LZH-YS1998
d14f3920e0
OBS-11 — stop/resume killed pure-native runs over a phantom external pin. Role templates' preferred_external_agent leaked into execution identity even when the user requested native and execution actually ran native; on resume the availability gate trusted the pin and failed every non-terminal item. Root fixes across the whole chain: - Staffing card per-role defaults are now the RESOLVED backend (explicit session agent choice > runnable template preference > native), never a hardcoded external default; seat enrichment and the dispatch selector's locked branch downgrade provably unavailable externals to native and record the wish in execution_agent_unavailable. - The resume availability gate fails closed only when a resumable external session actually exists; a bare pin heals to native (snapshot AND task durable identity) and the run resumes — mirroring dispatch fallback. - Suspend-checkpoint replies: force_resume (chat/headless spelling) is recognized alongside ui_force_resume, and bare continuation tokens (English and Chinese spellings) take the plain-resume path instead of being routed to the final decider as content, which reopened the already-approved intake card. OBS-5 — failed runs never closed and dropped new input. The dispatcher's convergence exit now settles terminally-failed runs (status=failed, lifecycle=closed_failed, run_failure metadata) and emits a company_run_failure_review card whose replies never swallow messages: dismiss acknowledges, content falls through so normal routing starts a fresh run. _maybe_resume_existing_company_runtime no longer re-executes a terminally-failed tree: control replies get an honest closed status, content-bearing input starts a new run. OBS-6 — provider quota exhaustion terminally failed work items. Rate-limit rejections are classified (LLMProvider.is_rate_limit_error, covering status codes, exception types, and English/Chinese provider error text), the agent runtime raises typed ProviderQuotaExhaustedError instead of burning conversation-feedback retries, and the company dispatcher parks: the item returns to READY (attempt interrupted, no terminal failure), the member session idles, and claiming backs off exponentially (60s doubling to a 900s cap; a quiet 30min resets the streak) before resuming automatically. Verified end-to-end on the real minimax-m3 campaign: same goal, same 300s stop point, same run shape that previously killed the whole tree within 90s now resumes cleanly and completes with all items approved; staffing defaults native for all 11 roles. Tests: test_stop_resume_native_pin (10), test_run_failure_settlement (6), test_provider_quota_park (9); attempt-ledger, recruiter, and suspend-resume suites updated to the new contracts (their old assertions pinned the defective behaviors). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
995 lines
37 KiB
Python
995 lines
37 KiB
Python
"""LLM provider layer built on LiteLLM for unified model access."""
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from __future__ import annotations
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import base64
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import hashlib
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import json
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import os
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from pathlib import Path
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from typing import Any, AsyncIterator
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from urllib.parse import urlparse
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from opc.core.windows_ssl import sanitize_windows_sslkeylogfile
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sanitize_windows_sslkeylogfile()
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import litellm
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from loguru import logger
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from opc.core.attachment_content import attachment_suffix
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from opc.core.attachment_store import AttachmentRef
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from opc.core.config import LLMConfig
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from opc.core.models import ModelCapabilitySet, RuntimeLLMEvent
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litellm.suppress_debug_info = True
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litellm.drop_params = True
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_MULTIMODAL_MODEL_HINTS = (
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"gpt-4.1",
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"gpt-4o",
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"gpt-4.5",
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"gpt-5",
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"o1",
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"o3",
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"o4",
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"claude-3",
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"claude-sonnet-4",
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"claude-opus-4",
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"gemini",
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"pixtral",
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"llava",
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"qwen-vl",
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"qwen2-vl",
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"qwen2.5-vl",
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"internvl",
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"minicpm-v",
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"glm-4v",
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)
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_DOCUMENT_MODEL_HINTS = (
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"gpt-4.1",
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"gpt-4o",
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"gpt-4.5",
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"gpt-5",
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"claude-3",
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"claude-sonnet-4",
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"claude-opus-4",
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"gemini",
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)
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_VIDEO_MODEL_HINTS = (
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"gemini",
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"veo",
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"video",
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)
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_TOOL_PROTOCOL_ERROR_HINTS = (
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"no tool output found for function call",
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"no tool output found",
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"messages with role 'tool' must be a response to a preceding message with 'tool_calls'",
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"assistant message with tool_calls",
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"tool_calls must be followed by tool messages",
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"missing tool response",
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"missing tool output",
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"tool_call_id",
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)
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def _normalized_model_name(model: str) -> str:
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if "/" in model:
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return model.split("/", 1)[1].strip().lower()
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return model.strip().lower()
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# Used when neither user config nor litellm can supply a window. Conservative
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# enough for modern models so compaction still has a real denominator.
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_CONTEXT_WINDOW_FALLBACK = 128_000
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_context_window_fallback_warned: set[str] = set()
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_max_tokens_clamp_warned: set[str] = set()
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def _clamp_max_tokens(model: str, requested: int) -> int:
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"""Cap the requested output tokens at the model's known output limit.
