refactor: centralize llm backend configuration
This commit is contained in:
liuwei
64
utils/ai/llm_registry.py
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64
utils/ai/llm_registry.py
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@@ -0,0 +1,64 @@
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from __future__ import annotations
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from pathlib import Path
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from typing import Any, Dict, Optional
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import yaml
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class LLMRegistry:
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"""从项目根 config.yaml 读取集中式 LLM 后端配置。"""
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_cache: Dict[str, Any] = {"mtime": None, "data": {}}
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@classmethod
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def get_root_config_path(cls) -> Path:
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return Path(__file__).resolve().parents[2] / "config.yaml"
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@classmethod
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def load_root_config(cls) -> Dict[str, Any]:
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path = cls.get_root_config_path()
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if not path.exists():
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return {}
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stat = path.stat()
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if cls._cache["mtime"] == stat.st_mtime and cls._cache["data"]:
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return cls._cache["data"]
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with open(path, "r", encoding="utf-8") as fp:
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data = yaml.safe_load(fp) or {}
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cls._cache = {"mtime": stat.st_mtime, "data": data}
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return data
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@classmethod
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def get_llm_config(cls) -> Dict[str, Any]:
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config = cls.load_root_config()
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llm_config = config.get("llm", {}) or {}
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return llm_config if isinstance(llm_config, dict) else {}
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@classmethod
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def get_backend(cls, backend_name: str) -> Dict[str, Any]:
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if not backend_name:
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return {}
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llm_config = cls.get_llm_config()
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backends = llm_config.get("backends", {}) or {}
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backend = backends.get(backend_name, {}) or {}
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return dict(backend) if isinstance(backend, dict) else {}
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@classmethod
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def resolve(cls, local_config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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local = dict(local_config or {})
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backend_name = (
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local.get("backend")
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or local.get("backend_name")
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or local.get("backend_ref")
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or ""
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)
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if not backend_name:
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return local
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merged = cls.get_backend(str(backend_name).strip())
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merged.update(local)
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merged["backend"] = backend_name
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return merged
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540
utils/ai/unified_llm.py
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540
utils/ai/unified_llm.py
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@@ -0,0 +1,540 @@
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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 Any, Dict, List, Optional, Tuple
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from urllib.parse import urlparse
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import requests
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from loguru import logger
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from utils.ai.llm_registry import LLMRegistry
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class UnifiedLLMClient:
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"""统一的 LLM 调用客户端，兼容 OpenAI-compatible 与 Dify。"""
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def __init__(self, config: Optional[Dict[str, Any]] = None):
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self.LOG = logger
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self.raw_config = config or {}
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self.config = self._normalize_config(self.raw_config)
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self.enabled = bool(self.config.get("enabled", True))
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self.provider = str(self.config.get("provider", "openai_compatible")).strip().lower()
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self.base_url = str(self.config.get("base_url", "")).rstrip("/")
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self.endpoint = str(self.config.get("endpoint", "")).lstrip("/")
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self.api_key = str(self.config.get("api_key", "")).strip()
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self.model = str(self.config.get("model", "")).strip()
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self.timeout_seconds = int(self.config.get("timeout_seconds", 60))
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self.timeout = self.timeout_seconds
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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", 1024))
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self.stream = bool(self.config.get("stream", False))
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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.mode = str(self.config.get("mode", "chat")).strip().lower()
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self.response_mode = str(self.config.get("response_mode", "blocking")).strip().lower()
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self.workflow_output_key = str(self.config.get("workflow_output_key", "text")).strip()
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self.default_system_prompt = str(self.config.get("system_prompt", "")).strip()
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self.last_error = ""
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def is_available(self) -> bool:
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if not self.enabled:
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return False
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if self.provider == "openai_compatible":
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return bool(self.base_url and self.endpoint and self.model)
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if self.provider == "dify":
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return bool(self.base_url and self.endpoint and self.api_key)
