分离流式与非流式响应处理
This commit is contained in:
Raven95676
@@ -189,15 +189,28 @@ class LLMRequestSubStage(Stage):
|
||||
)
|
||||
return
|
||||
|
||||
async for result in self._handle_llm_response(
|
||||
event, req, final_llm_response
|
||||
):
|
||||
if isinstance(result, ProviderRequest):
|
||||
# 有函数工具调用并且返回了结果，我们需要再次请求 LLM
|
||||
req = result
|
||||
need_loop = True
|
||||
else:
|
||||
yield
|
||||
if self.streaming_response:
|
||||
# 流式输出的处理
|
||||
async for result in self._handle_llm_stream_response(
|
||||
event, req, final_llm_response
|
||||
):
|
||||
if isinstance(result, ProviderRequest):
|
||||
# 有函数工具调用并且返回了结果，我们需要再次请求 LLM
|
||||
req = result
|
||||
need_loop = True
|
||||
else:
|
||||
yield
|
||||
else:
|
||||
# 非流式输出的处理
|
||||
async for result in self._handle_llm_response(
|
||||
event, req, final_llm_response
|
||||
):
|
||||
if isinstance(result, ProviderRequest):
|
||||
# 有函数工具调用并且返回了结果，我们需要再次请求 LLM
|
||||
req = result
|
||||
need_loop = True
|
||||
else:
|
||||
yield
|
||||
|
||||
asyncio.create_task(
|
||||
Metric.upload(
|
||||
@@ -210,14 +223,6 @@ class LLMRequestSubStage(Stage):
|
||||
# 保存到历史记录
|
||||
await self._save_to_history(event, req, final_llm_response)
|
||||
|
||||
# 流式输出完成后，将完整的LLM响应设置为事件结果
|
||||
if bool(self.streaming_response):
|
||||
event.clear_result()
|
||||
async for _ in self._handle_llm_response(
|
||||
event, req, final_llm_response
|
||||
):
|
||||
pass
|
||||
|
||||
except BaseException as e:
|
||||
logger.error(traceback.format_exc())
|
||||
event.set_result(
|
||||
@@ -243,38 +248,28 @@ class LLMRequestSubStage(Stage):
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse,
|
||||
) -> AsyncGenerator[None, None]:
|
||||
"""处理 LLM 响应。
|
||||
) -> AsyncGenerator[Union[None, ProviderRequest], None]:
|
||||
"""处理非流式 LLM 响应。
|
||||
|
||||
Returns:
|
||||
bool: 是否需要继续调用 LLM
|
||||
AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
|
||||
|
||||
Yields:
|
||||
Iterator[bool]: 将 event 交付给下一个 stage
|
||||
Iterator[Union[None, ProviderRequest]]: 将 event 交付给下一个 stage 或者返回 ProviderRequest 表示需要再次调用 LLM
|
||||
"""
|
||||
is_stream = bool(self.streaming_response)
|
||||
|
||||
if llm_response.role == "assistant":
|
||||
# text completion
|
||||
if llm_response.result_chain:
|
||||
event.set_result(
|
||||
MessageEventResult(
|
||||
chain=llm_response.result_chain.chain
|
||||
).set_result_content_type(
|
||||
ResultContentType.STREAMING_FINISH
|
||||
if is_stream
|
||||
else ResultContentType.LLM_RESULT
|
||||
)
|
||||
).set_result_content_type(ResultContentType.LLM_RESULT)
|
||||
)
|
||||
else:
|
||||
event.set_result(
|
||||
MessageEventResult()
|
||||
.message(llm_response.completion_text)
|
||||
.set_result_content_type(
|
||||
ResultContentType.STREAMING_FINISH
|
||||
if is_stream
|
||||
else ResultContentType.LLM_RESULT
|
||||
)
|
||||
.set_result_content_type(ResultContentType.LLM_RESULT)
|
||||
)
|
||||
elif llm_response.role == "err":
|
||||
event.set_result(
|
||||
@@ -283,83 +278,139 @@ class LLMRequestSubStage(Stage):
|
||||
)
|
||||
)
|
||||
elif llm_response.role == "tool":
|
||||
# function calling
|
||||
tool_call_result: list[ToolCallMessageSegment] = []
