ducoterra
055c0b5d85
Phase 74 (TODO.md L4): a follow-up question now reaches the model WITH the conversation so far — every prior user/brain turn and the prior thinking blocks on brain turns (preserve-thinking) — while POST /api/chat stays stateless (A10): the client provides the history in the request body and the server stores nothing new. Server (task 01): - ChatRequest.history: optional list[HistoryTurn] (who: user|brain, text, optional thinking) — absent/empty keeps the request byte-identical to pre-phase-74 (the two-message [system, user] request; the kill-switch semantics are pinned in the integration suite). - app.rag.prompts.history_to_messages: pure mapper — walks the turns newest-first against the settings budgets (history_max_turns=40 / history_max_chars=24000, BOR_HISTORY_MAX_TURNS / BOR_HISTORY_MAX_CHARS); a capped turn is dropped WHOLE (never cut mid-answer); the kept window is returned oldest-first; brain turns carry their thinking as reasoning_content (A4) only when non-empty. - Both branches feed it: the deflected path splices it between the system prompt and the current user message (the phase-71 recovery still rebuilds from messages[1:]), the grounded agent receives run_agent(..., history=hist); llm.py's message params widen to list[dict[str, Any]] (string-only messages stay byte-identical on the wire — the SDK passes message dicts through verbatim). - The per-turn log line (PLAN §9) gains history_msgs=N after kb_chars=N. - Pins: tests/unit/test_history.py (mapper: mapping, reasoning gating, both budgets, drop-whole, ordering, empty default), tests/unit/test_config.py (the two settings + env overrides), tests/unit/test_agent.py (the history splice + the default), tests/integration/test_chat_api.py (deflected AND grounded forward the history incl. reasoning_content, no-history byte-identity, 422 pins, the log field). Client (task 02): - runTurn — the single funnel for fresh send / phase-49 retry / phase-53 stale-regen — sends history = the conversation record minus the current question, with thinking only on brain records that streamed one (undefined drops the key from the JSON, the record's convention); the question is never duplicated into the history. Wire proof (task 03): - The mock's echo my history marker (HISTORY_TRIGGER) answers with the deterministic history echo — history: N prior messages; last answer tail: <last 24 chars>; thinking: yes|no — checked BEFORE the DEFLECT_MODE branch (like TABLE_TRIGGER), so it fires on both turn branches whatever the gate says; the module docstring records the user/assistant-only history invariant that keeps every existing (tool-result-classified) marker flow unaffected. - tests/e2e/test_llm_history.py (isolated): a grounded follow-up and a deflected follow-up both receive history: 2 prior messages + thinking: yes + the byte-exact tail of turn 1's answer (derived from the persisted bor.chat.v1 record — the same array the client maps into the body); a cold start receives history: 0 prior messages / last answer tail: none / thinking: no. - Regressions green in isolation: chat_rag, chat_history (phase 50), agent_document_tools, harness_aligned_tools, stop_generation, retry_answer, response_to_docs.
607 lines
27 KiB
Python
607 lines
27 KiB
Python
"""Async OpenAI-compatible client for the self-hosted aipi endpoint (PLAN A5).
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Provides the embeddings surface (importer, retrieval), one-shot chat
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completions (phase 30: the ``lite`` model summarizes non-markdown
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documents at import time), and chat streaming (PLAN A15) for the RAG
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pipeline. Chat streaming yields typed :class:`StreamPiece` values
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(phase 17) and — when the caller passes a ``tools`` list —
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:class:`ToolCallPiece` values (phase 37): aipi's ``turbo`` model streams
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its reasoning as ``delta.reasoning_content`` chunks (deepseek/litellm
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wire convention, verified live 2026-08-23) **before** the answer's
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``delta.content`` chunks, and reasoning counts against ``max_tokens``
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(an answer can in principle be empty).
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Fail-loud rule (PLAN A6): the ``chunks.embedding`` column is fixed at 768
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dimensions when the table is created, so a model that returns a different
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dimension must abort the import with an actionable error — never store
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vectors that pgvector rejects.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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from collections.abc import AsyncGenerator
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Literal, cast
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from openai import AsyncOpenAI, AsyncStream
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from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
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from app.config import Settings, get_settings
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if TYPE_CHECKING:
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# Phase 71: the filter type is only needed for typing (the module
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# stays import-graph-clean; callers pass their own instances).
