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fix:del some print ,and amend respback on stream judge ,and del in dingtalk this is_stream_output_supported() use

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This commit is contained in:
Dong_master
2025-07-29 23:09:02 +08:00
committed by Junyan Qin
parent 074d359c8e
commit a9776b7b53
10 changed files with 127 additions and 186 deletions
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pkg/provider/modelmgr/requesters/chatcmpl.py
+16 -31
View File
@@ -17,14 +17,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
"""OpenAI ChatCompletion API 请求器"""
client: openai.AsyncClient
is_content:bool
is_content: bool
default_config: dict[str, typing.Any] = {
'base_url': 'https://api.openai.com/v1',
'timeout': 120,
}
async def initialize(self):
self.client = openai.AsyncClient(
api_key='',
@@ -46,7 +45,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
args: dict,
extra_body: dict = {},
) -> chat_completion.ChatCompletion:
async for chunk in await self.client.chat.completions.create(**args, extra_body=extra_body):
yield chunk
@@ -66,23 +64,23 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
# deepseek的reasoner模型
if pipeline_config['trigger'].get('misc', '').get('remove_think'):
pass
else:
if reasoning_content is not None :
chatcmpl_message['content'] = '<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
if reasoning_content is not None:
chatcmpl_message['content'] = (
'<think>\n' + reasoning_content + '\n</think>\n' + chatcmpl_message['content']
)
message = llm_entities.Message(**chatcmpl_message)
return message
async def _make_msg_chunk(
self,
pipeline_config: dict[str, typing.Any],
chat_completion: chat_completion.ChatCompletion,
idx: int,
) -> llm_entities.MessageChunk:
# 处理流式chunk和完整响应的差异
# print(chat_completion.choices[0])
if hasattr(chat_completion, 'choices'):
@@ -98,7 +96,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
if 'role' not in delta or delta['role'] is None:
delta['role'] = 'assistant'
reasoning_content = delta['reasoning_content'] if 'reasoning_content' in delta else None
delta['content'] = '' if delta['content'] is None else delta['content']
@@ -106,13 +103,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
# deepseek的reasoner模型
if pipeline_config['trigger'].get('misc', '').get('remove_think'):
if reasoning_content is not None :
if reasoning_content is not None:
pass
else:
delta['content'] = delta['content']
else:
if reasoning_content is not None and idx == 0:
delta['content'] += f'<think>\n{reasoning_content}'
delta['content'] += f'<think>\n{reasoning_content}'
elif reasoning_content is None:
if self.is_content:
delta['content'] = delta['content']
@@ -122,7 +119,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
else:
delta['content'] += reasoning_content
message = llm_entities.MessageChunk(**delta)
return message
@@ -135,9 +131,10 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
use_funcs: list[tools_entities.LLMFunction] = None,
stream: bool = False,
extra_args: dict[str, typing.Any] = {},
) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
) ->llm_entities.MessageChunk:
self.client.api_key = use_model.token_mgr.get_token()
args = {}
args['model'] = use_model.model_entity.name
@@ -163,14 +160,14 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
if stream:
current_content = ''
args["stream"] = True
args['stream'] = True
chunk_idx = 0
self.is_content = False
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
pipeline_config = query.pipeline_config
async for chunk in self._req_stream(args, extra_body=extra_args):
# 处理流式消息
delta_message = await self._make_msg_chunk(pipeline_config,chunk,chunk_idx)
delta_message = await self._make_msg_chunk(pipeline_config, chunk, chunk_idx)
if delta_message.content:
current_content += delta_message.content
delta_message.content = current_content
@@ -182,15 +179,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
id=tool_call.id,
type=tool_call.type,
function=llm_entities.FunctionCall(
name=tool_call.function.name if tool_call.function else '',
arguments=''
name=tool_call.function.name if tool_call.function else '', arguments=''
),
)
if tool_call.function and tool_call.function.arguments:
# 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
chunk_idx += 1
chunk_choices = getattr(chunk, 'choices', None)
if chunk_choices and getattr(chunk_choices[0], 'finish_reason', None):
@@ -198,11 +193,9 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
delta_message.content = current_content
if chunk_idx % 64 == 0 or delta_message.is_final:
yield delta_message
# return
async def _closure(
self,
query: core_entities.Query,
@@ -211,7 +204,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
use_funcs: list[tools_entities.LLMFunction] = None,
stream: bool = False,
extra_args: dict[str, typing.Any] = {},
) -> llm_entities.Message | typing.AsyncGenerator[llm_entities.MessageChunk, None]:
) -> llm_entities.Message:
self.client.api_key = use_model.token_mgr.get_token()
args = {}
@@ -237,22 +230,15 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
args['messages'] = messages
# 发送请求
resp = await self._req(args, extra_body=extra_args)
# 处理请求结果
pipeline_config = query.pipeline_config
message = await self._make_msg(resp,pipeline_config)
