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✨ feat(llm): 实现LLM服务模块，支持多提供商统一接口和高级功能 (#1923)
* ✨ feat(llm): 实现LLM服务模块，支持多提供商统一接口和高级功能 * 🎨 Ruff * ✨ Config配置类支持BaseModel存储 * 🎨 代码格式化 * 🎨 代码格式化 * 🎨 格式化代码 * ✨ feat(llm): 添加 AI 对话历史管理 * ✨ feat(llmConfig): 引入 LLM 配置模型及管理功能 * 🎨 Ruff --------- Co-authored-by: fccckaug <xxxmio123123@gmail.com> Co-authored-by: HibiKier <45528451+HibiKier@users.noreply.github.com> Co-authored-by: HibiKier <775757368@qq.com> Co-authored-by: fccckaug <xxxmcsmiomio3@gmail.com> Co-authored-by: webjoin111 <455457521@qq.com>
This commit is contained in:
Rumio
co-authored by
fccckaug
HibiKier
HibiKier
fccckaug
webjoin111
fccckaug
HibiKier
HibiKier
fccckaug
webjoin111
parent
14f5842f10
commit
a020ea5c87
@@ -0,0 +1,632 @@
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"""
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LLM 模型实现类
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包含 LLM 模型的抽象基类和具体实现，负责与各种 AI 提供商的 API 交互。
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"""
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from abc import ABC, abstractmethod
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from collections.abc import Awaitable, Callable
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import json
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from typing import Any
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from zhenxun.services.log import logger
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from .config import LLMGenerationConfig
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from .config.providers import get_ai_config
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from .core import (
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KeyStatusStore,
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LLMHttpClient,
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RetryConfig,
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http_client_manager,
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with_smart_retry,
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)
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from .types import (
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EmbeddingTaskType,
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LLMErrorCode,
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LLMException,
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LLMMessage,
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LLMResponse,
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LLMTool,
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ModelDetail,
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ProviderConfig,
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)
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class LLMModelBase(ABC):
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"""LLM模型抽象基类"""
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@abstractmethod
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async def generate_text(
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self,
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prompt: str,
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history: list[dict[str, str]] | None = None,
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**kwargs: Any,
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) -> str:
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"""生成文本"""
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pass
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@abstractmethod
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async def generate_response(
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self,
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messages: list[LLMMessage],
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config: LLMGenerationConfig | None = None,
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tools: list[LLMTool] | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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**kwargs: Any,
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) -> LLMResponse:
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"""生成高级响应"""
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pass
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@abstractmethod
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async def generate_embeddings(
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self,
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texts: list[str],
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task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
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**kwargs: Any,
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) -> list[list[float]]:
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"""生成文本嵌入向量"""
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pass
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class LLMModel(LLMModelBase):
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"""LLM 模型实现类"""
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def __init__(
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self,
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provider_config: ProviderConfig,
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model_detail: ModelDetail,
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key_store: KeyStatusStore,
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http_client: LLMHttpClient,
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config_override: LLMGenerationConfig | None = None,
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):
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self.provider_config = provider_config
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self.model_detail = model_detail
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self.key_store = key_store
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self.http_client: LLMHttpClient = http_client
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self._generation_config = config_override
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self.provider_name = provider_config.name
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self.api_type = provider_config.api_type
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self.api_base = provider_config.api_base
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self.api_keys = (
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[provider_config.api_key]
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if isinstance(provider_config.api_key, str)
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else provider_config.api_key
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)
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self.model_name = model_detail.model_name
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self.temperature = model_detail.temperature
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self.max_tokens = model_detail.max_tokens
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self._is_closed = False
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async def _get_http_client(self) -> LLMHttpClient:
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"""获取HTTP客户端"""
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if self.http_client.is_closed:
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logger.debug(
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f"LLMModel {self.provider_name}/{self.model_name} 的 HTTP 客户端已关闭,"
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"正在获取新的客户端"
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)
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self.http_client = await http_client_manager.get_client(
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self.provider_config
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)
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return self.http_client
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async def _select_api_key(self, failed_keys: set[str] | None = None) -> str:
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"""选择可用的API密钥（使用轮询策略）"""
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if not self.api_keys:
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raise LLMException(
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f"提供商 {self.provider_name} 没有配置API密钥",
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code=LLMErrorCode.NO_AVAILABLE_KEYS,
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)
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selected_key = await self.key_store.get_next_available_key(
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self.provider_name, self.api_keys, failed_keys
