mirror of
https://github.com/zhenxun-org/zhenxun_bot.git
synced 2026-10-11 06:39:59 +08:00
✨ 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 +1,508 @@
|
||||
"""
|
||||
LLM 适配器基类和通用数据结构
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from zhenxun.services.log import logger
|
||||
|
||||
from ..types.exceptions import LLMErrorCode, LLMException
|
||||
from ..types.models import LLMToolCall
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..config.generation import LLMGenerationConfig
|
||||
from ..service import LLMModel
|
||||
from ..types.content import LLMMessage
|
||||
from ..types.enums import EmbeddingTaskType
|
||||
|
||||
|
||||
class RequestData(BaseModel):
|
||||
"""请求数据封装"""
|
||||
|
||||
url: str
|
||||
headers: dict[str, str]
|
||||
body: dict[str, Any]
|
||||
|
||||
|
||||
class ResponseData(BaseModel):
|
||||
"""响应数据封装 - 支持所有高级功能"""
|
||||
|
||||
text: str
|
||||
usage_info: dict[str, Any] | None = None
|
||||
raw_response: dict[str, Any] | None = None
|
||||
tool_calls: list[LLMToolCall] | None = None
|
||||
code_executions: list[Any] | None = None
|
||||
grounding_metadata: Any | None = None
|
||||
cache_info: Any | None = None
|
||||
|
||||
code_execution_results: list[dict[str, Any]] | None = None
|
||||
search_results: list[dict[str, Any]] | None = None
|
||||
function_calls: list[dict[str, Any]] | None = None
|
||||
safety_ratings: list[dict[str, Any]] | None = None
|
||||
citations: list[dict[str, Any]] | None = None
|
||||
|
||||
|
||||
class BaseAdapter(ABC):
|
||||
"""LLM API适配器基类"""
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def api_type(self) -> str:
|
||||
"""API类型标识"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def supported_api_types(self) -> list[str]:
|
||||
"""支持的API类型列表"""
|
||||
pass
|
||||
|
||||
def prepare_simple_request(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
api_key: str,
|
||||
prompt: str,
|
||||
history: list[dict[str, str]] | None = None,
|
||||
) -> RequestData:
|
||||
"""准备简单文本生成请求
|
||||
|
||||
默认实现：将简单请求转换为高级请求格式
|
||||
子类可以重写此方法以提供特定的优化实现
|
||||
"""
|
||||
from ..types.content import LLMMessage
|
||||
|
||||
messages: list[LLMMessage] = []
|
||||
|
||||
if history:
|
||||
for msg in history:
|
||||
role = msg.get("role", "user")
|
||||
content = msg.get("content", "")
|
||||
messages.append(LLMMessage(role=role, content=content))
|
||||
|
||||
messages.append(LLMMessage(role="user", content=prompt))
|
||||
|
||||
config = model._generation_config
|
||||
|
||||
return self.prepare_advanced_request(
|
||||
model=model,
|
||||
api_key=api_key,
|
||||
messages=messages,
|
||||
config=config,
|
||||
tools=None,
|
||||
tool_choice=None,
|
||||
)
|
||||
|
||||
@abstractmethod
|
||||
def prepare_advanced_request(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
api_key: str,
|
||||
messages: list["LLMMessage"],
|
||||
config: "LLMGenerationConfig | None" = None,
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
tool_choice: str | dict[str, Any] | None = None,
|
||||
) -> RequestData:
|
||||
"""准备高级请求"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def parse_response(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
response_json: dict[str, Any],
|
||||
is_advanced: bool = False,
|
||||
) -> ResponseData:
|
||||
"""解析API响应"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def prepare_embedding_request(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
api_key: str,
|
||||
texts: list[str],
|
||||
task_type: "EmbeddingTaskType | str",
|
||||
**kwargs: Any,
|
||||
) -> RequestData:
|
||||
"""准备文本嵌入请求"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def parse_embedding_response(
|
||||
self, response_json: dict[str, Any]
|
||||
) -> list[list[float]]:
|
||||
"""解析文本嵌入响应"""
|
||||
pass
|
||||
|
||||
