375 lines
13 KiB
Python
375 lines
13 KiB
Python
import json
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import logging
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from collections.abc import AsyncIterator
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from typing import Any
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from openai import AsyncOpenAI
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from app.config import get_settings
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logger = logging.getLogger(__name__)
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class LLMClient:
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def __init__(self) -> None:
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settings = get_settings()
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self.tools_enabled = settings.openrouter_tools_enabled
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self.client = AsyncOpenAI(
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api_key=settings.openrouter_api_key,
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base_url=settings.openrouter_base_url,
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)
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def _runtime(self) -> tuple[str, str, str]:
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from app.db.base import SessionLocal
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from app.settings.service import SettingsService
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settings = get_settings()
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db = SessionLocal()
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try:
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svc = SettingsService(db)
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model = str(svc.get_effective("openrouter_model"))
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extract = str(svc.get_effective("memory_extract_model"))
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effort = str(svc.get_effective("openrouter_reasoning_effort")).strip().lower()
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return model, extract, effort
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finally:
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db.close()
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def _vision_model_runtime(self) -> str:
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from app.db.base import SessionLocal
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from app.settings.service import SettingsService
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db = SessionLocal()
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try:
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return str(SettingsService(db).get_effective("openrouter_vision_model"))
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finally:
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db.close()
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@property
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def model(self) -> str:
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return self._runtime()[0]
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@property
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def memory_extract_model(self) -> str:
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return self._runtime()[1]
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@property
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def reasoning_effort(self) -> str:
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return self._runtime()[2]
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@property
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def vision_model(self) -> str:
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return self._vision_model_runtime()
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def _reasoning_extra_body(self) -> dict[str, Any] | None:
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if not self.reasoning_effort:
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return None
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if self.reasoning_effort == "none":
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return {"reasoning": {"effort": "none", "exclude": True}}
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return {"reasoning": {"effort": self.reasoning_effort}}
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@staticmethod
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def _delta_reasoning(delta: Any) -> tuple[str, list[Any]]:
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parts: list[str] = []
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for attr in ("reasoning", "reasoning_content"):
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value = getattr(delta, attr, None)
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if value:
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parts.append(str(value))
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details: list[Any] = []
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raw_details = getattr(delta, "reasoning_details", None)
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if raw_details:
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if isinstance(raw_details, list):
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details.extend(raw_details)
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else:
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details.append(raw_details)
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return "".join(parts), details
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@staticmethod
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def _normalize_reasoning_details(details: Any) -> list[Any] | None:
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if not details:
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return None
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items = details if isinstance(details, list) else [details]
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normalized: list[Any] = []
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for item in items:
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if hasattr(item, "model_dump"):
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normalized.append(item.model_dump())
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elif isinstance(item, dict):
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normalized.append(item)
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else:
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normalized.append(item)
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return normalized or None
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@staticmethod
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def attach_reasoning_to_message(
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message: dict[str, Any],
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*,
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reasoning: str = "",
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reasoning_details: list[Any] | None = None,
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) -> dict[str, Any]:
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if reasoning:
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message["reasoning"] = reasoning
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message["reasoning_content"] = reasoning
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normalized = LLMClient._normalize_reasoning_details(reasoning_details)
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if normalized:
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message["reasoning_details"] = normalized
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return message
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async def stream_chat(
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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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*,
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model: str | None = None,
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) -> AsyncIterator[dict[str, Any]]:
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use_tools = bool(tools) and self.tools_enabled
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kwargs: dict[str, Any] = {
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"model": model or self.model,
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"messages": messages,
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"stream": True,
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"temperature": 0.7,
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}
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if use_tools:
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kwargs["tools"] = tools
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extra_body = self._reasoning_extra_body()
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if extra_body:
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kwargs["extra_body"] = extra_body
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try:
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stream = await self.client.chat.completions.create(**kwargs)
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except Exception as exc:
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logger.exception("LLM stream failed: %s", exc)
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yield {"type": "error", "content": str(exc)}
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yield {"type": "done", "finish_reason": "error"}
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return
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tool_calls: dict[int, dict[str, Any]] = {}
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reasoning_parts: list[str] = []
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reasoning_details: list[Any] = []
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try:
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async for chunk in stream:
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if not chunk.choices:
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continue
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choice = chunk.choices[0]
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delta = choice.delta
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if delta.content:
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yield {"type": "content", "content": delta.content}
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reasoning_text, details = self._delta_reasoning(delta)
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if reasoning_text:
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reasoning_parts.append(reasoning_text)
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if details:
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reasoning_details.extend(details)
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if delta.tool_calls:
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for tool_call in delta.tool_calls:
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idx = tool_call.index
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if idx not in tool_calls:
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tool_calls[idx] = {
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"id": tool_call.id or "",
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"type": "function",
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"function": {"name": "", "arguments": ""},
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}
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if tool_call.id:
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tool_calls[idx]["id"] = tool_call.id
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if tool_call.function:
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if tool_call.function.name:
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tool_calls[idx]["function"]["name"] = tool_call.function.name
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if tool_call.function.arguments:
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tool_calls[idx]["function"]["arguments"] += tool_call.function.arguments
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usage = getattr(chunk, "usage", None)
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if usage is not None:
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logger.info(
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"LLM stream usage: prompt=%s completion=%s total=%s",
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getattr(usage, "prompt_tokens", None),
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getattr(usage, "completion_tokens", None),
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getattr(usage, "total_tokens", None),