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from app.rag.scaffolding import ScaffoldingFilter
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logger = logging.getLogger("app.llm")
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class EmbeddingError(RuntimeError):
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"""The embeddings endpoint failed (network, HTTP, or malformed reply)."""
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class EmbeddingDimensionError(EmbeddingError):
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"""Embedding dimension != BOR_EMBEDDING_DIM — import must fail loudly."""
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class LLMError(RuntimeError):
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"""The chat-completions endpoint failed (network, HTTP, or mid-stream)."""
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class ModelUnavailableError(LLMError):
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"""One of the models a sync needs is unreachable (phase 41 probe).
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Raised by :func:`check_models` when the pre-sync probe finds the
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embedding or summary model down; the message names the model so the
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admin can fix the right thing.
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"""
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@dataclass(frozen=True)
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class StreamPiece:
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"""One piece of a streamed chat turn (phase 17, PLAN §4 extension).
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``kind`` is ``"content"`` for answer text (an SSE ``delta`` frame)
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or ``"thinking"`` for the model's reasoning (an SSE ``thinking``
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frame). Frozen: pieces are immutable wire values, not accumulators.
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"""
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kind: Literal["content", "thinking"]
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text: str
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@dataclass(frozen=True)
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class ToolCallPiece:
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"""One model-requested tool call accumulated from stream deltas (phase 37).
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``id`` is the model's tool_call id (synthesized as ``call_<index>``
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when the wire never carried one), ``name`` is the function name
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(whatever the caller's ``tools`` list names — for the agent loop,
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``ls`` / ``read`` / ``grep``, phase 70), and ``arguments`` is the
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parsed JSON object (``{}`` when the model sent none).
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"""
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id: str # the model's tool_call id; synthesized "call_<index>" when absent
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name: str # "ls" | "read" | "grep" (whatever AGENT_TOOLS names, phase 70)
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arguments: dict[str, Any]
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@dataclass(frozen=True)
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class RetryPiece:
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"""One LLM request retry that is about to start (phase 67, locked A2).
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``attempt`` is the 1-based number of the attempt that is about to be
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tried — the one AFTER the attempt that just failed (a first-attempt
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failure carries ``attempt=2``, so the API's SSE ``retry`` frame reads
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"retrying (2 of N)" — the same convention the endpoint's embedding
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retry loop uses, phase 67 task 02); ``max_attempts`` is the total
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attempt budget (``llm_retries + 1``). One piece per wait: the API
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layer turns it into an SSE ``retry`` frame, and it always precedes
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the pre-retry sleep so the frame reaches the client before the wait
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starts.
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"""
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attempt: int
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max_attempts: int
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@dataclass
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class _ToolCallSlot:
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"""Mutable accumulator for one streamed tool call (phase 37, private).
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``id`` and ``function.name`` arrive on the first partial for an index;
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``function.arguments`` arrives in fragments to concatenate (OpenAI wire
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convention, verified live against aipi 2026-08-26).
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"""
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id: str | None = None
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name: str = ""
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arguments: str = ""
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def _materialize_tool_calls(
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slots: dict[int, _ToolCallSlot],
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) -> list[ToolCallPiece]:
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"""Turn accumulated slots into ordered :class:`ToolCallPiece` values.
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Malformed ``arguments`` JSON raises :class:`LLMError` — a silently
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dropped tool call would corrupt the agent loop (fail-loud house
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style). Empty/``null`` arguments become ``{}`` (a no-parameter call
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such as an unscoped ``ls``).
