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Robut ctf param

This commit is contained in:
luijait 2025-05-19 15:39:05 +02:00
parent e0bb5698e5
commit e3fe006f50
4 changed files with 103 additions and 28 deletions
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8
src/cai/sdk/agents/function_schema.py
View File

@ -263,7 +263,13 @@ def function_schema(
# field_name -> (type_annotation, default_value_or_Field(...))
fields: dict[str, Any] = {}
for name, param in filtered_params:
filtered_params_no_ctf = [
(name, param)
for name, param in filtered_params
if name.lower() != 'ctf'
]
for name, param in filtered_params_no_ctf:
ann = type_hints.get(name, param.annotation)
default = param.default

117
src/cai/sdk/agents/models/openai_chatcompletions.py
View File

@ -1067,7 +1067,7 @@ class OpenAIChatCompletionsModel(Model):
arguments=arguments_str,
name=parsed['name'],
type="function_call",
call_id=tool_call_id,
call_id=tool_call_id[:40],
)
# Display the tool call in CLI
@ -1089,7 +1089,7 @@ class OpenAIChatCompletionsModel(Model):
'name': parsed['name'],
'arguments': arguments_str
}),
'id': tool_call_id,
'id': tool_call_id[:40],
'type': 'function'
})
]
@ -1171,7 +1171,7 @@ class OpenAIChatCompletionsModel(Model):
yield ResponseOutputItemAddedEvent(
item=ResponseFunctionToolCall(
id=FAKE_RESPONSES_ID,
call_id=function_call.call_id,
call_id=function_call.call_id[:40],
arguments=function_call.arguments,
name=function_call.name,
type="function_call",
@ -1190,7 +1190,7 @@ class OpenAIChatCompletionsModel(Model):
yield ResponseOutputItemDoneEvent(
item=ResponseFunctionToolCall(
id=FAKE_RESPONSES_ID,
call_id=function_call.call_id,
call_id=function_call.call_id[:40],
arguments=function_call.arguments,
name=function_call.name,
type="function_call",
@ -1776,29 +1776,92 @@ class OpenAIChatCompletionsModel(Model):
stream: bool,
parallel_tool_calls: bool
) -> ChatCompletion | tuple[Response, AsyncStream[ChatCompletionChunk]]:
"""Handle standard LiteLLM API calls for OpenAI and compatible models."""
if stream:
# Standard LiteLLM handling for streaming
ret = litellm.completion(**kwargs)
stream_obj = await litellm.acompletion(**kwargs)
"""
Handle standard LiteLLM API calls for OpenAI and compatible models.
If a ContextWindowExceededError occurs due to a tool_call id being
too long, truncate all tool_call ids in the messages to 40 characters
and retry once silently.
"""
try:
if stream:
# Standard LiteLLM handling for streaming
ret = litellm.completion(**kwargs)
stream_obj = await litellm.acompletion(**kwargs)
response = Response(
id=FAKE_RESPONSES_ID,
created_at=time.time(),
model=self.model,
object="response",
output=[],
tool_choice="auto" if tool_choice is None or tool_choice == NOT_GIVEN else cast(Literal["auto", "required", "none"], tool_choice),
top_p=model_settings.top_p,
temperature=model_settings.temperature,
tools=[],
parallel_tool_calls=parallel_tool_calls or False,
)
return response, stream_obj
else:
# Standard OpenAI handling for non-streaming
ret = litellm.completion(**kwargs)
return ret
response = Response(
id=FAKE_RESPONSES_ID,
created_at=time.time(),
model=self.model,
object="response",
output=[],
tool_choice="auto" if tool_choice is None or tool_choice == NOT_GIVEN
else cast(Literal["auto", "required", "none"], tool_choice),
top_p=model_settings.top_p,
temperature=model_settings.temperature,
tools=[],
parallel_tool_calls=parallel_tool_calls or False,
)
return response, stream_obj
else:
# Standard OpenAI handling for non-streaming
ret = litellm.completion(**kwargs)
return ret
except Exception as e:
error_msg = str(e)
# Handle both OpenAI and Anthropic error messages for tool_call_id
if (
"string too long" in error_msg
or "Invalid 'messages" in error_msg
and "tool_call_id" in error_msg
and "maximum length" in error_msg
):
# Truncate all tool_call ids in all messages to 40 characters
messages = kwargs.get("messages", [])
for msg in messages:
# Truncate tool_call_id in the message itself if present
if (
"tool_call_id" in msg
and isinstance(msg["tool_call_id"], str)
and len(msg["tool_call_id"]) > 40
):
msg["tool_call_id"] = msg["tool_call_id"][:40]
# Truncate tool_call ids in tool_calls if present
if "tool_calls" in msg and isinstance(msg["tool_calls"], list):
for tool_call in msg["tool_calls"]:
if (
isinstance(tool_call, dict)
and "id" in tool_call
and isinstance(tool_call["id"], str)
and len(tool_call["id"]) > 40
):
tool_call["id"] = tool_call["id"][:40]
kwargs["messages"] = messages
# Retry once, silently
if stream:
ret = litellm.completion(**kwargs)
stream_obj = await litellm.acompletion(**kwargs)
response = Response(
id=FAKE_RESPONSES_ID,
created_at=time.time(),
model=self.model,
object="response",
output=[],
tool_choice="auto"
if tool_choice is None or tool_choice == NOT_GIVEN
else cast(
Literal["auto", "required", "none"], tool_choice
),
top_p=model_settings.top_p,
temperature=model_settings.temperature,
tools=[],
parallel_tool_calls=parallel_tool_calls or False,
)
return response, stream_obj
else:
ret = litellm.completion(**kwargs)
return ret
else:
raise
async def _fetch_response_litellm_ollama(
self,
@ -2032,7 +2095,7 @@ class _Converter:
items.append(
ResponseFunctionToolCall(
id=FAKE_RESPONSES_ID,
call_id=tool_call.id,
call_id=tool_call.id[:40],
arguments=tool_call.function.arguments,
name=tool_call.function.name,
type="function_call",

5
src/cai/sdk/agents/tool.py
View File

@ -272,6 +272,11 @@ def function_tool(
async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any:
try:
return await _on_invoke_tool_impl(ctx, input)
except KeyboardInterrupt:
logger.info(
f"Tool {schema.name} execution was interrupted by the user (Ctrl+C)."
)
return "execution was interrupted by the user (Ctrl+C)"
except Exception as e:
if failure_error_function is None:
raise

1
src/cai/tools/common.py
View File

@ -705,6 +705,7 @@ def run_command(command, ctf=None, stdout=False, # pylint: disable=too-many-arg
Returns:
str: Command output, status message, or session ID.
"""
print(ctf)
# Use the active timer during tool execution
stop_idle_timer()
start_active_timer()