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chore: clean up legacy anthropic path (#3905)

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cthomas
2025-08-13 15:09:13 -07:00
committed by GitHub
parent 15d8cc224c
commit edf39c565f
2 changed files with 2 additions and 414 deletions
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89
letta/llm_api/llm_api_tools.py
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@@ -7,11 +7,7 @@ import requests
from letta.constants import CLI_WARNING_PREFIX
from letta.errors import LettaConfigurationError, RateLimitExceededError
from letta.llm_api.anthropic import (
anthropic_bedrock_chat_completions_request,
anthropic_chat_completions_process_stream,
anthropic_chat_completions_request,
)
from letta.llm_api.anthropic import anthropic_bedrock_chat_completions_request
from letta.llm_api.aws_bedrock import has_valid_aws_credentials
from letta.llm_api.deepseek import build_deepseek_chat_completions_request, convert_deepseek_response_to_chatcompletion
from letta.llm_api.helpers import add_inner_thoughts_to_functions, unpack_all_inner_thoughts_from_kwargs
@@ -311,89 +307,6 @@ def create(
return response
elif llm_config.model_endpoint_type == "anthropic":
if not use_tool_naming:
raise NotImplementedError("Only tool calling supported on Anthropic API requests")
if llm_config.enable_reasoner:
llm_config.put_inner_thoughts_in_kwargs = False
# Force tool calling
tool_call = None
if functions is None:
# Special case for summarization path
tools = None
tool_choice = None
elif force_tool_call is not None:
# tool_call = {"type": "function", "function": {"name": force_tool_call}}
tool_choice = {"type": "tool", "name": force_tool_call}
tools = [{"type": "function", "function": f} for f in functions if f["name"] == force_tool_call]
assert functions is not None
# need to have this setting to be able to put inner thoughts in kwargs
llm_config.put_inner_thoughts_in_kwargs = True
else:
if llm_config.put_inner_thoughts_in_kwargs:
# tool_choice_type other than "auto" only plays nice if thinking goes inside the tool calls
tool_choice = {"type": "any", "disable_parallel_tool_use": True}
else:
tool_choice = {"type": "auto", "disable_parallel_tool_use": True}
tools = [{"type": "function", "function": f} for f in functions] if functions is not None else None
chat_completion_request = ChatCompletionRequest(
model=llm_config.model,
messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages],
tools=tools,
tool_choice=tool_choice,
max_tokens=llm_config.max_tokens, # Note: max_tokens is required for Anthropic API
temperature=llm_config.temperature,
stream=stream,
)
# Handle streaming
if stream: # Client requested token streaming
assert isinstance(stream_interface, (AgentChunkStreamingInterface, AgentRefreshStreamingInterface)), type(stream_interface)
stream_interface.inner_thoughts_in_kwargs = True
response = anthropic_chat_completions_process_stream(
chat_completion_request=chat_completion_request,
put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs,
stream_interface=stream_interface,
extended_thinking=llm_config.enable_reasoner,
max_reasoning_tokens=llm_config.max_reasoning_tokens,
provider_name=llm_config.provider_name,
provider_category=llm_config.provider_category,
name=name,
user_id=user_id,
)
else:
# Client did not request token streaming (expect a blocking backend response)
response = anthropic_chat_completions_request(
data=chat_completion_request,
put_inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs,
extended_thinking=llm_config.enable_reasoner,
max_reasoning_tokens=llm_config.max_reasoning_tokens,
provider_name=llm_config.provider_name,
provider_category=llm_config.provider_category,
user_id=user_id,
)
if llm_config.put_inner_thoughts_in_kwargs:
response = unpack_all_inner_thoughts_from_kwargs(response=response, inner_thoughts_key=INNER_THOUGHTS_KWARG)
telemetry_manager.create_provider_trace(
actor=actor,
provider_trace_create=ProviderTraceCreate(
request_json=chat_completion_request.model_json_schema(),
response_json=response.model_json_schema(),
step_id=step_id,
organization_id=actor.organization_id,
),
)
return response
# elif llm_config.model_endpoint_type == "cohere":
# if stream:
# raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}")