def validate_embedding_response(self, response_json: dict[str, Any]) -> None:
|
||||
"""验证嵌入API响应"""
|
||||
if "error" in response_json:
|
||||
error_info = response_json["error"]
|
||||
msg = (
|
||||
error_info.get("message", str(error_info))
|
||||
if isinstance(error_info, dict)
|
||||
else str(error_info)
|
||||
)
|
||||
raise LLMException(
|
||||
f"嵌入API错误: {msg}",
|
||||
code=LLMErrorCode.EMBEDDING_FAILED,
|
||||
details=response_json,
|
||||
)
|
||||
|
||||
def get_api_url(self, model: "LLMModel", endpoint: str) -> str:
|
||||
"""构建API URL"""
|
||||
if not model.api_base:
|
||||
raise LLMException(
|
||||
f"模型 {model.model_name} 的 api_base 未设置",
|
||||
code=LLMErrorCode.CONFIGURATION_ERROR,
|
||||
)
|
||||
return f"{model.api_base.rstrip('/')}{endpoint}"
|
||||
|
||||
def get_base_headers(self, api_key: str) -> dict[str, str]:
|
||||
"""获取基础请求头"""
|
||||
from zhenxun.utils.user_agent import get_user_agent
|
||||
|
||||
headers = get_user_agent()
|
||||
headers.update(
|
||||
{
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
}
|
||||
)
|
||||
return headers
|
||||
|
||||
def convert_messages_to_openai_format(
|
||||
self, messages: list["LLMMessage"]
|
||||
) -> list[dict[str, Any]]:
|
||||
"""将LLMMessage转换为OpenAI格式 - 通用方法"""
|
||||
openai_messages: list[dict[str, Any]] = []
|
||||
for msg in messages:
|
||||
openai_msg: dict[str, Any] = {"role": msg.role}
|
||||
|
||||
if msg.role == "tool":
|
||||
openai_msg["tool_call_id"] = msg.tool_call_id
|
||||
openai_msg["name"] = msg.name
|
||||
openai_msg["content"] = msg.content
|
||||
else:
|
||||
if isinstance(msg.content, str):
|
||||
openai_msg["content"] = msg.content
|
||||
else:
|
||||
content_parts = []
|
||||
for part in msg.content:
|
||||
if part.type == "text":
|
||||
content_parts.append({"type": "text", "text": part.text})
|
||||
elif part.type == "image":
|
||||
content_parts.append(
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"url": part.image_source},
|
||||
}
|
||||
)
|
||||
openai_msg["content"] = content_parts
|
||||
|
||||
if msg.role == "assistant" and msg.tool_calls:
|
||||
assistant_tool_calls = []
|
||||
for call in msg.tool_calls:
|
||||
assistant_tool_calls.append(
|
||||
{
|
||||
"id": call.id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": call.function.name,
|
||||
"arguments": call.function.arguments,
|
||||
},
|
||||
}
|
||||
)
|
||||
openai_msg["tool_calls"] = assistant_tool_calls
|
||||
|
||||
if msg.name and msg.role != "tool":
|
||||
openai_msg["name"] = msg.name
|
||||
|
||||
openai_messages.append(openai_msg)
|
||||
return openai_messages
|
||||
|
||||
def parse_openai_response(self, response_json: dict[str, Any]) -> ResponseData:
|
||||
"""解析OpenAI格式的响应 - 通用方法"""
|
||||
self.validate_response(response_json)
|
||||
|
||||
try:
|
||||
choices = response_json.get("choices", [])
|
||||
if not choices:
|
||||
logger.debug("OpenAI响应中没有choices，可能为空回复或流结束。")
|
||||
return ResponseData(text="", raw_response=response_json)
|
||||
|
||||
choice = choices[0]
|
||||
message = choice.get("message", {})
|
||||
content = message.get("content", "")
|
||||
|
||||
parsed_tool_calls: list[LLMToolCall] | None = None
|
||||
if message_tool_calls := message.get("tool_calls"):
|
||||
from ..types.models import LLMToolFunction
|
||||
|
||||
parsed_tool_calls = []
|
||||
for tc_data in message_tool_calls:
|
||||
try:
|
||||
if tc_data.get("type") == "function":
|
||||
parsed_tool_calls.append(
|
||||
LLMToolCall(
|
||||
id=tc_data["id"],
|
||||
function=LLMToolFunction(
|
||||
name=tc_data["function"]["name"],
|
||||
arguments=tc_data["function"]["arguments"],
|
||||
),
|
||||
)
|
||||
)
|
||||
except KeyError as e:
|
||||
logger.warning(
|
||||
f"解析OpenAI工具调用数据时缺少键: {tc_data}, 错误: {e}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"解析OpenAI工具调用数据时出错: {tc_data}, 错误: {e}"
|
||||
)
|
||||
if not parsed_tool_calls:
|
||||
parsed_tool_calls = None
|
||||
|
||||
final_text = content if content is not None else ""
|
||||
if not final_text and parsed_tool_calls:
|
||||
final_text = f"请求调用 {len(parsed_tool_calls)} 个工具。"
|
||||
|
||||
usage_info = response_json.get("usage")
|
||||
|
||||
return ResponseData(
|
||||
text=final_text,
|
||||
tool_calls=parsed_tool_calls,
|
||||
usage_info=usage_info,
|
||||
raw_response=response_json,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"解析OpenAI格式响应失败: {e}", e=e)
|
||||
raise LLMException(
|
||||
f"解析API响应失败: {e}",
|
||||
code=LLMErrorCode.RESPONSE_PARSE_ERROR,
|
||||
cause=e,
|
||||
)
|
||||
|
||||
def validate_response(self, response_json: dict[str, Any]) -> None:
|
||||
"""验证API响应，解析不同API的错误结构"""
|
||||
if "error" in response_json:
|
||||
error_info = response_json["error"]
|
||||
|
||||
if isinstance(error_info, dict):
|
||||
error_message = error_info.get("message", "未知错误")
|
||||
error_code = error_info.get("code", "unknown")
|
||||
error_type = error_info.get("type", "api_error")
|
||||
|
||||
error_code_mapping = {
|
||||
"invalid_api_key": LLMErrorCode.API_KEY_INVALID,
|
||||
"authentication_failed": LLMErrorCode.API_KEY_INVALID,
|
||||
"rate_limit_exceeded": LLMErrorCode.API_RATE_LIMITED,
|
||||
"quota_exceeded": LLMErrorCode.API_RATE_LIMITED,
|
||||
"model_not_found": LLMErrorCode.MODEL_NOT_FOUND,
|
||||
"invalid_model": LLMErrorCode.MODEL_NOT_FOUND,
|
||||
"context_length_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
|
||||
"max_tokens_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
|
||||
}
|
||||
|
||||
llm_error_code = error_code_mapping.get(
|
||||
error_code, LLMErrorCode.API_RESPONSE_INVALID
|
||||
)
|
||||
|
||||
logger.error(
|
||||
f"API返回错误: {error_message} "
|
||||
f"(代码: {error_code}, 类型: {error_type})"
|
||||
)
|
||||
else:
|
||||
error_message = str(error_info)
|
||||
error_code = "unknown"
|
||||
llm_error_code = LLMErrorCode.API_RESPONSE_INVALID
|
||||
|
||||
logger.error(f"API返回错误: {error_message}")
|
||||
|
||||
raise LLMException(
|
||||
f"API请求失败: {error_message}",
|
||||
code=llm_error_code,
|
||||
details={"api_error": error_info, "error_code": error_code},
|
||||
)
|
||||
|
||||
if "candidates" in response_json:
|
||||
candidates = response_json.get("candidates", [])
|
||||
if candidates:
|
||||
candidate = candidates[0]
|
||||
finish_reason = candidate.get("finishReason")
|
||||
if finish_reason in ["SAFETY", "RECITATION"]:
|
||||
safety_ratings = candidate.get("safetyRatings", [])
|
||||
logger.warning(
|
||||
f"Gemini内容被安全过滤: {finish_reason}, "
|
||||
f"安全评级: {safety_ratings}"
|
||||
)
|
||||
raise LLMException(
|
||||
f"内容被安全过滤: {finish_reason}",
|
||||
code=LLMErrorCode.CONTENT_FILTERED,
|
||||
details={
|
||||
"finish_reason": finish_reason,
|
||||
"safety_ratings": safety_ratings,
|
||||
},
|
||||
)
|
||||
|
||||
if not response_json:
|
||||
logger.error("API返回空响应")
|
||||
raise LLMException(
|
||||
"API返回空响应",
|
||||
code=LLMErrorCode.API_RESPONSE_INVALID,
|
||||
details={"response": response_json},
|
||||
)
|
||||
|
||||
def _apply_generation_config(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
config: "LLMGenerationConfig | None" = None,
|
||||
) -> dict[str, Any]:
|
||||
"""通用的配置应用逻辑"""
|
||||
if config is not None:
|
||||
return config.to_api_params(model.api_type, model.model_name)
|
||||
|
||||
if model._generation_config is not None:
|
||||
return model._generation_config.to_api_params(
|
||||
model.api_type, model.model_name
|
||||
)
|
||||
|
||||
base_config = {}
|
||||
if model.temperature is not None:
|
||||
base_config["temperature"] = model.temperature
|
||||
if model.max_tokens is not None:
|
