4b371dd6fb
* fix(core): patch bug w/ sleeptime agents and client-side tool executions * fix: add groupid to approvalcreate * chore: just stage-api && just publish-api
690 lines
27 KiB
Python
690 lines
27 KiB
Python
import asyncio
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import json
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import os
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import uuid
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from enum import Enum
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from typing import Any, AsyncGenerator, Dict, Iterable, List, Optional, Union, cast
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from fastapi import Header, HTTPException
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from openai.types.chat import ChatCompletionMessageParam
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from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall as OpenAIToolCall, Function as OpenAIFunction
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from openai.types.chat.completion_create_params import CompletionCreateParams
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from pydantic import BaseModel
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from letta.constants import (
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DEFAULT_MESSAGE_TOOL,
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DEFAULT_MESSAGE_TOOL_KWARG,
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FUNC_FAILED_HEARTBEAT_MESSAGE,
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REQ_HEARTBEAT_MESSAGE,
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REQUEST_HEARTBEAT_PARAM,
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)
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from letta.errors import ContextWindowExceededError, RateLimitExceededError
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from letta.helpers.datetime_helpers import get_utc_time, get_utc_timestamp_ns, ns_to_ms
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from letta.helpers.message_helper import convert_message_creates_to_messages
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from letta.log import get_logger
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from letta.otel.context import get_ctx_attributes
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from letta.otel.metric_registry import MetricRegistry
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from letta.otel.tracing import tracer
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from letta.schemas.agent import AgentState
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from letta.schemas.enums import MessageRole
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from letta.schemas.letta_message import ToolReturn as LettaToolReturn
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from letta.schemas.letta_message_content import (
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OmittedReasoningContent,
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ReasoningContent,
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RedactedReasoningContent,
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SummarizedReasoningContent,
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TextContent,
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)
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import ApprovalCreate, Message, MessageCreate, ToolReturn
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from letta.schemas.tool_execution_result import ToolExecutionResult
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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.system import get_heartbeat, package_function_response
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SENTRY_ENABLED = bool(os.getenv("SENTRY_DSN"))
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if SENTRY_ENABLED:
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import sentry_sdk
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SSE_PREFIX = "data: "
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SSE_SUFFIX = "\n\n"
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SSE_FINISH_MSG = "[DONE]" # mimic openai
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SSE_ARTIFICIAL_DELAY = 0.1
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logger = get_logger(__name__)
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def sse_formatter(data: Union[dict, str]) -> str:
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"""Prefix with 'data: ', and always include double newlines"""
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assert type(data) in [dict, str], f"Expected type dict or str, got type {type(data)}"
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data_str = json.dumps(data, separators=(",", ":")) if isinstance(data, dict) else data
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# print(f"data: {data_str}\n\n")
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return f"data: {data_str}\n\n"
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async def sse_async_generator(
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generator: AsyncGenerator,
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usage_task: Optional[asyncio.Task] = None,
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finish_message=True,
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request_start_timestamp_ns: Optional[int] = None,
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llm_config: Optional[LLMConfig] = None,
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):
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"""
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Wraps a generator for use in Server-Sent Events (SSE), handling errors and ensuring a completion message.
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Args:
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- generator: An asynchronous generator yielding data chunks.
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- usage_task: Optional task that will return usage statistics.
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- finish_message: Whether to send a completion message.
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- request_start_timestamp_ns: Optional ns timestamp when the request started, used to measure time to first token.
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Yields:
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- Formatted Server-Sent Event strings.