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)
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if choice.finish_reason:
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reasoning = "".join(reasoning_parts)
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normalized_details = self._normalize_reasoning_details(reasoning_details)
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if reasoning or normalized_details:
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yield {
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"type": "reasoning",
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"reasoning": reasoning,
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"reasoning_details": normalized_details,
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}
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if tool_calls:
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yield {"type": "tool_calls", "tool_calls": list(tool_calls.values())}
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logger.info(
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"LLM stream done: model=%s finish_reason=%s tool_calls=%d "
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"content_in_stream=%d reasoning_len=%d",
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model or self.model,
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choice.finish_reason,
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len(tool_calls),
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len(reasoning_parts),
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len(reasoning),
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)
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yield {"type": "done", "finish_reason": choice.finish_reason}
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except Exception as exc:
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logger.exception("LLM stream read failed: %s", exc)
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yield {"type": "error", "content": str(exc)}
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yield {"type": "done", "finish_reason": "error"}
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async def complete(
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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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*,
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temperature: float = 0.7,
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model: str | None = None,
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for_extraction: bool = False,
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visible_reply: bool = False,
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) -> dict[str, Any]:
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use_tools = bool(tools) and self.tools_enabled and not for_extraction
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kwargs: dict[str, Any] = {
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"model": model or self.model,
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"messages": messages,
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"temperature": temperature,
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}
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if use_tools:
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kwargs["tools"] = tools
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if for_extraction:
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kwargs["extra_body"] = {"reasoning": {"effort": "none"}}
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else:
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extra_body = self._reasoning_extra_body()
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if extra_body:
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kwargs["extra_body"] = extra_body
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response = await self.client.chat.completions.create(**kwargs)
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usage = getattr(response, "usage", None)
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if usage is not None:
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logger.info(
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"LLM complete usage: prompt=%s completion=%s total=%s model=%s",
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getattr(usage, "prompt_tokens", None),
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getattr(usage, "completion_tokens", None),
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getattr(usage, "total_tokens", None),
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kwargs.get("model"),
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)
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message = response.choices[0].message
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content = message.content or ""
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reasoning = ""
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for attr in ("reasoning", "reasoning_content"):
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value = getattr(message, attr, None)
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if value:
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reasoning = str(value)
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break
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if not content and reasoning and not visible_reply:
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content = reasoning
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result: dict[str, Any] = {
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"content": content,
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"tool_calls": [],
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"reasoning": reasoning,
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"reasoning_details": getattr(message, "reasoning_details", None),
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}
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if message.tool_calls:
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result["tool_calls"] = [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments,
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},
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}
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for tc in message.tool_calls
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]
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return result
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async def complete_vision(
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self,
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messages: list[dict[str, Any]],
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*,
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temperature: float = 0.1,
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model: str | None = None,
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) -> dict[str, Any]:
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use_model = model or self.vision_model
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kwargs: dict[str, Any] = {
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"model": use_model,
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"messages": messages,
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"temperature": temperature,
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"extra_body": {"reasoning": {"effort": "none", "exclude": True}},
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}
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response = await self.client.chat.completions.create(**kwargs)
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usage = getattr(response, "usage", None)
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usage_dict: dict[str, Any] = {}
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if usage is not None:
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usage_dict = {
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"prompt_tokens": getattr(usage, "prompt_tokens", None),
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"completion_tokens": getattr(usage, "completion_tokens", None),
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"total_tokens": getattr(usage, "total_tokens", None),
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}
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logger.info(
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"LLM vision usage: prompt=%s completion=%s total=%s model=%s",
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usage_dict.get("prompt_tokens"),
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usage_dict.get("completion_tokens"),
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usage_dict.get("total_tokens"),
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use_model,
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)
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message = response.choices[0].message
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return {
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"content": message.content or "",
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"model": use_model,
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"usage": usage_dict,
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}
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@staticmethod
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def parse_tool_arguments(arguments: str) -> dict[str, Any]:
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if not arguments:
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return {}
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try:
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return json.loads(arguments)
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except json.JSONDecodeError:
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return {}
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@staticmethod
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def serialize_reasoning(
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*,
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reasoning: str = "",
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reasoning_details: list[Any] | None = None,
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) -> str | None:
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payload: dict[str, Any] = {}
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if reasoning:
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payload["reasoning"] = reasoning
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payload["reasoning_content"] = reasoning
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if reasoning_details:
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payload["reasoning_details"] = reasoning_details
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if not payload:
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return None
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return json.dumps(payload, ensure_ascii=False)
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@staticmethod
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def deserialize_reasoning(raw: str | None) -> dict[str, Any]:
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if not raw:
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return {}
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try:
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data = json.loads(raw)
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except json.JSONDecodeError:
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return {"reasoning": raw}
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if isinstance(data, str):
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return {"reasoning": data, "reasoning_content": data}
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if isinstance(data, dict):
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return data
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return {}
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async def embed(self, texts: list[str]) -> list[list[float]]:
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settings = get_settings()
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if not texts:
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return []
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response = await self.client.embeddings.create(
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model=settings.embedding_model,
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input=texts,
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)
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return [item.embedding for item in response.data]
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