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fix：加入工具调用能力

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This commit is contained in:
SengokuCola
2025-04-10 22:13:17 +08:00
parent de061024c1
commit 110f94353f
6 changed files with 627 additions and 265 deletions
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52
src/plugins/models/utils_model.py
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@@ -342,6 +342,7 @@ class LLM_request:
"message": {
"content": accumulated_content,
"reasoning_content": reasoning_content,
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
}
}
],
@@ -366,6 +367,7 @@ class LLM_request:
"message": {
"content": accumulated_content,
"reasoning_content": reasoning_content,
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
}
}
],
@@ -384,7 +386,13 @@ class LLM_request:
# 构造一个伪result以便调用自定义响应处理器或默认处理器
result = {
"choices": [
{"message": {"content": content, "reasoning_content": reasoning_content}}
{
"message": {
"content": content,
"reasoning_content": reasoning_content,
# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
}
}
],
"usage": usage,
}
@@ -566,6 +574,9 @@ class LLM_request:
reasoning_content = message.get("reasoning_content", "")
if not reasoning_content:
reasoning_content = reasoning
# 提取工具调用信息
tool_calls = message.get("tool_calls", None)
# 记录token使用情况
usage = result.get("usage", {})
@@ -581,8 +592,12 @@ class LLM_request:
request_type=request_type if request_type is not None else self.request_type,
endpoint=endpoint,
)
return content, reasoning_content
# 只有当tool_calls存在且不为空时才返回
if tool_calls:
return content, reasoning_content, tool_calls
else:
return content, reasoning_content
return "没有返回结果", ""
@@ -605,21 +620,33 @@ class LLM_request:
return {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
# 防止小朋友们截图自己的key
async def generate_response(self, prompt: str) -> Tuple[str, str, str]:
async def generate_response(self, prompt: str) -> Tuple:
"""根据输入的提示生成模型的异步响应"""
content, reasoning_content = await self._execute_request(endpoint="/chat/completions", prompt=prompt)
return content, reasoning_content, self.model_name
response = await self._execute_request(endpoint="/chat/completions", prompt=prompt)
# 根据返回值的长度决定怎么处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, reasoning_content, self.model_name, tool_calls
else:
content, reasoning_content = response
return content, reasoning_content, self.model_name
async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple[str, str]:
async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple:
"""根据输入的提示和图片生成模型的异步响应"""
content, reasoning_content = await self._execute_request(
response = await self._execute_request(
endpoint="/chat/completions", prompt=prompt, image_base64=image_base64, image_format=image_format
)
return content, reasoning_content
# 根据返回值的长度决定怎么处理
if len(response) == 3:
content, reasoning_content, tool_calls = response
return content, reasoning_content, tool_calls
else:
content, reasoning_content = response
return content, reasoning_content
async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple[str, str]]:
async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
"""异步方式根据输入的提示生成模型的响应"""
# 构建请求体
data = {
@@ -630,10 +657,11 @@ class LLM_request:
**kwargs,
}
content, reasoning_content = await self._execute_request(
response = await self._execute_request(
endpoint="/chat/completions", payload=data, prompt=prompt
)
return content, reasoning_content
# 原样返回响应，不做处理
return response
async def get_embedding(self, text: str) -> Union[list, None]:
"""异步方法：获取文本的embedding向量