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Providers disagree on how to handle an oversized max_tokens: some clamp
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silently, others (e.g. DeepSeek) reject the request outright. Clamping
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here keeps a generous config default (32768) safe on small-cap models.
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Unknown models pass through unchanged.
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"""
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try:
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info = litellm.get_model_info(model)
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cap = info.get("max_output_tokens") or info.get("max_tokens")
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except Exception:
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return requested
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if not cap or requested <= int(cap):
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return requested
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if model not in _max_tokens_clamp_warned:
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_max_tokens_clamp_warned.add(model)
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logger.info(
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"max_tokens {} exceeds output limit {} of model={}; clamping.",
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requested,
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cap,
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model,
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)
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return int(cap)
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_CONTEXT_WINDOW_OVERRIDES: tuple[tuple[str, int], ...] = (
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("gpt-5.4-pro", 1_050_000),
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("gpt-5.4-mini", 400_000),
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("gpt-5.4-nano", 400_000),
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("gpt-5.4", 1_050_000),
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("gpt-5-pro", 400_000),
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("gpt-5", 400_000),
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)
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_POE_CONTEXT_WINDOW_OVERRIDES: tuple[tuple[str, int], ...] = (
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("claude-sonnet-4.5", 64_000),
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("claude-sonnet-4-5", 64_000),
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)
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def _context_window_override(model: str) -> int | None:
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normalized = _normalized_model_name(model)
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for prefix, window in _CONTEXT_WINDOW_OVERRIDES:
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if normalized == prefix or normalized.startswith(f"{prefix}-"):
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return window
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return None
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def _poe_context_window_override(model: str) -> int | None:
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normalized = _normalized_model_name(model)
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for prefix, window in _POE_CONTEXT_WINDOW_OVERRIDES:
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if normalized == prefix or normalized.startswith(f"{prefix}-"):
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return window
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return None
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def _is_official_openai_base(api_base: str | None) -> bool:
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normalized = str(api_base or "").strip()
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if not normalized:
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return True
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try:
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parsed = urlparse(normalized)
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except Exception:
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return False
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hostname = (parsed.hostname or "").strip().lower()
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return hostname in {"api.openai.com", "openai.com"}
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def _is_poe_base(api_base: str | None) -> bool:
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normalized = str(api_base or "").strip()
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if not normalized:
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return False
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try:
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parsed = urlparse(normalized)
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except Exception:
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return False
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hostname = (parsed.hostname or "").strip().lower()
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return hostname == "api.poe.com"
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def _looks_like_multimodal_model(model: str) -> bool:
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normalized = _normalized_model_name(model)
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if any(hint in normalized for hint in _MULTIMODAL_MODEL_HINTS):
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return True
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return normalized.endswith("-vl") or "-vl-" in normalized or "_vl" in normalized
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def _looks_like_document_capable_model(model: str) -> bool:
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normalized = _normalized_model_name(model)
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return any(hint in normalized for hint in _DOCUMENT_MODEL_HINTS)
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def _looks_like_video_capable_model(model: str) -> bool:
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normalized = _normalized_model_name(model)
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return any(hint in normalized for hint in _VIDEO_MODEL_HINTS)
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def _parse_tool_arguments(tool_name: str, arguments: Any) -> tuple[Any, str | None, str | None]:
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"""Parse tool-call arguments and preserve failures for downstream recovery."""
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if not isinstance(arguments, str):
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return arguments, None, None
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raw = arguments
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try:
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parsed = json.loads(raw)
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return parsed, raw, None
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except json.JSONDecodeError as e:
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snippet = raw[:500].replace("\n", "\\n")
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error = f"Invalid tool arguments JSON for `{tool_name}`: {e.msg} at char {e.pos}"
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logger.warning(f"{error}. Raw snippet: {snippet}")
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return raw, raw, error
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class ProviderQuotaExhaustedError(RuntimeError):
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"""The provider rejected the request for quota/rate-limit reasons.
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Raised by the agent runtime instead of retrying in place: replaying the
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same payload against an exhausted quota can only fail, so the company
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dispatcher parks the work (returns the item to READY and backs off)
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rather than failing it terminally (OBS-6).
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"""
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class LLMProvider:
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"""Unified LLM interface via LiteLLM supporting tool calls."""
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# Well-known provider API-key env vars that litellm reads directly when no
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# explicit api_key is passed. Used only by ``has_credentials()`` to avoid a
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# false "no credentials" verdict for env-based setups. Missing a provider
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# here just preserves the old behavior (a real LLM attempt), never a wrong
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# skip of a working key.
