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RuoYi-Vue3-FastAPI/ruoyi-fastapi-backend/utils/ai_util.py
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2026-02-12 10:46:37 +08:00

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6.3 KiB
Python

from importlib import import_module
from typing import TYPE_CHECKING
from config.database import async_engine
from config.env import DataBaseConfig
if TYPE_CHECKING:
from agno.db.base import AsyncBaseDb
from agno.models.base import Model
# 提供商名称 -> (模块路径, 类名) 的映射,延迟导入避免启动时加载所有AI SDK
_PROVIDER_REGISTRY: dict[str, tuple[str, str]] = {
'AIMLAPI': ('agno.models.aimlapi', 'AIMLAPI'),
'Anthropic': ('agno.models.anthropic', 'Claude'),
'Cerebras': ('agno.models.cerebras', 'Cerebras'),
'CerebrasOpenAI': ('agno.models.cerebras', 'CerebrasOpenAI'),
'Cohere': ('agno.models.cohere', 'Cohere'),
'CometAPI': ('agno.models.cometapi', 'CometAPI'),
'DashScope': ('agno.models.dashscope', 'DashScope'),
'DeepInfra': ('agno.models.deepinfra', 'DeepInfra'),
'DeepSeek': ('agno.models.deepseek', 'DeepSeek'),
'Fireworks': ('agno.models.fireworks', 'Fireworks'),
'Google': ('agno.models.google', 'Gemini'),
'Groq': ('agno.models.groq', 'Groq'),
'HuggingFace': ('agno.models.huggingface', 'HuggingFace'),
'LangDB': ('agno.models.langdb', 'LangDB'),
'LiteLLM': ('agno.models.litellm', 'LiteLLM'),
'LiteLLMOpenAI': ('agno.models.litellm', 'LiteLLMOpenAI'),
'LlamaCpp': ('agno.models.llama_cpp', 'LlamaCpp'),
'LMStudio': ('agno.models.lmstudio', 'LMStudio'),
'Meta': ('agno.models.meta', 'Llama'),
'Mistral': ('agno.models.mistral', 'MistralChat'),
'N1N': ('agno.models.n1n', 'N1N'),
'Nebius': ('agno.models.nebius', 'Nebius'),
'Nexus': ('agno.models.nexus', 'Nexus'),
'Nvidia': ('agno.models.nvidia', 'Nvidia'),
'Ollama': ('agno.models.ollama', 'Ollama'),
'OpenAI': ('agno.models.openai', 'OpenAIChat'),
'OpenAIResponses': ('agno.models.openai.responses', 'OpenAIResponses'),
'OpenRouter': ('agno.models.openrouter', 'OpenRouter'),
'Perplexity': ('agno.models.perplexity', 'Perplexity'),
'Portkey': ('agno.models.portkey', 'Portkey'),
'Requesty': ('agno.models.requesty', 'Requesty'),
'Sambanova': ('agno.models.sambanova', 'Sambanova'),
'SiliconFlow': ('agno.models.siliconflow', 'Siliconflow'),
'Together': ('agno.models.together', 'Together'),
'Vercel': ('agno.models.vercel', 'V0'),
'VLLM': ('agno.models.vllm', 'VLLM'),
'xAI': ('agno.models.xai', 'xAI'),
}
# 存储引擎名称 -> (模块路径, 类名) 的映射
_STORAGE_ENGINE_REGISTRY: dict[str, tuple[str, str]] = {
'mysql': ('agno.db.mysql', 'AsyncMySQLDb'),
'postgresql': ('agno.db.postgres', 'AsyncPostgresDb'),
}
# 已加载的提供商类缓存,避免重复import_module
_provider_class_cache: dict[str, 'type[Model]'] = {}
_storage_class_cache: dict[str, 'type[AsyncBaseDb]'] = {}
class AiUtil:
"""
AI工具类
"""
@classmethod
def _resolve_provider_class(cls, provider: str) -> 'type[Model] | None':
"""
按需加载并缓存提供商模型类
:param provider: 提供商名称
:return: 模型类,未找到返回None
"""
if provider in _provider_class_cache:
return _provider_class_cache[provider]
entry = _PROVIDER_REGISTRY.get(provider)
if entry is None:
return None
module_path, class_name = entry
provider_cls = getattr(import_module(module_path), class_name)
_provider_class_cache[provider] = provider_cls
return provider_cls
@classmethod
def _resolve_storage_class(cls, db_type: str) -> 'type[AsyncBaseDb]':
"""
按需加载并缓存存储引擎类
:param db_type: 数据库类型
:return: 存储引擎类
"""
if db_type in _storage_class_cache:
return _storage_class_cache[db_type]
entry = _STORAGE_ENGINE_REGISTRY.get(db_type)
if entry is None:
# 默认使用MySQL
entry = _STORAGE_ENGINE_REGISTRY['mysql']
module_path, class_name = entry
storage_cls = getattr(import_module(module_path), class_name)
_storage_class_cache[db_type] = storage_cls
return storage_cls
@classmethod
def get_storage_engine(cls) -> 'AsyncBaseDb':
"""
获取存储引擎实例
:return: 存储引擎实例
"""
storage_engine_class = cls._resolve_storage_class(DataBaseConfig.db_type)
return storage_engine_class(
db_engine=async_engine,
db_schema=DataBaseConfig.db_database if DataBaseConfig.db_type == 'mysql' else 'public',
session_table='ai_sessions',
memory_table='ai_memories',
metrics_table='ai_metrics',
eval_table='ai_eval_runs',
knowledge_table='ai_knowledge',
culture_table='ai_culture',
traces_table='ai_traces',
spans_table='ai_spans',
versions_table='ai_schema_versions',
create_schema=False,
)
@classmethod
def get_model_from_factory(
cls,
provider: str,
model_code: str,
model_name: str | None = None,
api_key: str | None = None,
base_url: str | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
**kwargs,
) -> 'Model':
"""
从工厂获取模型实例
:param provider: 提供商
:param model_code: 模型编码
:param model_name: 模型名称
:param api_key: API密钥
:param base_url: 基础URL
:param temperature: 温度
:param max_tokens: 最大令牌数
:return: 模型实例
"""
params = {
'id': model_code,
'name': model_name,
'base_url': base_url,
'api_key': api_key,
'temperature': temperature,
'max_tokens': max_tokens,
**kwargs,
}
params = {k: v for k, v in params.items() if v is not None}
if provider == 'Ollama':
params['host'] = base_url
if provider == 'DashScope' and not base_url:
params['base_url'] = 'https://dashscope.aliyuncs.com/compatible-mode/v1'
model_class = cls._resolve_provider_class(provider)
if model_class is None:
# 未知提供商,回退到OpenAI
model_class = cls._resolve_provider_class('OpenAI')
return model_class(**params)