e3eafb1977
LLMError exceptions are already properly formatted errors that should propagate directly. Without this check, they get unnecessarily wrapped by handle_llm_error, losing their original error information.
270 lines
12 KiB
Python
270 lines
12 KiB
Python
import json
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from typing import AsyncGenerator, List
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from letta.adapters.letta_llm_stream_adapter import LettaLLMStreamAdapter
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from letta.errors import LLMError
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from letta.log import get_logger
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logger = get_logger(__name__)
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from letta.helpers.datetime_helpers import get_utc_timestamp_ns
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from letta.interfaces.anthropic_parallel_tool_call_streaming_interface import SimpleAnthropicStreamingInterface
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from letta.interfaces.gemini_streaming_interface import SimpleGeminiStreamingInterface
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from letta.interfaces.openai_streaming_interface import SimpleOpenAIResponsesStreamingInterface, SimpleOpenAIStreamingInterface
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from letta.otel.tracing import log_attributes, safe_json_dumps, trace_method
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from letta.schemas.enums import ProviderType
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from letta.schemas.letta_message import LettaMessage
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from letta.schemas.letta_message_content import LettaMessageContentUnion
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from letta.schemas.provider_trace import ProviderTrace
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from letta.schemas.user import User
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from letta.server.rest_api.streaming_response import get_cancellation_event_for_run
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from letta.settings import settings
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from letta.utils import safe_create_task
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class SimpleLLMStreamAdapter(LettaLLMStreamAdapter):
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"""
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Adapter for handling streaming LLM requests with immediate token yielding.
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This adapter supports real-time streaming of tokens from the LLM, providing
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minimal time-to-first-token (TTFT) latency. It uses specialized streaming
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interfaces for different providers (OpenAI, Anthropic) to handle their
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specific streaming formats.
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"""
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def _extract_tool_calls(self) -> list:
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"""extract tool calls from interface, trying parallel API first then single API"""
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# try multi-call api if available
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if hasattr(self.interface, "get_tool_call_objects"):
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try:
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calls = self.interface.get_tool_call_objects()
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if calls:
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return calls
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except Exception:
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pass
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# fallback to single-call api
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try:
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single = self.interface.get_tool_call_object()
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return [single] if single else []
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except Exception:
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return []
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async def invoke_llm(
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self,
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request_data: dict,
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messages: list,
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tools: list,
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use_assistant_message: bool, # NOTE: not used
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requires_approval_tools: list[str] = [],
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step_id: str | None = None,
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actor: User | None = None,
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) -> AsyncGenerator[LettaMessage, None]:
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"""
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Execute a streaming LLM request and yield tokens/chunks as they arrive.
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This adapter:
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1. Makes a streaming request to the LLM
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2. Yields chunks immediately for minimal TTFT
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3. Accumulates response data through the streaming interface
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4. Updates all instance variables after streaming completes
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"""
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# Store request data
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self.request_data = request_data
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# Track request start time for latency calculation
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request_start_ns = get_utc_timestamp_ns()
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# Get cancellation event for this run to enable graceful cancellation (before branching)
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cancellation_event = get_cancellation_event_for_run(self.run_id) if self.run_id else None
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# Instantiate streaming interface
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if self.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock, ProviderType.minimax]:
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# NOTE: different
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self.interface = SimpleAnthropicStreamingInterface(
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requires_approval_tools=requires_approval_tools,
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run_id=self.run_id,
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step_id=step_id,
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)
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elif self.llm_config.model_endpoint_type in [
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ProviderType.openai,
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ProviderType.deepseek,
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ProviderType.openrouter,
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ProviderType.zai,
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ProviderType.chatgpt_oauth,
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]:
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# Decide interface based on payload shape
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use_responses = "input" in request_data and "messages" not in request_data
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# No support for Responses API proxy
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is_proxy = self.llm_config.provider_name == "lmstudio_openai"
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# ChatGPT OAuth always uses Responses API format
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if self.llm_config.model_endpoint_type == ProviderType.chatgpt_oauth:
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use_responses = True
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is_proxy = False
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if use_responses and not is_proxy:
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self.interface = SimpleOpenAIResponsesStreamingInterface(
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is_openai_proxy=False,
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messages=messages,
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tools=tools,
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requires_approval_tools=requires_approval_tools,
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run_id=self.run_id,
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step_id=step_id,
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cancellation_event=cancellation_event,
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)
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else:
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self.interface = SimpleOpenAIStreamingInterface(
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is_openai_proxy=self.llm_config.provider_name == "lmstudio_openai",
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messages=messages,
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tools=tools,
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requires_approval_tools=requires_approval_tools,
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model=self.llm_config.model,
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run_id=self.run_id,
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step_id=step_id,
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cancellation_event=cancellation_event,
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)
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elif self.llm_config.model_endpoint_type in [ProviderType.google_ai, ProviderType.google_vertex]:
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self.interface = SimpleGeminiStreamingInterface(
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requires_approval_tools=requires_approval_tools,
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run_id=self.run_id,
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step_id=step_id,
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cancellation_event=cancellation_event,
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)
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else:
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raise ValueError(f"Streaming not supported for provider {self.llm_config.model_endpoint_type}")
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# Start the streaming request (map provider errors to common LLMError types)
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try:
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# Gemini uses async generator pattern (no await) to maintain connection lifecycle
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# Other providers return awaitables that resolve to iterators
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if self.llm_config.model_endpoint_type in [ProviderType.google_ai, ProviderType.google_vertex]:
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stream = self.llm_client.stream_async(request_data, self.llm_config)
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else:
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stream = await self.llm_client.stream_async(request_data, self.llm_config)
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except Exception as e:
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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latency_ms = int((self.llm_request_finish_timestamp_ns - request_start_ns) / 1_000_000)
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await self.llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json=None,
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llm_config=self.llm_config,
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latency_ms=latency_ms,
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error_msg=str(e),
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error_type=type(e).__name__,
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)
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if isinstance(e, LLMError):
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raise
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raise self.llm_client.handle_llm_error(e, llm_config=self.llm_config)
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# Process the stream and yield chunks immediately for TTFT
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try:
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async for chunk in self.interface.process(stream): # TODO: add ttft span
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# Yield each chunk immediately as it arrives
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yield chunk
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except Exception as e:
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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latency_ms = int((self.llm_request_finish_timestamp_ns - request_start_ns) / 1_000_000)
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await self.llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json=None,
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llm_config=self.llm_config,
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latency_ms=latency_ms,
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error_msg=str(e),
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error_type=type(e).__name__,
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)
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if isinstance(e, LLMError):
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raise
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raise self.llm_client.handle_llm_error(e, llm_config=self.llm_config)
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# After streaming completes, extract the accumulated data
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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# extract tool calls from interface (supports both single and parallel calls)
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self.tool_calls = self._extract_tool_calls()
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# preserve legacy single-call field for existing consumers
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self.tool_call = self.tool_calls[-1] if self.tool_calls else None
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# Extract reasoning content from the interface
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# TODO this should probably just be called "content"?
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# self.reasoning_content = self.interface.get_reasoning_content()
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# Extract all content parts
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self.content: List[LettaMessageContentUnion] = self.interface.get_content()
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# Extract usage statistics from the interface
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# Each interface implements get_usage_statistics() with provider-specific logic
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self.usage = self.interface.get_usage_statistics()
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self.usage.step_count = 1
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# Store any additional data from the interface
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self.message_id = self.interface.letta_message_id
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# Log request and response data
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self.log_provider_trace(step_id=step_id, actor=actor)
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@trace_method
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def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
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"""
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Log provider trace data for telemetry purposes in a fire-and-forget manner.
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Creates an async task to log the request/response data without blocking
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the main execution flow. For streaming adapters, this includes the final
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tool call and reasoning content collected during streaming.
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Args:
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step_id: The step ID associated with this request for logging purposes
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actor: The user associated with this request for logging purposes
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"""
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if step_id is None or actor is None:
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return
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response_json = {
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"content": {
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"tool_call": self.tool_call.model_dump_json() if self.tool_call else None,
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# "reasoning": [content.model_dump_json() for content in self.reasoning_content],
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# NOTE: different
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# TODO potentially split this into both content and reasoning?
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"content": [content.model_dump_json() for content in self.content],
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},
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"id": self.interface.message_id,
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"model": self.interface.model,
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"role": "assistant",
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# "stop_reason": "",
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# "stop_sequence": None,
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"type": "message",
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# Use raw_usage if available for transparent provider trace logging, else fallback
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"usage": self.interface.raw_usage
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if hasattr(self.interface, "raw_usage") and self.interface.raw_usage
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else {
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"input_tokens": self.usage.prompt_tokens,
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"output_tokens": self.usage.completion_tokens,
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},
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}
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log_attributes(
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{
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"request_data": safe_json_dumps(self.request_data),
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"response_data": safe_json_dumps(response_json),
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}
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)
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if settings.track_provider_trace:
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safe_create_task(
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self.telemetry_manager.create_provider_trace_async(
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actor=actor,
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provider_trace=ProviderTrace(
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request_json=self.request_data,
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response_json=response_json,
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step_id=step_id,
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agent_id=self.agent_id,
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agent_tags=self.agent_tags,
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run_id=self.run_id,
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call_type=self.call_type,
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org_id=self.org_id,
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user_id=self.user_id,
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llm_config=self.llm_config.model_dump() if self.llm_config else None,
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),
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),
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label="create_provider_trace",
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)
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