feat: move new streaming adapters into own files (#5001)
This commit is contained in:
cthomas
@@ -1,4 +1,3 @@
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import asyncio
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from typing import AsyncGenerator
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from letta.adapters.letta_llm_adapter import LettaLLMAdapter
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@@ -110,81 +109,3 @@ class LettaLLMRequestAdapter(LettaLLMAdapter):
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),
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label="create_provider_trace",
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)
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class SimpleLettaLLMRequestAdapter(LettaLLMRequestAdapter):
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"""Simplifying assumptions:
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- No inner thoughts in kwargs
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- No forced tool calls
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- Content native as assistant message
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"""
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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: str | None = None,
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) -> AsyncGenerator[LettaMessage | None, None]:
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"""
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Execute a blocking LLM request and yield the response.
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This adapter:
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1. Makes a blocking request to the LLM
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2. Converts the response to chat completion format
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3. Extracts reasoning and tool call information
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4. Updates all instance variables
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5. Yields nothing (blocking mode doesn't stream)
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"""
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# Store request data
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self.request_data = request_data
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# Make the blocking LLM request
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self.response_data = await self.llm_client.request_async(request_data, self.llm_config)
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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# Convert response to chat completion format
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self.chat_completions_response = self.llm_client.convert_response_to_chat_completion(self.response_data, messages, self.llm_config)
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# Extract reasoning content from the response
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if self.chat_completions_response.choices[0].message.reasoning_content:
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self.reasoning_content = [
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ReasoningContent(
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reasoning=self.chat_completions_response.choices[0].message.reasoning_content,
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is_native=True,
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signature=self.chat_completions_response.choices[0].message.reasoning_content_signature,
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)
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]
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elif self.chat_completions_response.choices[0].message.omitted_reasoning_content:
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self.reasoning_content = [OmittedReasoningContent()]
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else:
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# logger.info("No reasoning content found.")
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self.reasoning_content = None
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if self.chat_completions_response.choices[0].message.content:
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# NOTE: big difference - 'content' goes into 'content'
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# Reasoning placed into content for legacy reasons
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self.content = [TextContent(text=self.chat_completions_response.choices[0].message.content)]
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else:
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self.content = None
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# Extract tool call
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if self.chat_completions_response.choices[0].message.tool_calls:
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self.tool_call = self.chat_completions_response.choices[0].message.tool_calls[0]
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else:
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self.tool_call = None
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# Extract usage statistics
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self.usage.step_count = 1
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self.usage.completion_tokens = self.chat_completions_response.usage.completion_tokens
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self.usage.prompt_tokens = self.chat_completions_response.usage.prompt_tokens
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self.usage.total_tokens = self.chat_completions_response.usage.total_tokens
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self.log_provider_trace(step_id=step_id, actor=actor)
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yield None
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return
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@@ -1,18 +1,12 @@
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import asyncio
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from typing import AsyncGenerator, List
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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, SimpleAnthropicStreamingInterface
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from letta.interfaces.openai_streaming_interface import (
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OpenAIStreamingInterface,
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SimpleOpenAIResponsesStreamingInterface,
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SimpleOpenAIStreamingInterface,
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)
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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.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 SummarizedReasoningContent, TextContent
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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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@@ -174,168 +168,3 @@ class LettaLLMStreamAdapter(LettaLLMAdapter):
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),
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label="create_provider_trace",
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)
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class SimpleLettaLLMStreamAdapter(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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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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|
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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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)
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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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)
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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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)
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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
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stream = await self.llm_client.stream_async(request_data, self.llm_config)
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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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# 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 non-reasoning content (eg text)
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self.content: List[TextContent | SummarizedReasoningContent] = 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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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 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 or not settings.track_provider_trace:
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return
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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={
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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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"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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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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84
letta/adapters/simple_llm_request_adapter.py
Normal file
84
letta/adapters/simple_llm_request_adapter.py
Normal file
@@ -0,0 +1,84 @@
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from typing import AsyncGenerator
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from letta.adapters.letta_llm_request_adapter import LettaLLMRequestAdapter
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from letta.helpers.datetime_helpers import get_utc_timestamp_ns
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from letta.schemas.letta_message import LettaMessage
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from letta.schemas.letta_message_content import OmittedReasoningContent, ReasoningContent, TextContent
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class SimpleLLMRequestAdapter(LettaLLMRequestAdapter):
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"""Simplifying assumptions:
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- No inner thoughts in kwargs
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- No forced tool calls
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- Content native as assistant message
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"""
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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: str | None = None,
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) -> AsyncGenerator[LettaMessage | None, None]:
|
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"""
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Execute a blocking LLM request and yield the response.
