04611b981c
* feat: filter messages search endpoint by agent id [LET-6229] * add autogenerated schema/types
2177 lines
96 KiB
Python
2177 lines
96 KiB
Python
from __future__ import annotations
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from letta.log import get_logger
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logger = get_logger(__name__)
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import copy
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import json
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import re
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import uuid
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from collections import OrderedDict
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from datetime import datetime, timezone
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from enum import Enum
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from typing import Annotated, Any, Dict, List, Literal, Optional, Union
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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.responses import ResponseReasoningItem
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from pydantic import BaseModel, Field, field_validator, model_validator
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from letta.constants import DEFAULT_MESSAGE_TOOL, DEFAULT_MESSAGE_TOOL_KWARG, REQUEST_HEARTBEAT_PARAM, TOOL_CALL_ID_MAX_LEN
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from letta.helpers.datetime_helpers import get_utc_time, is_utc_datetime
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from letta.helpers.json_helpers import json_dumps
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_VERTEX
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from letta.schemas.enums import MessageRole, PrimitiveType
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from letta.schemas.letta_base import OrmMetadataBase
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from letta.schemas.letta_message import (
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ApprovalRequestMessage,
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ApprovalResponseMessage,
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ApprovalReturn,
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AssistantMessage,
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HiddenReasoningMessage,
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LettaMessage,
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LettaMessageReturnUnion,
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MessageType,
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ReasoningMessage,
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SystemMessage,
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ToolCall,
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ToolCallMessage,
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ToolReturn as LettaToolReturn,
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ToolReturnMessage,
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UserMessage,
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)
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from letta.schemas.letta_message_content import (
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ImageContent,
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LettaMessageContentUnion,
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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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ToolCallContent,
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ToolReturnContent,
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get_letta_message_content_union_str_json_schema,
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)
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from letta.system import unpack_message
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from letta.utils import parse_json, validate_function_response
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def truncate_tool_return(content: Optional[str], limit: Optional[int]) -> Optional[str]:
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if limit is None or content is None:
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return content
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if len(content) <= limit:
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return content
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return content[:limit] + f"... [truncated {len(content) - limit} chars]"
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def add_inner_thoughts_to_tool_call(
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tool_call: OpenAIToolCall,
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inner_thoughts: str,
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inner_thoughts_key: str,
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) -> OpenAIToolCall:
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"""Add inner thoughts (arg + value) to a tool call"""
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try:
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# load the args list
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func_args = parse_json(tool_call.function.arguments)
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# create new ordered dict with inner thoughts first
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ordered_args = OrderedDict({inner_thoughts_key: inner_thoughts})
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# update with remaining args
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ordered_args.update(func_args)
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# create the updated tool call (as a string)
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updated_tool_call = copy.deepcopy(tool_call)
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updated_tool_call.function.arguments = json_dumps(ordered_args)
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return updated_tool_call
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except json.JSONDecodeError as e:
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logger.warning(f"Failed to put inner thoughts in kwargs: {e}")
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raise e
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class MessageCreateType(str, Enum):
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message = "message"
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approval = "approval"
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class MessageCreateBase(BaseModel):
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type: MessageCreateType = Field(..., description="The message type to be created.")
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class MessageCreate(MessageCreateBase):
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"""Request to create a message"""
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type: Optional[Literal[MessageCreateType.message]] = Field(
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default=MessageCreateType.message, description="The message type to be created."
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)
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# In the simplified format, only allow simple roles
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role: Literal[
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MessageRole.user,
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MessageRole.system,
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MessageRole.assistant,
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] = Field(..., description="The role of the participant.")
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content: Union[str, List[LettaMessageContentUnion]] = Field(
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...,
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description="The content of the message.",
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json_schema_extra=get_letta_message_content_union_str_json_schema(),
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)
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name: Optional[str] = Field(default=None, description="The name of the participant.")
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otid: Optional[str] = Field(default=None, description="The offline threading id associated with this message")
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sender_id: Optional[str] = Field(default=None, description="The id of the sender of the message, can be an identity id or agent id")
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batch_item_id: Optional[str] = Field(default=None, description="The id of the LLMBatchItem that this message is associated with")
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group_id: Optional[str] = Field(default=None, description="The multi-agent group that the message was sent in")
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def model_dump(self, to_orm: bool = False, **kwargs) -> Dict[str, Any]:
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data = super().model_dump(**kwargs)
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if to_orm and "content" in data:
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if isinstance(data["content"], str):
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data["content"] = [TextContent(text=data["content"])]
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return data
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class ApprovalCreate(MessageCreateBase):
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"""Input to approve or deny a tool call request"""
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type: Literal[MessageCreateType.approval] = Field(default=MessageCreateType.approval, description="The message type to be created.")
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approvals: Optional[List[LettaMessageReturnUnion]] = Field(default=None, description="The list of approval responses")
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approve: Optional[bool] = Field(None, description="Whether the tool has been approved", deprecated=True)
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approval_request_id: Optional[str] = Field(None, description="The message ID of the approval request", deprecated=True)
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reason: Optional[str] = Field(None, description="An optional explanation for the provided approval status", deprecated=True)
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group_id: Optional[str] = Field(default=None, description="The multi-agent group that the message was sent in")
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@model_validator(mode="after")
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def migrate_deprecated_fields(self):
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if not self.approvals and self.approve is not None and self.approval_request_id is not None:
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self.approvals = [
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ApprovalReturn(
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tool_call_id=self.approval_request_id,
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approve=self.approve,
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reason=self.reason,
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)
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]
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return self
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MessageCreateUnion = Union[MessageCreate, ApprovalCreate]
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class MessageUpdate(BaseModel):
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"""Request to update a message"""
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role: Optional[MessageRole] = Field(default=None, description="The role of the participant.")
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content: Optional[Union[str, List[LettaMessageContentUnion]]] = Field(
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default=None,
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description="The content of the message.",
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json_schema_extra=get_letta_message_content_union_str_json_schema(),
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)
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# NOTE: probably doesn't make sense to allow remapping user_id or agent_id (vs creating a new message)
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# user_id: Optional[str] = Field(None, description="The unique identifier of the user.")
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# agent_id: Optional[str] = Field(None, description="The unique identifier of the agent.")
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# NOTE: we probably shouldn't allow updating the model field, otherwise this loses meaning
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# model: Optional[str] = Field(None, description="The model used to make the function call.")
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name: Optional[str] = Field(default=None, description="The name of the participant.")
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# NOTE: we probably shouldn't allow updating the created_at field, right?
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# created_at: Optional[datetime] = Field(None, description="The time the message was created.")
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tool_calls: Optional[List[OpenAIToolCall,]] = Field(default=None, description="The list of tool calls requested.")
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tool_call_id: Optional[str] = Field(default=None, description="The id of the tool call.")
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def model_dump(self, to_orm: bool = False, **kwargs) -> Dict[str, Any]:
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data = super().model_dump(**kwargs)
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if to_orm and "content" in data:
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if isinstance(data["content"], str):
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data["content"] = [TextContent(text=data["content"])]
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return data
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class BaseMessage(OrmMetadataBase):
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__id_prefix__ = PrimitiveType.MESSAGE.value
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class Message(BaseMessage):
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"""
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Letta's internal representation of a message. Includes methods to convert to/from LLM provider formats.
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Attributes:
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id (str): The unique identifier of the message.
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role (MessageRole): The role of the participant.
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text (str): The text of the message.
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user_id (str): The unique identifier of the user.
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agent_id (str): The unique identifier of the agent.
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model (str): The model used to make the function call.
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name (str): The name of the participant.
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created_at (datetime): The time the message was created.
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tool_calls (List[OpenAIToolCall,]): The list of tool calls requested.
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tool_call_id (str): The id of the tool call.
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step_id (str): The id of the step that this message was created in.
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otid (str): The offline threading id associated with this message.
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tool_returns (List[ToolReturn]): The list of tool returns requested.
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group_id (str): The multi-agent group that the message was sent in.
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sender_id (str): The id of the sender of the message, can be an identity id or agent id.
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t
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"""
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id: str = BaseMessage.generate_id_field()
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agent_id: Optional[str] = Field(default=None, description="The unique identifier of the agent.")
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model: Optional[str] = Field(default=None, description="The model used to make the function call.")
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# Basic OpenAI-style fields
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role: MessageRole = Field(..., description="The role of the participant.")
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content: Optional[List[LettaMessageContentUnion]] = Field(default=None, description="The content of the message.")
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# NOTE: in OpenAI, this field is only used for roles 'user', 'assistant', and 'function' (now deprecated). 'tool' does not use it.
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name: Optional[str] = Field(
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default=None,
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description="For role user/assistant: the (optional) name of the participant. For role tool/function: the name of the function called.",
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)
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tool_calls: Optional[List[OpenAIToolCall]] = Field(
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default=None, description="The list of tool calls requested. Only applicable for role assistant."
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)
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tool_call_id: Optional[str] = Field(default=None, description="The ID of the tool call. Only applicable for role tool.")
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# Extras
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step_id: Optional[str] = Field(default=None, description="The id of the step that this message was created in.")
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run_id: Optional[str] = Field(default=None, description="The id of the run that this message was created in.")
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otid: Optional[str] = Field(default=None, description="The offline threading id associated with this message")
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tool_returns: Optional[List[ToolReturn]] = Field(default=None, description="Tool execution return information for prior tool calls")
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group_id: Optional[str] = Field(default=None, description="The multi-agent group that the message was sent in")
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sender_id: Optional[str] = Field(default=None, description="The id of the sender of the message, can be an identity id or agent id")
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batch_item_id: Optional[str] = Field(default=None, description="The id of the LLMBatchItem that this message is associated with")
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is_err: Optional[bool] = Field(
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default=None, description="Whether this message is part of an error step. Used only for debugging purposes."
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)
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approval_request_id: Optional[str] = Field(
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default=None, description="The id of the approval request if this message is associated with a tool call request."
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)
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approve: Optional[bool] = Field(default=None, description="Whether tool call is approved.")
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denial_reason: Optional[str] = Field(default=None, description="The reason the tool call request was denied.")
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approvals: Optional[List[ApprovalReturn | ToolReturn]] = Field(default=None, description="The list of approvals for this message.")