|
||||
logger.info(
|
||||
f"触发 {len(llm_response.tools_call_name)} 个函数调用: {llm_response.tools_call_name}"
|
||||
# 处理函数工具调用
|
||||
async for result in self._handle_function_tools(event, req, llm_response):
|
||||
yield result
|
||||
|
||||
async def _handle_llm_stream_response(
|
||||
self,
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse,
|
||||
) -> AsyncGenerator[Union[None, ProviderRequest], None]:
|
||||
"""处理流式 LLM 响应。
|
||||
|
||||
专门用于处理流式输出完成后的响应，与非流式响应处理分离。
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
|
||||
|
||||
Yields:
|
||||
Iterator[Union[None, ProviderRequest]]: 将 event 交付给下一个 stage 或者返回 ProviderRequest 表示需要再次调用 LLM
|
||||
"""
|
||||
if llm_response.role == "assistant":
|
||||
# text completion
|
||||
if llm_response.result_chain:
|
||||
event.set_result(
|
||||
MessageEventResult(
|
||||
chain=llm_response.result_chain.chain
|
||||
).set_result_content_type(ResultContentType.STREAMING_FINISH)
|
||||
)
|
||||
else:
|
||||
event.set_result(
|
||||
MessageEventResult()
|
||||
.message(llm_response.completion_text)
|
||||
.set_result_content_type(ResultContentType.STREAMING_FINISH)
|
||||
)
|
||||
elif llm_response.role == "err":
|
||||
event.set_result(
|
||||
MessageEventResult().message(
|
||||
f"AstrBot 请求失败。\n错误信息: {llm_response.completion_text}"
|
||||
)
|
||||
)
|
||||
for func_tool_name, func_tool_args, func_tool_id in zip(
|
||||
llm_response.tools_call_name,
|
||||
llm_response.tools_call_args,
|
||||
llm_response.tools_call_ids,
|
||||
):
|
||||
try:
|
||||
func_tool = req.func_tool.get_func(func_tool_name)
|
||||
if func_tool.origin == "mcp":
|
||||
logger.info(
|
||||
f"从 MCP 服务 {func_tool.mcp_server_name} 调用工具函数：{func_tool.name}，参数：{func_tool_args}"
|
||||
elif llm_response.role == "tool":
|
||||
# 处理函数工具调用
|
||||
async for result in self._handle_function_tools(event, req, llm_response):
|
||||
yield result
|
||||
|
||||
async def _handle_function_tools(
|
||||
self,
|
||||
event: AstrMessageEvent,
|
||||
req: ProviderRequest,
|
||||
llm_response: LLMResponse,
|
||||
) -> AsyncGenerator[Union[None, ProviderRequest], None]:
|
||||
"""处理函数工具调用。
|
||||
|
||||
Returns:
|
||||
AsyncGenerator[Union[None, ProviderRequest], None]: 如果返回 ProviderRequest，表示需要再次调用 LLM
|
||||
"""
|
||||
# function calling
|
||||
tool_call_result: list[ToolCallMessageSegment] = []
|
||||
logger.info(
|
||||
f"触发 {len(llm_response.tools_call_name)} 个函数调用: {llm_response.tools_call_name}"
|
||||
)
|
||||
for func_tool_name, func_tool_args, func_tool_id in zip(
|
||||
llm_response.tools_call_name,
|
||||
llm_response.tools_call_args,
|
||||
llm_response.tools_call_ids,
|
||||
):
|
||||
try:
|
||||
func_tool = req.func_tool.get_func(func_tool_name)
|
||||
if func_tool.origin == "mcp":
|
||||
logger.info(
|
||||
f"从 MCP 服务 {func_tool.mcp_server_name} 调用工具函数：{func_tool.name}，参数：{func_tool_args}"
|
||||
)
|
||||
client = req.func_tool.mcp_client_dict[
|
||||
func_tool.mcp_server_name
|
||||
]
|
||||
res = await client.session.call_tool(
|
||||
func_tool.name, func_tool_args
|
||||
)
|
||||
if res:
|
||||