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)
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if not selected_key:
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raise LLMException(
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f"提供商 {self.provider_name} 的所有API密钥当前都不可用",
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code=LLMErrorCode.NO_AVAILABLE_KEYS,
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details={
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"total_keys": len(self.api_keys),
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"failed_keys": len(failed_keys or set()),
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},
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)
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return selected_key
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async def _execute_embedding_request(
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self,
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adapter,
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texts: list[str],
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task_type: EmbeddingTaskType | str,
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http_client: LLMHttpClient,
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failed_keys: set[str] | None = None,
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) -> list[list[float]]:
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"""执行单次嵌入请求 - 供重试机制调用"""
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api_key = await self._select_api_key(failed_keys)
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try:
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request_data = adapter.prepare_embedding_request(
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model=self,
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api_key=api_key,
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texts=texts,
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task_type=task_type,
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)
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http_response = await http_client.post(
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request_data.url,
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headers=request_data.headers,
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json=request_data.body,
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)
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if http_response.status_code != 200:
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error_text = http_response.text
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logger.error(
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f"HTTP嵌入请求失败: {http_response.status_code} - {error_text}"
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)
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await self.key_store.record_failure(api_key, http_response.status_code)
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error_code = LLMErrorCode.API_REQUEST_FAILED
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if http_response.status_code in [401, 403]:
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error_code = LLMErrorCode.API_KEY_INVALID
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elif http_response.status_code == 429:
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error_code = LLMErrorCode.API_RATE_LIMITED
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raise LLMException(
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f"HTTP嵌入请求失败: {http_response.status_code}",
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code=error_code,
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details={
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"status_code": http_response.status_code,
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"response": error_text,
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"api_key": api_key,
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},
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)
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try:
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response_json = http_response.json()
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adapter.validate_embedding_response(response_json)
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embeddings = adapter.parse_embedding_response(response_json)
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except Exception as e:
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logger.error(f"解析嵌入响应失败: {e}", e=e)
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await self.key_store.record_failure(api_key, None)
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if isinstance(e, LLMException):
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raise
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else:
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raise LLMException(
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f"解析API嵌入响应失败: {e}",
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code=LLMErrorCode.RESPONSE_PARSE_ERROR,
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cause=e,
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)
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await self.key_store.record_success(api_key)
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return embeddings
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except LLMException:
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raise
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except Exception as e:
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logger.error(f"生成嵌入时发生未预期错误: {e}", e=e)
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await self.key_store.record_failure(api_key, None)
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raise LLMException(
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f"生成嵌入失败: {e}",
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code=LLMErrorCode.EMBEDDING_FAILED,
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cause=e,
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)
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async def _execute_with_smart_retry(
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self,
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adapter,
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messages: list[LLMMessage],
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config: LLMGenerationConfig | None,
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tools_dict: list[dict[str, Any]] | None,
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tool_choice: str | dict[str, Any] | None,
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http_client: LLMHttpClient,
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):
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"""智能重试机制 - 使用统一的重试装饰器"""
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ai_config = get_ai_config()
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max_retries = ai_config.get("max_retries_llm", 3)
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retry_delay = ai_config.get("retry_delay_llm", 2)
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retry_config = RetryConfig(max_retries=max_retries, retry_delay=retry_delay)
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return await with_smart_retry(
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self._execute_single_request,
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adapter,
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messages,
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config,
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tools_dict,
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tool_choice,
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http_client,
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retry_config=retry_config,
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key_store=self.key_store,
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provider_name=self.provider_name,
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)
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async def _execute_single_request(
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self,
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adapter,
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messages: list[LLMMessage],
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config: LLMGenerationConfig | None,
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tools_dict: list[dict[str, Any]] | None,
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tool_choice: str | dict[str, Any] | None,