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"""
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pieces: list[ToolCallPiece] = []
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for index in sorted(slots):
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slot = slots[index]
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raw = slot.arguments.strip()
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label = slot.name or f"index {index}"
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if raw:
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try:
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parsed: Any = json.loads(raw)
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except json.JSONDecodeError as e:
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raise LLMError(
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f"model sent malformed tool-call arguments for '{label}': "
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f"{raw[:200]!r} ({e})"
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) from e
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else:
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parsed = None
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if parsed is None:
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arguments: dict[str, Any] = {}
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elif isinstance(parsed, dict):
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arguments = cast("dict[str, Any]", parsed)
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else:
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raise LLMError(
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f"model sent non-object tool-call arguments for '{label}': "
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f"{raw[:200]!r}"
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)
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pieces.append(
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ToolCallPiece(
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id=slot.id or f"call_{index}", name=slot.name, arguments=arguments
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)
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)
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return pieces
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# aipi's local embedding model rejects requests over ~1024 input tokens
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# ("input is too large to process"). Batch by estimated tokens, with a
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# safety margin under that cap — code-dense text can tokenize at ~3
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# chars/token, so stay well below the ceiling. A retry that halves an
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# over-large batch (:meth:`LLMClient._embed_batch`) is the backstop.
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_TOKENS_PER_CHAR = 0.25
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_REQUEST_TOKEN_BUDGET = 700
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class _TooLarge(RuntimeError):
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"""Internal: the endpoint rejected the request's input size."""
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class LLMClient:
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"""Thin async wrapper over the aipi OpenAI-compatible API."""
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def __init__(self, settings: Settings | None = None) -> None:
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self.settings = settings or get_settings()
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#: Number of embedding HTTP requests made so far (importer logging).
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self.embed_batches: int = 0
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self._client = AsyncOpenAI(
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base_url=self.settings.llm_base_url,
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api_key=self.settings.effective_api_key,
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timeout=120.0,
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)
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async def _post_embeddings(self, texts: list[str]) -> list[list[float]]:
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"""One POST /embeddings with a minimal OpenAI-compatible payload.
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The ``openai`` SDK (1.x and 2.x) injects ``encoding_format`` into
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every embeddings request (defaulting to ``"base64"``), and aipi's
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litellm ``openai_like`` model group rejects that parameter in any
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form — so we reuse the openai client's own httpx transport (same
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base URL, TLS, and connection pooling) and send a clean payload.
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The endpoint's default is floats, which is what pgvector needs.
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"""
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http = self._client._client # pyright: ignore[reportAttributeAccessIssue]
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resp = await http.post(
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"embeddings",
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json={"model": self.settings.llm_embed_model, "input": texts},
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headers={"Authorization": f"Bearer {self.settings.effective_api_key}"},
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)
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if resp.status_code >= 400:
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if "too large" in resp.text:
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raise _TooLarge(resp.text[:300])
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raise EmbeddingError(
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f"embeddings endpoint returned HTTP {resp.status_code}: {resp.text[:300]}"
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)
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payload = resp.json()
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rows = payload.get("data") if isinstance(payload, dict) else None
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if not isinstance(rows, list) or len(rows) != len(texts):
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n = len(rows) if isinstance(rows, list) else "?"
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raise EmbeddingError(
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f"embeddings endpoint returned {n} vectors for {len(texts)} inputs — "
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"refusing to guess which is which"
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)
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ordered = sorted(rows, key=lambda r: r["index"])
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return [[float(x) for x in row["embedding"]] for row in ordered]
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async def _embed_batch(self, chunk: list[str]) -> list[list[float]]:
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"""Embed *chunk*, halving the request if the endpoint says the input
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is too large (tokenizer estimates can be wrong for dense content).
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A single text that still fails is a hard, actionable error."""
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try:
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return await self._post_embeddings(chunk)
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except _TooLarge:
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if len(chunk) == 1:
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raise EmbeddingError(
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f"a single {len(chunk[0])}-char chunk exceeded the endpoint's "
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"per-request input token cap — lower BOR_CHUNK_TARGET_CHARS "
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"and re-import"
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) from None
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mid = len(chunk) // 2
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left = await self._embed_batch(chunk[:mid])
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right = await self._embed_batch(chunk[mid:])
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return [*left, *right]
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def _check_dims(self, vecs: list[list[float]]) -> None:
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expected = self.settings.embedding_dim
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dims = sorted({len(v) for v in vecs})
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if dims != [expected]:
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raise EmbeddingDimensionError(
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f"embedding dimension mismatch: model '{self.settings.llm_embed_model}' "
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f"returned dims {dims} but BOR_EMBEDDING_DIM={expected}. The chunks "
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"table stores a fixed dimension — run `uv run python -m scripts.llm_probe`,"
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" update BOR_EMBEDDING_DIM, and recreate the schema (README → "
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"Troubleshooting: 'Embedding dimension mismatch')."