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"""
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first_chunk = True
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ttft_span = None
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if request_start_timestamp_ns is not None:
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ttft_span = tracer.start_span("time_to_first_token", start_time=request_start_timestamp_ns)
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ttft_span.set_attributes({f"llm_config.{k}": v for k, v in llm_config.model_dump().items() if v is not None})
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try:
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async for chunk in generator:
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# Measure time to first token
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if first_chunk and ttft_span is not None:
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now = get_utc_timestamp_ns()
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ttft_ns = now - request_start_timestamp_ns
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ttft_span.add_event(name="time_to_first_token_ms", attributes={"ttft_ms": ns_to_ms(ttft_ns)})
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ttft_span.end()
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metric_attributes = get_ctx_attributes()
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if llm_config:
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metric_attributes["model.name"] = llm_config.model
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MetricRegistry().ttft_ms_histogram.record(ns_to_ms(ttft_ns), metric_attributes)
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first_chunk = False
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# yield f"data: {json.dumps(chunk)}\n\n"
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if isinstance(chunk, BaseModel):
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chunk = chunk.model_dump()
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elif isinstance(chunk, Enum):
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chunk = str(chunk.value)
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elif not isinstance(chunk, dict):
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chunk = str(chunk)
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yield sse_formatter(chunk)
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# If we have a usage task, wait for it and send its result
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if usage_task is not None:
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try:
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usage = await usage_task
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# Double-check the type
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if not isinstance(usage, LettaUsageStatistics):
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err_msg = f"Expected LettaUsageStatistics, got {type(usage)}"
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logger.error(err_msg)
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raise ValueError(err_msg)
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yield sse_formatter(usage.model_dump())
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except ContextWindowExceededError as e:
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capture_sentry_exception(e)
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logger.error(f"ContextWindowExceededError error: {e}")
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yield sse_formatter({"error": f"Stream failed: {e}", "code": str(e.code.value) if e.code else None})
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except RateLimitExceededError as e:
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capture_sentry_exception(e)
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logger.error(f"RateLimitExceededError error: {e}")
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yield sse_formatter({"error": f"Stream failed: {e}", "code": str(e.code.value) if e.code else None})
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except Exception as e:
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capture_sentry_exception(e)
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logger.error(f"Caught unexpected Exception: {e}")
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yield sse_formatter({"error": "Stream failed (internal error occurred)"})
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except Exception as e:
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capture_sentry_exception(e)
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logger.error(f"Caught unexpected Exception: {e}")
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yield sse_formatter({"error": "Stream failed (decoder encountered an error)"})
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finally:
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if finish_message:
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# Signal that the stream is complete
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yield sse_formatter(SSE_FINISH_MSG)
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def capture_sentry_exception(e: BaseException):
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"""This will capture the exception in sentry, since the exception handler upstack (in FastAPI) won't catch it, because this may be a 200 response"""
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if SENTRY_ENABLED:
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sentry_sdk.capture_exception(e)
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def create_input_messages(input_messages: List[MessageCreate], agent_id: str, timezone: str, run_id: str, actor: User) -> List[Message]:
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"""
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Converts a user input message into the internal structured format.
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TODO (cliandy): this effectively duplicates the functionality of `convert_message_creates_to_messages`,
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we should unify this when it's clear what message attributes we need.
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"""
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messages = convert_message_creates_to_messages(
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input_messages, agent_id, timezone, run_id, wrap_user_message=False, wrap_system_message=False
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)
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return messages
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def create_approval_response_message_from_input(
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agent_state: AgentState, input_message: ApprovalCreate, run_id: Optional[str] = None
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) -> List[Message]:
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def maybe_convert_tool_return_message(maybe_tool_return: LettaToolReturn):
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if isinstance(maybe_tool_return, LettaToolReturn):
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packaged_function_response = package_function_response(
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maybe_tool_return.status == "success", maybe_tool_return.tool_return, agent_state.timezone
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)
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return ToolReturn(
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tool_call_id=maybe_tool_return.tool_call_id,
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status=maybe_tool_return.status,
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func_response=packaged_function_response,
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stdout=maybe_tool_return.stdout,
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stderr=maybe_tool_return.stderr,
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)
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return maybe_tool_return
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return [
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Message(
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role=MessageRole.approval,
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agent_id=agent_state.id,
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model=agent_state.llm_config.model,
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approval_request_id=input_message.approval_request_id,
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approve=input_message.approve,
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denial_reason=input_message.reason,
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approvals=[maybe_convert_tool_return_message(approval) for approval in input_message.approvals],
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run_id=run_id,
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group_id=input_message.group_id
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if input_message.group_id
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else (agent_state.multi_agent_group.id if agent_state.multi_agent_group else None),
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)
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]
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def create_approval_request_message_from_llm_response(
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agent_id: str,
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model: str,
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requested_tool_calls: List[OpenAIToolCall],
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allowed_tool_calls: List[OpenAIToolCall] = [],
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reasoning_content: Optional[List[Union[TextContent, ReasoningContent, RedactedReasoningContent, OmittedReasoningContent]]] = None,
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pre_computed_assistant_message_id: Optional[str] = None,
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step_id: str | None = None,
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run_id: str = None,
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) -> Message:
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messages = []
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if allowed_tool_calls:
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oai_tool_calls = [
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OpenAIToolCall(
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id=tool_call.id,
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function=OpenAIFunction(
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name=tool_call.function.name,
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arguments=tool_call.function.arguments,
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),
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type="function",
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)
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for tool_call in allowed_tool_calls
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]
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tool_message = Message(
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role=MessageRole.assistant,
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content=reasoning_content if reasoning_content else [],
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agent_id=agent_id,
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model=model,
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tool_calls=oai_tool_calls,
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tool_call_id=allowed_tool_calls[0].id,
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created_at=get_utc_time(),
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step_id=step_id,
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run_id=run_id,
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)
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if pre_computed_assistant_message_id:
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tool_message.id = pre_computed_assistant_message_id
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messages.append(tool_message)
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# Construct the tool call with the assistant's message
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oai_tool_calls = [
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OpenAIToolCall(
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id=tool_call.id,
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function=OpenAIFunction(
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name=tool_call.function.name,
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arguments=tool_call.function.arguments,
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),
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type="function",
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)
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for tool_call in requested_tool_calls
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]
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# TODO: Use ToolCallContent instead of tool_calls
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# TODO: This helps preserve ordering
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approval_message = Message(
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role=MessageRole.approval,
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content=reasoning_content if reasoning_content and not allowed_tool_calls else [],
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agent_id=agent_id,
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model=model,
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tool_calls=oai_tool_calls,
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tool_call_id=oai_tool_calls[0].id,
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created_at=get_utc_time(),
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step_id=step_id,
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run_id=run_id,
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)
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if pre_computed_assistant_message_id:
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approval_message.id = decrement_message_uuid(pre_computed_assistant_message_id)
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messages.append(approval_message)
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return messages
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def decrement_message_uuid(message_id: str):
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message_uuid = uuid.UUID(message_id.split("-", maxsplit=1)[1])
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uuid_as_int = message_uuid.int
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decremented_int = uuid_as_int - 1
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decremented_uuid = uuid.UUID(int=decremented_int)
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return "message-" + str(decremented_uuid)
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def create_letta_messages_from_llm_response(
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agent_id: str,
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model: str,
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function_name: Optional[str],
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function_arguments: Optional[Dict],
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tool_execution_result: Optional[ToolExecutionResult],
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tool_call_id: Optional[str],
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function_response: Optional[str],
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timezone: str,
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run_id: str | None = None,
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step_id: str = None,
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continue_stepping: bool = False,
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heartbeat_reason: Optional[str] = None,
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reasoning_content: Optional[
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List[Union[TextContent, ReasoningContent, RedactedReasoningContent, OmittedReasoningContent | SummarizedReasoningContent]]
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] = None,
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pre_computed_assistant_message_id: Optional[str] = None,
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llm_batch_item_id: Optional[str] = None,
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is_approval_response: bool | None = None,
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# force set request_heartbeat, useful for v2 loop to ensure matching tool rules
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force_set_request_heartbeat: bool = True,
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add_heartbeat_on_continue: bool = True,
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) -> List[Message]:
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messages = []
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if not is_approval_response: # Skip approval responses (omit them)
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if function_name is not None:
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# Construct the tool call with the assistant's message
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# Force set request_heartbeat in tool_args to calculated continue_stepping
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if force_set_request_heartbeat:
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function_arguments[REQUEST_HEARTBEAT_PARAM] = continue_stepping
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tool_call = OpenAIToolCall(
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id=tool_call_id,
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function=OpenAIFunction(
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name=function_name,
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arguments=json.dumps(function_arguments),
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),
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type="function",
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)
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# TODO: Use ToolCallContent instead of tool_calls
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# TODO: This helps preserve ordering
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# Safeguard against empty text messages
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content = []
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if reasoning_content:
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for content_part in reasoning_content:
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if isinstance(content_part, TextContent) and content_part.text == "":
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continue
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content.append(content_part)
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assistant_message = Message(
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role=MessageRole.assistant,
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content=content,
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agent_id=agent_id,
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model=model,
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tool_calls=[tool_call],
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tool_call_id=tool_call_id,
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created_at=get_utc_time(),
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batch_item_id=llm_batch_item_id,
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run_id=run_id,
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)
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else:
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# Safeguard against empty text messages
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content = []
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if reasoning_content:
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for content_part in reasoning_content:
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if isinstance(content_part, TextContent) and content_part.text == "":
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continue
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content.append(content_part)
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# Should only hit this if using react agents
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if content and len(content) > 0:
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assistant_message = Message(
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role=MessageRole.assistant,
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# NOTE: weird that this is called "reasoning_content" here, since it's not
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content=content,
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agent_id=agent_id,
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model=model,
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tool_calls=None,
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tool_call_id=None,
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created_at=get_utc_time(),
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batch_item_id=llm_batch_item_id,
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run_id=run_id,
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)
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else:
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assistant_message = None
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if assistant_message:
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if pre_computed_assistant_message_id:
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assistant_message.id = pre_computed_assistant_message_id
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messages.append(assistant_message)
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# TODO: Use ToolReturnContent instead of TextContent
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# TODO: This helps preserve ordering
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if tool_execution_result is not None:
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packaged_function_response = package_function_response(tool_execution_result.success_flag, function_response, timezone)
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tool_message = Message(
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role=MessageRole.tool,
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content=[TextContent(text=packaged_function_response)],
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agent_id=agent_id,
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model=model,
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tool_calls=[],
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tool_call_id=tool_call_id,
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created_at=get_utc_time(),
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name=function_name,
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batch_item_id=llm_batch_item_id,
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tool_returns=[
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ToolReturn(
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tool_call_id=tool_call_id,
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status=tool_execution_result.status,
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stderr=tool_execution_result.stderr,
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stdout=tool_execution_result.stdout,
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func_response=packaged_function_response,
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)
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],
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run_id=run_id,
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)
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messages.append(tool_message)
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if continue_stepping and add_heartbeat_on_continue:
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# TODO skip this for react agents, instead we just force looping
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heartbeat_system_message = create_heartbeat_system_message(
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agent_id=agent_id,
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model=model,
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function_call_success=(tool_execution_result.success_flag if tool_execution_result is not None else True),
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timezone=timezone,
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heartbeat_reason=heartbeat_reason,
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run_id=run_id,
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)
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messages.append(heartbeat_system_message)
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for message in messages:
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message.step_id = step_id
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return messages
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def create_parallel_tool_messages_from_llm_response(
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agent_id: str,
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model: str,
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tool_call_specs: List[Dict[str, Any]], # List of tool call specs: {"name": str, "arguments": Dict, "id": Optional[str]}
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tool_execution_results: List[ToolExecutionResult],
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function_responses: List[Optional[str]],
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timezone: str,
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run_id: Optional[str] = None,
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step_id: Optional[str] = None,
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reasoning_content: Optional[
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List[Union[TextContent, ReasoningContent, RedactedReasoningContent, OmittedReasoningContent | SummarizedReasoningContent]]
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] = None,
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pre_computed_assistant_message_id: Optional[str] = None,
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llm_batch_item_id: Optional[str] = None,
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is_approval_response: bool = False,
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tool_returns: List[ToolReturn] = [],
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) -> List[Message]:
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"""
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Build two messages representing a parallel tool-call step:
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- One assistant message with ALL tool_calls populated (tool_call_id left empty)
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- One tool message with ALL tool_returns populated (tool_call_id left empty)
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Notes:
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- Consumers should read tool_calls/tool_returns arrays for per-call details.
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- The tool message's content includes only the first call's packaged response for
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backward-compatibility with legacy renderers. UIs should prefer tool_returns.
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- When invoked for an approval response, the assistant message is omitted (the approval
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tool call was previously surfaced).