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http_client: LLMHttpClient,
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failed_keys: set[str] | None = None,
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) -> LLMResponse:
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"""执行单次请求 - 供重试机制调用，直接返回 LLMResponse"""
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api_key = await self._select_api_key(failed_keys)
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try:
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request_data = adapter.prepare_advanced_request(
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model=self,
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api_key=api_key,
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messages=messages,
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config=config,
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tools=tools_dict,
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tool_choice=tool_choice,
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)
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http_response = await http_client.post(
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request_data.url,
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headers=request_data.headers,
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json=request_data.body,
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)
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if http_response.status_code != 200:
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error_text = http_response.text
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logger.error(
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f"HTTP请求失败: {http_response.status_code} - {error_text}"
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)
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await self.key_store.record_failure(api_key, http_response.status_code)
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if http_response.status_code in [401, 403]:
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error_code = LLMErrorCode.API_KEY_INVALID
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elif http_response.status_code == 429:
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error_code = LLMErrorCode.API_RATE_LIMITED
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elif http_response.status_code in [402, 413]:
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error_code = LLMErrorCode.API_QUOTA_EXCEEDED
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else:
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error_code = LLMErrorCode.API_REQUEST_FAILED
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raise LLMException(
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f"HTTP请求失败: {http_response.status_code}",
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code=error_code,
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details={
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"status_code": http_response.status_code,
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"response": error_text,
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"api_key": api_key,
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},
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)
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try:
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response_json = http_response.json()
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response_data = adapter.parse_response(
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model=self,
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response_json=response_json,
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is_advanced=True,
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)
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from .types.models import LLMToolCall
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response_tool_calls = []
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if response_data.tool_calls:
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for tc_data in response_data.tool_calls:
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if isinstance(tc_data, LLMToolCall):
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response_tool_calls.append(tc_data)
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elif isinstance(tc_data, dict):
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try:
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response_tool_calls.append(LLMToolCall(**tc_data))
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except Exception as e:
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logger.warning(
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f"无法将工具调用数据转换为LLMToolCall: {tc_data}, "
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f"error: {e}"
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)
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else:
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logger.warning(f"工具调用数据格式未知: {tc_data}")
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llm_response = LLMResponse(
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text=response_data.text,
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usage_info=response_data.usage_info,
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raw_response=response_data.raw_response,
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tool_calls=response_tool_calls if response_tool_calls else None,
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code_executions=response_data.code_executions,
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grounding_metadata=response_data.grounding_metadata,
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cache_info=response_data.cache_info,
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)
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except Exception as e:
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logger.error(f"解析响应失败: {e}", e=e)
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await self.key_store.record_failure(api_key, None)
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if isinstance(e, LLMException):
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raise
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else:
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raise LLMException(
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f"解析API响应失败: {e}",
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code=LLMErrorCode.RESPONSE_PARSE_ERROR,
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cause=e,
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)
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await self.key_store.record_success(api_key)
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return llm_response
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except LLMException:
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raise
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except Exception as e:
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logger.error(f"生成响应时发生未预期错误: {e}", e=e)
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await self.key_store.record_failure(api_key, None)
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raise LLMException(
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f"生成响应失败: {e}",
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code=LLMErrorCode.GENERATION_FAILED,
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cause=e,
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)
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async def close(self):
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"""
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标记模型实例的当前使用周期结束。
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共享的 HTTP 客户端由 LLMHttpClientManager 管理，不由 LLMModel 关闭。
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"""
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if self._is_closed:
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return
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self._is_closed = True
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logger.debug(
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f"LLMModel实例的使用周期已结束: {self} (共享HTTP客户端状态不受影响)"
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)
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async def __aenter__(self):
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if self._is_closed:
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logger.debug(
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f"Re-entering context for closed LLMModel {self}. "
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f"Resetting _is_closed to False."