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_CREDENTIAL_ENV_VARS = (
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"OPENAI_API_KEY",
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"ANTHROPIC_API_KEY",
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"OPENROUTER_API_KEY",
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"AZURE_API_KEY",
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"AZURE_OPENAI_API_KEY",
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"GEMINI_API_KEY",
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"GOOGLE_API_KEY",
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"MISTRAL_API_KEY",
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"GROQ_API_KEY",
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"DEEPSEEK_API_KEY",
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"TOGETHERAI_API_KEY",
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"ARK_API_KEY",
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)
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def __init__(self, config: LLMConfig, opc_home: Path | None = None) -> None:
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self.config = config
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self.opc_home = opc_home
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self._total_tokens_in = 0
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self._total_tokens_out = 0
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self._total_cost = 0.0
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self._api_key = config.api_key or (
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os.environ.get(config.api_key_env) if config.api_key_env else None
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) or None
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self._api_base = config.api_base or None
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def has_credentials(self) -> bool:
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"""Whether an LLM call can plausibly authenticate.
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True when a key is configured (``api_key`` / ``api_key_env``) or a
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well-known provider env var is present. False only when no credential
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is found anywhere — callers use that to skip LLM work that would
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certainly fail (e.g. native agent selection when an external agent can
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run the task instead). A False at worst degrades to rule-based behavior,
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which stays functional; it never blocks execution.
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"""
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if self._api_key:
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return True
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return any(os.environ.get(var) for var in self._CREDENTIAL_ENV_VARS)
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@property
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def stats(self) -> dict[str, Any]:
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return {
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"tokens_in": self._total_tokens_in,
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"tokens_out": self._total_tokens_out,
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"estimated_cost": self._total_cost,
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}
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def _select_model(self, task_type: str | None = None) -> str:
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if task_type and task_type in self.config.routing:
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return self.config.routing[task_type]
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return self.config.default_model
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def _config_context_window_override(self, model: str) -> int | None:
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"""User-configured context window for models litellm cannot map.
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Per-model overrides win over the scalar default. Both come from
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``LLMConfig`` so proxy/self-hosted models (doubao, minimax, glm, …)
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can report a real context window to the usage ring and compaction.
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"""
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per_model = getattr(self.config, "context_window_overrides", None) or {}
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normalized = _normalized_model_name(model)
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for key, window in per_model.items():
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candidate = _normalized_model_name(str(key))
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if candidate and (normalized == candidate or normalized.startswith(f"{candidate}-")):
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try:
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value = int(window)
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except (TypeError, ValueError):
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continue
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if value > 0:
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return value
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try:
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scalar = int(getattr(self.config, "context_window", 0) or 0)
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except (TypeError, ValueError):
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scalar = 0
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return scalar if scalar > 0 else None
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def get_context_window(self, task_type: str | None = None, model: str | None = None) -> int | None:
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resolved_model = model or self._select_model(task_type)
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config_override = self._config_context_window_override(resolved_model)
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if config_override is not None:
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return config_override
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poe_override = _poe_context_window_override(resolved_model) if _is_poe_base(self._api_base) else None
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if poe_override is not None:
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return poe_override
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override = _context_window_override(resolved_model) if _is_official_openai_base(self._api_base) else None
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if override is not None:
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return override
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try:
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# max_input_tokens is the context window; litellm.get_max_tokens()
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# returns the "max_tokens" map entry, which for many models (e.g.
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# deepseek) is the OUTPUT cap and would wildly under-report here.
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info = litellm.get_model_info(resolved_model)
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limit = info.get("max_input_tokens") or info.get("max_tokens")
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if limit:
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return int(limit)
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reason = "model is not mapped in litellm"
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except Exception as e:
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reason = str(e)
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if resolved_model not in _context_window_fallback_warned:
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_context_window_fallback_warned.add(resolved_model)
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logger.warning(
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"Unable to resolve context window for model={} ({}); assuming {} tokens. "
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"Set llm.context_window or llm.context_window_overrides in llm_config.yaml "
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"to use the model's real window.",
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resolved_model,
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reason,
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_CONTEXT_WINDOW_FALLBACK,
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)
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return _CONTEXT_WINDOW_FALLBACK
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def count_input_tokens(
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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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task_type: str | None = None,
