codebase injects is quoted and attributed because it came out of a file, a
|
|
page or a machine; this one genuinely *is* the person at the keyboard,
|
|
authenticated by the session cookie and stored as a `Message` whose role
|
|
says so. Wrapping it would teach a model that a user turn can be a
|
|
quotation, which is the exact distinction the other two rely on. What the
|
|
model needs -- that this can happen at all -- is one sentence in the
|
|
harness, where authored wording lives.
|
|
"""
|
|
try:
|
|
with session_scope() as db:
|
|
waiting = _next_waiting(db, chat_id)
|
|
if waiting is None:
|
|
return None
|
|
|
|
waiting.queued = False
|
|
entry = chat_service.message_payload(waiting, vision=vision)
|
|
# The reply that answers it must sort *before* it, or the next
|
|
# turn's transcript reads "answer, then the question it answered"
|
|
# and a small model dutifully answers again. Moving the placeholder
|
|
# rather than the prompt keeps several interjections in the order
|
|
# they were typed.
|
|
placeholder = db.get(Message, generation.message_id)
|
|
if placeholder is not None:
|
|
placeholder.created_at = datetime.now(UTC)
|
|
generation.injected_ids.append(waiting.id)
|
|
except Exception: # noqa: BLE001 - a lost interjection is not a failed reply
|
|
log.exception("could not take a queued prompt into chat %s", chat_id)
|
|
return None
|
|
|
|
generation.status = "Taking in what you just added…"
|
|
generation.touch()
|
|
return entry
|
|
|
|
|
|
def _persist(generation: Generation, title: str, elapsed: float) -> None:
|
|
"""Write the finished reply, name the chat, and set the unread flag.
|
|
|
|
A generation another one has replaced may not write. A regeneration cancels
|
|
its predecessor, whose `finally:` then runs this on the same row -- and it
|
|
would overwrite the fresh reply with the abandoned one.
|
|
|
|
The test is "someone else owns this row now", not "this one is registered":
|
|
an unregistered generation still writes, because that is a direct call
|
|
rather than a superseded one.
|
|
"""
|
|
owner = _RUNNING.get(generation.message_id)
|
|
if owner is not None and owner is not generation:
|
|
log.debug("skipping persist for superseded generation %s", generation.message_id)
|
|
return
|
|
|
|
try:
|
|
with session_scope() as db:
|
|
message = db.get(Message, generation.message_id)
|
|
chat = db.get(Chat, generation.chat_id)
|
|
if message is None or chat is None:
|
|
return
|
|
|
|
message.content = generation.text
|
|
message.reasoning = generation.thinking
|
|
message.reasoning_ms = generation.reasoning_ms
|
|
message.tool_calls_json = generation.tool_events
|
|
message.plan_json = generation.plan or {}
|
|
if generation.plan:
|
|
# This bubble now carries the plan in force, and the chat points
|
|
# at it so the harness can find it with one primary-key lookup
|
|
# rather than a scan. Older bubbles keep the plan as it was then,
|
|
# which is what a transcript is for -- the card is never
|
|
# re-rendered in place.
|
|
chat.plan_message_id = message.id
|
|
message.usage_json = metrics_service.to_json(
|
|
metrics_service.from_generation(generation)
|
|
)
|
|
message.error = generation.error
|
|
message.stopped = generation.stopped
|
|
message.complete = True
|
|
|
|
if title and not chat.title_generated:
|
|
chat.title = title
|
|
chat.title_generated = True
|
|
|
|
# Nobody watching when it landed, so it is news. The chat page
|
|
# clears this when it is next opened. Not for a temporary chat:
|
|
# there is no sidebar row for the dot, and the toast would name a
|
|
# chat nobody can navigate to.
|
|
if generation.followers == 0 and not chat.temporary:
|
|
chat.unread = True
|
|
chat.unread_notified = False
|
|
|
|
db.commit()
|
|
log.debug(
|
|
"chat %s finished: %d chars, %d reasoning, %.1fs",
|
|
generation.chat_id,
|
|
len(message.content),
|
|
len(message.reasoning),
|
|
elapsed,
|
|
)
|
|
except Exception: # noqa: BLE001 - the task is ending either way
|
|
log.exception("could not persist generation for chat %s", generation.chat_id)
|
|
|
|
|
|
__all__ = [
|
|
"RENDER_INTERVAL",
|
|
"Generation",
|
|
"ROLE_ASSISTANT",
|
|
"ensure",
|
|
"get",
|
|
"request_stop",
|
|
"restart",
|
|
"shutdown",
|
|
]
|