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)
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self._is_closed = False
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self._check_not_closed()
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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"""异步上下文管理器出口"""
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_ = exc_type, exc_val, exc_tb
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await self.close()
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def _check_not_closed(self):
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"""检查实例是否已关闭"""
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if self._is_closed:
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raise RuntimeError(f"LLMModel实例已关闭: {self}")
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async def generate_text(
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self,
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prompt: str,
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history: list[dict[str, str]] | None = None,
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**kwargs: Any,
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) -> str:
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"""生成文本 - 通过 generate_response 实现"""
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self._check_not_closed()
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messages: list[LLMMessage] = []
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if history:
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for msg in history:
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role = msg.get("role", "user")
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content_text = msg.get("content", "")
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messages.append(LLMMessage(role=role, content=content_text))
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messages.append(LLMMessage.user(prompt))
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model_fields = getattr(LLMGenerationConfig, "model_fields", {})
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request_specific_config_dict = {
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k: v for k, v in kwargs.items() if k in model_fields
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}
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request_specific_config = None
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if request_specific_config_dict:
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request_specific_config = LLMGenerationConfig(
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**request_specific_config_dict
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)
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for key in request_specific_config_dict:
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kwargs.pop(key, None)
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response = await self.generate_response(
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messages,
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config=request_specific_config,
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**kwargs,
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)
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return response.text
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async def generate_response(
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self,
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messages: list[LLMMessage],
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config: LLMGenerationConfig | None = None,
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tools: list[LLMTool] | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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tool_executor: Callable[[str, dict[str, Any]], Awaitable[Any]] | None = None,
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max_tool_iterations: int = 5,
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**kwargs: Any,
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) -> LLMResponse:
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"""生成高级响应 - 实现完整的工具调用循环"""
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self._check_not_closed()
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from .adapters import get_adapter_for_api_type
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from .config.generation import create_generation_config_from_kwargs
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adapter = get_adapter_for_api_type(self.api_type)
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if not adapter:
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raise LLMException(
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f"未找到适用于 API 类型 '{self.api_type}' 的适配器",
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code=LLMErrorCode.CONFIGURATION_ERROR,
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)
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final_request_config = self._generation_config or LLMGenerationConfig()
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||||
if kwargs:
|
||||
kwargs_config = create_generation_config_from_kwargs(**kwargs)
|
||||
merged_dict = final_request_config.to_dict()
|
||||
merged_dict.update(kwargs_config.to_dict())
|
||||
final_request_config = LLMGenerationConfig(**merged_dict)
|
||||
|
||||
if config is not None:
|
||||
merged_dict = final_request_config.to_dict()
|
||||
merged_dict.update(config.to_dict())
|
||||
final_request_config = LLMGenerationConfig(**merged_dict)
|
||||
|
||||
tools_dict: list[dict[str, Any]] | None = None
|
||||
if tools:
|
||||
tools_dict = []
|
||||
for tool in tools:
|
||||
if hasattr(tool, "model_dump"):
|
||||
model_dump_func = getattr(tool, "model_dump")
|
||||
tools_dict.append(model_dump_func(exclude_none=True))
|
||||
elif isinstance(tool, dict):
|
||||
tools_dict.append(tool)
|
||||
else:
|
||||
try:
|
||||
tools_dict.append(dict(tool))
|
||||
except (TypeError, ValueError):
|
||||
logger.warning(f"工具 '{tool}' 无法转换为字典，已忽略。")
|
||||
|
||||
http_client = await self._get_http_client()
|
||||
current_messages = list(messages)
|
||||
|
||||
for iteration in range(max_tool_iterations):
|
||||
logger.debug(f"工具调用循环迭代: {iteration + 1}/{max_tool_iterations}")
|
||||
|
||||
llm_response = await self._execute_with_smart_retry(
|
||||
adapter,
|
||||
current_messages,
|
||||
final_request_config,
|
||||
tools_dict if iteration == 0 else None,
|
||||
tool_choice if iteration == 0 else None,
|
||||
http_client,
|
||||
)
|
||||
|
||||
response_tool_calls = llm_response.tool_calls or []
|
||||
|
||||
if not response_tool_calls or not tool_executor:
|
||||
logger.debug("模型未请求工具调用，或未提供工具执行器。返回当前响应。")
|
||||
return llm_response
|
||||
|
||||
logger.info(f"模型请求执行 {len(response_tool_calls)} 个工具。")
|
||||
|
||||
assistant_message_content = llm_response.text if llm_response.text else ""
|
||||
current_messages.append(
|
||||
LLMMessage.assistant_tool_calls(
|
||||
content=assistant_message_content, tool_calls=response_tool_calls
|
||||
)
|
||||
)
|
||||
|
||||
tool_response_messages: list[LLMMessage] = []
|
||||
for tool_call in response_tool_calls:
|
||||
tool_name = tool_call.function.name
|
||||
try:
|
||||
tool_args_dict = json.loads(tool_call.function.arguments)
|
||||
logger.debug(f"执行工具: {tool_name}，参数: {tool_args_dict}")
|
||||
|
||||
tool_result = await tool_executor(tool_name, tool_args_dict)
|
||||
logger.debug(
|
||||
f"工具 '{tool_name}' 执行结果: {str(tool_result)[:200]}..."
