198 lines
8.5 KiB
Python
198 lines
8.5 KiB
Python
from typing import AsyncGenerator
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from letta.adapters.letta_llm_adapter import LettaLLMAdapter
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from letta.helpers.datetime_helpers import get_utc_timestamp_ns
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from letta.interfaces.anthropic_streaming_interface import AnthropicStreamingInterface
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from letta.interfaces.openai_streaming_interface import OpenAIStreamingInterface
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from letta.llm_api.llm_client_base import LLMClientBase
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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.llm_config import LLMConfig
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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 LettaLLMStreamAdapter(LettaLLMAdapter):
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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 __init__(self, llm_client: LLMClientBase, llm_config: LLMConfig, run_id: str | None = None) -> None:
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super().__init__(llm_client, llm_config)
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self.run_id = run_id
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self.interface: OpenAIStreamingInterface | AnthropicStreamingInterface | None = None
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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,
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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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self.interface = AnthropicStreamingInterface(
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use_assistant_message=use_assistant_message,
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put_inner_thoughts_in_kwarg=self.llm_config.put_inner_thoughts_in_kwargs,
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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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# For non-v1 agents, always use Chat Completions streaming interface
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self.interface = OpenAIStreamingInterface(
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use_assistant_message=use_assistant_message,
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is_openai_proxy=self.llm_config.provider_name == "lmstudio_openai",
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put_inner_thoughts_in_kwarg=self.llm_config.put_inner_thoughts_in_kwargs,
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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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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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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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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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# 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 call from the interface
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try:
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self.tool_call = self.interface.get_tool_call_object()
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except ValueError as e:
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# No tool call, handle upstream
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self.tool_call = None
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# Extract reasoning content from the interface
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self.reasoning_content = self.interface.get_reasoning_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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# NOTE: For Anthropic, input_tokens is NON-cached only, so total_tokens here
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# undercounts the actual total (missing cache_read + cache_creation tokens).
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# For OpenAI/Gemini, input_tokens is already the total, so this is correct.
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# See simple_llm_stream_adapter.py for the proper provider-aware calculation.
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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=(input_tokens or 0) + (output_tokens or 0),
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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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def supports_token_streaming(self) -> bool:
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return True
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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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},
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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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"usage": {
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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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# Store response data for future reference
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self.response_data = response_json
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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_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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),
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),
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label="create_provider_trace",
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)
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