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chore: clean up legacy bedrock path (#3906)

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cthomas
2025-08-13 15:26:27 -07:00
committed by GitHub
parent edf39c565f
commit 48f78991a8
3 changed files with 3 additions and 156 deletions
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35
letta/llm_api/llm_api_tools.py
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@@ -7,8 +7,6 @@ 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
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
from letta.llm_api.openai import (
@@ -25,7 +23,7 @@ from letta.otel.tracing import log_event, trace_method
from letta.schemas.enums import ProviderCategory
from letta.schemas.llm_config import LLMConfig
from letta.schemas.message import Message
from letta.schemas.openai.chat_completion_request import ChatCompletionRequest, cast_message_to_subtype
from letta.schemas.openai.chat_completion_request import ChatCompletionRequest
from letta.schemas.openai.chat_completion_response import ChatCompletionResponse
from letta.schemas.provider_trace import ProviderTraceCreate
from letta.services.telemetry_manager import TelemetryManager
@@ -384,37 +382,6 @@ def create(
return response
elif llm_config.model_endpoint_type == "bedrock":
"""Anthropic endpoint that goes via /embeddings instead of /chat/completions"""
if stream:
raise NotImplementedError("Streaming not yet implemented for Anthropic (via the /embeddings endpoint).")
if not use_tool_naming:
raise NotImplementedError("Only tool calling supported on Anthropic API requests")
if not has_valid_aws_credentials():
raise LettaConfigurationError(message="Invalid or missing AWS credentials. Please configure valid AWS credentials.")
tool_call = None
if force_tool_call is not None:
tool_call = {"type": "function", "function": {"name": force_tool_call}}
assert functions is not None
return anthropic_bedrock_chat_completions_request(
data=ChatCompletionRequest(
model=llm_config.model,
messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages],
tools=[{"type": "function", "function": f} for f in functions] if functions else None,
tool_choice=tool_call,
# user=str(user_id),
# NOTE: max_tokens is required for Anthropic API
max_tokens=llm_config.max_tokens,
),
provider_name=llm_config.provider_name,
provider_category=llm_config.provider_category,
user_id=user_id,
)
elif llm_config.model_endpoint_type == "deepseek":
if model_settings.deepseek_api_key is None and llm_config.model_endpoint == "":
# only is a problem if we are *not* using an openai proxy