Jaroslav Beneš
ecb52e9978
A server is a row with a URL; its tools are discovered by a button and cached, then offered beside the built-in ones. Written by hand rather than taken from the reference SDK, because that SDK's transport does its own connecting -- and the one thing that must not be bypassed is check_url on every hop. Owning the transport is the point; the framing beside it is the small part. Sessions are per call: initialize, initialized, the call, a best-effort DELETE. Caching one wants an owner, a TTL, eviction, a lock and a shutdown hook, and the server may expire it under all of that anyway -- ToolContext is a session-free snapshot precisely so nothing in a tool holds live state. A server's names and descriptions reach the model as instructions and are bounded before they do; what it returns is escaped preformatted text, never markdown. Tools are namespaced per server, so two servers exposing "search" do not collide and neither shadows a built-in. Also: a round's calls now run together under a semaphore, results indexed so each tool turn stays paired with its call, and generation.status names what is running -- a remote tool is latency-bound, and a silent pause is what a hang looks like. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
613 lines
24 KiB
Python
613 lines
24 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 logging
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import time
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from dataclasses import dataclass, field
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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 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 metrics as metrics_service
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from lembas.services import prompts as prompts_service
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from lembas.services import tokens
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from lembas.services import tools as tools_service
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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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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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@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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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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return True
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def _prune() -> None:
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cutoff = datetime.now(UTC) - KEEP_FINISHED
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for message_id, generation in list(_RUNNING.items()):
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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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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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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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generation.prompt_estimate = tokens.estimate_request(payload)
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for round_number in range(tools_service.MAX_ROUNDS + 1):
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generation.rounds = round_number + 1
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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)
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if thought:
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if reasoning_started is None:
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reasoning_started = time.monotonic()
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generation.reasoning.append(thought)
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generation.touch()
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if offered:
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fragments = delta_tool_calls(chunk)
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if fragments:
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accumulator.feed(fragments)
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text = delta_text(chunk)
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if text:
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for kind, piece in splitter.feed(text):
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if kind == REASONING:
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if reasoning_started is None:
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reasoning_started = time.monotonic()
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generation.reasoning.append(piece)
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else:
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if reasoning_started is not None and not generation.reasoning_ms:
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generation.reasoning_ms = int(
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(time.monotonic() - reasoning_started) * 1000
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)
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generation.content.append(piece)
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round_text.append(piece)
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generation.touch()
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if generation.cancel:
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generation.stopped = True
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break
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# Let followers and other tasks run between chunks.
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await asyncio.sleep(0)
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calls = accumulator.calls
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if generation.stopped or not calls:
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break
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if round_number == tools_service.MAX_ROUNDS:
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# Out of rounds with the model still asking for tools. Recorded
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# rather than silently dropped: an answer that stops here needs
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# to be explicable.
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generation.tool_events.append(
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{
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"name": calls[0]["name"],
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"status": "error",
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"error": (
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f"Stopped after {tools_service.MAX_ROUNDS} rounds of tool "
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f"calls without an answer."
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),
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}
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)
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generation.touch()
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break
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messages = [
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*payload["messages"],
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tools_service.assistant_turn(calls, "".join(round_text)),
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]
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generation.status = _tool_status(calls)
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generation.touch()
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try:
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outcomes = await _run_calls(tool_context, calls)
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finally:
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generation.status = ""
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generation.touch()
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for call, outcome in zip(calls, outcomes, strict=True):
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generation.tool_events.append(outcome.event)
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messages.append(tools_service.tool_turn(call, outcome.content))
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generation.touch()
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payload = {**payload, "messages": messages}
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for kind, piece in splitter.flush():
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(generation.reasoning if kind == REASONING else generation.content).append(piece)
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generation.touch()
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except LLMError as exc:
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generation.error = exc.message
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log.info("generation failed for chat %s: %s", generation.chat_id, exc.message)
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except asyncio.CancelledError:
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# Shutdown, not a reader navigating away -- that no longer reaches here.
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generation.stopped = True
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raise
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except Exception: # noqa: BLE001 - a task that dies silently is worse
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generation.error = "Something went wrong while generating this reply."