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# This overrides the optional base orm schema, created_at MUST exist on all messages objects
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created_at: datetime = Field(default_factory=get_utc_time, description="The timestamp when the object was created.")
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# validate that run_id is set
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# @model_validator(mode="after")
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# def validate_run_id(self):
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# if self.run_id is None:
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# raise ValueError("Run ID is required")
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# return self
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@field_validator("role")
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@classmethod
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def validate_role(cls, v: str) -> str:
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roles = ["system", "assistant", "user", "tool", "approval"]
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assert v in roles, f"Role must be one of {roles}"
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return v
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def to_json(self):
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json_message = vars(self)
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if json_message["tool_calls"] is not None:
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json_message["tool_calls"] = [vars(tc) for tc in json_message["tool_calls"]]
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# turn datetime to ISO format
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# also if the created_at is missing a timezone, add UTC
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if not is_utc_datetime(self.created_at):
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self.created_at = self.created_at.replace(tzinfo=timezone.utc)
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json_message["created_at"] = self.created_at.isoformat()
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json_message.pop("is_err", None) # make sure we don't include this debugging information
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return json_message
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@staticmethod
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def generate_otid():
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return str(uuid.uuid4())
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@staticmethod
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def to_letta_messages_from_list(
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messages: List[Message],
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use_assistant_message: bool = True,
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assistant_message_tool_name: str = DEFAULT_MESSAGE_TOOL,
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assistant_message_tool_kwarg: str = DEFAULT_MESSAGE_TOOL_KWARG,
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reverse: bool = True,
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include_err: Optional[bool] = None,
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text_is_assistant_message: bool = False,
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) -> List[LettaMessage]:
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if use_assistant_message:
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message_ids_to_remove = []
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assistant_messages_by_tool_call = {
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tool_call.id: msg
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for msg in messages
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if msg.role == MessageRole.assistant and msg.tool_calls
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for tool_call in msg.tool_calls
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}
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for message in messages:
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if (
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message.role == MessageRole.tool
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and message.tool_call_id in assistant_messages_by_tool_call
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and assistant_messages_by_tool_call[message.tool_call_id].tool_calls
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and assistant_message_tool_name
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in [tool_call.function.name for tool_call in assistant_messages_by_tool_call[message.tool_call_id].tool_calls]
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):
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message_ids_to_remove.append(message.id)
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messages = [msg for msg in messages if msg.id not in message_ids_to_remove]
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# Convert messages to LettaMessages
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return [
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msg
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for m in messages
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for msg in m.to_letta_messages(
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use_assistant_message=use_assistant_message,
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assistant_message_tool_name=assistant_message_tool_name,
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assistant_message_tool_kwarg=assistant_message_tool_kwarg,
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reverse=reverse,
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include_err=include_err,
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text_is_assistant_message=text_is_assistant_message,
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)
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]
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def to_letta_messages(
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self,
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use_assistant_message: bool = False,
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assistant_message_tool_name: str = DEFAULT_MESSAGE_TOOL,
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assistant_message_tool_kwarg: str = DEFAULT_MESSAGE_TOOL_KWARG,
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reverse: bool = True,
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include_err: Optional[bool] = None,
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text_is_assistant_message: bool = False,
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) -> List[LettaMessage]:
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"""Convert message object (in DB format) to the style used by the original Letta API"""
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messages = []
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if self.role == MessageRole.assistant:
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if self.content:
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messages.extend(self._convert_reasoning_messages(text_is_assistant_message=text_is_assistant_message))
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if self.tool_calls is not None:
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messages.extend(
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self._convert_tool_call_messages(
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current_message_count=len(messages),
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use_assistant_message=use_assistant_message,
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assistant_message_tool_name=assistant_message_tool_name,
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assistant_message_tool_kwarg=assistant_message_tool_kwarg,
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),
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)
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elif self.role == MessageRole.tool:
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messages.append(self._convert_tool_return_message())
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elif self.role == MessageRole.user:
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messages.append(self._convert_user_message())
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elif self.role == MessageRole.system:
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messages.append(self._convert_system_message())
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elif self.role == MessageRole.approval:
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if self.content:
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messages.extend(self._convert_reasoning_messages(text_is_assistant_message=text_is_assistant_message))
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if self.tool_calls is not None:
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messages.append(self._convert_approval_request_message())
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else:
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if self.approvals:
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first_approval = [a for a in self.approvals if isinstance(a, ApprovalReturn)]
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def maybe_convert_tool_return_message(maybe_tool_return):
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if isinstance(maybe_tool_return, ToolReturn):
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parsed_data = self._parse_tool_response(maybe_tool_return.func_response)
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return LettaToolReturn(
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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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tool_return=parsed_data["message"],
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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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approval_response_message = ApprovalResponseMessage(
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id=self.id,
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date=self.created_at,
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otid=self.otid,
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approvals=[maybe_convert_tool_return_message(approval) for approval in self.approvals],
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run_id=self.run_id,
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# TODO: temporary populate these fields for backwards compatibility
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approve=first_approval[0].approve if first_approval else None,
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approval_request_id=first_approval[0].tool_call_id if first_approval else None,
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reason=first_approval[0].reason if first_approval else None,
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)
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else:
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approval_response_message = ApprovalResponseMessage(
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id=self.id,
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date=self.created_at,
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otid=self.otid,
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approve=self.approve,
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approval_request_id=self.approval_request_id,
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reason=self.denial_reason,
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approvals=[
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# TODO: temporary workaround to populate from legacy fields
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ApprovalReturn(
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tool_call_id=self.approval_request_id,
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approve=self.approve,
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reason=self.denial_reason,
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)
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],
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run_id=self.run_id,
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)
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messages.append(approval_response_message)
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else:
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raise ValueError(f"Unknown role: {self.role}")
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return messages[::-1] if reverse else messages
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def _convert_reasoning_messages(
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self,
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current_message_count: int = 0,
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text_is_assistant_message: bool = False, # For v3 loop, set to True
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) -> List[LettaMessage]:
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messages = []
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for content_part in self.content:
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otid = Message.generate_otid_from_id(self.id, current_message_count + len(messages))
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if isinstance(content_part, TextContent):
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if text_is_assistant_message:
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# .content is assistant message
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if messages and messages[-1].message_type == MessageType.assistant_message:
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messages[-1].content += content_part.text
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else:
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messages.append(
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AssistantMessage(
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id=self.id,
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date=self.created_at,
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content=content_part.text,
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name=self.name,
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otid=otid,
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sender_id=self.sender_id,
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step_id=self.step_id,
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is_err=self.is_err,
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run_id=self.run_id,
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)
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)
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else:
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# .content is COT
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messages.append(
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ReasoningMessage(
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id=self.id,
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date=self.created_at,
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reasoning=content_part.text,
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name=self.name,
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otid=otid,
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sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
|
|
elif isinstance(content_part, ReasoningContent):
|
|
# "native" COT
|
|
if messages and messages[-1].message_type == MessageType.reasoning_message:
|
|
messages[-1].reasoning += content_part.reasoning
|
|
else:
|
|
messages.append(
|
|
ReasoningMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
reasoning=content_part.reasoning,
|
|
source="reasoner_model", # TODO do we want to tag like this?
|
|
signature=content_part.signature,
|
|
name=self.name,
|
|
otid=otid,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
|
|
elif isinstance(content_part, SummarizedReasoningContent):
|
|
# TODO remove the cast and just return the native type
|
|
casted_content_part = content_part.to_reasoning_content()
|
|
if casted_content_part is not None:
|
|
messages.append(
|
|
ReasoningMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
reasoning=casted_content_part.reasoning,
|
|
source="reasoner_model", # TODO do we want to tag like this?
|
|
signature=casted_content_part.signature,
|
|
name=self.name,
|
|
otid=otid,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
|
|
elif isinstance(content_part, RedactedReasoningContent):
|
|
# "native" redacted/hidden COT
|
|
messages.append(
|
|
HiddenReasoningMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
state="redacted",
|
|
hidden_reasoning=content_part.data,
|
|
name=self.name,
|
|
otid=otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
|
|
elif isinstance(content_part, OmittedReasoningContent):
|
|
# Special case for "hidden reasoning" models like o1/o3
|
|
# NOTE: we also have to think about how to return this during streaming
|
|
messages.append(
|
|
HiddenReasoningMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
state="omitted",
|
|
name=self.name,
|
|
otid=otid,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
|
|
else:
|
|
logger.warning(f"Unrecognized content part in assistant message: {content_part}")
|
|
|
|
return messages
|
|
|
|
def _convert_assistant_message(
|
|
self,
|
|
) -> AssistantMessage:
|
|
if self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent):
|
|
text_content = self.content[0].text
|
|
else:
|
|
raise ValueError(f"Invalid assistant message (no text object on message): {self.content}")
|
|
|
|
return AssistantMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
content=text_content,
|
|
name=self.name,
|
|
otid=self.otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
# is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
|
|
def _convert_tool_call_messages(
|
|
self,
|
|
current_message_count: int = 0,
|
|
use_assistant_message: bool = False,
|
|
assistant_message_tool_name: str = DEFAULT_MESSAGE_TOOL,
|
|
assistant_message_tool_kwarg: str = DEFAULT_MESSAGE_TOOL_KWARG,
|
|
) -> List[LettaMessage]:
|
|
messages = []
|
|
|
|
# If assistant mode is off, just create one ToolCallMessage with all tool calls
|
|
if not use_assistant_message:
|
|
all_tool_call_objs = [
|
|
ToolCall(
|
|
name=tool_call.function.name,
|
|
arguments=tool_call.function.arguments,
|
|
tool_call_id=tool_call.id,
|
|
)
|
|
for tool_call in self.tool_calls
|
|
]
|
|
|
|
if all_tool_call_objs:
|
|
otid = Message.generate_otid_from_id(self.id, current_message_count)
|
|
messages.append(
|
|
ToolCallMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
# use first tool call for the deprecated field
|
|
tool_call=all_tool_call_objs[0],
|
|
tool_calls=all_tool_call_objs,
|
|
name=self.name,
|
|
otid=otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
return messages
|
|
|
|
collected_tool_calls = []
|
|
|
|
for tool_call in self.tool_calls:
|
|
otid = Message.generate_otid_from_id(self.id, current_message_count + len(messages))
|
|
|
|
if tool_call.function.name == assistant_message_tool_name:
|
|
if collected_tool_calls:
|
|
tool_call_message = ToolCallMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
# use first tool call for the deprecated field
|
|
tool_call=collected_tool_calls[0],
|
|
tool_calls=collected_tool_calls.copy(),
|
|
name=self.name,
|
|
otid=Message.generate_otid_from_id(self.id, current_message_count + len(messages)),
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
messages.append(tool_call_message)
|
|
collected_tool_calls = [] # reset the collection
|
|
|
|
try:
|
|
func_args = parse_json(tool_call.function.arguments)
|
|
message_string = validate_function_response(func_args[assistant_message_tool_kwarg], 0, truncate=False)
|
|
except KeyError:
|
|
raise ValueError(f"Function call {tool_call.function.name} missing {assistant_message_tool_kwarg} argument")
|
|
messages.append(
|
|
AssistantMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
content=message_string,
|
|
name=self.name,
|
|
otid=otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
)
|
|
else:
|
|
# non-assistant tool call, collect it
|
|
tool_call_obj = ToolCall(
|
|
name=tool_call.function.name,
|
|
arguments=tool_call.function.arguments,
|
|
tool_call_id=tool_call.id,
|
|
)
|
|
collected_tool_calls.append(tool_call_obj)
|
|
|
|
# flush any remaining collected tool calls
|
|
if collected_tool_calls:
|
|
tool_call_message = ToolCallMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
# use first tool call for the deprecated field
|
|
tool_call=collected_tool_calls[0],
|
|
tool_calls=collected_tool_calls,
|
|
name=self.name,
|
|
otid=Message.generate_otid_from_id(self.id, current_message_count + len(messages)),
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
messages.append(tool_call_message)
|
|
|
|
return messages
|
|
|
|
def _convert_tool_return_message(self) -> ToolReturnMessage:
|
|
"""Convert tool role message to ToolReturnMessage.