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"""
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# Construct OpenAI-style tool_calls for the assistant message
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openai_tool_calls: List[OpenAIToolCall] = []
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for spec in tool_call_specs:
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name = spec.get("name")
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args = spec.get("arguments", {})
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call_id = spec.get("id") or str(uuid.uuid4())
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# Ensure the spec carries the resolved id so returns/content can reference it
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if not spec.get("id"):
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spec["id"] = call_id
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openai_tool_calls.append(
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OpenAIToolCall(
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id=call_id,
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function=OpenAIFunction(name=name, arguments=json.dumps(args)),
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type="function",
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)
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)
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messages: List[Message] = []
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if not is_approval_response:
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# Assistant message with all tool_calls (no single tool_call_id)
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# Safeguard against empty text messages
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content: List[
|
|
Union[TextContent, ReasoningContent, RedactedReasoningContent, OmittedReasoningContent, SummarizedReasoningContent]
|
|
] = []
|
|
if reasoning_content:
|
|
for content_part in reasoning_content:
|
|
if isinstance(content_part, TextContent) and content_part.text == "":
|
|
continue
|
|
content.append(content_part)
|
|
|
|
assistant_message = Message(
|
|
role=MessageRole.assistant,
|
|
content=content,
|
|
agent_id=agent_id,
|
|
model=model,
|
|
tool_calls=openai_tool_calls,
|
|
tool_call_id=None,
|
|
created_at=get_utc_time(),
|
|
batch_item_id=llm_batch_item_id,
|
|
run_id=run_id,
|
|
)
|
|
if step_id:
|
|
assistant_message.step_id = step_id
|
|
if pre_computed_assistant_message_id:
|
|
assistant_message.id = pre_computed_assistant_message_id
|
|
messages.append(assistant_message)
|
|
|
|
content: List[TextContent] = []
|
|
for spec, exec_result, response in zip(tool_call_specs, tool_execution_results, function_responses):
|
|
packaged = package_function_response(exec_result.success_flag, response, timezone)
|
|
content.append(TextContent(text=packaged))
|
|
tool_returns.append(
|
|
ToolReturn(
|
|
tool_call_id=spec.get("id"),
|
|
status=exec_result.status,
|
|
stdout=exec_result.stdout,
|
|
stderr=exec_result.stderr,
|
|
func_response=packaged,
|
|
)
|
|
)
|
|
|
|
tool_message = Message(
|
|
role=MessageRole.tool,
|
|
content=content,
|
|
agent_id=agent_id,
|
|
model=model,
|
|
tool_calls=[],
|
|
tool_call_id=tool_returns[0].tool_call_id, # For legacy reasons, set to first one
|
|
created_at=get_utc_time(),
|
|
batch_item_id=llm_batch_item_id,
|
|
tool_returns=tool_returns,
|
|
run_id=run_id,
|
|
)
|
|
if step_id:
|
|
tool_message.step_id = step_id
|
|
|
|
messages.append(tool_message)
|
|
return messages
|
|
|
|
|
|
def create_heartbeat_system_message(
|
|
agent_id: str,
|
|
model: str,
|
|
function_call_success: bool,
|
|
timezone: str,
|
|
llm_batch_item_id: Optional[str] = None,
|
|
heartbeat_reason: Optional[str] = None,
|
|
run_id: Optional[str] = None,
|
|
) -> Message:
|
|
if heartbeat_reason:
|
|
text_content = heartbeat_reason
|
|
else:
|
|
text_content = REQ_HEARTBEAT_MESSAGE if function_call_success else FUNC_FAILED_HEARTBEAT_MESSAGE
|
|
|
|
heartbeat_system_message = Message(
|
|
role=MessageRole.user,
|
|
content=[TextContent(text=get_heartbeat(timezone, text_content))],
|
|
agent_id=agent_id,
|
|
model=model,
|
|
tool_calls=[],
|
|
tool_call_id=None,
|
|
created_at=get_utc_time(),
|
|
batch_item_id=llm_batch_item_id,
|
|
run_id=run_id,
|
|
)
|
|
return heartbeat_system_message
|
|
|
|
|
|
def create_assistant_messages_from_openai_response(
|
|
response_text: str,
|
|
agent_id: str,
|
|
model: str,
|
|
timezone: str,
|
|
) -> List[Message]:
|
|
"""
|
|
Converts an OpenAI response into Messages that follow the internal
|
|
paradigm where LLM responses are structured as tool calls instead of content.
|
|
"""
|
|
tool_call_id = str(uuid.uuid4())
|
|
|
|
return create_letta_messages_from_llm_response(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
function_name=DEFAULT_MESSAGE_TOOL,
|
|
function_arguments={DEFAULT_MESSAGE_TOOL_KWARG: response_text}, # Avoid raw string manipulation
|
|
tool_execution_result=ToolExecutionResult(status="success"),
|
|
tool_call_id=tool_call_id,
|
|
function_response=None,
|
|
timezone=timezone,
|
|
continue_stepping=False,
|
|
)
|
|
|
|
|
|
def convert_in_context_letta_messages_to_openai(in_context_messages: List[Message], exclude_system_messages: bool = False) -> List[dict]:
|
|
"""
|
|
Flattens Letta's messages (with system, user, assistant, tool roles, etc.)
|
|
into standard OpenAI chat messages (system, user, assistant).
