refactor: centralize llm backend configuration
This commit is contained in:
liuwei
@@ -9,17 +9,7 @@ max_reply_sentences = 3
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familiarity_hint = "有熟悉感，但不过度装熟"
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[api]
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provider = "openai_compatible"
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api_base_url = "http://192.168.2.240:3000/v1"
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endpoint = "chat/completions"
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api_key = "sk-hC6WMLAsTdItpywyrYdxT6pQ4E7NARGbUKuPWRH0zMheen9e"
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model = "gpt-5.4"
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timeout_seconds = 45
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temperature = 0.35
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max_tokens = 120
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stream = true
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max_retries = 3
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retry_delay_seconds = 1.0
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backend = "openai_compatible_ai_auto_response"
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[mode]
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group_default_mode = "social"
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@@ -1,199 +1,6 @@
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from __future__ import annotations
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import json
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import time
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from typing import Dict, List, Optional
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import requests
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from utils.ai.unified_llm import UnifiedLLMClient
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class LLMClient:
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def __init__(self, config: Dict):
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self.config = config or {}
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self.provider = self.config.get("provider", "openai_compatible")
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self.base_url = str(self.config.get("api_base_url", "")).rstrip("/")
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self.endpoint = str(self.config.get("endpoint", "chat/completions")).lstrip("/")
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self.api_key = self.config.get("api_key", "")
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self.model = self.config.get("model", "")
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self.timeout_seconds = int(self.config.get("timeout_seconds", 45))
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self.temperature = float(self.config.get("temperature", 0.7))
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self.max_tokens = int(self.config.get("max_tokens", 500))
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self.stream = bool(self.config.get("stream", True))
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self.max_retries = max(int(self.config.get("max_retries", 3) or 3), 1)
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self.retry_delay_seconds = float(self.config.get("retry_delay_seconds", 1.0) or 1.0)
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self.last_error = ""
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class LLMClient(UnifiedLLMClient):
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"""兼容旧调用方式的统一 LLM 客户端别名。"""
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def chat(
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self,
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system_prompt: str,
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user_prompt: str,
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user_id: str,
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image_urls: Optional[List[str]] = None,
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) -> str:
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self.last_error = ""
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if not self.base_url:
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self.last_error = "empty_base_url"
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return ""
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if self.provider == "openai_compatible":
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return self._chat_openai_compatible(system_prompt, user_prompt, user_id, image_urls or [])
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self.last_error = f"unsupported_provider:{self.provider}"
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return ""
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def _chat_openai_compatible(
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self,
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system_prompt: str,
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user_prompt: str,
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user_id: str,
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image_urls: List[str],
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) -> str:
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if not self.model:
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return ""
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payload = {
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"model": self.model,
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"messages": self._build_messages(system_prompt, user_prompt, image_urls),
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"temperature": self.temperature,
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"max_tokens": self.max_tokens,
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"user": user_id,
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}
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if self.stream:
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payload["stream"] = True
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headers = {
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"Content-Type": "application/json",
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}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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for attempt in range(1, self.max_retries + 1):
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try:
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if self.stream:
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text = self._chat_streaming(payload, headers)
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else:
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text = self._chat_non_streaming(payload, headers)
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if text:
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return text
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except Exception as exc:
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self.last_error = f"request_failed:attempt_{attempt}:{exc}"
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if attempt < self.max_retries:
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time.sleep(self.retry_delay_seconds * attempt)
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return ""
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def _chat_non_streaming(self, payload: Dict, headers: Dict[str, str]) -> str:
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response = requests.post(
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f"{self.base_url}/{self.endpoint}",
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json=payload,
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headers=headers,
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timeout=self.timeout_seconds,
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)
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response.raise_for_status()
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data = response.json()
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text = self._extract_text(data)
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if text:
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return text
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self.last_error = f"empty_model_output:{self.model}"
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return ""
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def _chat_streaming(self, payload: Dict, headers: Dict[str, str]) -> str:
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chunks: List[str] = []
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with requests.post(
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f"{self.base_url}/{self.endpoint}",
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json=payload,
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headers=headers,
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timeout=self.timeout_seconds,
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stream=True,
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) as response:
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response.raise_for_status()
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buffer = b""
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for part in response.iter_content(chunk_size=None):
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if not part:
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continue
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buffer += part
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while b"\n\n" in buffer:
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event, buffer = buffer.split(b"\n\n", 1)
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try:
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event_text = event.decode("utf-8")
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except UnicodeDecodeError:
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buffer = event + b"\n\n" + buffer
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break
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text_piece, done = self._parse_sse_event(event_text)
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if text_piece:
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chunks.append(text_piece)
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if done:
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final_text = "".join(chunks).strip()
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if final_text:
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return final_text
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self.last_error = f"empty_stream_output:{self.model}"
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return ""
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final_text = "".join(chunks).strip()
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if final_text:
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return final_text
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self.last_error = f"empty_stream_output:{self.model}"
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return ""
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@staticmethod
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def _build_messages(system_prompt: str, user_prompt: str, image_urls: List[str]) -> List[Dict]:
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user_content: str | List[Dict[str, object]]
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if image_urls:
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content_parts: List[Dict[str, object]] = [{"type": "text", "text": user_prompt}]
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for image_url in image_urls:
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if image_url:
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content_parts.append({"type": "image_url", "image_url": {"url": image_url}})
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user_content = content_parts
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else:
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user_content = user_prompt
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return [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_content},
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]
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@staticmethod
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def _extract_text(data: Dict) -> str:
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choices = data.get("choices") or []
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if choices:
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message = choices[0].get("message", {}) or {}
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content = message.get("content")
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if isinstance(content, str) and content.strip():
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return content.strip()
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if isinstance(content, list):
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parts = []
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for item in content:
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if isinstance(item, dict):
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text = item.get("text") or item.get("content")
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if isinstance(text, str) and text.strip():
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parts.append(text.strip())
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if parts:
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return "\n".join(parts).strip()
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for key in ("reasoning_content", "text", "output_text"):
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value = message.get(key)
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if isinstance(value, str) and value.strip():
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return value.strip()
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for key in ("output_text", "text", "answer", "response"):
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value = data.get(key)
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if isinstance(value, str) and value.strip():
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return value.strip()
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return ""
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@classmethod
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def _parse_sse_event(cls, event_text: str) -> tuple[str, bool]:
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lines = [line.strip() for line in event_text.splitlines() if line.strip()]
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data_lines = [line[5:].strip() for line in lines if line.startswith("data:")]
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if not data_lines:
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return "", False
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data = "\n".join(data_lines)
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if data == "[DONE]":
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return "", True
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obj = json.loads(data)
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choice = (obj.get("choices") or [{}])[0]
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delta = choice.get("delta") or {}
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content = delta.get("content")
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if isinstance(content, str):
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return content, False
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if isinstance(content, list):
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parts = []
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for item in content:
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if isinstance(item, dict):
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text = item.get("text") or item.get("content")
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if isinstance(text, str):
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parts.append(text)
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return "".join(parts), False
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return "", False
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