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tools系统

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This commit is contained in:
UnCLAS-Prommer
2025-07-31 11:41:15 +08:00
parent 483c8fb547
commit 37e52a1566
10 changed files with 95 additions and 323 deletions
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2
src/llm_models/model_client/gemini_client.py
View File

@@ -1,4 +1,4 @@
raise DeprecationWarning("Genimi Client is not fully available yet.")
raise DeprecationWarning("Genimi Client is not fully available yet. Please remove your Gemini API Provider")
import asyncio
import io
from collections.abc import Iterable

3
src/llm_models/payload_content/__init__.py
View File

@@ -0,0 +1,3 @@
from .tool_option import ToolCall
__all__ = ["ToolCall"]

64
src/llm_models/utils_model.py
View File

@@ -11,7 +11,7 @@ from src.config.config import model_config
from src.config.api_ada_configs import APIProvider, ModelInfo, TaskConfig
from .payload_content.message import MessageBuilder, Message
from .payload_content.resp_format import RespFormat
from .payload_content.tool_option import ToolOption, ToolCall
from .payload_content.tool_option import ToolOption, ToolCall, ToolOptionBuilder, ToolParamType
from .model_client.base_client import BaseClient, APIResponse, client_registry
from .utils import compress_messages, llm_usage_recorder
from .exceptions import NetworkConnectionError, ReqAbortException, RespNotOkException, RespParseException
@@ -60,7 +60,7 @@ class LLMRequest:
image_format: str,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
) -> Tuple[str, Tuple[str, str, Optional[List[Dict[str, Any]]]]]:
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
"""
为图像生成响应
Args:
@@ -68,7 +68,7 @@ class LLMRequest:
image_base64 (str): 图像的Base64编码字符串
image_format (str): 图像格式（如 'png', 'jpeg' 等）
Returns:
(Tuple[str, str, str, Optional[List[Dict[str, Any]]]]): 响应内容、推理内容、模型名称、工具调用列表
(Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
"""
# 请求体构建
message_builder = MessageBuilder()
@@ -104,18 +104,18 @@ class LLMRequest:
request_type=self.request_type,
endpoint="/chat/completions",
)
return content, (
reasoning_content,
model_info.name,
self._convert_tool_calls(tool_calls) if tool_calls else None,
)
return content, (reasoning_content, model_info.name, tool_calls)
async def generate_response_for_voice(self):
pass
async def generate_response_async(
self, prompt: str, temperature: Optional[float] = None, max_tokens: Optional[int] = None
) -> Tuple[str, Tuple[str, str, Optional[List[Dict[str, Any]]]]]:
self,
prompt: str,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
"""
异步生成响应
Args:
@@ -123,13 +123,13 @@ class LLMRequest:
temperature (float, optional): 温度参数
max_tokens (int, optional): 最大token数
Returns:
(Tuple[str, str, str, Optional[List[Dict[str, Any]]]]): 响应内容、推理内容、模型名称、工具调用列表
(Tuple[str, str, str, Optional[List[ToolCall]]]): 响应内容、推理内容、模型名称、工具调用列表
"""
# 请求体构建
message_builder = MessageBuilder()
message_builder.add_text_content(prompt)
messages = [message_builder.build()]
tool_built = self._build_tool_options(tools)
# 模型选择
model_info, api_provider, client = self._select_model()
@@ -142,6 +142,7 @@ class LLMRequest:
message_list=messages,
temperature=temperature,
max_tokens=max_tokens,
tool_options=tool_built,
)
content = response.content
reasoning_content = response.reasoning_content or ""
@@ -161,11 +162,7 @@ class LLMRequest:
if not content:
raise RuntimeError("获取LLM生成内容失败")
return content, (
reasoning_content,
model_info.name,
self._convert_tool_calls(tool_calls) if tool_calls else None,
)
return content, (reasoning_content, model_info.name, tool_calls)
async def get_embedding(self, embedding_input: str) -> Tuple[List[float], str]:
"""获取嵌入向量
@@ -214,10 +211,6 @@ class LLMRequest:
client = client_registry.get_client_class(api_provider.client_type)(copy.deepcopy(api_provider))
return model_info, api_provider, client
def _convert_tool_calls(self, tool_calls: List[ToolCall]) -> List[Dict[str, Any]]:
"""将ToolCall对象转换为Dict列表"""
pass
async def _execute_request(
self,
api_provider: APIProvider,
@@ -435,6 +428,35 @@ class LLMRequest:
)
return -1, None
def _build_tool_options(self, tools: Optional[List[Dict[str, Any]]]) -> Optional[List[ToolOption]]:
"""构建工具选项列表"""
if not tools:
return None
tool_options: List[ToolOption] = []
for tool in tools:
tool_legal = True
tool_options_builder = ToolOptionBuilder()
tool_options_builder.set_name(tool.get("name", ""))
tool_options_builder.set_description(tool.get("description", ""))
parameters: List[Tuple[str, str, str, bool]] = tool.get("parameters", [])
for param in parameters:
try:
tool_options_builder.add_param(
name=param[0],
param_type=ToolParamType(param[1]),
description=param[2],
required=param[3],
)
except ValueError as ve:
tool_legal = False
logger.error(f"{param[1]} 参数类型错误: {str(ve)}")
except Exception as e:
tool_legal = False
logger.error(f"构建工具参数失败: {str(e)}")
if tool_legal:
tool_options.append(tool_options_builder.build())
return tool_options or None
@staticmethod
def _extract_reasoning(content: str) -> Tuple[str, str]:
"""CoT思维链提取，向后兼容"""