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✨ feat(llm): 全面重构LLM服务模块，增强多模态与工具支持 (#1953)

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* ✨ feat(llm): 全面重构LLM服务模块，增强多模态与工具支持

🚀 核心功能增强
- 多模型链式调用：新增 `pipeline_chat` 支持复杂任务流处理
- 扩展提供商支持：新增 ARK(火山方舟)、SiliconFlow(硅基流动) 适配器
- 多模态处理增强：支持URL媒体文件下载转换，提升输入灵活性
- 历史对话支持：AI.analyze 方法支持历史消息上下文和可选 UniMessage 参数
- 文本嵌入功能：新增 `embed`、`analyze_multimodal`、`search_multimodal` 等API
- 模型能力系统：新增 `ModelCapabilities` 统一管理模型特性（多模态、工具调用等）

🔧 架构重构与优化
- MCP工具系统重构：配置独立化至 `data/llm/mcp_tools.json`，预置常用工具
- API调用逻辑统一：提取通用 `_perform_api_call` 方法，消除代码重复
- 跨平台兼容：Windows平台MCP工具npx命令自动包装处理
- HTTP客户端增强：兼容不同版本httpx代理配置（0.28+版本适配）

🛠️ API与配置完善
- 统一返回类型：`AI.analyze` 统一返回 `LLMResponse` 类型
- 消息转换工具：新增 `message_to_unimessage` 转换函数
- Gemini适配器增强：URL图片下载编码、动态安全阈值配置
- 缓存管理：新增模型实例缓存和管理功能
- 配置预设：扩展 CommonOverrides 预设配置选项
- 历史管理优化：支持多模态内容占位符替换，提升效率

📚 文档与开发体验
- README全面重写：新增完整使用指南、API参考和架构概览
- 文档内容扩充：补充嵌入模型、缓存管理、工具注册等功能说明
- 日志记录增强：支持详细调试信息输出
- API简化：移除冗余函数，优化接口设计

* 🎨  feat(llm): 统一LLM服务函数文档格式

* ✨ feat(llm): 添加新模型并简化提供者配置加载

* 🚨 auto fix by pre-commit hooks

---------

Co-authored-by: webjoin111 <455457521@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Rumio
2025-07-08 11:15:15 +08:00
committed by GitHub
co-authored by webjoin111 pre-commit-ci[bot]
parent 1e7ae38684
commit 48cbb2bf1d
24 changed files with 2128 additions and 1338 deletions
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zhenxun/services/llm/adapters/base.py
+57 -15
View File
@@ -17,6 +17,7 @@ if TYPE_CHECKING:
from ..service import LLMModel
from ..types.content import LLMMessage
from ..types.enums import EmbeddingTaskType
from ..types.models import LLMTool
class RequestData(BaseModel):
@@ -60,7 +61,7 @@ class BaseAdapter(ABC):
"""支持的API类型列表"""
pass
def prepare_simple_request(
async def prepare_simple_request(
self,
model: "LLMModel",
api_key: str,
@@ -86,7 +87,7 @@ class BaseAdapter(ABC):
config = model._generation_config
return self.prepare_advanced_request(
return await self.prepare_advanced_request(
model=model,
api_key=api_key,
messages=messages,
@@ -96,13 +97,13 @@ class BaseAdapter(ABC):
)
@abstractmethod
def prepare_advanced_request(
async def prepare_advanced_request(
self,
model: "LLMModel",
api_key: str,
messages: list["LLMMessage"],
config: "LLMGenerationConfig | None" = None,
tools: list[dict[str, Any]] | None = None,
tools: list["LLMTool"] | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> RequestData:
"""准备高级请求"""
@@ -238,6 +239,9 @@ class BaseAdapter(ABC):
message = choice.get("message", {})
content = message.get("content", "")
if content:
content = content.strip()
parsed_tool_calls: list[LLMToolCall] | None = None
if message_tool_calls := message.get("tool_calls"):
from ..types.models import LLMToolFunction
@@ -375,7 +379,7 @@ class BaseAdapter(ABC):
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"]:
if model.api_type == "gemini":
base_config["maxOutputTokens"] = model.max_tokens
else:
base_config["max_tokens"] = model.max_tokens
@@ -401,26 +405,51 @@ class OpenAICompatAdapter(BaseAdapter):
"""
@abstractmethod
def get_chat_endpoint(self) -> str:
def get_chat_endpoint(self, model: "LLMModel") -> str:
"""子类必须实现，返回 chat completions 的端点"""
pass
@abstractmethod
def get_embedding_endpoint(self) -> str:
def get_embedding_endpoint(self, model: "LLMModel") -> str:
"""子类必须实现，返回 embeddings 的端点"""
pass
def prepare_advanced_request(
async 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 - OpenAI兼容API的通用实现"""
url = self.get_api_url(model, self.get_chat_endpoint(model))
headers = self.get_base_headers(api_key)
messages = []
if history:
messages.extend(history)
messages.append({"role": "user", "content": prompt})
body = {
"model": model.model_name,
"messages": messages,
}
body = self.apply_config_override(model, body)
return RequestData(url=url, headers=headers, body=body)
async def prepare_advanced_request(
self,
model: "LLMModel",
api_key: str,
messages: list["LLMMessage"],
config: "LLMGenerationConfig | None" = None,
tools: list[dict[str, Any]] | None = None,
tools: list["LLMTool"] | 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())
url = self.get_api_url(model, self.get_chat_endpoint(model))
headers = self.get_base_headers(api_key)
openai_messages = self.convert_messages_to_openai_format(messages)
@@ -430,7 +459,21 @@ class OpenAICompatAdapter(BaseAdapter):
}
if tools:
body["tools"] = tools
openai_tools = []
for tool in tools:
if tool.type == "function" and tool.function:
openai_tools.append({"type": "function", "function": tool.function})
elif tool.type == "mcp" and tool.mcp_session:
if callable(tool.mcp_session):
raise ValueError(
"适配器接收到未激活的 MCP 会话工厂。"
"会话工厂应该在 LLMModel.generate_response 中被激活。"
)
openai_tools.append(
tool.mcp_session.to_api_tool(api_type=self.api_type)
)
if openai_tools:
body["tools"] = openai_tools
if tool_choice:
body["tool_choice"] = tool_choice
@@ -444,7 +487,7 @@ class OpenAICompatAdapter(BaseAdapter):
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
_ = model, is_advanced
return self.parse_openai_response(response_json)
def prepare_embedding_request(
@@ -456,8 +499,8 @@ class OpenAICompatAdapter(BaseAdapter):
**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())
_ = task_type
url = self.get_api_url(model, self.get_embedding_endpoint(model))
headers = self.get_base_headers(api_key)
body = {
@@ -465,7 +508,6 @@ class OpenAICompatAdapter(BaseAdapter):
"input": texts,
}
# 应用额外的配置参数
if kwargs:
body.update(kwargs)