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fixes for anthropic function calling

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This commit is contained in:
Kwindla Hultman Kramer
2024-11-08 16:33:02 -08:00
parent 4379db43ed
commit a54ca08405
2 changed files with 17 additions and 14 deletions
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3
examples/foundational/14b-function-calling-anthropic-video.py
View File

@@ -67,7 +67,8 @@ async def main():
llm = AnthropicLLMService(
api_key=os.getenv("ANTHROPIC_API_KEY"),
model="claude-3-5-sonnet-20240620",
# model="claude-3-5-sonnet-20240620",
model="claude-3-5-sonnet-latest",
enable_prompt_caching_beta=True,
)
llm.register_function("get_weather", get_weather)

28
src/pipecat/services/anthropic.py
View File

@@ -671,6 +671,7 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
):
self._function_call_in_progress = None
self._function_call_result = frame
await self._push_aggregation()
else:
logger.warning(
"FunctionCallResultFrame tool_call_id != InProgressFrame tool_call_id"
@@ -679,9 +680,12 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
self._function_call_result = None
elif isinstance(frame, AnthropicImageMessageFrame):
self._pending_image_frame_message = frame
await self._push_aggregation()
async def _push_aggregation(self):
if not self._aggregation:
if not (
self._aggregation or self._function_call_result or self._pending_image_frame_message
):
return
run_llm = False
@@ -694,20 +698,18 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
frame = self._function_call_result
self._function_call_result = None
if frame.result:
self._context.add_message(
assistant_message = {"role": "assistant", "content": []}
if aggregation:
assistant_message["content"].append({"type": "text", "text": aggregation})
assistant_message["content"].append(
{
"role": "assistant",
"content": [
{"type": "text", "text": aggregation},
{
"type": "tool_use",
"id": frame.tool_call_id,
"name": frame.function_name,
"input": frame.arguments,
},
],
"type": "tool_use",
"id": frame.tool_call_id,
"name": frame.function_name,
"input": frame.arguments,
}
)
self._context.add_message(assistant_message)
self._context.add_message(
{
"role": "user",
@@ -721,7 +723,7 @@ class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator):
}
)
run_llm = True
else:
elif aggregation:
self._context.add_message({"role": "assistant", "content": aggregation})
if self._pending_image_frame_message: