111 lines
4.7 KiB
Python
111 lines
4.7 KiB
Python
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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from letta.schemas.usage import normalize_cache_tokens, normalize_reasoning_tokens
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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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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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# Set telemetry context and make the blocking LLM request
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self.llm_client.set_telemetry_context(
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telemetry_manager=self.telemetry_manager,
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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="agent_step",
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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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try:
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self.response_data = await self.llm_client.request_async_with_telemetry(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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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 = await self.llm_client.convert_response_to_chat_completion(
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self.response_data, messages, self.llm_config
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)
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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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if self.reasoning_content and len(self.reasoning_content) > 0:
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# Temp workaround to consolidate parts to persist reasoning content, this should be integrated better
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self.content = self.reasoning_content + (self.content or [])
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# Extract tool call
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tool_calls = self.chat_completions_response.choices[0].message.tool_calls or []
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self.tool_calls = list(tool_calls)
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self.tool_call = self.tool_calls[0] if self.tool_calls else 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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# Extract cache and reasoning token details using normalized helpers
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usage = self.chat_completions_response.usage
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self.usage.cached_input_tokens, self.usage.cache_write_tokens = normalize_cache_tokens(usage.prompt_tokens_details)
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self.usage.reasoning_tokens = normalize_reasoning_tokens(usage.completion_tokens_details)
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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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