# TODO content的类型可能包括list[TextContent | ImageContent | EmbeddedResource]，这里只处理了TextContent。
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=res.content[0].text,
|
||||
)
|
||||
)
|
||||
client = req.func_tool.mcp_client_dict[
|
||||
func_tool.mcp_server_name
|
||||
]
|
||||
res = await client.session.call_tool(
|
||||
func_tool.name, func_tool_args
|
||||
)
|
||||
if res:
|
||||
# TODO content的类型可能包括list[TextContent | ImageContent | EmbeddedResource]，这里只处理了TextContent。
|
||||
else:
|
||||
logger.info(
|
||||
f"调用工具函数：{func_tool_name}，参数：{func_tool_args}"
|
||||
)
|
||||
# 尝试调用工具函数
|
||||
wrapper = self._call_handler(
|
||||
self.ctx, event, func_tool.handler, **func_tool_args
|
||||
)
|
||||
async for resp in wrapper:
|
||||
if resp is not None: # 有 return 返回
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=res.content[0].text,
|
||||
content=resp,
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
f"调用工具函数：{func_tool_name}，参数：{func_tool_args}"
|
||||
)
|
||||
# 尝试调用工具函数
|
||||
wrapper = self._call_handler(
|
||||
self.ctx, event, func_tool.handler, **func_tool_args
|
||||
)
|
||||
async for resp in wrapper:
|
||||
if resp is not None: # 有 return 返回
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=resp,
|
||||
)
|
||||
)
|
||||
else:
|
||||
yield # 有生成器返回
|
||||
event.clear_result() # 清除上一个 handler 的结果
|
||||
except BaseException as e:
|
||||
logger.warning(traceback.format_exc())
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=f"error: {str(e)}",
|
||||
)
|
||||
else:
|
||||
yield # 有生成器返回
|
||||
event.clear_result() # 清除上一个 handler 的结果
|
||||
except BaseException as e:
|
||||
logger.warning(traceback.format_exc())
|
||||
tool_call_result.append(
|
||||
ToolCallMessageSegment(
|
||||
role="tool",
|
||||
tool_call_id=func_tool_id,
|
||||
content=f"error: {str(e)}",
|
||||
)
|
||||
if tool_call_result:
|
||||
# 函数调用结果
|
||||
req.func_tool = None # 暂时不支持递归工具调用
|
||||
assistant_msg_seg = AssistantMessageSegment(
|
||||
role="assistant", tool_calls=llm_response.to_openai_tool_calls()
|
||||
)
|
||||
# 在多轮 Tool 调用的情况下，这里始终保持最新的 Tool 调用结果，减少上下文长度。
|
||||
req.tool_calls_result = ToolCallsResult(
|
||||
tool_calls_info=assistant_msg_seg,
|
||||
tool_calls_result=tool_call_result,
|
||||
if tool_call_result:
|
||||
# 函数调用结果
|
||||
req.func_tool = None # 暂时不支持递归工具调用
|
||||
assistant_msg_seg = AssistantMessageSegment(
|
||||
role="assistant", tool_calls=llm_response.to_openai_tool_calls()
|
||||
)
|
||||
# 在多轮 Tool 调用的情况下，这里始终保持最新的 Tool 调用结果，减少上下文长度。
|
||||
req.tool_calls_result = ToolCallsResult(
|
||||
tool_calls_info=assistant_msg_seg,
|
||||
tool_calls_result=tool_call_result,
|
||||
)
|
||||
yield req # 再次执行 LLM 请求
|
||||
else:
|
||||
if llm_response.completion_text:
|
||||
event.set_result(
|
||||
MessageEventResult().message(llm_response.completion_text)
|
||||
)
|
||||
yield req # 再次执行 LLM 请求
|
||||
else:
|
||||
if llm_response.completion_text:
|
||||
event.set_result(
|
||||
MessageEventResult().message(llm_response.completion_text)
|
||||
)
|
||||
|
||||
async def _save_to_history(
|
||||
self, event: AstrMessageEvent, req: ProviderRequest, llm_response: LLMResponse
|
||||
|
||||
Reference in New Issue
Block a user