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Code Issues Packages Projects Releases Wiki Activity

fix: In the runner, every 8 tokens yield

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This commit is contained in:
Dong_master
2025-08-03 23:23:51 +08:00
committed by Junyan Qin
parent c3ed4ef6a1
commit fcef784180
4 changed files with 21 additions and 22 deletions
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3
pkg/provider/modelmgr/requesters/chatcmpl.py
View File

@@ -189,8 +189,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
delta_message.is_final = True
delta_message.content = current_content
if chunk_idx % 64 == 0 or delta_message.is_final:
yield delta_message
yield delta_message
# return
async def _closure(

3
pkg/provider/modelmgr/requesters/giteeaichatcmpl.py
View File

@@ -195,5 +195,4 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
delta_message.is_final = True
delta_message.content = current_content
if chunk_idx % 64 == 0 or delta_message.is_final:
yield delta_message
yield delta_message

3
pkg/provider/modelmgr/requesters/modelscopechatcmpl.py
View File

@@ -286,8 +286,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
delta_message.is_final = True
delta_message.content = current_content
if chunk_idx % 64 == 0 or delta_message.is_final:
yield delta_message
yield delta_message
# return
async def invoke_llm(

34
pkg/provider/runners/localagent.py
View File

@@ -111,15 +111,17 @@ class LocalAgentRunner(runner.RequestRunner):
else:
# 流式输出，需要处理工具调用
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
msg_idx = 0
async for msg in query.use_llm_model.requester.invoke_llm_stream(
query,
query.use_llm_model,
req_messages,
query.use_funcs,
extra_args=query.use_llm_model.model_entity.extra_args,
):
assert isinstance(msg, llm_entities.MessageChunk)
yield msg
):
msg_idx = msg_idx + 1
if msg_idx % 8 == 0 or msg.is_final:
yield msg
if msg.tool_calls:
for tool_call in msg.tool_calls:
if tool_call.id not in tool_calls_map:
@@ -180,19 +182,19 @@ class LocalAgentRunner(runner.RequestRunner):
extra_args=query.use_llm_model.model_entity.extra_args,
):
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,