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model: str | None = None,
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) -> int | None:
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resolved_model = model or self._select_model(task_type)
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try:
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return int(litellm.token_counter(
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model=resolved_model,
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messages=messages,
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tools=tools,
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))
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except Exception as e:
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logger.warning(f"Unable to count prompt tokens for model={resolved_model}: {e}")
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return None
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def count_text_tokens(
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self,
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text: str,
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task_type: str | None = None,
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model: str | None = None,
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) -> int | None:
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resolved_model = model or self._select_model(task_type)
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try:
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return int(litellm.token_counter(
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model=resolved_model,
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text=text,
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))
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except Exception as e:
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logger.warning(f"Unable to count text tokens for model={resolved_model}: {e}")
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return None
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def get_capabilities(
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self,
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task_type: str | None = None,
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model: str | None = None,
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) -> ModelCapabilitySet:
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resolved_model = model or self._select_model(task_type)
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normalized = _normalized_model_name(resolved_model)
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provider_family = resolved_model.split("/", 1)[0].strip().lower() if "/" in resolved_model else ""
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supports_thinking = any(hint in normalized for hint in ("o1", "o3", "o4", "gpt-5", "claude", "reason"))
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return ModelCapabilitySet(
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model=resolved_model,
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supports_streaming=True,
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supports_tool_calling=True,
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supports_streaming_tool_calls=True,
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supports_thinking=supports_thinking,
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supports_multimodal=_looks_like_multimodal_model(resolved_model),
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supports_documents=_looks_like_document_capable_model(resolved_model),
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supports_video=_looks_like_video_capable_model(resolved_model),
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provider_family=provider_family,
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metadata={
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"api_base": self._api_base or "",
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},
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)
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def build_cache_fingerprint(
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self,
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*,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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task_type: str | None = None,
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model: str | None = None,
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extra: dict[str, Any] | None = None,
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) -> str:
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resolved_model = model or self._select_model(task_type)
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payload = {
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"model": resolved_model,
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"messages": messages,
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"tools": tools or [],
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"extra": extra or {},
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}
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raw = json.dumps(payload, ensure_ascii=False, sort_keys=True, default=str)
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return hashlib.sha256(raw.encode("utf-8")).hexdigest()
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def is_rate_limit_error(self, error: Exception) -> bool:
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"""Classify provider quota / rate-limit rejections.
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These never produce model output and never get better by replaying
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the identical payload, so callers must park/back off instead of
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burning conversation-feedback retries (OBS-6). Classification is by
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exception type when available and by error text otherwise — the
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streaming path re-raises provider errors as plain RuntimeError with
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only the message preserved.
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"""
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if isinstance(error, litellm.exceptions.RateLimitError):
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return True
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if "ratelimit" in type(error).__name__.lower():
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return True
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if getattr(error, "status_code", None) == 429:
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return True
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message = str(error).lower()
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keywords = (
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"rate limit",
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"rate_limit",
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"ratelimit",
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"too many requests",
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"insufficient_quota",
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"quota exceeded",
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"exceeded your quota",
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"quota exhausted",
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"error code: 429",
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"status code: 429",
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"http 429",
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# Chinese-provider spellings of the same rejection (Volces/DeepSeek
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# and other domestic endpoints return localized error text).
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"请求过于频繁",
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"配额已用完",
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"配额耗尽",
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"触发限流",
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)
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return any(keyword in message for keyword in keywords)
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def is_context_overflow_error(self, error: Exception) -> bool:
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|
if isinstance(error, litellm.exceptions.ContextWindowExceededError):
|
|
return True
|
|
message = str(error).lower()
|
|
keywords = (
|
|
"context window",
|
|
"context length",
|
|
"maximum context length",
|
|
"prompt is too long",
|
|
"too many tokens",
|
|
"context_length_exceeded",
|
|
"token limit exceeded",
|
|
)
|
|
return any(keyword in message for keyword in keywords)
|
|
|
|
def is_tool_protocol_error(self, error: Exception) -> bool:
|
|
message = str(error).lower()
|
|
return any(hint in message for hint in _TOOL_PROTOCOL_ERROR_HINTS)
|
|
|
|
@staticmethod
|
|
def sanitize_tool_call_history(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
|
"""Drop incomplete or stray assistant/tool tool-call transcripts."""
|
|
sanitized: list[dict[str, Any]] = []
|
|
pending_ids: list[str] = []
|
|
buffered_block: list[dict[str, Any]] = []
|
|
|
|
def _copy_message(message: dict[str, Any]) -> dict[str, Any]:
|
|
cloned = dict(message)
|
|
if isinstance(message.get("tool_calls"), list):
|
|
cloned["tool_calls"] = [dict(item) for item in message["tool_calls"]]
|
|
return cloned
|
|
|
|
for message in messages:
|
|
role = str(message.get("role", "") or "").strip()
|
|
if not pending_ids:
|
|
if role == "assistant" and isinstance(message.get("tool_calls"), list) and message["tool_calls"]:
|
|
ids = [
|
|
str(item.get("id", "") or "").strip()
|
|
for item in message["tool_calls"]
|
|
if isinstance(item, dict) and str(item.get("id", "") or "").strip()
|
|
]
|
|
if not ids:
|
|
continue
|
|
buffered_block = [_copy_message(message)]
|
|
pending_ids = ids
|
|
continue
|
|
if role == "tool":
|
|
continue
|
|
sanitized.append(_copy_message(message))
|
|
continue
|
|
|
|
if role == "tool":
|
|
tool_call_id = str(message.get("tool_call_id", "") or "").strip()
|
|
if tool_call_id and tool_call_id in pending_ids:
|
|
buffered_block.append(_copy_message(message))
|
|
pending_ids = [item for item in pending_ids if item != tool_call_id]
|
|
if not pending_ids:
|
|
sanitized.extend(buffered_block)
|
|
buffered_block = []
|
|
continue
|
|
|
|
if role == "assistant" and isinstance(message.get("tool_calls"), list) and message["tool_calls"]:
|
|
ids = [
|
|
str(item.get("id", "") or "").strip()
|
|
for item in message["tool_calls"]
|
|
if isinstance(item, dict) and str(item.get("id", "") or "").strip()
|
|
]
|
|
buffered_block = [_copy_message(message)] if ids else []
|
|
pending_ids = ids
|
|
continue
|
|
|
|
buffered_block = []
|
|
pending_ids = []
|
|
sanitized.append(_copy_message(message))
|
|
|
|
return sanitized
|
|
|
|
def prepare_user_message_content(
|
|
self,
|
|
content: str,
|
|
*,
|
|
attachment_refs: list[dict[str, Any]] | None = None,
|
|
task_type: str | None = None,
|
|
) -> str | list[dict[str, Any]]:
|
|
text = str(content or "")
|
|
refs = list(attachment_refs or [])
|
|
if not refs:
|
|
return text
|
|
|
|
model = self._select_model(task_type)
|
|
parts = self._build_direct_attachment_parts(model, refs)
|
|
if not parts:
|
|
return text
|
|
|
|
content_parts: list[dict[str, Any]] = []
|
|
if text:
|
|
content_parts.append({"type": "text", "text": text})
|
|
content_parts.extend(parts)
|
|
return content_parts
|
|
|
|
async def chat(
|
|
self,
|
|
messages: list[dict[str, Any]],
|
|
tools: list[dict[str, Any]] | None = None,
|
|
task_type: str | None = None,
|
|
temperature: float | None = None,
|
|
max_tokens: int | None = None,
|
|
**kwargs: Any,
|
|
) -> dict[str, Any]:
|
|
model = self._select_model(task_type)
|
|
temp = temperature if temperature is not None else self.config.temperature
|
|
max_tok = _clamp_max_tokens(model, max_tokens if max_tokens is not None else self.config.max_tokens)
|
|
|
|
call_kwargs: dict[str, Any] = {
|
|
"model": model,
|
|
"messages": messages,
|
|
"temperature": temp,
|
|
"max_tokens": max_tok,
|
|
**kwargs,
|
|
}
|
|
if self._api_base:
|
|
call_kwargs["api_base"] = self._api_base
|
|
if self._api_key:
|
|
call_kwargs["api_key"] = self._api_key
|
|
if tools:
|
|
call_kwargs["tools"] = tools
|
|
call_kwargs["tool_choice"] = "auto"
|
|
|
|
logger.debug(f"LLM call: model={model}, base={self._api_base or 'default'}, msgs={len(messages)}, tools={len(tools or [])}")
|
|
|
|
try:
|
|
response = await litellm.acompletion(**call_kwargs)
|
|
except Exception as e:
|
|
logger.error(f"LLM call failed: {e}")
|
|
raise
|
|
|
|
usage = getattr(response, "usage", None)
|
|
cost = 0.0
|
|
if usage:
|
|
self._total_tokens_in += getattr(usage, "prompt_tokens", 0)
|
|
self._total_tokens_out += getattr(usage, "completion_tokens", 0)
|
|
try:
|
|
cost = litellm.completion_cost(completion_response=response)
|
|
self._total_cost += cost
|
|
except Exception:
|
|
pass
|
|
|
|
choice = response.choices[0]
|
|
message = choice.message
|
|
|
|
result: dict[str, Any] = {
|
|
"content": message.content or "",
|
|
"tool_calls": [],
|
|
"finish_reason": choice.finish_reason,
|
|
"model": model,
|
|
"cost": cost,
|
|
"usage": {
|
|
"prompt_tokens": getattr(usage, "prompt_tokens", 0) if usage else 0,
|
|
"completion_tokens": getattr(usage, "completion_tokens", 0) if usage else 0,
|
|
},
|
|
}
|
|
|
|
if message.tool_calls:
|
|
for tc in message.tool_calls:
|
|
args, raw_args, parse_error = _parse_tool_arguments(tc.function.name, tc.function.arguments)
|
|
tool_call = {
|
|
"id": tc.id,
|
|
"function": tc.function.name,
|
|
"arguments": args,
|
|
}
|
|
if raw_args is not None:
|
|
tool_call["arguments_raw"] = raw_args
|
|
if parse_error:
|
|
