158 lines
6.4 KiB
Python
158 lines
6.4 KiB
Python
import logging
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import os
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import time
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from typing import Optional
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import httpx
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from openai import AsyncOpenAI, APIError, APITimeoutError
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from app.core.config import Settings
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class LLMClientError(Exception):
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pass
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class LLMClient:
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"""Asynchronous LLM client using OpenAI SDK with provider-agnostic config."""
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def __init__(self, settings: Settings):
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self.settings = settings
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self.model = settings.llm_model_name
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self.enable_thinking = settings.llm_enable_thinking
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self.logger = logging.getLogger(__name__)
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self._client = AsyncOpenAI(
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base_url=settings.llm_base_url.rstrip("/"),
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api_key=settings.llm_api_key,
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timeout=settings.llm_timeout,
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http_client=httpx.AsyncClient(
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headers={"Content-Type": "application/json"},
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),
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)
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self._langchain_model = None
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def _truncate_prompt_for_log(self, prompt: str, first_chars: int = 100, last_chars: int = 100) -> str:
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"""Truncate prompt for logging: show first N and last N chars with ellipsis."""
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if len(prompt) <= first_chars + last_chars:
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return prompt
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return f"{prompt[:first_chars]}...({len(prompt) - first_chars - last_chars} chars omitted)...{prompt[-last_chars:]}"
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async def complete(self, prompt: str, temperature: float = 0.7, step_name: str = "LLM") -> str:
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"""Send a chat completion request with optional thinking control.
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Args:
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prompt: The prompt to send to the LLM.
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temperature: Sampling temperature.
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step_name: Identifier for the processing step (e.g., "QueryDecomposer").
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Returns:
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The LLM response text.
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"""
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messages = [{"role": "user", "content": prompt}]
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extra_body = self._build_extra_body()
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prompt_preview = self._truncate_prompt_for_log(prompt)
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self.logger.info("[%s] LLM request started. Prompt: %s", step_name, prompt_preview)
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start_time = time.perf_counter()
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self.logger.info("LLM Extra Body %s", str(extra_body))
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try:
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response = await self._client.chat.completions.create(
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model=self.model,
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messages=messages,
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temperature=temperature,
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extra_body=extra_body if extra_body else None,
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)
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self.logger.info("LLM Response: %s",str(response))
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content = response.choices[0].message.content or ""
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elapsed_ms = (time.perf_counter() - start_time) * 1000
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self.logger.info(
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"[%s] LLM request completed in %.2fms (prompt_tokens=%s, completion_tokens=%s)",
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step_name,
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elapsed_ms,
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response.usage.prompt_tokens if response.usage else "?",
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response.usage.completion_tokens if response.usage else "?",
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)
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return content
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except (APITimeoutError, APIError) as exc:
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elapsed_ms = (time.perf_counter() - start_time) * 1000
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self.logger.error("[%s] LLM API error after %.2fms: %s", step_name, elapsed_ms, exc)
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raise LLMClientError from exc
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except Exception as exc:
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elapsed_ms = (time.perf_counter() - start_time) * 1000
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self.logger.error("[%s] Unexpected LLM error after %.2fms: %s", step_name, elapsed_ms, exc)
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raise LLMClientError from exc
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def _build_extra_body(self) -> dict:
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"""Build extra_body for provider-specific parameters.
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When thinking is enabled, no extra params are passed
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and the model uses its default thinking mode.
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vLLM: {"chat_template_kwargs": {"enable_thinking": False}}
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OpenRouter: {"reasoning": {"enabled": False}}
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"""
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if self.enable_thinking:
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return {}
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if self.settings.vllm_engine:
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return {
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"chat_template_kwargs": {"enable_thinking": False},
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}
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return {"reasoning": {"enabled": False}}
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async def close(self):
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await self._client.close()
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def _get_langchain_model(self):
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if self._langchain_model is None:
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from langchain.chat_models import init_chat_model
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os.environ.setdefault("OPENAI_API_KEY", self.settings.llm_api_key)
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os.environ.setdefault("OPENAI_BASE_URL", self.settings.llm_base_url)
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# vLLM's chat_template_kwargs is incompatible with LangChain's
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# with_structured_output() — it leaks into AsyncCompletions.parse()
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# which rejects the unknown kwarg. Only provider-agnostic params
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# (e.g. OpenAI's reasoning) are safe to pass via model_kwargs.
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model_kwargs: dict[str, Any] | None = None
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if not self.settings.vllm_engine and not self.enable_thinking:
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model_kwargs = {"reasoning": {"enabled": False}}
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self._langchain_model = init_chat_model(
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model=self.model,
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model_provider="openai",
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temperature=0.0,
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model_kwargs=model_kwargs,
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)
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return self._langchain_model
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async def complete_structured(self, prompt: str, pydantic_model, step_name: str = "LLM"):
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prompt_preview = self._truncate_prompt_for_log(prompt)
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self.logger.info("[%s] Structured LLM request started. Prompt: %s", step_name, prompt_preview)
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start_time = time.perf_counter()
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extra_body = self._build_extra_body()
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self.logger.info("[%s] Structured LLM Extra Body: %s", step_name, str(extra_body))
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try:
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model = self._get_langchain_model()
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structured = model.with_structured_output(pydantic_model, method="json_schema")
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result = await structured.ainvoke(prompt)
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elapsed_ms = (time.perf_counter() - start_time) * 1000
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self.logger.info(
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"[%s] Structured LLM Response: %s",
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step_name,
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getattr(result, "model_dump", lambda: result)(),
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)
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self.logger.info(
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"[%s] Structured LLM request completed in %.2fms",
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step_name,
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elapsed_ms,
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)
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return result
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except Exception as exc:
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elapsed_ms = (time.perf_counter() - start_time) * 1000
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self.logger.error("[%s] Structured LLM error after %.2fms: %s", step_name, elapsed_ms, exc)
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raise LLMClientError from exc
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