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log.exception("unexpected generation failure for chat %s", generation.chat_id)
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finally:
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if reasoning_started is not None and not generation.reasoning_ms:
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generation.reasoning_ms = int((time.monotonic() - reasoning_started) * 1000)
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generation.elapsed_ms = int((time.monotonic() - started) * 1000)
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if not generation.completion_tokens:
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# The endpoint reported nothing, so fall back to the estimate. Marked
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# as such everywhere it is shown -- four characters to a token is
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# wrong enough on code and CJK to be worth saying out loud.
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generation.completion_tokens = tokens.estimate(
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generation.text + generation.thinking
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)
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generation.prompt_tokens = generation.prompt_estimate
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generation.context_tokens = generation.prompt_tokens + generation.completion_tokens
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# Naming the chat is a second, short completion, so it has to happen
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# here rather than in the synchronous persist step below. Best-effort:
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# a chat title is never worth surfacing an error for.
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title = ""
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if needs_title and question:
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if generation.error or endpoint is None:
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title = chat_service.fallback_title(question)
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else:
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with contextlib.suppress(Exception):
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title = await chat_service.generate_title(
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endpoint,
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model_id,
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question,
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generation.text,
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template=title_prompt,
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)
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title = title or chat_service.fallback_title(question)
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# Written *before* `done`, because `_follow` breaks out of its loop the
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# moment it sees that flag and immediately re-renders the bubble from
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# the row. The other order left a window in which the finished frame
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# showed the previous turn's stored values.
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_persist(generation, title, time.monotonic() - started)
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generation.done = True
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generation.finished_at = datetime.now(UTC)
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generation.touch()
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async def _maybe_compact(generation: Generation) -> None:
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"""Summarise the earlier turns if the window is about to be full.
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Never raises. A failed compaction logs and sends the uncompacted request,
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which either works or fails upstream with a message that says what actually
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happened -- refusing to answer because the summariser was unavailable would
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be a worse trade.
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The awaited call is deliberately outside any session, the same shape titling
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uses: read everything needed, close, ask, reopen to write.
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"""
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try:
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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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return
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pending = _pending_text(db, message)
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if not compaction_service.should_compact(db, chat, pending=pending):
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return
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template = prompts_service.resolve(db, "task.compact")
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upto = compaction_service.last_complete(db, chat)
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if not template.strip() or upto is None:
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return
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endpoint, model_id = chat_service.resolve_endpoint(db, chat)
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transcript = compaction_service.transcript(db, chat, upto=upto)
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previous = compaction_service.previous_summary_block(chat)
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upto_id = upto.id
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generation.status = "Summarising earlier messages…"
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generation.touch()
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summary = await chat_service.summarise_for_compaction(
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endpoint,
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model_id,
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transcript=transcript,
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previous_summary=previous,
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template=template,
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)
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if not summary:
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return
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with session_scope() as db:
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chat = db.get(Chat, generation.chat_id)
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upto = db.get(Message, upto_id)
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if chat is None or upto is None:
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return
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compaction_service.apply(chat, summary=summary, upto=upto)
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db.commit()
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log.info("chat %s compacted automatically through %s", chat.id, upto_id)
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except Exception: # noqa: BLE001 - the reply matters more than the tidy-up
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log.exception("automatic compaction failed for chat %s", generation.chat_id)
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finally:
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generation.status = ""
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generation.touch()
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# How many of a round's tool calls may be in flight at once. A bound rather
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# than none: a model that asks for eight would otherwise open eight sockets and
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# eight database sessions at the same moment.
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MAX_PARALLEL_TOOLS = 4
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def _tool_status(calls: list[dict]) -> str:
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"""What to show while tools run.
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A remote tool -- an HTTP endpoint, an MCP server -- can take seconds with
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nothing streaming, and a silent pause is exactly what a hang looks like.
|
|
"""
|
|
if len(calls) == 1:
|
|
return f"Running {calls[0]['name']}…"
|
|
return f"Running {len(calls)} tools…"
|
|
|
|
|
|
async def _run_calls(context, calls: list[dict]) -> 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(call: dict):
|
|
async with limit:
|
|
return await tools_service.run_tool(context, call["name"], call["arguments"])
|
|
|
|
return list(await asyncio.gather(*(one(call) for call in 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 _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.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",
|
|
]
|