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return False
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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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result = self.generate(
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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user=user_id,
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image_urls=image_urls or [],
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)
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return (result or {}).get("text", "") or ""
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def run(
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self,
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prompt: str,
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user: str,
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inputs: Optional[Dict[str, Any]] = None,
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tag: str = "",
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) -> Optional[Dict[str, Any]]:
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if self.provider == "dify":
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return self.generate(prompt=prompt, user=user, inputs=inputs or {}, tag=tag)
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effective_prompt = prompt or self._stringify_inputs(inputs or {})
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return self.generate(
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system_prompt=self.default_system_prompt,
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user_prompt=effective_prompt,
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user=user,
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inputs=inputs or {},
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tag=tag,
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)
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def generate(
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self,
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prompt: str = "",
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user: str = "",
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inputs: Optional[Dict[str, Any]] = None,
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tag: str = "",
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system_prompt: str = "",
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user_prompt: str = "",
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image_urls: Optional[List[str]] = None,
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files: Optional[List[Dict[str, Any]]] = None,
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) -> Optional[Dict[str, Any]]:
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self.last_error = ""
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if not self.is_available():
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self.last_error = "client_unavailable"
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return None
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if self.provider == "dify":
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return self._generate_dify(
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prompt=prompt,
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user=user,
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inputs=inputs or {},
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tag=tag,
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files=files or [],
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)
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if self.provider == "openai_compatible":
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return self._generate_openai(
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system_prompt=system_prompt,
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user_prompt=user_prompt or prompt,
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user=user,
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image_urls=image_urls or [],
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)
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self.last_error = f"unsupported_provider:{self.provider}"
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return None
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def _generate_openai(
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self,
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system_prompt: str,
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user_prompt: str,
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user: str,
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image_urls: List[str],
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) -> Optional[Dict[str, Any]]:
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payload = {
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"model": self.model,
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"messages": self._build_messages(system_prompt or self.default_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,
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"stream": self.stream,
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}
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headers = {"Content-Type": "application/json"}
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if self.api_key:
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headers["Authorization"] = self._build_auth_header(self.api_key)
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url = f"{self.base_url}/{self.endpoint}"
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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, raw = self._request_openai_stream(url, payload, headers)
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else:
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text, raw = self._request_openai_json(url, payload, headers)
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if text:
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return {
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"text": text,
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"usage": self._extract_openai_usage(raw),
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"raw": raw,
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}
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self.last_error = f"empty_model_output:{self.model}"
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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 None
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def _generate_dify(
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self,
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prompt: str,
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user: str,
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inputs: Dict[str, Any],
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tag: str,
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files: List[Dict[str, Any]],
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) -> Optional[Dict[str, Any]]:
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headers = {
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"Authorization": self._build_auth_header(self.api_key),
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"Content-Type": "application/json",
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}
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payload_inputs = dict(inputs or {})
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if self.mode == "workflow":
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if prompt and "query" not in payload_inputs:
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payload_inputs["query"] = prompt
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payload = {
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"inputs": payload_inputs,