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)
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def _batch_texts(self, texts: list[str]) -> list[list[str]]:
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"""Group *texts* into requests under the endpoint's token cap.
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``BOR_EMBED_BATCH_SIZE`` stays a hard cap on *texts* per request;
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the token budget usually binds first for 2000-char chunks.
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"""
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budget_chars = int(_REQUEST_TOKEN_BUDGET / _TOKENS_PER_CHAR)
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max_texts = max(1, self.settings.embed_batch_size)
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batches: list[list[str]] = []
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cur: list[str] = []
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cur_chars = 0
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for text in texts:
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if cur and (len(cur) >= max_texts or cur_chars + len(text) > budget_chars):
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batches.append(cur)
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cur, cur_chars = [], 0
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cur.append(text)
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cur_chars += len(text)
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if cur:
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batches.append(cur)
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return batches
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async def embed(self, texts: list[str]) -> list[list[float]]:
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"""Embed *texts* in token-budgeted batches (order always kept)."""
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if not texts:
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return []
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out: list[list[float]] = []
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for chunk in self._batch_texts(texts):
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try:
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vecs = await self._embed_batch(chunk)
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except EmbeddingError:
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raise
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except Exception as e: # noqa: BLE001 — wrap transport-level failures
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raise EmbeddingError(
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f"embeddings request to {self.settings.llm_base_url} failed: {e}"
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) from e
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self.embed_batches += 1
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self._check_dims(vecs)
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out.extend(vecs)
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return out
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async def embed_one(self, text: str) -> list[float]:
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"""Convenience: embed a single text (retrieval path)."""
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(vec,) = await self.embed([text])
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return vec
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async def chat(
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self, messages: list[dict[str, Any]], model: str | None = None
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) -> str:
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"""One-shot (non-streaming) completion (A5 extended, phase 30).
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Short, low-temperature request (``temperature=0.2``, 2048-token
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cap — summaries and outlines are small, so a fixed budget is
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enough) against ``BOR_LLM_SUMMARY_MODEL`` (default ``lite``)
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unless *model* names another. Used by the document summarizer
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(phase 30) and the KB overview generator (phase 31).
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Any transport/HTTP/malformed failure, a choiceless reply, or an
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empty/missing ``content`` field raises :class:`LLMError` — a
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silent empty summary must never be stored.
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"""
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try:
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resp = await self._client.chat.completions.create(
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model=model or self.settings.llm_summary_model,
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messages=cast("list[ChatCompletionMessageParam]", messages),
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temperature=0.2,
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max_tokens=2048,
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stream=False,
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)
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except LLMError:
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raise
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except Exception as e: # noqa: BLE001 — wrap transport-level failures
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raise LLMError(
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f"chat completion from {self.settings.llm_base_url} failed: {e}"
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) from e
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if not resp.choices:
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raise LLMError(
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f"chat completion from {self.settings.llm_base_url} "
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"returned no choices"
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)
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content = resp.choices[0].message.content
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if content is None or not content.strip():
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raise LLMError(
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f"chat completion from {self.settings.llm_base_url} returned "
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"empty content — refusing to store a silent summary"
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)
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return content.strip()
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async def chat_stream(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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scaffolding: ScaffoldingFilter | None = None,
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) -> AsyncGenerator[StreamPiece | ToolCallPiece, None]:
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"""Stream assistant pieces from the chat model (PLAN A5/A15, phase 17).
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Messages are passed to the request body VERBATIM: string-only
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``{role, content}`` dicts are byte-identical on the wire to the
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pre-phase-74 requests, and an assistant message may additionally
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carry ``reasoning_content`` (the client's prior thinking, phase
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74 — the same wire field the model uses for its OWN reasoning on
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the response side; the ``openai`` SDK passes message dicts
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through untouched, so no transport change).