tool_call["arguments_parse_error"] = parse_error
|
|
result["tool_calls"].append(tool_call)
|
|
|
|
return result
|
|
|
|
def normalize_stream_event(
|
|
self,
|
|
chunk: Any,
|
|
*,
|
|
model: str,
|
|
) -> list[RuntimeLLMEvent]:
|
|
events: list[RuntimeLLMEvent] = []
|
|
usage = getattr(chunk, "usage", None)
|
|
if usage:
|
|
events.append(RuntimeLLMEvent(
|
|
event_type="usage",
|
|
model=model,
|
|
payload={
|
|
"prompt_tokens": getattr(usage, "prompt_tokens", 0),
|
|
"completion_tokens": getattr(usage, "completion_tokens", 0),
|
|
"context_window": self.get_context_window(model=model),
|
|
},
|
|
))
|
|
|
|
choices = getattr(chunk, "choices", None) or []
|
|
if not choices:
|
|
return events
|
|
|
|
choice = choices[0]
|
|
delta = getattr(choice, "delta", None)
|
|
if delta is not None:
|
|
text = getattr(delta, "content", None)
|
|
if text:
|
|
events.append(RuntimeLLMEvent(
|
|
event_type="assistant_delta",
|
|
model=model,
|
|
payload={"text": text},
|
|
))
|
|
thinking_text = (
|
|
getattr(delta, "reasoning", None)
|
|
or getattr(delta, "reasoning_content", None)
|
|
or getattr(delta, "thinking", None)
|
|
)
|
|
if thinking_text:
|
|
events.append(RuntimeLLMEvent(
|
|
event_type="thinking_delta",
|
|
model=model,
|
|
payload={"text": str(thinking_text)},
|
|
))
|
|
tool_calls = getattr(delta, "tool_calls", None) or []
|
|
for tool_call in tool_calls:
|
|
function = getattr(tool_call, "function", None)
|
|
events.append(RuntimeLLMEvent(
|
|
event_type="tool_call_delta",
|
|
model=model,
|
|
payload={
|
|
"index": getattr(tool_call, "index", 0),
|
|
"id": getattr(tool_call, "id", ""),
|
|
"name": getattr(function, "name", "") if function else "",
|
|
"arguments": getattr(function, "arguments", "") if function else "",
|
|
},
|
|
))
|
|
|
|
finish_reason = getattr(choice, "finish_reason", None)
|
|
if finish_reason:
|
|
events.append(RuntimeLLMEvent(
|
|
event_type="message_stop",
|
|
model=model,
|
|
payload={"finish_reason": finish_reason},
|
|
))
|
|
return events
|
|
|
|
async def chat_stream(
|
|
self,
|
|
messages: list[dict[str, Any]],
|
|
tools: list[dict[str, Any]] | None = None,
|
|
task_type: str | None = None,
|
|
temperature: float | None = None,
|
|
max_tokens: int | None = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[RuntimeLLMEvent]:
|
|
model = self._select_model(task_type)
|
|
temp = temperature if temperature is not None else self.config.temperature
|
|
max_tok = _clamp_max_tokens(model, max_tokens if max_tokens is not None else self.config.max_tokens)
|
|
|
|
call_kwargs: dict[str, Any] = {
|
|
"model": model,
|
|
"messages": messages,
|
|
"temperature": temp,
|
|
"max_tokens": max_tok,
|
|
"stream": True,
|
|
**kwargs,
|
|
}
|
|
if self._api_base:
|
|
call_kwargs["api_base"] = self._api_base
|
|
if self._api_key:
|
|
call_kwargs["api_key"] = self._api_key
|
|
if tools:
|
|
call_kwargs["tools"] = tools
|
|
call_kwargs["tool_choice"] = "auto"
|
|
|
|
logger.debug(
|
|
f"LLM stream call: model={model}, base={self._api_base or 'default'}, msgs={len(messages)}, tools={len(tools or [])}"
|
|
)
|
|
|
|
last_usage = {"prompt_tokens": 0, "completion_tokens": 0}
|
|
yield RuntimeLLMEvent(event_type="message_start", model=model, payload={"model": model})
|
|
|
|
try:
|
|
stream = await litellm.acompletion(**call_kwargs)
|
|
if hasattr(stream, "__aiter__"):
|
|
async for chunk in stream:
|
|
for event in self.normalize_stream_event(chunk, model=model):
|
|
if event.event_type == "usage":
|
|
total_prompt = int(event.payload.get("prompt_tokens", 0) or 0)
|
|
total_completion = int(event.payload.get("completion_tokens", 0) or 0)
|
|
delta_prompt = max(0, total_prompt - last_usage["prompt_tokens"])
|
|
delta_completion = max(0, total_completion - last_usage["completion_tokens"])
|
|
last_usage["prompt_tokens"] = total_prompt
|
|
last_usage["completion_tokens"] = total_completion
|
|
cost = 0.0
|
|
try:
|
|
prompt_cost, completion_cost = litellm.cost_per_token(
|
|
model=model,
|
|
prompt_tokens=delta_prompt,
|
|
completion_tokens=delta_completion,
|
|
)
|
|
cost = float(prompt_cost or 0.0) + float(completion_cost or 0.0)
|
|
except Exception:
|
|
cost = 0.0
|
|
self._total_tokens_in += delta_prompt
|
|
self._total_tokens_out += delta_completion
|
|
self._total_cost += cost
|
|
event.payload = {
|
|
**dict(event.payload),
|
|
"prompt_tokens": delta_prompt,
|
|
"completion_tokens": delta_completion,
|
|
"prompt_tokens_total": total_prompt,
|
|
"completion_tokens_total": total_completion,
|
|
"estimated_cost_delta": cost,
|
|
"estimated_cost_total": self._total_cost,
|
|
"context_window": event.payload.get("context_window") or self.get_context_window(model=model),
|
|
"model": model,
|
|
}
|
|
yield event
|
|
else:
|
|
# Provider fallback: treat the response as a single non-streaming completion.
|
|
choice = stream.choices[0]
|
|
message = choice.message
|
|
if getattr(message, "content", None):
|
|
yield RuntimeLLMEvent(
|
|
event_type="assistant_delta",
|
|
model=model,
|
|
payload={"text": message.content},
|
|
)
|
|
for tc in getattr(message, "tool_calls", None) or []:
|
|
yield RuntimeLLMEvent(
|
|
event_type="tool_call_delta",
|
|
model=model,
|
|
payload={
|
|
"index": 0,
|
|
"id": getattr(tc, "id", ""),
|
|
"name": getattr(getattr(tc, "function", None), "name", ""),
|
|
"arguments": getattr(getattr(tc, "function", None), "arguments", ""),
|
|
},
|
|
)
|
|
usage = getattr(stream, "usage", None)
|
|
if usage:
|
|
cost = 0.0
|
|
prompt_tokens = int(getattr(usage, "prompt_tokens", 0) or 0)
|
|
completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0)
|
|
try:
|
|
prompt_cost, completion_cost = litellm.cost_per_token(
|
|
model=model,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
)
|
|
cost = float(prompt_cost or 0.0) + float(completion_cost or 0.0)
|
|
except Exception:
|
|
cost = 0.0
|
|
self._total_tokens_in += prompt_tokens
|
|
self._total_tokens_out += completion_tokens
|
|
self._total_cost += cost
|
|
yield RuntimeLLMEvent(
|
|
event_type="usage",
|
|
model=model,
|
|
payload={
|
|
"prompt_tokens": prompt_tokens,
|
|
"completion_tokens": completion_tokens,
|
|
"prompt_tokens_total": prompt_tokens,
|
|
"completion_tokens_total": completion_tokens,
|
|
"estimated_cost_delta": cost,
|
|
"estimated_cost_total": self._total_cost,
|
|
"context_window": self.get_context_window(model=model),
|
|
"model": model,
|
|
},
|
|
)
|
|
yield RuntimeLLMEvent(
|
|
event_type="message_stop",
|
|
model=model,
|
|
payload={"finish_reason": getattr(choice, "finish_reason", "stop")},
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"LLM stream failed: {e}")
|
|
yield RuntimeLLMEvent(
|
|
event_type="error",
|
|
model=model,
|
|
payload={"message": str(e)},
|
|
)
|
|
raise
|
|
|
|
async def simple_chat(
|
|
self,
|
|
prompt: str,
|
|
system: str | None = None,
|
|
task_type: str | None = None,
|
|
) -> str:
|
|
messages: list[dict[str, Any]] = []
|
|
if system:
|
|
messages.append({"role": "system", "content": system})
|
|
messages.append({"role": "user", "content": prompt})
|
|
result = await self.chat(messages, task_type=task_type)
|
|
return result["content"]
|
|
|
|
def get_tool_definitions(self, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
|
"""Convert internal tool definitions to OpenAI function-calling format."""