|
|
|
|
The tool return is packaged as follows:
|
|
packaged_message = {
|
|
"status": "OK" if was_success else "Failed",
|
|
"message": response_string,
|
|
"time": formatted_time,
|
|
}
|
|
|
|
Returns:
|
|
ToolReturnMessage: Converted tool return message
|
|
|
|
Raises:
|
|
ValueError: If message role is not 'tool', parsing fails, or no valid content exists
|
|
"""
|
|
if self.role != MessageRole.tool:
|
|
raise ValueError(f"Cannot convert message of type {self.role} to ToolReturnMessage")
|
|
|
|
# This is a very special buggy case during the double writing period
|
|
# where there is no tool call id on the tool return object, but it exists top level
|
|
# This is meant to be a short term patch - this can happen when people are using old agent files that were exported
|
|
# during a specific migration state
|
|
if len(self.tool_returns) == 1 and self.tool_call_id and not self.tool_returns[0].tool_call_id:
|
|
self.tool_returns[0].tool_call_id = self.tool_call_id
|
|
|
|
if self.tool_returns:
|
|
return self._convert_explicit_tool_returns()
|
|
|
|
return self._convert_legacy_tool_return()
|
|
|
|
def _convert_explicit_tool_returns(self) -> ToolReturnMessage:
|
|
"""Convert explicit tool returns to a single ToolReturnMessage."""
|
|
# build list of all tool return objects
|
|
all_tool_returns = []
|
|
for tool_return in self.tool_returns:
|
|
parsed_data = self._parse_tool_response(tool_return.func_response)
|
|
|
|
tool_return_obj = LettaToolReturn(
|
|
tool_return=parsed_data["message"],
|
|
status=parsed_data["status"],
|
|
tool_call_id=tool_return.tool_call_id,
|
|
stdout=tool_return.stdout,
|
|
stderr=tool_return.stderr,
|
|
)
|
|
all_tool_returns.append(tool_return_obj)
|
|
|
|
if not all_tool_returns:
|
|
# this should not happen if tool_returns is non-empty, but handle gracefully
|
|
raise ValueError("No tool returns to convert")
|
|
|
|
first_tool_return = all_tool_returns[0]
|
|
|
|
return ToolReturnMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
# deprecated top-level fields populated from first tool return
|
|
tool_return=first_tool_return.tool_return,
|
|
status=first_tool_return.status,
|
|
tool_call_id=first_tool_return.tool_call_id,
|
|
stdout=first_tool_return.stdout,
|
|
stderr=first_tool_return.stderr,
|
|
tool_returns=all_tool_returns,
|
|
name=self.name,
|
|
otid=Message.generate_otid_from_id(self.id, 0),
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
|
|
def _convert_legacy_tool_return(self) -> ToolReturnMessage:
|
|
"""Convert legacy single text content to ToolReturnMessage."""
|
|
if not self._has_single_text_content():
|
|
raise ValueError(f"No valid tool returns to convert: {self}")
|
|
|
|
text_content = self.content[0].text
|
|
parsed_data = self._parse_tool_response(text_content)
|
|
|
|
return self._create_tool_return_message(
|
|
message_text=parsed_data["message"],
|
|
status=parsed_data["status"],
|
|
tool_call_id=self.tool_call_id,
|
|
stdout=None,
|
|
stderr=None,
|
|
otid_index=0,
|
|
)
|
|
|
|
def _has_single_text_content(self) -> bool:
|
|
"""Check if message has exactly one text content item."""
|
|
return self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent)
|
|
|
|
def _parse_tool_response(self, response_text: str) -> dict:
|
|
"""Parse tool response JSON and extract message and status.
|
|
|
|
Args:
|
|
response_text: Raw JSON response text
|
|
|
|
Returns:
|
|
Dictionary with 'message' and 'status' keys
|
|
|
|
Raises:
|
|
ValueError: If JSON parsing fails
|
|
"""
|
|
try:
|
|
function_return = parse_json(response_text)
|
|
return {
|
|
"message": str(function_return.get("message", response_text)),
|
|
"status": self._parse_tool_status(function_return.get("status", "OK")),
|
|
}
|
|
except json.JSONDecodeError as e:
|
|
raise ValueError(f"Failed to decode function return: {response_text}") from e
|
|
|
|
def _create_tool_return_message(
|
|
self,
|
|
message_text: str,
|
|
status: str,
|
|
tool_call_id: Optional[str],
|
|
stdout: Optional[str],
|
|
stderr: Optional[str],
|
|
otid_index: int,
|
|
) -> ToolReturnMessage:
|
|
"""Create a ToolReturnMessage with common attributes.
|
|
|
|
Args:
|
|
message_text: The tool return message text
|
|
status: Tool execution status
|
|
tool_call_id: Optional tool call identifier
|
|
stdout: Optional standard output
|
|
stderr: Optional standard error
|
|
otid_index: Index for OTID generation
|
|
|
|
Returns:
|
|
Configured ToolReturnMessage instance
|
|
"""
|
|
tool_return_obj = LettaToolReturn(
|
|
tool_return=message_text,
|
|
status=status,
|
|
tool_call_id=tool_call_id,
|
|
stdout=stdout,
|
|
stderr=stderr,
|
|
)
|
|
|
|
return ToolReturnMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
tool_return=message_text,
|
|
status=status,
|
|
tool_call_id=tool_call_id,
|
|
stdout=stdout,
|
|
stderr=stderr,
|
|
tool_returns=[tool_return_obj],
|
|
name=self.name,
|
|
otid=Message.generate_otid_from_id(self.id, otid_index),
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
|
|
@staticmethod
|
|
def _parse_tool_status(status: str) -> Literal["success", "error"]:
|
|
"""Convert tool status string to enum value"""
|
|
if status == "OK":
|
|
return "success"
|
|
elif status == "Failed":
|
|
return "error"
|
|
else:
|
|
raise ValueError(f"Invalid status: {status}")
|
|
|
|
def _convert_approval_request_message(self) -> ApprovalRequestMessage:
|
|
"""Convert approval request message to ApprovalRequestMessage"""
|
|
|
|
def _convert_tool_call(tool_call):
|
|
return ToolCall(
|
|
name=tool_call.function.name,
|
|
arguments=tool_call.function.arguments,
|
|
tool_call_id=tool_call.id,
|
|
)
|
|
|
|
return ApprovalRequestMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
otid=self.otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
run_id=self.run_id,
|
|
tool_call=_convert_tool_call(self.tool_calls[0]), # backwards compatibility
|
|
tool_calls=[_convert_tool_call(tc) for tc in self.tool_calls],
|
|
name=self.name,
|
|
)
|
|
|
|
def _convert_user_message(self) -> UserMessage:
|
|
"""Convert user role message to UserMessage"""
|
|
# Extract text content
|
|
if self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent):
|
|
text_content = self.content[0].text
|
|
elif self.content:
|
|
text_content = self.content
|
|
else:
|
|
raise ValueError(f"Invalid user message (no text object on message): {self.content}")
|
|
|
|
message = unpack_message(text_content)
|
|
|
|
return UserMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
content=message,
|
|
name=self.name,
|
|
otid=self.otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
is_err=self.is_err,
|
|
run_id=self.run_id,
|
|
)
|
|
|
|
def _convert_system_message(self) -> SystemMessage:
|
|
"""Convert system role message to SystemMessage"""
|
|
if self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent):
|
|
text_content = self.content[0].text
|
|
else:
|
|
raise ValueError(f"Invalid system message (no text object on system): {self.content}")
|
|
|
|
return SystemMessage(
|
|
id=self.id,
|
|
date=self.created_at,
|
|
content=text_content,
|
|
name=self.name,
|
|
otid=self.otid,
|
|
sender_id=self.sender_id,
|
|
step_id=self.step_id,
|
|
run_id=self.run_id,
|
|
)
|
|
|
|
@staticmethod
|
|
def dict_to_message(
|
|
agent_id: str,
|
|
openai_message_dict: dict,
|
|
model: Optional[str] = None, # model used to make function call
|
|
allow_functions_style: bool = False, # allow deprecated functions style?