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``stream=True`` against the OpenAI-compatible endpoint, yielding
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typed :class:`StreamPiece` values. Wire convention (verified live
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against aipi's ``turbo`` on 2026-08-23): the model's reasoning
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arrives as ``delta.reasoning_content`` chunks (deepseek/litellm
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convention) **before** the first ``delta.content`` chunk, so in
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practice thinking pieces precede content pieces. The ``openai``
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SDK keeps unknown delta fields in ``model_extra``, so ``getattr``
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is the right accessor — no raw-HTTP parsing is needed. A chunk
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carrying both fields yields the thinking piece **first**.
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Reasoning counts against ``max_tokens``: an answer can in principle
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be empty (thinking with no content) — the UI handles that.
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Answers are allowed up to ``BOR_MAX_OUTPUT_TOKENS`` (default
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32 768) output tokens — the old hard 700-token cap cut long
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answers off mid-sentence (owner report 2026-08-22).
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Tool calls (phase 37): when *tools* (an OpenAI ``tools`` list) is
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not None it is passed through as ``tools=…``; when None the key is
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**not** included, so the request is byte-identical to pre-phase-37
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and no tool pieces can be produced. A tool-calling model replies
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with ``delta.tool_calls`` partials — keyed by ``index``, with
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``id`` and ``function.name`` on the first partial and
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``function.arguments`` in fragments — which are accumulated into
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one :class:`ToolCallPiece` per call, yielded in index order at
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stream end (after the stream's chunks are exhausted — aipi ends
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the stream at ``finish_reason="tool_calls"``, so this is the
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wire's emission point). Malformed ``arguments`` JSON raises
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:class:`LLMError`. Wire convention verified live against
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aipi's ``turbo`` on 2026-08-26 via
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``uv run python -m scripts.llm_probe --tools`` (phase 37, task 01:
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``probe: turbo tool_calls=supported 2026-08-26``).
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Scaffolding guardrail (phase 71): the caller may pass a
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``ScaffoldingFilter`` — one per request, caller-owned (this
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method never creates or resets one). When present, only
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**content** is filtered: ``delta.content`` is fed through the
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filter and only the clean text is yielded (an empty clean
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result yields **no** piece — no empty ``delta`` frames); thinking
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pieces are never filtered (the scratchpad stays raw, phase 17).
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At stream end the filter's held tail is flushed to a content
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piece **before** any tool-call materialization (content-
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before-tools wire convention). ``None`` (the default) keeps
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today's byte-identical raw path for callers that opt out.
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Any failure (network, HTTP, malformed stream) surfaces as
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:class:`LLMError` so the API layer can turn it into an SSE
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``error`` event instead of a hung request.
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Teardown (phase 48, 2026-08-29, ``TODO.md`` L3): once
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``create()`` succeeded, the endpoint stream's lifetime is
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explicit — it is closed on **every** exit: normal exhaustion
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(a quiet no-op on the already-ended SDK stream, so the
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completed path stays byte-identical), a wrapped mid-stream
|
|
failure, and consumer abandon (stop/cancel — ``GeneratorExit``;
|
|
awaiting in the ``finally`` is safe because it does not yield).
|
|
The SDK's ``close()`` awaits the underlying httpx response's
|
|
``aclose()``, so the local model stops generating as soon as
|
|
the SSE consumer goes away.
|
|
"""
|
|
kwargs: dict[str, Any] = {
|
|
# ``{role, content}`` dicts are exactly what the message params
|
|
# accept; the cast keeps pyright honest about the SDK's union.
|
|
"model": self.settings.llm_chat_model,
|
|
"messages": cast("list[ChatCompletionMessageParam]", messages),
|
|
"temperature": 0.4,
|
|
"max_tokens": self.settings.max_output_tokens,
|
|
"stream": True,
|
|
}
|
|
if tools is not None:
|
|
kwargs["tools"] = tools
|
|
stream: AsyncStream[ChatCompletionChunk] | None = None
|
|
try:
|
|
stream = cast(
|
|
"AsyncStream[ChatCompletionChunk]",
|
|
await self._client.chat.completions.create(**kwargs),
|
|
)
|
|
calls: dict[int, _ToolCallSlot] = {}
|
|
async for chunk in stream:
|
|
if not chunk.choices:
|
|
continue
|
|
choice = chunk.choices[0]
|
|
delta = choice.delta
|
|
# Tool-call partials (phase 37) accumulate across chunks,
|
|
# keyed by index; a missing index (not seen on aipi) falls
|
|
# back to the next synthetic slot.
|
|
for tc in getattr(delta, "tool_calls", None) or []:
|
|
idx = getattr(tc, "index", None)
|
|
key = idx if isinstance(idx, int) else (max(calls) + 1 if calls else 0)
|
|
slot = calls.setdefault(key, _ToolCallSlot())
|
|
tc_id = getattr(tc, "id", None)
|
|
if tc_id and slot.id is None:
|
|
slot.id = tc_id
|
|
fn = getattr(tc, "function", None)
|
|
if fn is not None:
|
|
if fn.name:
|
|
slot.name += fn.name
|
|
if fn.arguments:
|
|
slot.arguments += fn.arguments
|
|
reasoning = getattr(delta, "reasoning_content", None)
|
|
if not reasoning:
|
|
# Future-proofing: the same wire convention under a
|
|
# shorter field name.
|
|
reasoning = getattr(delta, "reasoning", None)
|
|
if reasoning:
|
|
yield StreamPiece("thinking", reasoning)
|
|
content = delta.content
|
|
if content:
|
|
if scaffolding is not None:
|
|
# Phase 71: content only — an empty clean result
|
|
# yields nothing (no empty delta frames).
|
|
cleaned = scaffolding.feed(content)
|
|
if cleaned:
|
|
yield StreamPiece("content", cleaned)
|
|
else:
|
|
yield StreamPiece("content", content)
|
|
# Phase 71: flush the filter's held tail at stream end, BEFORE
|
|
# any tool-call materialization — flushed-tail content precedes
|
|
# ToolCallPieces (content-before-tools wire convention).
|
|
if scaffolding is not None:
|
|
tail = scaffolding.flush()
|
|
if tail:
|
|
yield StreamPiece("content", tail)
|
|
if calls:
|
|
for piece in _materialize_tool_calls(calls):
|
|
yield piece
|
|
except LLMError:
|
|
raise
|
|
except Exception as e: # noqa: BLE001 — wrap transport-level failures
|
|
raise LLMError(f"chat stream from {self.settings.llm_base_url} failed: {e}") from e
|
|
finally:
|
|
# Phase 48: deterministic teardown — whenever ``create()``
|
|
# succeeded, close the endpoint's stream on every subsequent
|
|
# exit (normal exhaustion, wrapped failures, and consumer
|
|
# abandon). A failure of ``create()`` itself never sets
|
|
# ``stream``, so it stays the plain wrap above.
|
|
if stream is not None:
|
|
await stream.close()
|
|
|
|
|
|
async def chat_stream_retried(
|
|
llm: LLMClient,
|
|
messages: list[dict[str, Any]],
|
|
*,
|
|
tools: list[dict[str, Any]] | None = None,
|
|
retries: int = 0,
|
|
delay: float = 0.0,
|
|
scaffolding: ScaffoldingFilter | None = None,
|
|
) -> AsyncGenerator[StreamPiece | ToolCallPiece | RetryPiece, None]:
|
|
"""Stream a chat turn, retrying a dead endpoint (phase 67).
|
|
|
|
Wraps :meth:`LLMClient.chat_stream` with the retry-before-first-piece
|
|
rule (owner-locked A2): a request is restarted **only** while no output
|
|
piece (thinking/tool/delta) has been yielded for it. Once pieces have
|
|
flowed, an :class:`LLMError` is re-raised unchanged — a partial answer
|
|
is never redone, and the API layer's terminal ``error`` frame applies.
|
|
This primitive is the ONLY place that rule lives (the chat endpoint
|
|
and the agent loop both build on it).