||||
if model.api_type in ["gemini", "gemini_native"]:
|
||||
base_config["maxOutputTokens"] = model.max_tokens
|
||||
else:
|
||||
base_config["max_tokens"] = model.max_tokens
|
||||
|
||||
return base_config
|
||||
|
||||
def apply_config_override(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
body: dict[str, Any],
|
||||
config: "LLMGenerationConfig | None" = None,
|
||||
) -> dict[str, Any]:
|
||||
"""应用配置覆盖"""
|
||||
config_params = self._apply_generation_config(model, config)
|
||||
body.update(config_params)
|
||||
return body
|
||||
|
||||
|
||||
class OpenAICompatAdapter(BaseAdapter):
|
||||
"""
|
||||
处理所有 OpenAI 兼容 API 的通用适配器。
|
||||
消除 OpenAIAdapter 和 ZhipuAdapter 之间的代码重复。
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def get_chat_endpoint(self) -> str:
|
||||
"""子类必须实现，返回 chat completions 的端点"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_embedding_endpoint(self) -> str:
|
||||
"""子类必须实现，返回 embeddings 的端点"""
|
||||
pass
|
||||
|
||||
def prepare_advanced_request(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
api_key: str,
|
||||
messages: list["LLMMessage"],
|
||||
config: "LLMGenerationConfig | None" = None,
|
||||
tools: list[dict[str, Any]] | None = None,
|
||||
tool_choice: str | dict[str, Any] | None = None,
|
||||
) -> RequestData:
|
||||
"""准备高级请求 - OpenAI兼容格式"""
|
||||
url = self.get_api_url(model, self.get_chat_endpoint())
|
||||
headers = self.get_base_headers(api_key)
|
||||
openai_messages = self.convert_messages_to_openai_format(messages)
|
||||
|
||||
body = {
|
||||
"model": model.model_name,
|
||||
"messages": openai_messages,
|
||||
}
|
||||
|
||||
if tools:
|
||||
body["tools"] = tools
|
||||
if tool_choice:
|
||||
body["tool_choice"] = tool_choice
|
||||
|
||||
body = self.apply_config_override(model, body, config)
|
||||
return RequestData(url=url, headers=headers, body=body)
|
||||
|
||||
def parse_response(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
response_json: dict[str, Any],
|
||||
is_advanced: bool = False,
|
||||
) -> ResponseData:
|
||||
"""解析响应 - 直接使用基类的 OpenAI 格式解析"""
|
||||
_ = model, is_advanced # 未使用的参数
|
||||
return self.parse_openai_response(response_json)
|
||||
|
||||
def prepare_embedding_request(
|
||||
self,
|
||||
model: "LLMModel",
|
||||
api_key: str,
|
||||
texts: list[str],
|
||||
task_type: "EmbeddingTaskType | str",
|
||||
**kwargs: Any,
|
||||
) -> RequestData:
|
||||
"""准备嵌入请求 - OpenAI兼容格式"""
|
||||
_ = task_type # 未使用的参数
|
||||
url = self.get_api_url(model, self.get_embedding_endpoint())
|
||||
headers = self.get_base_headers(api_key)
|
||||
|
||||
body = {
|
||||
"model": model.model_name,
|
||||
"input": texts,
|
||||
}
|
||||
|
||||
# 应用额外的配置参数
|
||||
if kwargs:
|
||||
body.update(kwargs)
|
||||
|
||||
return RequestData(url=url, headers=headers, body=body)
|
||||
|
||||
def parse_embedding_response(
|
||||
self, response_json: dict[str, Any]
|
||||
) -> list[list[float]]:
|
||||
"""解析嵌入响应 - OpenAI兼容格式"""
|
||||
self.validate_embedding_response(response_json)
|
||||
|
||||
try:
|
||||
data = response_json.get("data", [])
|
||||
if not data:
|
||||
raise LLMException(
|
||||
"嵌入响应中没有数据",
|
||||
code=LLMErrorCode.EMBEDDING_FAILED,
|
||||
details=response_json,
|
||||
)
|
||||
|
||||
embeddings = []
|
||||
for item in data:
|
||||
if "embedding" in item:
|
||||
embeddings.append(item["embedding"])
|
||||
else:
|
||||
raise LLMException(
|
||||
"嵌入响应格式错误：缺少embedding字段",
|
||||
code=LLMErrorCode.EMBEDDING_FAILED,
|
||||
details=item,
|
||||
)
|
||||
|
||||
return embeddings
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"解析嵌入响应失败: {e}", e=e)
|
||||
raise LLMException(
|
||||
f"解析嵌入响应失败: {e}",
|
||||
code=LLMErrorCode.EMBEDDING_FAILED,
|
||||
cause=e,
|
||||
)
|
||||
Reference in New Issue
Block a user