|
|
created_at: Optional[datetime] = None,
|
|
id: Optional[str] = None,
|
|
name: Optional[str] = None,
|
|
group_id: Optional[str] = None,
|
|
tool_returns: Optional[List[ToolReturn]] = None,
|
|
run_id: Optional[str] = None,
|
|
) -> Message:
|
|
"""Convert a ChatCompletion message object into a Message object (synced to DB)"""
|
|
if not created_at:
|
|
# timestamp for creation
|
|
created_at = get_utc_time()
|
|
|
|
assert "role" in openai_message_dict, openai_message_dict
|
|
assert "content" in openai_message_dict, openai_message_dict
|
|
|
|
# TODO(caren) implicit support for only non-parts/list content types
|
|
if openai_message_dict["content"] is not None and type(openai_message_dict["content"]) is not str:
|
|
raise ValueError(f"Invalid content type: {type(openai_message_dict['content'])}")
|
|
content: List[LettaMessageContentUnion] = (
|
|
[TextContent(text=openai_message_dict["content"])] if openai_message_dict["content"] else []
|
|
)
|
|
|
|
# This is really hacky and this interface is poorly designed, we should auto derive tool_returns instead of passing it in
|
|
if not tool_returns:
|
|
tool_returns = []
|
|
if "tool_returns" in openai_message_dict:
|
|
tool_returns = [ToolReturn(**tr) for tr in openai_message_dict["tool_returns"]]
|
|
|
|
# TODO(caren) bad assumption here that "reasoning_content" always comes before "redacted_reasoning_content"
|
|
if "reasoning_content" in openai_message_dict and openai_message_dict["reasoning_content"]:
|
|
content.append(
|
|
ReasoningContent(
|
|
reasoning=openai_message_dict["reasoning_content"],
|
|
is_native=True,
|
|
signature=(
|
|
str(openai_message_dict["reasoning_content_signature"])
|
|
if "reasoning_content_signature" in openai_message_dict
|
|
else None
|
|
),
|
|
),
|
|
)
|
|
if "redacted_reasoning_content" in openai_message_dict and openai_message_dict["redacted_reasoning_content"]:
|
|
content.append(
|
|
RedactedReasoningContent(
|
|
data=str(openai_message_dict["redacted_reasoning_content"]),
|
|
),
|
|
)
|
|
if "omitted_reasoning_content" in openai_message_dict and openai_message_dict["omitted_reasoning_content"]:
|
|
content.append(
|
|
OmittedReasoningContent(),
|
|
)
|
|
|
|
# If we're going from deprecated function form
|
|
if openai_message_dict["role"] == "function":
|
|
if not allow_functions_style:
|
|
raise DeprecationWarning(openai_message_dict)
|
|
assert "tool_call_id" in openai_message_dict, openai_message_dict
|
|
|
|
# Convert from 'function' response to a 'tool' response
|
|
if id is not None:
|
|
return Message(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
# standard fields expected in an OpenAI ChatCompletion message object
|
|
role=MessageRole.tool, # NOTE
|
|
content=content,
|
|
name=name,
|
|
tool_calls=openai_message_dict["tool_calls"] if "tool_calls" in openai_message_dict else None,
|
|
tool_call_id=openai_message_dict["tool_call_id"] if "tool_call_id" in openai_message_dict else None,
|
|
created_at=created_at,
|
|
id=str(id),
|
|
tool_returns=tool_returns,
|
|
group_id=group_id,
|
|
run_id=run_id,
|
|
)
|
|
else:
|
|
return Message(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
# standard fields expected in an OpenAI ChatCompletion message object
|
|
role=MessageRole.tool, # NOTE
|
|
content=content,
|
|
name=name,
|
|
tool_calls=openai_message_dict["tool_calls"] if "tool_calls" in openai_message_dict else None,
|
|
tool_call_id=openai_message_dict["tool_call_id"] if "tool_call_id" in openai_message_dict else None,
|
|
created_at=created_at,
|
|
tool_returns=tool_returns,
|
|
group_id=group_id,
|
|
run_id=run_id,
|
|
)
|
|
|
|
elif "function_call" in openai_message_dict and openai_message_dict["function_call"] is not None:
|
|
if not allow_functions_style:
|
|
raise DeprecationWarning(openai_message_dict)
|
|
assert openai_message_dict["role"] == "assistant", openai_message_dict
|
|
assert "tool_call_id" in openai_message_dict, openai_message_dict
|
|
|
|
# Convert a function_call (from an assistant message) into a tool_call
|
|
# NOTE: this does not conventionally include a tool_call_id (ToolCall.id), it's on the caster to provide it
|
|
tool_calls = [
|
|
OpenAIToolCall(
|
|
id=openai_message_dict["tool_call_id"], # NOTE: unconventional source, not to spec
|
|
type="function",
|
|
function=OpenAIFunction(
|
|
name=openai_message_dict["function_call"]["name"],
|
|
arguments=openai_message_dict["function_call"]["arguments"],
|
|
),
|
|
)
|
|
]
|
|
|
|
if id is not None:
|
|
return Message(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
# standard fields expected in an OpenAI ChatCompletion message object
|
|
role=MessageRole(openai_message_dict["role"]),
|
|
content=content,
|
|
name=name,
|
|
tool_calls=tool_calls,
|
|
tool_call_id=None, # NOTE: None, since this field is only non-null for role=='tool'
|
|
created_at=created_at,
|
|
id=str(id),
|
|
tool_returns=tool_returns,
|
|
group_id=group_id,
|
|
run_id=run_id,
|
|
)
|
|
else:
|
|
return Message(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
# standard fields expected in an OpenAI ChatCompletion message object
|
|
role=MessageRole(openai_message_dict["role"]),
|
|
content=content,
|
|
name=openai_message_dict["name"] if "name" in openai_message_dict else None,
|
|
tool_calls=tool_calls,
|
|
tool_call_id=None, # NOTE: None, since this field is only non-null for role=='tool'
|
|
created_at=created_at,
|
|
tool_returns=tool_returns,
|
|
group_id=group_id,
|
|
run_id=run_id,
|
|
)
|
|
|
|
else:
|
|
# Basic sanity check
|
|
if openai_message_dict["role"] == "tool":
|
|
assert "tool_call_id" in openai_message_dict and openai_message_dict["tool_call_id"] is not None, openai_message_dict
|
|
else:
|
|
if "tool_call_id" in openai_message_dict:
|
|
assert openai_message_dict["tool_call_id"] is None, openai_message_dict
|
|
|
|
if "tool_calls" in openai_message_dict and openai_message_dict["tool_calls"] is not None:
|
|
assert openai_message_dict["role"] == "assistant", openai_message_dict
|
|
|
|
tool_calls = [
|
|
OpenAIToolCall(id=tool_call["id"], type=tool_call["type"], function=tool_call["function"])
|
|
for tool_call in openai_message_dict["tool_calls"]
|
|
]
|
|
else:
|
|
tool_calls = None
|
|
|
|
# If we're going from tool-call style
|
|
if id is not None:
|
|
return Message(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
# standard fields expected in an OpenAI ChatCompletion message object
|
|
role=MessageRole(openai_message_dict["role"]),
|
|
content=content,
|
|
name=openai_message_dict["name"] if "name" in openai_message_dict else name,
|
|
tool_calls=tool_calls,
|
|
tool_call_id=openai_message_dict["tool_call_id"] if "tool_call_id" in openai_message_dict else None,
|
|
created_at=created_at,
|
|
id=str(id),
|
|
tool_returns=tool_returns,
|
|
group_id=group_id,
|
|
run_id=run_id,
|
|
)
|
|
else:
|
|
return Message(
|
|
agent_id=agent_id,
|
|
model=model,
|
|
# standard fields expected in an OpenAI ChatCompletion message object
|
|
role=MessageRole(openai_message_dict["role"]),
|
|
content=content,
|
|
name=openai_message_dict["name"] if "name" in openai_message_dict else name,
|
|
tool_calls=tool_calls,
|
|
tool_call_id=openai_message_dict["tool_call_id"] if "tool_call_id" in openai_message_dict else None,
|
|
created_at=created_at,
|
|
tool_returns=tool_returns,
|
|
group_id=group_id,
|
|
run_id=run_id,
|
|
)
|
|
|
|
def to_openai_dict_search_results(self, max_tool_id_length: int = TOOL_CALL_ID_MAX_LEN) -> dict:
|
|
result_json = self.to_openai_dict()
|
|
search_result_json = {"timestamp": self.created_at, "message": {"content": result_json["content"], "role": result_json["role"]}}
|
|
return search_result_json
|
|
|
|
def to_openai_dict(
|
|
self,
|
|
max_tool_id_length: int = TOOL_CALL_ID_MAX_LEN,
|
|
put_inner_thoughts_in_kwargs: bool = False,
|
|
use_developer_message: bool = False,
|
|
# if true, then treat the content field as AssistantMessage
|
|
native_content: bool = False,
|
|
strip_request_heartbeat: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> dict | None:
|
|
"""Go from Message class to ChatCompletion message object"""
|
|
assert not (native_content and put_inner_thoughts_in_kwargs), "native_content and put_inner_thoughts_in_kwargs cannot both be true"
|
|
|
|
if self.role == "approval" and self.tool_calls is None:
|
|
return None
|
|
|
|
# TODO change to pydantic casting, eg `return SystemMessageModel(self)`
|
|
# If we only have one content part and it's text, treat it as COT
|
|
parse_content_parts = False
|
|
if self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent):
|
|
text_content = self.content[0].text
|
|
elif self.content and len(self.content) == 1 and isinstance(self.content[0], ToolReturnContent):
|
|
text_content = self.content[0].content
|
|
elif self.content and len(self.content) == 1 and isinstance(self.content[0], ImageContent):
|
|
text_content = "[Image Here]"
|
|
# Otherwise, check if we have TextContent and multiple other parts
|
|
elif self.content and len(self.content) > 1:
|
|
text_parts = [content for content in self.content if isinstance(content, TextContent)]
|
|
# assert len(text) == 1, f"multiple text content parts found in a single message: {self.content}"
|
|
text_content = "\n\n".join([t.text for t in text_parts])
|
|
# Summarizer transcripts use this OpenAI-style dict; include a compact image placeholder
|
|
image_count = len([c for c in self.content if isinstance(c, ImageContent)])
|
|
if image_count > 0:
|
|
placeholder = "[Image omitted]" if image_count == 1 else f"[{image_count} images omitted]"
|
|
text_content = (text_content + (" " if text_content else "")) + placeholder
|
|
parse_content_parts = True
|
|
else:
|
|
text_content = None
|
|
|
|
# TODO(caren) we should eventually support multiple content parts here?
|
|
# ie, actually make dict['content'] type list
|
|
# But for now, it's OK until we support multi-modal,
|
|
# since the only "parts" we have are for supporting various COT
|
|
|
|
if self.role == "system":
|
|
openai_message = {
|
|
"content": text_content,
|
|
"role": "developer" if use_developer_message else self.role,
|
|
}
|
|
|
|
elif self.role == "user":
|
|
assert text_content is not None, vars(self)
|
|
openai_message = {
|
|
"content": text_content,
|
|
"role": self.role,
|
|
}
|
|
|
|
elif self.role == "assistant" or self.role == "approval":
|
|
try:
|
|
assert self.tool_calls is not None or text_content is not None, vars(self)
|
|
except AssertionError as e:
|
|
# relax check if this message only contains reasoning content
|
|
if self.content is not None and len(self.content) > 0:
|
|
# Check if all non-empty content is reasoning-related
|
|
all_reasoning = all(
|
|
isinstance(c, (ReasoningContent, SummarizedReasoningContent, OmittedReasoningContent, RedactedReasoningContent))
|
|
for c in self.content
|
|
)
|
|
if all_reasoning:
|
|
return None
|
|
raise e
|
|
|
|
# if native content, then put it directly inside the content
|
|
if native_content:
|
|
openai_message = {
|
|
# TODO support listed content (if it's possible for role assistant?)
|
|
# "content": self.content,
|
|
"content": text_content, # here content is not reasoning, it's assistant message
|
|
"role": "assistant",
|
|
}
|
|
# otherwise, if inner_thoughts_in_kwargs, hold it for the tool calls
|
|
else:
|
|
openai_message = {
|
|
"content": None if (put_inner_thoughts_in_kwargs and self.tool_calls is not None) else text_content,
|
|
"role": "assistant",
|
|
}
|
|
|
|
if self.tool_calls is not None:
|
|
if put_inner_thoughts_in_kwargs:
|
|
# put the inner thoughts inside the tool call before casting to a dict
|
|
openai_message["tool_calls"] = [
|
|
add_inner_thoughts_to_tool_call(
|
|
tool_call,
|
|
inner_thoughts=text_content,
|
|
inner_thoughts_key=INNER_THOUGHTS_KWARG,
|
|
).model_dump()
|
|
for tool_call in self.tool_calls
|
|
]
|
|
else:
|
|
openai_message["tool_calls"] = [tool_call.model_dump() for tool_call in self.tool_calls]
|
|
|
|
if strip_request_heartbeat:
|
|
for tool_call_dict in openai_message["tool_calls"]:
|
|
tool_call_dict.pop(REQUEST_HEARTBEAT_PARAM, None)
|
|
|
|
if max_tool_id_length:
|
|
for tool_call_dict in openai_message["tool_calls"]:
|
|
tool_call_dict["id"] = tool_call_dict["id"][:max_tool_id_length]
|
|
|
|
elif self.role == "tool":
|
|
# Handle tool returns - if tool_returns exists, use the first one
|
|
if self.tool_returns and len(self.tool_returns) > 0:
|
|
tool_return = self.tool_returns[0]
|
|
if not tool_return.tool_call_id:
|
|
raise TypeError("OpenAI API requires tool_call_id to be set.")
|
|
func_response = truncate_tool_return(tool_return.func_response, tool_return_truncation_chars)
|
|
openai_message = {
|
|
"content": func_response,
|
|
"role": self.role,
|
|
"tool_call_id": tool_return.tool_call_id[:max_tool_id_length] if max_tool_id_length else tool_return.tool_call_id,
|
|
}
|
|
else:
|
|
# Legacy fallback for old message format
|
|
assert self.tool_call_id is not None, vars(self)
|
|
legacy_content = truncate_tool_return(text_content, tool_return_truncation_chars)
|
|
openai_message = {
|
|
"content": legacy_content,
|
|
"role": self.role,
|
|
"tool_call_id": self.tool_call_id[:max_tool_id_length] if max_tool_id_length else self.tool_call_id,
|
|
}
|
|
|
|
else:
|
|
raise ValueError(self.role)
|
|
|
|
# Optional field, do not include if null or invalid
|
|
if self.name is not None:
|
|
if bool(re.match(r"^[^\s<|\\/>]+$", self.name)):
|
|
openai_message["name"] = self.name
|
|
else:
|
|
logger.warning(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
|
|
|
|
if parse_content_parts and self.content is not None:
|
|
for content in self.content:
|
|
if isinstance(content, ReasoningContent):
|
|
openai_message["reasoning_content"] = content.reasoning
|
|
if content.signature:
|
|
openai_message["reasoning_content_signature"] = content.signature
|
|
if isinstance(content, RedactedReasoningContent):
|
|
openai_message["redacted_reasoning_content"] = content.data
|
|
|
|
return openai_message
|
|
|
|
@staticmethod
|
|
def to_openai_dicts_from_list(
|
|
messages: List[Message],
|
|
max_tool_id_length: int = TOOL_CALL_ID_MAX_LEN,
|
|
put_inner_thoughts_in_kwargs: bool = False,
|
|
use_developer_message: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> List[dict]:
|
|
messages = Message.filter_messages_for_llm_api(messages)
|
|
result: List[dict] = []
|
|
|
|
for m in messages:
|
|
# Special case: OpenAI Chat Completions requires a separate tool message per tool_call_id
|
|
# If we have multiple explicit tool_returns on a single Message, expand into one dict per return
|
|
if m.role == MessageRole.tool and m.tool_returns and len(m.tool_returns) > 0:
|
|
for tr in m.tool_returns:
|
|
if not tr.tool_call_id:
|
|
raise TypeError("ToolReturn came back without a tool_call_id.")
|
|
# Ensure explicit tool_returns are truncated for Chat Completions
|
|
func_response = truncate_tool_return(tr.func_response, tool_return_truncation_chars)
|
|
result.append(
|
|
{
|
|
"content": func_response,
|
|
"role": "tool",
|
|
"tool_call_id": tr.tool_call_id[:max_tool_id_length] if max_tool_id_length else tr.tool_call_id,
|
|
}
|
|
)
|
|
continue
|
|
|
|
d = m.to_openai_dict(
|
|
max_tool_id_length=max_tool_id_length,
|
|
put_inner_thoughts_in_kwargs=put_inner_thoughts_in_kwargs,
|
|
use_developer_message=use_developer_message,
|
|
tool_return_truncation_chars=tool_return_truncation_chars,
|
|
)
|
|
if d is not None:
|
|
result.append(d)
|
|
|
|
return result
|
|
|
|
def to_openai_responses_dicts(
|
|
self,
|
|
max_tool_id_length: int = TOOL_CALL_ID_MAX_LEN,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> List[dict]:
|
|
"""Go from Message class to ChatCompletion message object"""
|
|
|
|
if self.role == "approval" and self.tool_calls is None:
|
|
return []
|
|
|
|
message_dicts = []
|
|
|
|
if self.role == "system":
|
|
assert len(self.content) == 1 and isinstance(self.content[0], TextContent), vars(self)
|
|
message_dicts.append(
|
|
{
|
|
"role": "developer",
|
|
"content": self.content[0].text,
|
|
}
|
|
)
|
|
|
|
elif self.role == "user":
|
|
# TODO do we need to do a swap to placeholder text here for images?
|
|
assert all([isinstance(c, TextContent) or isinstance(c, ImageContent) for c in self.content]), vars(self)
|
|
|
|
user_dict = {
|
|
"role": self.role.value if hasattr(self.role, "value") else self.role,
|
|
# TODO support multi-modal
|
|
"content": self.content[0].text,
|
|
}
|
|
|
|
# Optional field, do not include if null or invalid
|
|
if self.name is not None:
|
|
if bool(re.match(r"^[^\s<|\\/>]+$", self.name)):
|
|
user_dict["name"] = self.name
|
|
else:
|
|
logger.warning(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
|
|
|
|
message_dicts.append(user_dict)
|
|
|
|
elif self.role == "assistant" or self.role == "approval":
|
|
assert self.tool_calls is not None or (self.content is not None and len(self.content) > 0)
|
|
|
|
# A few things may be in here, firstly reasoning content, secondly assistant messages, thirdly tool calls
|
|
# TODO check if OpenAI Responses is capable of R->A->T like Anthropic?
|
|
|
|
if self.content is not None:
|
|
for content_part in self.content:
|
|
if isinstance(content_part, SummarizedReasoningContent):
|
|
message_dicts.append(
|
|
{
|
|
"type": "reasoning",
|
|
"id": content_part.id,
|
|
"summary": [{"type": "summary_text", "text": s.text} for s in content_part.summary],
|
|
"encrypted_content": content_part.encrypted_content,
|
|
}
|
|
)
|
|
elif isinstance(content_part, TextContent):
|
|
message_dicts.append(
|
|
{
|
|
"role": "assistant",
|
|
"content": content_part.text,
|
|
}
|
|
)
|
|
# else skip
|
|
|
|
if self.tool_calls is not None:
|
|
for tool_call in self.tool_calls:
|
|
message_dicts.append(
|
|
{
|
|
"type": "function_call",
|
|
"call_id": tool_call.id[:max_tool_id_length] if max_tool_id_length else tool_call.id,
|
|
"name": tool_call.function.name,
|
|
"arguments": tool_call.function.arguments,
|
|
"status": "completed", # TODO check if needed?
|
|
}
|
|
)
|
|
|
|
elif self.role == "tool":
|
|
# Handle tool returns - similar pattern to Anthropic
|
|
if self.tool_returns:
|
|
for tool_return in self.tool_returns:
|
|
if not tool_return.tool_call_id:
|
|
raise TypeError("OpenAI Responses API requires tool_call_id to be set.")
