a38475f23d
json.dumps on request data. Also remove step and actor since they are already present in the span
278 lines
13 KiB
Python
278 lines
13 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.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, 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 ProviderTraceCreate
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from letta.schemas.usage import LettaUsageStatistics
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from letta.schemas.user import User
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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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# Instantiate streaming interface
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if self.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock]:
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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 == ProviderType.openai:
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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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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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)
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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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)
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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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)
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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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# Extract optional parameters
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# ttft_span = kwargs.get('ttft_span', None)
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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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raise self.llm_client.handle_llm_error(e)
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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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# Map provider-specific errors during streaming to common LLMError types
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raise self.llm_client.handle_llm_error(e)
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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
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# Some providers don't provide usage in streaming, use fallback if needed
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if hasattr(self.interface, "input_tokens") and hasattr(self.interface, "output_tokens"):
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# Handle cases where tokens might not be set (e.g., LMStudio)
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input_tokens = self.interface.input_tokens
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output_tokens = self.interface.output_tokens
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# Fallback to estimated values if not provided
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if not input_tokens and hasattr(self.interface, "fallback_input_tokens"):
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input_tokens = self.interface.fallback_input_tokens
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if not output_tokens and hasattr(self.interface, "fallback_output_tokens"):
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output_tokens = self.interface.fallback_output_tokens
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# Extract cache token data (OpenAI/Gemini use cached_tokens)
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# None means provider didn't report, 0 means provider reported 0
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cached_input_tokens = None
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if hasattr(self.interface, "cached_tokens") and self.interface.cached_tokens is not None:
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cached_input_tokens = self.interface.cached_tokens
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# Anthropic uses cache_read_tokens for cache hits
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elif hasattr(self.interface, "cache_read_tokens") and self.interface.cache_read_tokens is not None:
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cached_input_tokens = self.interface.cache_read_tokens
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# Extract cache write tokens (Anthropic only)
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# None means provider didn't report, 0 means provider reported 0
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cache_write_tokens = None
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if hasattr(self.interface, "cache_creation_tokens") and self.interface.cache_creation_tokens is not None:
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cache_write_tokens = self.interface.cache_creation_tokens
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# Extract reasoning tokens (OpenAI o1/o3 models use reasoning_tokens, Gemini uses thinking_tokens)
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# None means provider didn't report, 0 means provider reported 0
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reasoning_tokens = None
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if hasattr(self.interface, "reasoning_tokens") and self.interface.reasoning_tokens is not None:
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reasoning_tokens = self.interface.reasoning_tokens
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elif hasattr(self.interface, "thinking_tokens") and self.interface.thinking_tokens is not None:
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reasoning_tokens = self.interface.thinking_tokens
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# Calculate actual total input tokens for context window limit checks (summarization trigger).
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#
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# ANTHROPIC: input_tokens is NON-cached only, must add cache tokens
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# Total = input_tokens + cache_read_input_tokens + cache_creation_input_tokens
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#
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# OPENAI/GEMINI: input_tokens (prompt_tokens/prompt_token_count) is already TOTAL
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# cached_tokens is a subset, NOT additive
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# Total = input_tokens (don't add cached_tokens or it double-counts!)
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is_anthropic = hasattr(self.interface, "cache_read_tokens") or hasattr(self.interface, "cache_creation_tokens")
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if is_anthropic:
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actual_input_tokens = (input_tokens or 0) + (cached_input_tokens or 0) + (cache_write_tokens or 0)
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else:
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actual_input_tokens = input_tokens or 0
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self.usage = LettaUsageStatistics(
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step_count=1,
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completion_tokens=output_tokens or 0,
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prompt_tokens=input_tokens or 0,
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total_tokens=actual_input_tokens + (output_tokens or 0),
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cached_input_tokens=cached_input_tokens,
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cache_write_tokens=cache_write_tokens,
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reasoning_tokens=reasoning_tokens,
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)
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else:
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# Default usage statistics if not available
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self.usage = LettaUsageStatistics(step_count=1, completion_tokens=0, prompt_tokens=0, total_tokens=0)
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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": json.dumps(self.request_data),
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"response_data": 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_create=ProviderTraceCreate(
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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, # Use original step_id for telemetry
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organization_id=actor.organization_id,
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),
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),
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label="create_provider_trace",
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)
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