|
|
|
|
Transformation rules:
|
|
1. Assistant + send_message tool_call => content = tool_call's "message"
|
|
2. Tool (role=tool) referencing send_message => skip
|
|
3. User messages might store actual text inside JSON => parse that into content
|
|
4. System => pass through as normal
|
|
"""
|
|
# Always include the system prompt
|
|
# TODO: This is brittle
|
|
openai_messages = [in_context_messages[0].to_openai_dict()]
|
|
|
|
for msg in in_context_messages[1:]:
|
|
if msg.role == MessageRole.system and exclude_system_messages:
|
|
# Skip if exclude_system_messages is set to True
|
|
continue
|
|
|
|
# 1. Assistant + 'send_message' tool_calls => flatten
|
|
if msg.role == MessageRole.assistant and msg.tool_calls:
|
|
# Find any 'send_message' tool_calls
|
|
send_message_calls = [tc for tc in msg.tool_calls if tc.function.name == "send_message"]
|
|
if send_message_calls:
|
|
# If we have multiple calls, just pick the first or merge them
|
|
# Typically there's only one.
|
|
tc = send_message_calls[0]
|
|
arguments = json.loads(tc.function.arguments)
|
|
# Extract the "message" string
|
|
extracted_text = arguments.get("message", "")
|
|
|
|
# Create a new content with the extracted text
|
|
msg = Message(
|
|
id=msg.id,
|
|
role=msg.role,
|
|
content=[TextContent(text=extracted_text)],
|
|
agent_id=msg.agent_id,
|
|
model=msg.model,
|
|
name=msg.name,
|
|
tool_calls=None, # no longer needed
|
|
tool_call_id=None,
|
|
created_at=msg.created_at,
|
|
)
|
|
|
|
# 2. If role=tool and it's referencing send_message => skip
|
|
if msg.role == MessageRole.tool and msg.name == "send_message":
|
|
# Usually 'tool' messages with `send_message` are just status/OK messages
|
|
# that OpenAI doesn't need to see. So skip them.
|
|
continue
|
|
|
|
# 3. User messages might store text in JSON => parse it
|
|
if msg.role == MessageRole.user:
|
|
# Example: content=[TextContent(text='{"type": "user_message","message":"Hello"}')]
|
|
# Attempt to parse JSON and extract "message"
|
|
if msg.content and msg.content[0].text.strip().startswith("{"):
|
|
try:
|
|
parsed = json.loads(msg.content[0].text)
|
|
# If there's a "message" field, use that as the content
|
|
if "message" in parsed:
|
|
actual_user_text = parsed["message"]
|
|
msg = Message(
|
|
id=msg.id,
|
|
role=msg.role,
|
|
content=[TextContent(text=actual_user_text)],
|
|
agent_id=msg.agent_id,
|
|
model=msg.model,
|
|
name=msg.name,
|
|
tool_calls=msg.tool_calls,
|
|
tool_call_id=msg.tool_call_id,
|
|
created_at=msg.created_at,
|
|
)
|
|
except json.JSONDecodeError:
|
|
pass # It's not JSON, leave as-is
|
|
|
|
# Finally, convert to dict using your existing method
|
|
m = msg.to_openai_dict()
|
|
assert m is not None
|
|
openai_messages.append(m)
|
|
|
|
return openai_messages
|
|
|
|
|
|
def get_user_message_from_chat_completions_request(completion_request: CompletionCreateParams) -> List[MessageCreate]:
|
|
try:
|
|
messages = list(cast(Iterable[ChatCompletionMessageParam], completion_request["messages"]))
|
|
except KeyError:
|
|
# Handle the case where "messages" is not present in the request
|
|
raise HTTPException(status_code=400, detail="The 'messages' field is missing in the request.")
|
|
except TypeError:
|
|
# Handle the case where "messages" is not iterable
|
|
raise HTTPException(status_code=400, detail="The 'messages' field must be an iterable.")
|
|
except Exception as e:
|
|
# Catch any other unexpected errors and include the exception message
|
|
raise HTTPException(status_code=400, detail=f"An error occurred while processing 'messages': {str(e)}")
|
|
|
|
if messages[-1]["role"] != "user":
|
|
logger.error(f"The last message does not have a `user` role: {messages}")
|
|
raise HTTPException(status_code=400, detail="'messages[-1].role' must be a 'user'")
|
|
|
|
input_message = messages[-1]
|
|
if not isinstance(input_message["content"], str):
|
|
logger.error(f"The input message does not have valid content: {input_message}")
|
|
raise HTTPException(status_code=400, detail="'messages[-1].content' must be a 'string'")
|
|
|
|
for message in reversed(messages):
|
|
if message["role"] == "user":
|
|
return [MessageCreate(role=MessageRole.user, content=[TextContent(text=message["content"])])]
|