|
|
func_response = truncate_tool_return(tool_return.func_response, tool_return_truncation_chars)
|
|
message_dicts.append(
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": tool_return.tool_call_id[:max_tool_id_length] if max_tool_id_length else tool_return.tool_call_id,
|
|
"output": func_response,
|
|
}
|
|
)
|
|
else:
|
|
# Legacy fallback for old message format
|
|
assert self.tool_call_id is not None, vars(self)
|
|
assert len(self.content) == 1 and isinstance(self.content[0], TextContent), vars(self)
|
|
legacy_output = truncate_tool_return(self.content[0].text, tool_return_truncation_chars)
|
|
message_dicts.append(
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": self.tool_call_id[:max_tool_id_length] if max_tool_id_length else self.tool_call_id,
|
|
"output": legacy_output,
|
|
}
|
|
)
|
|
|
|
else:
|
|
raise ValueError(self.role)
|
|
|
|
return message_dicts
|
|
|
|
@staticmethod
|
|
def to_openai_responses_dicts_from_list(
|
|
messages: List[Message],
|
|
max_tool_id_length: int = TOOL_CALL_ID_MAX_LEN,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> List[dict]:
|
|
messages = Message.filter_messages_for_llm_api(messages)
|
|
result = []
|
|
for message in messages:
|
|
result.extend(
|
|
message.to_openai_responses_dicts(
|
|
max_tool_id_length=max_tool_id_length, tool_return_truncation_chars=tool_return_truncation_chars
|
|
)
|
|
)
|
|
return result
|
|
|
|
def to_anthropic_dict(
|
|
self,
|
|
current_model: str,
|
|
inner_thoughts_xml_tag="thinking",
|
|
put_inner_thoughts_in_kwargs: bool = False,
|
|
# if true, then treat the content field as AssistantMessage
|
|
native_content: bool = False,
|
|
strip_request_heartbeat: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> dict | None:
|
|
"""
|
|
Convert to an Anthropic message dictionary
|
|
|
|
Args:
|
|
inner_thoughts_xml_tag (str): The XML tag to wrap around inner thoughts
|
|
"""
|
|
assert not (native_content and put_inner_thoughts_in_kwargs), "native_content and put_inner_thoughts_in_kwargs cannot both be true"
|
|
|
|
if self.role == "approval" and self.tool_calls is None:
|
|
return None
|
|
|
|
# Check for COT
|
|
if self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent):
|
|
text_content = self.content[0].text
|
|
else:
|
|
text_content = None
|
|
|
|
def add_xml_tag(string: str, xml_tag: Optional[str]):
|
|
# NOTE: Anthropic docs recommends using <thinking> tag when using CoT + tool use
|
|
if f"<{xml_tag}>" in string and f"</{xml_tag}>" in string:
|
|
# don't nest if tags already exist
|
|
return string
|
|
return f"<{xml_tag}>{string}</{xml_tag}" if xml_tag else string
|
|
|
|
if self.role == "system":
|
|
# NOTE: this is not for system instructions, but instead system "events"
|
|
|
|
assert text_content is not None, vars(self)
|
|
# Two options here, we would use system.package_system_message,
|
|
# or use a more Anthropic-specific packaging ie xml tags
|
|
user_system_event = add_xml_tag(string=f"SYSTEM ALERT: {text_content}", xml_tag="event")
|
|
anthropic_message = {
|
|
"content": user_system_event,
|
|
"role": "user",
|
|
}
|
|
|
|
elif self.role == "user":
|
|
# special case for text-only message
|
|
if text_content is not None:
|
|
anthropic_message = {
|
|
"content": text_content,
|
|
"role": self.role,
|
|
}
|
|
else:
|
|
content_parts = []
|
|
for content in self.content:
|
|
if isinstance(content, TextContent):
|
|
content_parts.append({"type": "text", "text": content.text})
|
|
elif isinstance(content, ImageContent):
|
|
content_parts.append(
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"data": content.source.data,
|
|
"media_type": content.source.media_type,
|
|
},
|
|
}
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported content type: {content.type}")
|
|
|
|
anthropic_message = {
|
|
"content": content_parts,
|
|
"role": self.role,
|
|
}
|
|
|
|
elif self.role == "assistant" or self.role == "approval":
|
|
# assert self.tool_calls is not None or text_content is not None, vars(self)
|
|
assert self.tool_calls is not None or len(self.content) > 0
|
|
anthropic_message = {
|
|
"role": "assistant",
|
|
}
|
|
content = []
|
|
if native_content:
|
|
# No special handling for TextContent
|
|
if self.content is not None:
|
|
for content_part in self.content:
|
|
# TextContent, ImageContent, ToolCallContent, ToolReturnContent, ReasoningContent, RedactedReasoningContent, OmittedReasoningContent
|
|
if isinstance(content_part, ReasoningContent):
|
|
if current_model == self.model:
|
|
content.append(
|
|
{
|
|
"type": "thinking",
|
|
"thinking": content_part.reasoning,
|
|
"signature": content_part.signature,
|
|
}
|
|
)
|
|
elif isinstance(content_part, RedactedReasoningContent):
|
|
if current_model == self.model:
|
|
content.append(
|
|
{
|
|
"type": "redacted_thinking",
|
|
"data": content_part.data,
|
|
}
|
|
)
|
|
elif isinstance(content_part, TextContent):
|
|
content.append(
|
|
{
|
|
"type": "text",
|
|
"text": content_part.text,
|
|
}
|
|
)
|
|
else:
|
|
# Skip unsupported types eg OmmitedReasoningContent
|
|
pass
|
|
|
|
else:
|
|
# COT / reasoning / thinking
|
|
if self.content is not None and len(self.content) >= 1:
|
|
for content_part in self.content:
|
|
if isinstance(content_part, ReasoningContent):
|
|
if current_model == self.model:
|
|
content.append(
|
|
{
|
|
"type": "thinking",
|
|
"thinking": content_part.reasoning,
|
|
"signature": content_part.signature,
|
|
}
|
|
)
|
|
if isinstance(content_part, RedactedReasoningContent):
|
|
if current_model == self.model:
|
|
content.append(
|
|
{
|
|
"type": "redacted_thinking",
|
|
"data": content_part.data,
|
|
}
|
|
)
|
|
if isinstance(content_part, TextContent):
|
|
content.append(
|
|
{
|
|
"type": "text",
|
|
"text": content_part.text,
|
|
}
|
|
)
|
|
elif text_content is not None:
|
|
content.append(
|
|
{
|
|
"type": "text",
|
|
"text": add_xml_tag(string=text_content, xml_tag=inner_thoughts_xml_tag),
|
|
}
|
|
)
|
|
# Tool calling
|
|
if self.tool_calls is not None:
|
|
for tool_call in self.tool_calls:
|
|
if put_inner_thoughts_in_kwargs:
|
|
tool_call_input = add_inner_thoughts_to_tool_call(
|
|
tool_call,
|
|
inner_thoughts=text_content,
|
|
inner_thoughts_key=INNER_THOUGHTS_KWARG,
|
|
).model_dump()
|
|
else:
|
|
tool_call_input = parse_json(tool_call.function.arguments)
|
|
|
|
if strip_request_heartbeat:
|
|
tool_call_input.pop(REQUEST_HEARTBEAT_PARAM, None)
|
|
|
|
content.append(
|
|
{
|
|
"type": "tool_use",
|
|
"id": tool_call.id,
|
|
"name": tool_call.function.name,
|
|
"input": tool_call_input,
|
|
}
|
|
)
|
|
|
|
anthropic_message["content"] = content
|
|
|
|
elif self.role == "tool":
|
|
# NOTE: Anthropic uses role "user" for "tool" responses
|
|
content = []
|
|
for tool_return in self.tool_returns:
|
|
if not tool_return.tool_call_id:
|
|
from letta.log import get_logger
|
|
|
|
logger = get_logger(__name__)
|
|
logger.error(
|
|
f"Missing tool_call_id in tool return. "
|
|
f"Message ID: {self.id}, "
|
|
f"Tool name: {getattr(tool_return, 'name', 'unknown')}, "
|
|
f"Tool return: {tool_return}"
|
|
)
|
|
raise TypeError(
|
|
f"Anthropic API requires tool_use_id to be set. "
|
|
f"Message ID: {self.id}, Tool: {getattr(tool_return, 'name', 'unknown')}"
|
|
)
|
|
func_response = truncate_tool_return(tool_return.func_response, tool_return_truncation_chars)
|
|
content.append(
|
|
{
|
|
"type": "tool_result",
|
|
"tool_use_id": tool_return.tool_call_id,
|
|
"content": func_response,
|
|
}
|
|
)
|
|
if content:
|
|
anthropic_message = {
|
|
"role": "user",
|
|
"content": content,
|
|
}
|
|
else:
|
|
if not self.tool_call_id:
|
|
raise TypeError("Anthropic API requires tool_use_id to be set.")
|
|
|
|
# This is for legacy reasons
|
|
legacy_content = truncate_tool_return(text_content, tool_return_truncation_chars)
|
|
anthropic_message = {
|
|
"role": "user", # NOTE: diff
|
|
"content": [
|
|
# TODO support error types etc
|
|
{
|
|
"type": "tool_result",
|
|
"tool_use_id": self.tool_call_id,
|
|
"content": legacy_content,
|
|
}
|
|
],
|
|
}
|
|
|
|
else:
|
|
raise ValueError(self.role)
|
|
|
|
return anthropic_message
|
|
|
|
@staticmethod
|
|
def to_anthropic_dicts_from_list(
|
|
messages: List[Message],
|
|
current_model: str,
|
|
inner_thoughts_xml_tag: str = "thinking",
|
|
put_inner_thoughts_in_kwargs: bool = False,
|
|
# if true, then treat the content field as AssistantMessage
|
|
native_content: bool = False,
|
|
strip_request_heartbeat: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> List[dict]:
|
|
messages = Message.filter_messages_for_llm_api(messages)
|
|
result = [
|
|
m.to_anthropic_dict(
|
|
current_model=current_model,
|
|
inner_thoughts_xml_tag=inner_thoughts_xml_tag,
|
|
put_inner_thoughts_in_kwargs=put_inner_thoughts_in_kwargs,
|
|
native_content=native_content,
|
|
strip_request_heartbeat=strip_request_heartbeat,
|
|
tool_return_truncation_chars=tool_return_truncation_chars,
|
|
)
|
|
for m in messages
|
|
]
|
|
result = [m for m in result if m is not None]
|
|
return result
|
|
|
|
def to_google_dict(
|
|
self,
|
|
current_model: str,
|
|
put_inner_thoughts_in_kwargs: bool = True,
|
|
# if true, then treat the content field as AssistantMessage
|
|
native_content: bool = False,
|
|
strip_request_heartbeat: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
) -> dict | None:
|
|
"""
|
|
Go from Message class to Google AI REST message object
|
|
"""
|
|
assert not (native_content and put_inner_thoughts_in_kwargs), "native_content and put_inner_thoughts_in_kwargs cannot both be true"
|
|
|
|
if self.role == "approval" and self.tool_calls is None:
|
|
return None
|
|
|
|
# type Content: https://ai.google.dev/api/rest/v1/Content / https://ai.google.dev/api/rest/v1beta/Content
|
|
# parts[]: Part
|
|
# role: str ('user' or 'model')
|
|
if self.content and len(self.content) == 1 and isinstance(self.content[0], TextContent):
|
|
text_content = self.content[0].text
|
|
elif self.content and len(self.content) == 1 and isinstance(self.content[0], ToolReturnContent):
|
|
text_content = self.content[0].content
|
|
else:
|
|
text_content = None
|
|
|
|
if self.role != "tool" and self.name is not None:
|
|
logger.warning(f"Using Google AI with non-null 'name' field (name={self.name} role={self.role}), not yet supported.")