|
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|
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This adapter:
|
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1. Makes a blocking request to the LLM
|
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2. Converts the response to chat completion format
|
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3. Extracts reasoning and tool call information
|
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4. Updates all instance variables
|
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5. Yields nothing (blocking mode doesn't stream)
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"""
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# Store request data
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self.request_data = request_data
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# Make the blocking LLM request
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self.response_data = await self.llm_client.request_async(request_data, self.llm_config)
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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# Convert response to chat completion format
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self.chat_completions_response = self.llm_client.convert_response_to_chat_completion(self.response_data, messages, self.llm_config)
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# Extract reasoning content from the response
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if self.chat_completions_response.choices[0].message.reasoning_content:
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self.reasoning_content = [
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ReasoningContent(
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reasoning=self.chat_completions_response.choices[0].message.reasoning_content,
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is_native=True,
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signature=self.chat_completions_response.choices[0].message.reasoning_content_signature,
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)
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]
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elif self.chat_completions_response.choices[0].message.omitted_reasoning_content:
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self.reasoning_content = [OmittedReasoningContent()]
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else:
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# logger.info("No reasoning content found.")
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self.reasoning_content = None
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if self.chat_completions_response.choices[0].message.content:
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# NOTE: big difference - 'content' goes into 'content'
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# Reasoning placed into content for legacy reasons
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self.content = [TextContent(text=self.chat_completions_response.choices[0].message.content)]
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else:
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self.content = None
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# Extract tool call
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if self.chat_completions_response.choices[0].message.tool_calls:
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self.tool_call = self.chat_completions_response.choices[0].message.tool_calls[0]
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else:
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self.tool_call = None
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# Extract usage statistics
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self.usage.step_count = 1
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self.usage.completion_tokens = self.chat_completions_response.usage.completion_tokens
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self.usage.prompt_tokens = self.chat_completions_response.usage.prompt_tokens
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self.usage.total_tokens = self.chat_completions_response.usage.total_tokens
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self.log_provider_trace(step_id=step_id, actor=actor)
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yield None
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return
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179
letta/adapters/simple_llm_stream_adapter.py
Normal file
179
letta/adapters/simple_llm_stream_adapter.py
Normal file
@@ -0,0 +1,179 @@
|
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from typing import AsyncGenerator, List
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|
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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
|
||||
from letta.interfaces.anthropic_streaming_interface import SimpleAnthropicStreamingInterface
|
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from letta.interfaces.openai_streaming_interface import SimpleOpenAIStreamingInterface
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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 SummarizedReasoningContent, TextContent
|
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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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|
||||
|
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class SimpleLLMStreamAdapter(LettaLLMStreamAdapter):
|
||||
"""
|
||||
Adapter for handling streaming LLM requests with immediate token yielding.
|
||||
|
||||
This adapter supports real-time streaming of tokens from the LLM, providing
|
||||
minimal time-to-first-token (TTFT) latency. It uses specialized streaming
|
||||
interfaces for different providers (OpenAI, Anthropic) to handle their
|
||||
specific streaming formats.
|
||||
"""
|
||||
|
||||
async def invoke_llm(
|
||||
self,
|
||||
request_data: dict,
|
||||
messages: list,
|
||||
tools: list,
|
||||
use_assistant_message: bool, # NOTE: not used
|
||||
requires_approval_tools: list[str] = [],
|
||||
step_id: str | None = None,
|
||||
actor: User | None = None,
|
||||
) -> AsyncGenerator[LettaMessage, None]:
|
||||
"""
|
||||
Execute a streaming LLM request and yield tokens/chunks as they arrive.
|
||||
|
||||
This adapter:
|
||||
1. Makes a streaming request to the LLM
|
||||
2. Yields chunks immediately for minimal TTFT
|
||||
3. Accumulates response data through the streaming interface
|
||||
4. Updates all instance variables after streaming completes
|
||||
"""
|
||||
# Store request data
|
||||
self.request_data = request_data
|
||||
|
||||
# Instantiate streaming interface
|
||||
if self.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock]:
|
||||
# NOTE: different
|
||||
self.interface = SimpleAnthropicStreamingInterface(
|
||||
requires_approval_tools=requires_approval_tools,
|
||||
)
|
||||
elif self.llm_config.model_endpoint_type == ProviderType.openai:
|
||||
# Decide interface based on payload shape
|
||||
use_responses = "input" in request_data and "messages" not in request_data
|
||||
# No support for Responses API proxy
|
||||
is_proxy = self.llm_config.provider_name == "lmstudio_openai"
|
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if use_responses and not is_proxy:
|
||||
self.interface = SimpleOpenAIResponsesStreamingInterface(
|
||||
is_openai_proxy=False,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
requires_approval_tools=requires_approval_tools,
|
||||
)
|
||||
else:
|
||||
self.interface = SimpleOpenAIStreamingInterface(
|
||||
is_openai_proxy=self.llm_config.provider_name == "lmstudio_openai",
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
requires_approval_tools=requires_approval_tools,
|
||||
model=self.llm_config.model,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Streaming not supported for provider {self.llm_config.model_endpoint_type}")
|
||||
|
||||
# Extract optional parameters
|
||||
# ttft_span = kwargs.get('ttft_span', None)
|
||||
|
||||
# Start the streaming request
|
||||
stream = await self.llm_client.stream_async(request_data, self.llm_config)
|
||||
|
||||
# Process the stream and yield chunks immediately for TTFT
|
||||
async for chunk in self.interface.process(stream): # TODO: add ttft span
|
||||
# Yield each chunk immediately as it arrives
|
||||
yield chunk
|
||||
|
||||
# After streaming completes, extract the accumulated data
|
||||
self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
|
||||
|
||||
# Extract tool call from the interface
|
||||
try:
|
||||
self.tool_call = self.interface.get_tool_call_object()
|
||||
except ValueError as e:
|
||||
# No tool call, handle upstream
|
||||
self.tool_call = None
|
||||
|
||||
# Extract reasoning content from the interface
|
||||
# TODO this should probably just be called "content"?
|
||||
# self.reasoning_content = self.interface.get_reasoning_content()
|
||||
|
||||
# Extract non-reasoning content (eg text)
|
||||
self.content: List[TextContent | SummarizedReasoningContent] = self.interface.get_content()
|
||||
|
||||
# Extract usage statistics
|
||||
# Some providers don't provide usage in streaming, use fallback if needed
|
||||
if hasattr(self.interface, "input_tokens") and hasattr(self.interface, "output_tokens"):
|
||||
# Handle cases where tokens might not be set (e.g., LMStudio)
|
||||
input_tokens = self.interface.input_tokens
|
||||
output_tokens = self.interface.output_tokens
|
||||
|
||||
# Fallback to estimated values if not provided
|
||||
if not input_tokens and hasattr(self.interface, "fallback_input_tokens"):
|
||||
input_tokens = self.interface.fallback_input_tokens
|
||||
if not output_tokens and hasattr(self.interface, "fallback_output_tokens"):
|
||||
output_tokens = self.interface.fallback_output_tokens
|
||||
|
||||
self.usage = LettaUsageStatistics(
|
||||
step_count=1,
|
||||
completion_tokens=output_tokens or 0,
|
||||
prompt_tokens=input_tokens or 0,
|
||||
total_tokens=(input_tokens or 0) + (output_tokens or 0),
|
||||
)
|
||||
else:
|
||||
# Default usage statistics if not available
|
||||
self.usage = LettaUsageStatistics(step_count=1, completion_tokens=0, prompt_tokens=0, total_tokens=0)
|
||||
|
||||
# Store any additional data from the interface
|
||||
self.message_id = self.interface.letta_message_id
|
||||
|
||||
# Log request and response data
|
||||
self.log_provider_trace(step_id=step_id, actor=actor)
|
||||
|
||||
def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
|
||||
"""
|
||||
Log provider trace data for telemetry purposes in a fire-and-forget manner.
|
||||
|
||||
Creates an async task to log the request/response data without blocking
|
||||
the main execution flow. For streaming adapters, this includes the final
|
||||
tool call and reasoning content collected during streaming.
|
||||
|
||||
Args:
|
||||
step_id: The step ID associated with this request for logging purposes
|
||||
actor: The user associated with this request for logging purposes
|
||||
"""
|
||||
if step_id is None or actor is None or not settings.track_provider_trace:
|
||||
return
|
||||
|
||||
safe_create_task(
|
||||
self.telemetry_manager.create_provider_trace_async(
|
||||
actor=actor,
|
||||
provider_trace_create=ProviderTraceCreate(
|
||||
request_json=self.request_data,
|
||||
response_json={
|
||||
"content": {
|
||||
"tool_call": self.tool_call.model_dump_json() if self.tool_call else None,
|
||||
# "reasoning": [content.model_dump_json() for content in self.reasoning_content],
|
||||
# NOTE: different
|
||||
# TODO potentially split this into both content and reasoning?
|
||||
"content": [content.model_dump_json() for content in self.content],
|
||||
},
|
||||
"id": self.interface.message_id,
|
||||
"model": self.interface.model,
|
||||
"role": "assistant",
|
||||
# "stop_reason": "",
|
||||
# "stop_sequence": None,
|
||||
"type": "message",
|
||||
"usage": {
|
||||
"input_tokens": self.usage.prompt_tokens,
|
||||
"output_tokens": self.usage.completion_tokens,
|
||||
},
|
||||
},
|
||||
step_id=step_id, # Use original step_id for telemetry
|
||||
organization_id=actor.organization_id,
|
||||
),
|
||||
),
|
||||
label="create_provider_trace",
|
||||
)
|
||||
Reference in New Issue
Block a user