b888c4c17a
* feat: add conversation_id parameter to context endpoint [LET-6989] Add optional conversation_id query parameter to retrieve_agent_context_window. When provided, the endpoint uses messages from the specific conversation instead of the agent's default message_ids. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * chore: regenerate SDK after context endpoint update [LET-6989] 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * feat: add isolated blocks support for conversations Allows conversations to have their own copies of specific memory blocks (e.g., todo_list) that override agent defaults, enabling conversation-specific state isolation. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * undo * update apis * test * cleanup * fix tests * simplify * move override logic * patch --------- Co-authored-by: Letta <noreply@letta.com>
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
import uuid
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from typing import TYPE_CHECKING, List, Optional
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from sqlalchemy import ForeignKey, Index, String
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from letta.orm.mixins import OrganizationMixin
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from letta.orm.sqlalchemy_base import SqlalchemyBase
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from letta.schemas.conversation import Conversation as PydanticConversation
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if TYPE_CHECKING:
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from letta.orm.agent import Agent
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from letta.orm.block import Block
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from letta.orm.conversation_messages import ConversationMessage
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class Conversation(SqlalchemyBase, OrganizationMixin):
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"""Conversations that can be created on an agent for concurrent messaging."""
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__tablename__ = "conversations"
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__pydantic_model__ = PydanticConversation
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__table_args__ = (
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Index("ix_conversations_agent_id", "agent_id"),
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Index("ix_conversations_org_agent", "organization_id", "agent_id"),
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)
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id: Mapped[str] = mapped_column(String, primary_key=True, default=lambda: f"conv-{uuid.uuid4()}")
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agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"), nullable=False)
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summary: Mapped[Optional[str]] = mapped_column(String, nullable=True, doc="Summary of the conversation")
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# Relationships
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agent: Mapped["Agent"] = relationship("Agent", back_populates="conversations", lazy="raise")
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message_associations: Mapped[List["ConversationMessage"]] = relationship(
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"ConversationMessage",
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back_populates="conversation",
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cascade="all, delete-orphan",
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lazy="selectin",
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)
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isolated_blocks: Mapped[List["Block"]] = relationship(
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"Block",
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secondary="blocks_conversations",
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lazy="selectin",
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passive_deletes=True,
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doc="Conversation-specific blocks that override agent defaults for isolated memory.",
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)
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def to_pydantic(self) -> PydanticConversation:
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"""Converts the SQLAlchemy model to its Pydantic counterpart."""
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return self.__pydantic_model__(
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id=self.id,
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agent_id=self.agent_id,
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summary=self.summary,
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created_at=self.created_at,
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updated_at=self.updated_at,
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created_by_id=self.created_by_id,
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last_updated_by_id=self.last_updated_by_id,
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isolated_block_ids=[b.id for b in self.isolated_blocks] if self.isolated_blocks else [],
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)
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