|
|
|
|
if self.role == "system":
|
|
# NOTE: Gemini API doesn't have a 'system' role, use 'user' instead
|
|
# https://www.reddit.com/r/Bard/comments/1b90i8o/does_gemini_have_a_system_prompt_option_while/
|
|
google_ai_message = {
|
|
"role": "user", # NOTE: no 'system'
|
|
"parts": [{"text": text_content}],
|
|
}
|
|
|
|
elif self.role == "user":
|
|
assert self.content, vars(self)
|
|
|
|
content_parts = []
|
|
for content in self.content:
|
|
if isinstance(content, TextContent):
|
|
content_parts.append({"text": content.text})
|
|
elif isinstance(content, ImageContent):
|
|
content_parts.append(
|
|
{
|
|
"inline_data": {
|
|
"data": content.source.data,
|
|
"mime_type": content.source.media_type,
|
|
}
|
|
}
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported content type: {content.type}")
|
|
|
|
google_ai_message = {
|
|
"role": "user",
|
|
"parts": content_parts,
|
|
}
|
|
|
|
elif self.role == "assistant" or self.role == "approval":
|
|
assert self.tool_calls is not None or text_content is not None or len(self.content) > 1
|
|
google_ai_message = {
|
|
"role": "model", # NOTE: different
|
|
}
|
|
|
|
# NOTE: Google AI API doesn't allow non-null content + function call
|
|
# To get around this, just two a two part message, inner thoughts first then
|
|
parts = []
|
|
|
|
if native_content and text_content is not None:
|
|
# TODO support multi-part assistant content
|
|
parts.append({"text": text_content})
|
|
|
|
elif not put_inner_thoughts_in_kwargs and text_content is not None:
|
|
# NOTE: ideally we do multi-part for CoT / inner thoughts + function call, but Google AI API doesn't allow it
|
|
raise NotImplementedError
|
|
parts.append({"text": text_content})
|
|
|
|
if self.tool_calls is not None:
|
|
# Check if there's a signature in the content that should be included with function calls
|
|
# Google Vertex requires thought_signature to be echoed back in function calls
|
|
thought_signature = None
|
|
if self.content and current_model == self.model:
|
|
for content in self.content:
|
|
# Check for signature in ReasoningContent, TextContent, or ToolCallContent
|
|
if isinstance(content, (ReasoningContent, TextContent, ToolCallContent)):
|
|
thought_signature = getattr(content, "signature", None)
|
|
|
|
# NOTE: implied support for multiple calls
|
|
for tool_call in self.tool_calls:
|
|
function_name = tool_call.function.name
|
|
function_args = tool_call.function.arguments
|
|
try:
|
|
# NOTE: Google AI wants actual JSON objects, not strings
|
|
function_args = parse_json(function_args)
|
|
except:
|
|
raise UserWarning(f"Failed to parse JSON function args: {function_args}")
|
|
function_args = {"args": function_args}
|
|
|
|
if put_inner_thoughts_in_kwargs and text_content is not None:
|
|
assert INNER_THOUGHTS_KWARG not in function_args, function_args
|
|
assert len(self.tool_calls) == 1
|
|
function_args[INNER_THOUGHTS_KWARG_VERTEX] = text_content
|
|
|
|
if strip_request_heartbeat:
|
|
function_args.pop(REQUEST_HEARTBEAT_PARAM, None)
|
|
|
|
# Build the function call part
|
|
function_call_part = {
|
|
"functionCall": {
|
|
"name": function_name,
|
|
"args": function_args,
|
|
}
|
|
}
|
|
|
|
# Include thought_signature if we found one
|
|
if thought_signature is not None:
|
|
function_call_part["thought_signature"] = thought_signature
|
|
|
|
parts.append(function_call_part)
|
|
else:
|
|
if not native_content:
|
|
assert text_content is not None
|
|
parts.append({"text": text_content})
|
|
|
|
if self.content and len(self.content) > 1:
|
|
native_google_content_parts = []
|
|
for content in self.content:
|
|
if isinstance(content, TextContent):
|
|
native_part = {"text": content.text}
|
|
if content.signature and current_model == self.model:
|
|
native_part["thought_signature"] = content.signature
|
|
native_google_content_parts.append(native_part)
|
|
elif isinstance(content, ReasoningContent):
|
|
if current_model == self.model:
|
|
native_google_content_parts.append({"text": content.reasoning, "thought": True})
|
|
elif isinstance(content, ToolCallContent):
|
|
native_part = {
|
|
"function_call": {
|
|
"name": content.name,
|
|
"args": content.input,
|
|
},
|
|
}
|
|
if content.signature and current_model == self.model:
|
|
native_part["thought_signature"] = content.signature
|
|
native_google_content_parts.append(native_part)
|
|
else:
|
|
# silently drop other content types
|
|
pass
|
|
if native_google_content_parts:
|
|
parts = native_google_content_parts
|
|
|
|
google_ai_message["parts"] = parts
|
|
|
|
elif self.role == "tool":
|
|
# NOTE: Significantly different tool calling format, more similar to function calling format
|
|
|
|
# Handle tool returns - similar pattern to Anthropic
|
|
if self.tool_returns:
|
|
parts = []
|
|
for tool_return in self.tool_returns:
|
|
if not tool_return.tool_call_id:
|
|
raise TypeError("Google AI API requires tool_call_id to be set.")
|
|
|
|
# Use the function name if available, otherwise use tool_call_id
|
|
function_name = self.name if self.name else tool_return.tool_call_id
|
|
|
|
# Truncate the tool return if needed
|
|
func_response = truncate_tool_return(tool_return.func_response, tool_return_truncation_chars)
|
|
|
|
# NOTE: Google AI API wants the function response as JSON only, no string
|
|
try:
|
|
function_response = parse_json(func_response)
|
|
except:
|
|
function_response = {"function_response": func_response}
|
|
|
|
parts.append(
|
|
{
|
|
"functionResponse": {
|
|
"name": function_name,
|
|
"response": {
|
|
"name": function_name, # NOTE: name twice... why?
|
|
"content": function_response,
|
|
},
|
|
}
|
|
}
|
|
)
|
|
|
|
google_ai_message = {
|
|
"role": "function",
|
|
"parts": parts,
|
|
}
|
|
else:
|
|
# Legacy fallback for old message format
|
|
assert self.tool_call_id is not None, vars(self)
|
|
|
|
if self.name is None:
|
|
logger.warning("Couldn't find function name on tool call, defaulting to tool ID instead.")
|
|
function_name = self.tool_call_id
|
|
else:
|
|
function_name = self.name
|
|
|
|
# Truncate the legacy content if needed
|
|
legacy_content = truncate_tool_return(text_content, tool_return_truncation_chars)
|
|
|
|
# NOTE: Google AI API wants the function response as JSON only, no string
|
|
try:
|
|
function_response = parse_json(legacy_content)
|
|
except:
|
|
function_response = {"function_response": legacy_content}
|
|
|
|
google_ai_message = {
|
|
"role": "function",
|
|
"parts": [
|
|
{
|
|
"functionResponse": {
|
|
"name": function_name,
|
|
"response": {
|
|
"name": function_name, # NOTE: name twice... why?