|
|
formatted = []
|
|
for tool in tools:
|
|
formatted.append({
|
|
"type": "function",
|
|
"function": {
|
|
"name": tool["name"],
|
|
"description": tool.get("description", ""),
|
|
"parameters": tool.get("parameters", {"type": "object", "properties": {}}),
|
|
},
|
|
})
|
|
return formatted
|
|
|
|
def _build_direct_attachment_parts(
|
|
self,
|
|
model: str,
|
|
attachment_refs: list[dict[str, Any]],
|
|
) -> list[dict[str, Any]]:
|
|
capabilities = self._attachment_capabilities(model)
|
|
if not capabilities["enabled"]:
|
|
return []
|
|
|
|
parts: list[dict[str, Any]] = []
|
|
for ref_dict in attachment_refs:
|
|
try:
|
|
ref = AttachmentRef.from_dict(ref_dict)
|
|
data_url = self._attachment_data_url(ref)
|
|
except Exception as exc:
|
|
logger.warning(f"Skipping direct attachment payload: {exc}")
|
|
continue
|
|
|
|
if not data_url:
|
|
continue
|
|
|
|
if ref.mime_type.startswith("image/") and capabilities["image_mode"]:
|
|
parts.append(self._build_attachment_part("image_url", ref, data_url))
|
|
elif ref.mime_type == "application/pdf" and capabilities["pdf_mode"]:
|
|
parts.append(self._build_attachment_part(str(capabilities["pdf_mode"]), ref, data_url))
|
|
elif ref.mime_type.startswith("video/") and capabilities["video_mode"]:
|
|
parts.append(self._build_attachment_part(str(capabilities["video_mode"]), ref, data_url))
|
|
|
|
return parts
|
|
|
|
def _build_attachment_part(
|
|
self,
|
|
mode: str,
|
|
ref: AttachmentRef,
|
|
data_url: str,
|
|
) -> dict[str, Any]:
|
|
if mode == "image_url":
|
|
return {
|
|
"type": "image_url",
|
|
"image_url": {"url": data_url},
|
|
}
|
|
if mode == "video_url":
|
|
return {
|
|
"type": "video_url",
|
|
"video_url": {"url": data_url},
|
|
}
|
|
if mode == "file":
|
|
return {
|
|
"type": "file",
|
|
"file": {
|
|
"file_data": data_url,
|
|
"filename": ref.filename,
|
|
},
|
|
}
|
|
raise ValueError(f"Unsupported attachment transport mode: {mode}")
|
|
|
|
def _attachment_capabilities(self, model: str) -> dict[str, Any]:
|
|
provider = model.split("/", 1)[0].strip().lower()
|
|
api_base = (self._api_base or "").lower()
|
|
|
|
image_mode: str | None = None
|
|
pdf_mode: str | None = None
|
|
video_mode: str | None = None
|
|
|
|
if "api.poe.com" in api_base:
|
|
image_mode = "image_url"
|
|
pdf_mode = "file"
|
|
if _looks_like_multimodal_model(model):
|
|
video_mode = "file"
|
|
elif "openrouter.ai" in api_base:
|
|
image_mode = "image_url"
|
|
pdf_mode = "file"
|
|
if _looks_like_video_capable_model(model):
|
|
video_mode = "video_url"
|
|
elif provider in {"openai", "azure", "anthropic"}:
|
|
image_mode = "image_url"
|
|
pdf_mode = "file"
|
|
elif provider in {"google", "gemini", "vertex_ai", "vertex"}:
|
|
image_mode = "image_url"
|
|
pdf_mode = "file"
|
|
if _looks_like_video_capable_model(model):
|
|
video_mode = "video_url"
|
|
elif _looks_like_multimodal_model(model):
|
|
image_mode = "image_url"
|
|
pdf_mode = "file" if _looks_like_document_capable_model(model) else None
|
|
if _looks_like_video_capable_model(model):
|
|
video_mode = "video_url"
|
|
|
|
return {
|
|
"enabled": bool(image_mode or pdf_mode or video_mode),
|
|
"image_mode": image_mode,
|
|
"pdf_mode": pdf_mode,
|
|
"video_mode": video_mode,
|
|
}
|
|
|
|
def _attachment_data_url(self, ref: AttachmentRef) -> str | None:
|
|
path = self._resolve_attachment_path(ref)
|
|
if path is None:
|
|
return None
|
|
payload = base64.b64encode(path.read_bytes()).decode("ascii")
|
|
mime_type = ref.mime_type or _guess_mime_from_filename(ref.filename)
|
|
return f"data:{mime_type};base64,{payload}"
|
|
|
|
def _resolve_attachment_path(self, ref: AttachmentRef) -> Path | None:
|
|
if not self.opc_home or not ref.disk_path:
|
|
return None
|
|
resolved = (self.opc_home / ref.disk_path).resolve()
|
|
opc_home_resolved = self.opc_home.resolve()
|
|
if not str(resolved).startswith(str(opc_home_resolved)):
|
|
raise ValueError(f"Attachment path escapes OPC home: {ref.disk_path}")
|
|
return resolved
|
|
|
|
|
|
def _guess_mime_from_filename(filename: str) -> str:
|
|
suffix = attachment_suffix(filename)
|
|
if suffix == ".pdf":
|
|
return "application/pdf"
|
|
if suffix == ".docx":
|
|
return "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
|
|
if suffix == ".xlsx":
|
|
return "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
|
|
if suffix == ".pptx":
|
|
return "application/vnd.openxmlformats-officedocument.presentationml.presentation"
|
|
if suffix == ".mp4":
|
|
return "video/mp4"
|
|
if suffix in {".mpeg", ".mpg"}:
|
|
return "video/mpeg"
|
|
if suffix == ".mov":
|
|
return "video/quicktime"
|
|
if suffix == ".webm":
|
|
return "video/webm"
|
|
return "application/octet-stream"
|