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"response_mode": self.response_mode,
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"user": user,
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"files": files,
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}
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elif self.mode == "completion":
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payload = {
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"inputs": payload_inputs,
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"query": prompt,
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"response_mode": self.response_mode,
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"user": user,
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"files": files,
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}
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else:
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payload = {
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"inputs": payload_inputs,
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"query": prompt,
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"response_mode": self.response_mode,
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"conversation_id": "",
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"user": user,
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"files": files,
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}
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url = f"{self.base_url}/{self.endpoint}"
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for attempt in range(1, self.max_retries + 1):
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try:
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if self.response_mode == "streaming":
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parsed = self._request_dify_stream(url, payload, headers, tag)
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else:
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response = requests.post(url, headers=headers, json=payload, timeout=self.timeout_seconds)
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response.raise_for_status()
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parsed = self._parse_dify_response(response.json())
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if parsed and parsed.get("text"):
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return parsed
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self.last_error = f"empty_model_output:{self.mode}"
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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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self.LOG.warning(f"[UnifiedLLMClient] Dify 请求失败: tag={tag}, attempt={attempt}, error={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 None
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def _request_openai_json(self, url: str, payload: Dict[str, Any], headers: Dict[str, str]) -> Tuple[str, Dict[str, Any]]:
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response = requests.post(url, json=payload, headers=headers, timeout=self.timeout_seconds)
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response.raise_for_status()
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data = response.json()
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return self._extract_openai_text(data), data
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def _request_openai_stream(
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self,
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url: str,
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payload: Dict[str, Any],
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headers: Dict[str, str],
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) -> Tuple[str, Dict[str, Any]]:
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chunks: List[str] = []
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with requests.post(url, json=payload, headers=headers, timeout=self.timeout_seconds, stream=True) 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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text_piece, done = self._parse_openai_sse_event(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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if text_piece:
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chunks.append(text_piece)
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if done:
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break
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return "".join(chunks).strip(), {"stream_text": "".join(chunks).strip()}
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def _request_dify_stream(
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self,
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url: str,
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payload: Dict[str, Any],
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headers: Dict[str, str],
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tag: str,
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) -> Optional[Dict[str, Any]]:
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with requests.post(url, headers=headers, json=payload, timeout=self.timeout_seconds, stream=True) as response:
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response.raise_for_status()
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event_name = ""
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text_fragments: List[str] = []
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final_payload = None
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for raw_line in response.iter_lines(decode_unicode=True):
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if raw_line is None:
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continue
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line = str(raw_line).strip()
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if not line:
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continue
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if line.startswith("event:"):
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event_name = line[6:].strip()
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continue
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if not line.startswith("data:"):
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continue
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data_text = line[5:].strip()
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if not data_text or data_text == "[DONE]":
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continue
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try:
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chunk = json.loads(data_text)
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except Exception:
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continue
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candidate_text = self._extract_dify_stream_text(chunk)
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if candidate_text:
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text_fragments.append(candidate_text)
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chunk_event = str(chunk.get("event") or event_name or "").strip()
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if chunk_event in {"workflow_finished", "message_end"}:
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final_payload = chunk
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if final_payload:
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parsed = self._parse_dify_response(final_payload)
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if parsed and parsed.get("text"):
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return parsed
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text = "".join(fragment for fragment in text_fragments if fragment).strip()
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if text:
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return {"text": text, "usage": {}, "raw": final_payload or {}}
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self.LOG.warning(f"[UnifiedLLMClient] Dify 流式响应未产出有效内容: tag={tag}")
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return None
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@staticmethod
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def _build_messages(system_prompt: str, user_prompt: str, image_urls: List[str]) -> List[Dict[str, Any]]:
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user_content: str | List[Dict[str, Any]]
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if image_urls:
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content_parts: List[Dict[str, Any]] = [{"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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messages: List[Dict[str, Any]] = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": user_content})
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return messages
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|
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@staticmethod
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def _extract_openai_text(data: Dict[str, Any]) -> 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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|
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@classmethod
|
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def _parse_openai_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)
|
||||
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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|
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def _parse_dify_response(self, data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
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if self.mode == "workflow":
|
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return self._parse_dify_workflow_response(data)
|
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answer = str(data.get("answer", "") or "").strip()
|
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usage = (data.get("metadata") or {}).get("usage", {}) or {}
|
||||
return {"text": answer, "usage": usage, "raw": data}
|
||||
|
||||
def _parse_dify_workflow_response(self, data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
|
||||
payload = (data or {}).get("data", {}) or {}
|
||||
outputs = payload.get("outputs", {}) or {}
|
||||
text = ""
|
||||
|
||||
for key in filter(None, [self.workflow_output_key, "text", "answer", "result_json", "result"]):
|
||||
if outputs.get(key) is not None:
|
||||
text = self._stringify_output(outputs.get(key))
|
||||
if text:
|
||||
break
|
||||
|
||||
if not text:
|
||||
for value in outputs.values():
|
||||
text = self._stringify_output(value)
|
||||
if text:
|
||||
break
|
||||
|
||||
usage = {
|
||||
"total_tokens": payload.get("total_tokens"),
|
||||
"latency": payload.get("elapsed_time"),
|
||||
}
|
||||
return {"text": text.strip(), "usage": usage, "raw": data}
|
||||
|
||||
def _extract_dify_stream_text(self, chunk: Dict[str, Any]) -> str:
|
||||
if not isinstance(chunk, dict):
|
||||
return ""
|
||||
payload = (chunk.get("data") or {}) if isinstance(chunk.get("data"), dict) else {}
|
||||
outputs = payload.get("outputs", {}) if isinstance(payload.get("outputs"), dict) else {}
|
||||
|
||||
for key in filter(None, [self.workflow_output_key, "text", "answer", "result_json", "result"]):
|
||||
if outputs.get(key) is not None:
|
||||
return self._stringify_output(outputs.get(key))
|
||||
|
||||
for key in ("text", "answer"):
|
||||
if chunk.get(key) is not None:
|
||||
return self._stringify_output(chunk.get(key))
|
||||
|
||||
return ""
|
||||
|
||||
@staticmethod
|
||||
def _extract_openai_usage(data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
usage = data.get("usage", {}) or {}
|
||||
if usage:
|
||||
return usage
|
||||
return {}
|
||||
|
||||
@staticmethod
|
||||
def _stringify_output(value: Any) -> str:
|
||||
if value is None:
|
||||
return ""
|
||||
if isinstance(value, str):
|
||||
return value.strip()
|
||||
if isinstance(value, (dict, list)):
|
||||
return json.dumps(value, ensure_ascii=False)
|
||||
return str(value).strip()
|
||||
|
||||
@classmethod
|
||||
def _normalize_config(cls, config: Dict[str, Any]) -> Dict[str, Any]:
|
||||
normalized = LLMRegistry.resolve(config or {})
|
||||
normalized["enabled"] = bool(
|
||||
normalized.get("enabled", normalized.get("enable", True))
|
||||
)
|
||||
|
||||
if not normalized.get("provider"):
|
||||
normalized["provider"] = cls._guess_provider(normalized)
|
||||
|
||||
parsed_url = cls._split_url(
|
||||
normalized.get("api_url")
|
||||
or normalized.get("url")
|
||||
)
|
||||
base_url = (
|
||||
normalized.get("base_url")
|
||||
or normalized.get("api_base_url")
|
||||
or parsed_url[0]
|
||||
or ""
|
||||
)
|
||||
endpoint = (
|
||||
normalized.get("endpoint")
|
||||
or parsed_url[1]
|
||||
or ""
|
||||
)
|
||||
|
||||
normalized["base_url"] = str(base_url).rstrip("/")
|
||||
normalized["endpoint"] = str(endpoint).lstrip("/")
|
||||
normalized["api_key"] = (
|
||||
normalized.get("api_key")
|
||||
or normalized.get("api-key")
|
||||
or normalized.get("authorization")
|
||||
or ""
|
||||
)
|
||||
normalized["timeout_seconds"] = int(
|
||||
normalized.get("timeout_seconds")
|
||||
or normalized.get("request_timeout_seconds")
|
||||
or normalized.get("request_timeout")
|
||||
or 60
|
||||
)
|
||||
normalized["max_retries"] = int(normalized.get("max_retries", len(normalized.get("retry_delays_seconds", [])) + 1 or 3))
|
||||
normalized["retry_delay_seconds"] = float(normalized.get("retry_delay_seconds", 1.0))
|
||||
normalized["response_mode"] = normalized.get("response_mode", "blocking")
|
||||
normalized["workflow_output_key"] = normalized.get("workflow_output_key", "text")
|
||||
|
||||
if normalized["provider"] == "dify":
|
||||
default_endpoint = cls._guess_dify_endpoint(normalized)
|
||||
if not normalized["endpoint"]:
|
||||
normalized["endpoint"] = default_endpoint
|
||||
else:
|
||||
if not normalized["endpoint"]:
|
||||
normalized["endpoint"] = "chat/completions"
|
||||
|
||||
return normalized
|
||||
|
||||
@staticmethod
|
||||
def _guess_provider(config: Dict[str, Any]) -> str:
|
||||
api_key = str(
|
||||
config.get("api_key")
|
||||
or config.get("api-key")
|
||||
or config.get("authorization")
|
||||
or ""
|
||||
).strip()
|
||||
url = str(config.get("api_url") or config.get("url") or config.get("endpoint") or "").lower()
|
||||
mode = str(config.get("mode", "")).lower()
|
||||
if "workflows/run" in url or "chat-messages" in url or "completion-messages" in url:
|
||||
return "dify"
|
||||
if api_key.startswith("app-") or mode in {"workflow", "completion"}:
|
||||
return "dify"
|
||||
return "openai_compatible"
|
||||
|
||||
@staticmethod
|
||||
def _guess_dify_endpoint(config: Dict[str, Any]) -> str:
|
||||
mode = str(config.get("mode", "chat")).strip().lower()
|
||||
if mode == "workflow":
|
||||
return "workflows/run"
|
||||
if mode == "completion":
|
||||
return "completion-messages"
|
||||
return "chat-messages"
|
||||
|
||||
@staticmethod
|
||||
def _split_url(url: Optional[str]) -> Tuple[str, str]:
|
||||
if not url:
|
||||
return "", ""
|
||||
parsed = urlparse(str(url))
|
||||
if not parsed.scheme or not parsed.netloc:
|
||||
return "", str(url)
|
||||
base = f"{parsed.scheme}://{parsed.netloc}"
|
||||
return base, parsed.path.lstrip("/")
|
||||
|
||||
@staticmethod
|
||||
def _build_auth_header(value: str) -> str:
|
||||
token = str(value or "").strip()
|
||||
if not token:
|
||||
return ""
|
||||
if token.lower().startswith("bearer "):
|
||||
return token
|
||||
return f"Bearer {token}"
|
||||
|
||||
@staticmethod
|
||||
def _stringify_inputs(inputs: Dict[str, Any]) -> str:
|
||||
if not inputs:
|
||||
return ""
|
||||
try:
|
||||
return json.dumps(inputs, ensure_ascii=False)
|
||||
except Exception:
|
||||
return str(inputs)
|
||||
Reference in New Issue
Block a user