333 lines
10 KiB
Python
333 lines
10 KiB
Python
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"""
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LLM Provider Factory and Configuration Management.
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[AC-ASA-14, AC-ASA-15, AC-ASA-16, AC-ASA-17, AC-ASA-18] LLM provider management.
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Design pattern: Factory pattern for pluggable LLM providers.
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"""
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import logging
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from dataclasses import dataclass, field
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from typing import Any
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from app.services.llm.base import LLMClient, LLMConfig
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from app.services.llm.openai_client import OpenAIClient
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logger = logging.getLogger(__name__)
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@dataclass
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class LLMProviderInfo:
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"""Information about an LLM provider."""
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name: str
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display_name: str
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description: str
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config_schema: dict[str, Any]
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LLM_PROVIDERS: dict[str, LLMProviderInfo] = {
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"openai": LLMProviderInfo(
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name="openai",
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display_name="OpenAI",
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description="OpenAI GPT 系列模型 (GPT-4, GPT-3.5 等)",
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config_schema={
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"api_key": {
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"type": "string",
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"description": "API Key",
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"required": True,
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"secret": True,
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},
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"base_url": {
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"type": "string",
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"description": "API Base URL",
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"default": "https://api.openai.com/v1",
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},
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"model": {
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"type": "string",
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"description": "模型名称",
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"default": "gpt-4o-mini",
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},
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"max_tokens": {
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"type": "integer",
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"description": "最大输出 Token 数",
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"default": 2048,
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},
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"temperature": {
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"type": "number",
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"description": "温度参数 (0-2)",
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"default": 0.7,
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},
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},
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),
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"ollama": LLMProviderInfo(
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name="ollama",
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display_name="Ollama",
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description="Ollama 本地模型 (Llama, Qwen 等)",
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config_schema={
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"base_url": {
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"type": "string",
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"description": "Ollama API 地址",
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"default": "http://localhost:11434/v1",
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},
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"model": {
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"type": "string",
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"description": "模型名称",
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"default": "llama3.2",
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},
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"max_tokens": {
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"type": "integer",
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"description": "最大输出 Token 数",
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"default": 2048,
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},
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"temperature": {
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"type": "number",
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"description": "温度参数 (0-2)",
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"default": 0.7,
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},
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},
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),
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"azure": LLMProviderInfo(
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name="azure",
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display_name="Azure OpenAI",
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description="Azure OpenAI 服务",
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config_schema={
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"api_key": {
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"type": "string",
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"description": "API Key",
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"required": True,
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"secret": True,
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},
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"base_url": {
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"type": "string",
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"description": "Azure Endpoint",
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"required": True,
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},
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"model": {
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"type": "string",
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"description": "部署名称",
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"required": True,
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},
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"api_version": {
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"type": "string",
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"description": "API 版本",
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"default": "2024-02-15-preview",
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},
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"max_tokens": {
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"type": "integer",
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"description": "最大输出 Token 数",
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"default": 2048,
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},
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"temperature": {
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"type": "number",
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"description": "温度参数 (0-2)",
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"default": 0.7,
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},
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},
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),
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}
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class LLMProviderFactory:
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"""
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Factory for creating LLM clients.
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[AC-ASA-14, AC-ASA-15] Dynamic provider creation.
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"""
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@classmethod
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def get_providers(cls) -> list[LLMProviderInfo]:
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"""Get all registered LLM providers."""
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return list(LLM_PROVIDERS.values())
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@classmethod
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def get_provider_info(cls, name: str) -> LLMProviderInfo | None:
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"""Get provider info by name."""
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return LLM_PROVIDERS.get(name)
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@classmethod
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def create_client(
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cls,
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provider: str,
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config: dict[str, Any],
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) -> LLMClient:
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"""
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Create an LLM client for the specified provider.
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[AC-ASA-15] Factory method for client creation.
