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fix:The handling logic of remove_think in the connector and Temporarily blocked the processing of streaming tool calls in the runner.

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This commit is contained in:
Dong_master
2025-08-05 04:24:03 +08:00
parent 5597dffaeb
commit e88302f1b4
5 changed files with 145 additions and 129 deletions
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pkg/provider/runners/localagent.py
+16 -11
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@@ -122,10 +122,11 @@ class LocalAgentRunner(runner.RequestRunner):
remove_think=remove_think,
):
msg_idx = msg_idx + 1
tool_msg = msg
if msg_idx % 8 == 0 or msg.is_final:
yield msg
if msg.tool_calls:
for tool_call in msg.tool_calls:
if tool_msg.tool_calls:
for tool_call in tool_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,
@@ -137,9 +138,9 @@ class LocalAgentRunner(runner.RequestRunner):
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,
final_msg = llm_entities.MessageChunk(
role="tool",
content='',
tool_calls=list(tool_calls_map.values()),
)
@@ -176,6 +177,7 @@ class LocalAgentRunner(runner.RequestRunner):
if is_stream:
tool_calls_map = {}
msg_idx = 0
async for msg in await query.use_llm_model.requester.invoke_llm_stream(
query,
query.use_llm_model,
@@ -184,9 +186,12 @@ class LocalAgentRunner(runner.RequestRunner):
extra_args=query.use_llm_model.model_entity.extra_args,
remove_think=remove_think,
):
yield msg
if msg.tool_calls:
for tool_call in msg.tool_calls:
msg_idx += 1
tool_msg = msg
if msg_idx % 8 == 0 or msg.is_final:
yield msg
if tool_msg.tool_calls:
for tool_call in tool_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,
@@ -198,9 +203,9 @@ class LocalAgentRunner(runner.RequestRunner):
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,
final_msg = llm_entities.MessageChunk(
role="tool",
content='',
tool_calls=list(tool_calls_map.values()),
)
else: