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feat: refactor with cursor max mode claude 4.1 opus

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This commit is contained in:
Junyan Qin
2025-08-07 15:47:57 +08:00
parent 9736d0708a
commit 261f50b8ec
3 changed files with 201 additions and 104 deletions
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pkg/provider/runners/localagent.py
+58 -17
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@@ -113,6 +113,9 @@ class LocalAgentRunner(runner.RequestRunner):
# 流式输出，需要处理工具调用
tool_calls_map: dict[str, llm_entities.ToolCall] = {}
msg_idx = 0
accumulated_content = '' # 从开始累积的所有内容
last_role = 'assistant'
async for msg in query.use_llm_model.requester.invoke_llm_stream(
query,
query.use_llm_model,
@@ -122,11 +125,18 @@ 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 tool_msg.tool_calls:
for tool_call in tool_msg.tool_calls:
# 记录角色
if msg.role:
last_role = msg.role
# 累积内容
if msg.content:
accumulated_content += msg.content
# 处理工具调用
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,
@@ -138,10 +148,21 @@ 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
# 每8个chunk或最后一个chunk时，输出所有累积的内容
if msg_idx % 8 == 0 or msg.is_final:
yield llm_entities.MessageChunk(
role=last_role,
content=accumulated_content, # 输出所有累积内容
tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
is_final=msg.is_final,
)
# 创建最终消息用于后续处理
final_msg = llm_entities.MessageChunk(
role="tool",
content='',
tool_calls=list(tool_calls_map.values()),
role=last_role,
content=accumulated_content,
tool_calls=list(tool_calls_map.values()) if tool_calls_map else None,
)
pending_tool_calls = final_msg.tool_calls
@@ -178,7 +199,10 @@ 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(
accumulated_content = '' # 从开始累积的所有内容
last_role = 'assistant'
async for msg in query.use_llm_model.requester.invoke_llm_stream(
query,
query.use_llm_model,
req_messages,
@@ -187,11 +211,18 @@ class LocalAgentRunner(runner.RequestRunner):
remove_think=remove_think,
):
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:
# 记录角色
if msg.role:
last_role = msg.role
# 累积内容
if msg.content:
accumulated_content += msg.content
# 处理工具调用
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,
@@ -203,10 +234,20 @@ 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
# 每8个chunk或最后一个chunk时，输出所有累积的内容
if msg_idx % 8 == 0 or msg.is_final:
yield llm_entities.MessageChunk(
role=last_role,
content=accumulated_content, # 输出所有累积内容
tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
is_final=msg.is_final,
)
final_msg = llm_entities.MessageChunk(
role="tool",
content='',
tool_calls=list(tool_calls_map.values()),
role=last_role,
content=accumulated_content,
tool_calls=list(tool_calls_map.values()) if tool_calls_map else None,
)
else:
# 处理完所有调用，再次请求