|
||||
)
|
||||
|
||||
tool_response_messages.append(
|
||||
LLMMessage.tool_response(
|
||||
tool_call_id=tool_call.id,
|
||||
function_name=tool_name,
|
||||
result=tool_result,
|
||||
)
|
||||
)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(
|
||||
f"工具 '{tool_name}' 参数JSON解析失败: "
|
||||
f"{tool_call.function.arguments}, 错误: {e}"
|
||||
)
|
||||
tool_response_messages.append(
|
||||
LLMMessage.tool_response(
|
||||
tool_call_id=tool_call.id,
|
||||
function_name=tool_name,
|
||||
result={
|
||||
"error": "Argument JSON parsing failed",
|
||||
"details": str(e),
|
||||
},
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"执行工具 '{tool_name}' 失败: {e}", e=e)
|
||||
tool_response_messages.append(
|
||||
LLMMessage.tool_response(
|
||||
tool_call_id=tool_call.id,
|
||||
function_name=tool_name,
|
||||
result={
|
||||
"error": "Tool execution failed",
|
||||
"details": str(e),
|
||||
},
|
||||
)
|
||||
)
|
||||
|
||||
current_messages.extend(tool_response_messages)
|
||||
|
||||
logger.warning(f"已达到最大工具调用迭代次数 ({max_tool_iterations})。")
|
||||
raise LLMException(
|
||||
"已达到最大工具调用迭代次数，但模型仍在请求工具调用或未提供最终文本回复。",
|
||||
code=LLMErrorCode.GENERATION_FAILED,
|
||||
details={
|
||||
"iterations": max_tool_iterations,
|
||||
"last_messages": current_messages[-2:],
|
||||
},
|
||||
)
|
||||
|
||||
async def generate_embeddings(
|
||||
self,
|
||||
texts: list[str],
|
||||
task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
|
||||
**kwargs: Any,
|
||||
) -> list[list[float]]:
|
||||
"""生成文本嵌入向量"""
|
||||
self._check_not_closed()
|
||||
if not texts:
|
||||
return []
|
||||
|
||||
from .adapters import get_adapter_for_api_type
|
||||
|
||||
adapter = get_adapter_for_api_type(self.api_type)
|
||||
if not adapter:
|
||||
raise LLMException(
|
||||
f"未找到适用于 API 类型 '{self.api_type}' 的嵌入适配器",
|
||||
code=LLMErrorCode.CONFIGURATION_ERROR,
|
||||
)
|
||||
|
||||
http_client = await self._get_http_client()
|
||||
|
||||
ai_config = get_ai_config()
|
||||
default_max_retries = ai_config.get("max_retries_llm", 3)
|
||||
default_retry_delay = ai_config.get("retry_delay_llm", 2)
|
||||
max_retries_embed = kwargs.get(
|
||||
"max_retries_embed", max(1, default_max_retries // 2)
|
||||
)
|
||||
retry_delay_embed = kwargs.get("retry_delay_embed", default_retry_delay / 2)
|
||||
|
||||
retry_config = RetryConfig(
|
||||
max_retries=max_retries_embed,
|
||||
retry_delay=retry_delay_embed,
|
||||
exponential_backoff=True,
|
||||
key_rotation=True,
|
||||
)
|
||||
|
||||
return await with_smart_retry(
|
||||
self._execute_embedding_request,
|
||||
adapter,
|
||||
texts,
|
||||
task_type,
|
||||
http_client,
|
||||
retry_config=retry_config,
|
||||
key_store=self.key_store,
|
||||
provider_name=self.provider_name,
|
||||
)
|
||||
|
||||
def __str__(self) -> str:
|
||||
status = "closed" if self._is_closed else "active"
|
||||
return f"LLMModel({self.provider_name}/{self.model_name}, {status})"
|
||||
|
||||
def __repr__(self) -> str:
|
||||
status = "closed" if self._is_closed else "active"
|
||||
return (
|
||||
f"LLMModel(provider={self.provider_name}, model={self.model_name}, "
|
||||
f"api_type={self.api_type}, status={status})"
|
||||
)
|
||||
Reference in New Issue
Block a user