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Args:
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provider: Provider name (openai, ollama, azure)
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config: Provider configuration
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Returns:
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LLMClient instance
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Raises:
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ValueError: If provider is not supported
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"""
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if provider not in LLM_PROVIDERS:
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raise ValueError(f"Unsupported LLM provider: {provider}")
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if provider in ("openai", "ollama", "azure"):
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return OpenAIClient(
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api_key=config.get("api_key"),
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base_url=config.get("base_url"),
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model=config.get("model"),
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default_config=LLMConfig(
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model=config.get("model", "gpt-4o-mini"),
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max_tokens=config.get("max_tokens", 2048),
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temperature=config.get("temperature", 0.7),
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),
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)
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raise ValueError(f"Unsupported LLM provider: {provider}")
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class LLMConfigManager:
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"""
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Manager for LLM configuration.
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[AC-ASA-16, AC-ASA-17, AC-ASA-18] Configuration management with hot-reload.
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"""
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def __init__(self):
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self._current_provider: str = "openai"
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self._current_config: dict[str, Any] = {}
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self._client: LLMClient | None = None
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def get_current_config(self) -> dict[str, Any]:
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"""Get current LLM configuration."""
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return {
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"provider": self._current_provider,
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"config": self._current_config,
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}
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async def update_config(
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self,
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provider: str,
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config: dict[str, Any],
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) -> bool:
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"""
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Update LLM configuration.
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[AC-ASA-16] Hot-reload configuration.
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Args:
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provider: Provider name
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config: New configuration
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Returns:
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True if update successful
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"""
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if provider not in LLM_PROVIDERS:
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raise ValueError(f"Unsupported LLM provider: {provider}")
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provider_info = LLM_PROVIDERS[provider]
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validated_config = self._validate_config(provider_info, config)
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if self._client:
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await self._client.close()
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self._client = None
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self._current_provider = provider
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self._current_config = validated_config
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logger.info(f"[AC-ASA-16] LLM config updated: provider={provider}")
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return True
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def _validate_config(
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self,
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provider_info: LLMProviderInfo,
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config: dict[str, Any],
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) -> dict[str, Any]:
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"""Validate configuration against provider schema."""
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validated = {}
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for key, schema in provider_info.config_schema.items():
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if key in config:
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validated[key] = config[key]
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elif "default" in schema:
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validated[key] = schema["default"]
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elif schema.get("required"):
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raise ValueError(f"Missing required config: {key}")
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return validated
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def get_client(self) -> LLMClient:
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"""Get or create LLM client with current config."""
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if self._client is None:
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self._client = LLMProviderFactory.create_client(
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self._current_provider,
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self._current_config,
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)
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return self._client
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async def test_connection(
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self,
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test_prompt: str = "你好，请简单介绍一下自己。",
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provider: str | None = None,
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config: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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"""
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Test LLM connection.
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[AC-ASA-17, AC-ASA-18] Connection testing.
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Args:
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test_prompt: Test prompt to send
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provider: Optional provider to test (uses current if not specified)
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config: Optional config to test (uses current if not specified)
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Returns:
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Test result with success status, response, and metrics
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"""
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import time
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test_provider = provider or self._current_provider
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test_config = config or self._current_config
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if test_provider not in LLM_PROVIDERS:
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return {
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"success": False,
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"error": f"Unsupported provider: {test_provider}",
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}
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try:
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client = LLMProviderFactory.create_client(test_provider, test_config)
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start_time = time.time()
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response = await client.generate(
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messages=[{"role": "user", "content": test_prompt}],
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)
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latency_ms = (time.time() - start_time) * 1000
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await client.close()
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return {
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"success": True,
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"response": response.content,
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"latency_ms": round(latency_ms, 2),
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"prompt_tokens": response.usage.get("prompt_tokens", 0),
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"completion_tokens": response.usage.get("completion_tokens", 0),
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"total_tokens": response.usage.get("total_tokens", 0),
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"model": response.model,
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"message": f"连接成功，模型: {response.model}",
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}
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except Exception as e:
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logger.error(f"[AC-ASA-18] LLM test failed: {e}")
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return {
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"success": False,
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"error": str(e),
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"message": f"连接失败: {str(e)}",
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}
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async def close(self) -> None:
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"""Close the current client."""
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if self._client:
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await self._client.close()
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self._client = None
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_llm_config_manager: LLMConfigManager | None = None
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def get_llm_config_manager() -> LLMConfigManager:
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"""Get or create LLM config manager instance."""
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global _llm_config_manager
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if _llm_config_manager is None:
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_llm_config_manager = LLMConfigManager()
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return _llm_config_manager
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