message = await self._make_msg(resp, pipeline_config)
return message
async def invoke_llm(
self,
query: core_entities.Query,
@@ -273,7 +259,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
req_messages.append(msg_dict)
try:
msg = await self._closure(
query=query,
req_messages=req_messages,
@@ -334,7 +319,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
funcs: typing.List[tools_entities.LLMFunction] = None,
stream: bool = False,
extra_args: dict[str, typing.Any] = {},
) -> llm_entities.MessageChunk:
) -> llm_entities.MessageChunk:
req_messages = [] # req_messages 仅用于类内，外部同步由 query.messages 进行
for m in messages:
msg_dict = m.dict(exclude_none=True)
pkg/provider/modelmgr/requesters/deepseekchatcmpl.py
+1 -1
View File
@@ -55,6 +55,6 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
raise errors.RequesterError('接口返回为空，请确定模型提供商服务是否正常')
pipeline_config = query.pipeline_config
# 处理请求结果
message = await self._make_msg(resp,pipeline_config)
message = await self._make_msg(resp, pipeline_config)
return message
pkg/provider/runners/dashscopeapi.py
-4
View File
@@ -185,8 +185,6 @@ class DashScopeAPIRunner(runner.RequestRunner):
# 将参考资料替换到文本中
pending_content = self._replace_references(pending_content, references_dict)
yield llm_entities.Message(
role='assistant',
content=pending_content,
@@ -261,13 +259,11 @@ class DashScopeAPIRunner(runner.RequestRunner):
role='assistant',
content=pending_content,
is_final=is_final,
)
# 保存当前会话的session_id用于下次对话的语境
query.session.using_conversation.uuid = stream_output.get('session_id')
else:
for chunk in response:
if chunk.get('status_code') != 200:
pkg/provider/runners/difysvapi.py
-2
View File
@@ -148,7 +148,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
if mode == 'workflow':
if chunk['event'] == 'node_finished':
if not is_stream:
if chunk['data']['node_type'] == 'answer':
yield llm_entities.Message(
role='assistant',
@@ -274,7 +273,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
content=self._try_convert_thinking(pending_agent_message),
)
if chunk['event'] == 'agent_thought':
if chunk['tool'] != '' and chunk['observation'] != '': # 工具调用结果，跳过
continue
pkg/provider/runners/localagent.py
+22 -26
View File
@@ -2,7 +2,6 @@ from __future__ import annotations
import json
import copy
from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE
import typing
from .. import runner
from ...core import entities as core_entities
@@ -30,11 +29,14 @@ class LocalAgentRunner(runner.RequestRunner):
class ToolCallTracker:
"""工具调用追踪器"""
def __init__(self):
self.active_calls: dict[str,dict] = {}
self.active_calls: dict[str, dict] = {}
self.completed_calls: list[llm_entities.ToolCall] = []
async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
async def run(
self, query: core_entities.Query
) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
"""运行请求"""
pending_tool_calls = []
@@ -89,16 +91,14 @@ class LocalAgentRunner(runner.RequestRunner):
is_stream = query.adapter.is_stream_output_supported()
try:
# print(await query.adapter.is_stream_output_supported())
is_stream = await query.adapter.is_stream_output_supported()
except AttributeError:
is_stream = False
# while True:
# pass
if not is_stream:
# 非流式输出，直接请求
# print(123)
msg = await query.use_llm_model.requester.invoke_llm(
query,
query.use_llm_model,
@@ -108,7 +108,6 @@ class LocalAgentRunner(runner.RequestRunner):
)
yield msg
final_msg = msg
print(final_msg)
else:
# 流式输出，需要处理工具调用
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
@@ -122,27 +121,26 @@ class LocalAgentRunner(runner.RequestRunner):
):
assert isinstance(msg, llm_entities.MessageChunk)
yield msg
# if msg.tool_calls:
# for tool_call in msg.tool_calls:
# if tool_call.id not in tool_calls_map:
# tool_calls_map[tool_call.id] = llm_entities.ToolCall(
# id=tool_call.id,
# type=tool_call.type,
# function=llm_entities.FunctionCall(
# name=tool_call.function.name if tool_call.function else '',
# arguments=''
# ),
# )
# if tool_call.function and tool_call.function.arguments:
# # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
# tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
if msg.tool_calls:
for tool_call in msg.tool_calls:
if tool_call.id not in tool_calls_map:
tool_calls_map[tool_call.id] = llm_entities.ToolCall(
id=tool_call.id,
type=tool_call.type,
function=llm_entities.FunctionCall(
name=tool_call.function.name if tool_call.function else '',
arguments=''
),
)
if tool_call.function and tool_call.function.arguments:
# 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
final_msg = llm_entities.Message(
role=msg.role,
content=msg.all_content,
tool_calls=list(tool_calls_map.values()),
)
pending_tool_calls = final_msg.tool_calls
req_messages.append(final_msg)
@@ -193,8 +191,7 @@ class LocalAgentRunner(runner.RequestRunner):
id=tool_call.id,
type=tool_call.type,
function=llm_entities.FunctionCall(
name=tool_call.function.name if tool_call.function else '',
arguments=''
name=tool_call.function.name if tool_call.function else '', arguments=''
),
)
if tool_call.function and tool_call.function.arguments:
@@ -206,7 +203,6 @@ class LocalAgentRunner(runner.RequestRunner):
tool_calls=list(tool_calls_map.values()),
)
else:
print("非流式")
# 处理完所有调用，再次请求
msg = await query.use_llm_model.requester.invoke_llm(
query,