|
|
"content": function_response,
|
|
},
|
|
}
|
|
}
|
|
],
|
|
}
|
|
|
|
else:
|
|
raise ValueError(self.role)
|
|
|
|
# Validate that parts is never empty before returning
|
|
if "parts" not in google_ai_message or not google_ai_message["parts"]:
|
|
# If parts is empty, add a default text part
|
|
google_ai_message["parts"] = [{"text": "empty message"}]
|
|
logger.warning(
|
|
f"Empty 'parts' detected in message with role '{self.role}'. Added default empty text part. Full message:\n{vars(self)}"
|
|
)
|
|
|
|
return google_ai_message
|
|
|
|
@staticmethod
|
|
def to_google_dicts_from_list(
|
|
messages: List[Message],
|
|
current_model: str,
|
|
put_inner_thoughts_in_kwargs: bool = True,
|
|
native_content: bool = False,
|
|
tool_return_truncation_chars: Optional[int] = None,
|
|
):
|
|
messages = Message.filter_messages_for_llm_api(messages)
|
|
result = [
|
|
m.to_google_dict(
|
|
current_model=current_model,
|
|
put_inner_thoughts_in_kwargs=put_inner_thoughts_in_kwargs,
|
|
native_content=native_content,
|
|
tool_return_truncation_chars=tool_return_truncation_chars,
|
|
)
|
|
for m in messages
|
|
]
|
|
result = [m for m in result if m is not None]
|
|
return result
|
|
|
|
def is_approval_request(self) -> bool:
|
|
return self.role == "approval" and self.tool_calls is not None and len(self.tool_calls) > 0
|
|
|
|
def is_approval_response(self) -> bool:
|
|
return self.role == "approval" and self.tool_calls is None and self.approve is not None
|
|
|
|
def is_summarization_message(self) -> bool:
|
|
return (
|
|
self.role == "user"
|
|
and self.content is not None
|
|
and len(self.content) == 1
|
|
and isinstance(self.content[0], TextContent)
|
|
and "system_alert" in self.content[0].text
|
|
)
|
|
|
|
@staticmethod
|
|
def filter_messages_for_llm_api(
|
|
messages: List[Message],
|
|
) -> List[Message]:
|
|
messages = [m for m in messages if m is not None]
|
|
if len(messages) == 0:
|
|
return []
|
|
# Add special handling for legacy bug where summarization triggers in the middle of hitl
|
|
messages_to_filter = []
|
|
for i in range(len(messages) - 1):
|
|
first_message_is_approval = messages[i].is_approval_request()
|
|
second_message_is_summary = messages[i + 1].is_summarization_message()
|
|
third_message_is_optional_approval = i + 2 >= len(messages) or messages[i + 2].is_approval_response()
|
|
if first_message_is_approval and second_message_is_summary and third_message_is_optional_approval:
|
|
messages_to_filter.append(messages[i])
|
|
for idx in reversed(messages_to_filter): # reverse to avoid index shift
|
|
messages.remove(idx)
|
|
|
|
# Filter last message if it is a lone approval request without a response - this only occurs for token counting
|
|
if messages[-1].role == "approval" and messages[-1].tool_calls is not None and len(messages[-1].tool_calls) > 0:
|
|
messages.remove(messages[-1])
|
|
# Also filter pending tool call message if this turn invoked parallel tool calling
|
|
if messages and messages[-1].role == "assistant" and messages[-1].tool_calls is not None and len(messages[-1].tool_calls) > 0:
|
|
messages.remove(messages[-1])
|
|
|
|
# Filter last message if it is a lone reasoning message without assistant message or tool call
|
|
if (
|
|
messages[-1].role == "assistant"
|
|
and messages[-1].tool_calls is None
|
|
and (not messages[-1].content or all(not isinstance(content_part, TextContent) for content_part in messages[-1].content))
|
|
):
|
|
messages.remove(messages[-1])
|
|
|
|
# Collapse adjacent tool call and approval messages
|
|
messages = Message.collapse_tool_call_messages_for_llm_api(messages)
|
|
|
|
# Dedupe duplicate tool-return payloads across tool messages so downstream providers
|
|
# never see the same tool_call_id's result twice in a single request
|
|
messages = Message.dedupe_tool_messages_for_llm_api(messages)
|
|
|
|
# Dedupe duplicate tool calls within assistant messages so a single assistant message
|
|
# cannot emit multiple tool_use blocks with the same id (Anthropic requirement)
|
|
messages = Message.dedupe_tool_calls_for_llm_api(messages)
|
|
|
|
return messages
|
|
|
|
@staticmethod
|
|
def collapse_tool_call_messages_for_llm_api(
|
|
messages: List[Message],
|
|
) -> List[Message]:
|
|
adjacent_tool_call_approval_messages = []
|
|
for i in range(len(messages) - 1):
|
|
if (
|
|
messages[i].role == MessageRole.assistant
|
|
and messages[i].tool_calls is not None
|
|
and messages[i + 1].role == MessageRole.approval
|
|
and messages[i + 1].tool_calls is not None
|
|
):
|
|
adjacent_tool_call_approval_messages.append(i)
|
|
for i in reversed(adjacent_tool_call_approval_messages):
|
|
messages[i].content = messages[i].content + messages[i + 1].content
|
|
messages[i].tool_calls = messages[i].tool_calls + messages[i + 1].tool_calls
|
|
messages.remove(messages[i + 1])
|
|
return messages
|
|
|
|
@staticmethod
|
|
def dedupe_tool_messages_for_llm_api(messages: List[Message]) -> List[Message]:
|
|
"""Dedupe duplicate tool returns across tool-role messages by tool_call_id.
|
|
|
|
- For explicit tool_returns arrays: keep the first occurrence of each tool_call_id,
|
|
drop subsequent duplicates within the request.
|
|
- For legacy single tool_call_id + content messages: keep the first, drop duplicates.
|
|
- If a tool message has neither unique tool_returns nor content, drop it.
|
|
|
|
This runs prior to provider-specific formatting to reduce duplicate tool_result blocks downstream.
|
|
"""
|
|
if not messages:
|
|
return messages
|
|
|
|
from letta.log import get_logger
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
seen_ids: set[str] = set()
|
|
removed_tool_msgs = 0
|
|
removed_tool_returns = 0
|
|
result: List[Message] = []
|
|
|
|
for m in messages:
|
|
if m.role != MessageRole.tool:
|
|
result.append(m)
|
|
continue
|
|
|
|
# Prefer explicit tool_returns when present
|
|
if m.tool_returns and len(m.tool_returns) > 0:
|
|
unique_returns = []
|
|
for tr in m.tool_returns:
|
|
tcid = getattr(tr, "tool_call_id", None)
|
|
if tcid and tcid in seen_ids:
|
|
removed_tool_returns += 1
|
|
continue
|
|
if tcid:
|
|
seen_ids.add(tcid)
|
|
unique_returns.append(tr)
|
|
|
|
if unique_returns:
|
|
# Replace with unique set; keep message
|
|
m.tool_returns = unique_returns
|
|
result.append(m)
|
|
else:
|
|
# No unique returns left; if legacy content exists, fall back to legacy handling below
|
|
if m.tool_call_id and m.content and len(m.content) > 0:
|
|
tcid = m.tool_call_id
|
|
if tcid in seen_ids:
|
|
removed_tool_msgs += 1
|
|
continue
|
|
seen_ids.add(tcid)
|
|
result.append(m)
|
|
else:
|
|
removed_tool_msgs += 1
|
|
continue
|
|
|
|
else:
|
|
# Legacy single-response path
|
|
tcid = getattr(m, "tool_call_id", None)
|
|
if tcid:
|
|
if tcid in seen_ids:
|
|
removed_tool_msgs += 1
|
|
continue
|
|
seen_ids.add(tcid)
|
|
result.append(m)
|
|
|
|
if removed_tool_msgs or removed_tool_returns:
|
|
logger.error(
|
|
"[Message] Deduped duplicate tool messages for request: removed_messages=%d, removed_returns=%d",
|
|
removed_tool_msgs,
|
|
removed_tool_returns,
|
|
)
|
|
|
|
return result
|
|
|
|
@staticmethod
|
|
def dedupe_tool_calls_for_llm_api(messages: List[Message]) -> List[Message]:
|
|
"""Ensure each assistant message contains unique tool_calls by id.
|
|
|
|
Anthropic requires tool_use ids to be unique within a single assistant message. When
|
|
collapsing adjacent assistant/approval messages, duplicates can sneak in. This pass keeps
|
|
the first occurrence per id and drops subsequent duplicates.
|
|
"""
|
|
if not messages:
|
|
return messages
|
|
|
|
from letta.log import get_logger
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
removed_counts_total = 0
|
|
for m in messages:
|
|
if m.role != MessageRole.assistant or not m.tool_calls:
|
|
continue
|
|
seen: set[str] = set()
|
|
unique_tool_calls = []
|
|
removed = 0
|
|
for tc in m.tool_calls:
|
|
tcid = getattr(tc, "id", None)
|
|
if tcid and tcid in seen:
|
|
removed += 1
|
|
continue
|
|
if tcid:
|
|
seen.add(tcid)
|
|
unique_tool_calls.append(tc)
|
|
if removed:
|
|
m.tool_calls = unique_tool_calls
|
|
removed_counts_total += removed
|
|
if removed_counts_total:
|
|
logger.error("[Message] Deduped duplicate tool_calls in assistant messages: removed=%d", removed_counts_total)
|
|
return messages
|
|
|
|
@staticmethod
|
|
def generate_otid_from_id(message_id: str, index: int) -> str:
|
|
"""
|
|
Convert message id to bits and change the list bit to the index
|
|
"""
|
|
if index == -1:
|
|
return message_id
|
|
|
|
if not 0 <= index < 128:
|
|
raise ValueError("Index must be between 0 and 127")
|
|
|
|
message_uuid = message_id.replace("message-", "")
|
|
uuid_int = int(message_uuid.replace("-", ""), 16)
|
|
|
|
# Clear last 7 bits and set them to index; supports up to 128 unique indices
|
|
uuid_int = (uuid_int & ~0x7F) | (index & 0x7F)
|
|
|
|
hex_str = f"{uuid_int:032x}"
|
|
return f"{hex_str[:8]}-{hex_str[8:12]}-{hex_str[12:16]}-{hex_str[16:20]}-{hex_str[20:]}"
|
|
|
|
|
|
class ToolReturn(BaseModel):
|
|
tool_call_id: Optional[Any] = Field(None, description="The ID for the tool call")
|
|
status: Literal["success", "error"] = Field(..., description="The status of the tool call")
|
|
stdout: Optional[List[str]] = Field(default=None, description="Captured stdout (e.g. prints, logs) from the tool invocation")
|
|
stderr: Optional[List[str]] = Field(default=None, description="Captured stderr from the tool invocation")
|
|
func_response: Optional[str] = Field(None, description="The function response string")
|
|
|
|
|
|
class MessageSearchRequest(BaseModel):
|
|
"""Request model for searching messages across the organization"""
|
|
|
|
query: Optional[str] = Field(None, description="Text query for full-text search")
|
|
search_mode: Literal["vector", "fts", "hybrid"] = Field("hybrid", description="Search mode to use")
|
|
roles: Optional[List[MessageRole]] = Field(None, description="Filter messages by role")
|
|
agent_id: Optional[str] = Field(None, description="Filter messages by agent ID")
|
|
project_id: Optional[str] = Field(None, description="Filter messages by project ID")
|
|
template_id: Optional[str] = Field(None, description="Filter messages by template ID")
|
|
limit: int = Field(50, description="Maximum number of results to return", ge=1, le=100)
|
|
start_date: Optional[datetime] = Field(None, description="Filter messages created after this date")
|
|
end_date: Optional[datetime] = Field(None, description="Filter messages created on or before this date")
|
|
|
|
|
|
class MessageSearchResult(BaseModel):
|
|
"""Result from a message search operation with scoring details."""
|
|
|
|
embedded_text: str = Field(..., description="The embedded content (LLM-friendly)")
|
|
message: Message = Field(..., description="The raw message object")
|
|
fts_rank: Optional[int] = Field(None, description="Full-text search rank position if FTS was used")
|
|
vector_rank: Optional[int] = Field(None, description="Vector search rank position if vector search was used")
|
|
rrf_score: float = Field(..., description="Reciprocal Rank Fusion combined score")
|