|
|
|
|
Up to *retries* restarts after the initial attempt (``retries + 1``
|
|
attempts total; ``retries=0`` is exactly one attempt with no
|
|
:class:`RetryPiece` — the pre-phase-67 kill-switch path). Each restart
|
|
is preceded by one :class:`RetryPiece` (``attempt`` = the 1-based
|
|
number of the attempt about to be tried — the failed attempt + 1 —
|
|
and ``max_attempts`` = ``retries + 1``) and a flat
|
|
``asyncio.sleep(delay)`` — the TODO-locked fixed interval, no
|
|
backoff. The ``RetryPiece`` always precedes its sleep: the API frame
|
|
must reach the client before the wait starts.
|
|
|
|
The request is restarted byte-identical: ``chat_stream`` is stateless,
|
|
so every attempt is opened with the SAME *messages*/*tools*.
|
|
|
|
Scaffolding guardrail (phase 71): *scaffolding* is passed through to
|
|
every attempt's ``chat_stream``. The SAME caller-owned filter object
|
|
across the retry attempts of one logical request is safe by
|
|
construction: a restarted attempt only happens while no piece was
|
|
emitted, i.e. the filter was never fed (its pending buffer is still
|
|
empty).
|
|
|
|
Teardown (phase 48, extended): every attempt's stream is explicitly
|
|
closed in a ``finally`` — normal exhaustion, a terminal
|
|
:class:`LLMError`, and a consumer abandon (``GeneratorExit`` mid-attempt
|
|
or during the pre-retry sleep) all run it, so an abandoned turn never
|
|
leaves the endpoint's stream open.
|
|
"""
|
|
max_attempts = retries + 1
|
|
for attempt in range(1, max_attempts + 1):
|
|
emitted = False
|
|
stream = llm.chat_stream(messages, tools=tools, scaffolding=scaffolding)
|
|
try:
|
|
async for piece in stream:
|
|
emitted = True
|
|
yield piece
|
|
except LLMError as e:
|
|
if emitted:
|
|
# Locked A2: tokens already flowed — the failure is
|
|
# terminal, never redo a partial answer.
|
|
raise
|
|
if attempt >= max_attempts:
|
|
# Retries exhausted — the API layer turns this into the
|
|
# terminal error frame.
|
|
raise
|
|
logger.warning(
|
|
"llm stream failed before the first piece (attempt %d/%d) — "
|
|
"retrying in %.1fs: %s",
|
|
attempt,
|
|
max_attempts,
|
|
delay,
|
|
e,
|
|
)
|
|
yield RetryPiece(attempt + 1, max_attempts)
|
|
await asyncio.sleep(delay)
|
|
else:
|
|
# A fully consumed attempt — the turn is done; the loop must
|
|
# NOT open another stream for an attempt that never failed.
|
|
return
|
|
finally:
|
|
# Phase 48 teardown for THIS attempt's stream (a no-op once the
|
|
# stream already ended; the real close on a consumer abandon).
|
|
await stream.aclose()
|
|
|
|
|
|
async def check_models(llm: LLMClient) -> None:
|
|
"""Verify the models a sync needs (embed + summary) before any
|
|
expensive work; raise ModelUnavailableError naming the model.
|
|
|
|
The probe is deliberately tiny — one short embedding
|
|
(``sync model check``) and one 1-token-scale completion (``ping``)
|
|
— so a dead endpoint is discovered cheaper than a single git
|
|
clone. The sync sanitizer downstream (``app.api.sync
|
|
._sanitize_error``) still masks any credentials embedded in the
|
|
wrapped error text, so the raw endpoint URL in the original
|
|
exception is safe to include.
|
|
"""
|
|
embed_model = llm.settings.llm_embed_model
|
|
try:
|
|
await llm.embed_one("sync model check")
|
|
except Exception as e: # noqa: BLE001 — wrap EmbeddingError + transport failures
|
|
raise ModelUnavailableError(
|
|
f"The embedding model ('{embed_model}') is not available — "
|
|
f"check the model endpoint and retry. ({e})"
|
|
) from e
|
|
summary_model = llm.settings.llm_summary_model
|
|
try:
|
|
await llm.chat([{"role": "user", "content": "ping"}])
|
|
except Exception as e: # noqa: BLE001 — wrap LLMError + transport failures
|
|
raise ModelUnavailableError(
|
|
f"The summary model ('{summary_model}') is not available — "
|
|
f"check the model endpoint and retry. ({e})"
|
|
) from e
|