1aa60dada2
Implements three-phase AI assistant feature: - Phase 0: RAG infrastructure with pgvector embeddings, Voyage AI integration, tree chunking service, and semantic search over team's flow library - Phase 1: In-session copilot panel during flow navigation with contextual AI help, current step awareness, and suggested related flows - Phase 2: Standalone AI chat page with persistent conversation history, pin/delete, and configurable retention policies (account-level) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
"""Tree embedding storage for RAG-powered AI assistant.
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Stores vector embeddings of tree content chunks for semantic search.
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Each tree is split into multiple chunks (node, solution, tree_summary)
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and embedded via Voyage AI for cosine similarity retrieval.
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"""
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import uuid
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from datetime import datetime, timezone
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from typing import Optional, Any
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from sqlalchemy import String, Text, DateTime, ForeignKey, Index
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from sqlalchemy.orm import Mapped, mapped_column
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from sqlalchemy.dialects.postgresql import UUID, JSONB
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from app.core.database import Base
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# pgvector column type — imported at runtime to avoid import errors
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# when pgvector is not installed locally
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try:
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from pgvector.sqlalchemy import Vector
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except ImportError:
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Vector = None
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class TreeEmbedding(Base):
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__tablename__ = "tree_embeddings"
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__table_args__ = (
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Index("ix_tree_embeddings_account_id", "account_id"),
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Index("ix_tree_embeddings_tree_id", "tree_id"),
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)
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id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
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)
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tree_id: Mapped[uuid.UUID] = mapped_column(
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UUID(as_uuid=True),
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ForeignKey("trees.id", ondelete="CASCADE"),
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nullable=False,
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)
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account_id: Mapped[Optional[uuid.UUID]] = mapped_column(
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UUID(as_uuid=True),
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ForeignKey("accounts.id", ondelete="CASCADE"),
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nullable=True,
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)
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chunk_type: Mapped[str] = mapped_column(
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String(30),
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nullable=False,
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comment="node | solution | tree_summary",
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)
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node_type: Mapped[Optional[str]] = mapped_column(
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String(30), nullable=True
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)
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node_id: Mapped[Optional[str]] = mapped_column(
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String(100), nullable=True
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)
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chunk_text: Mapped[str] = mapped_column(Text, nullable=False)
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embedding_model: Mapped[str] = mapped_column(
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String(50), nullable=False, default="voyage-3.5"
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)
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# The embedding column is created via migration with vector(1024) type
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# We store it as a generic column here and handle it in queries
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meta: Mapped[dict[str, Any]] = mapped_column(
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JSONB, nullable=False, default=dict
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)
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True),
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default=lambda: datetime.now(timezone.utc),
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onupdate=lambda: datetime.now(timezone.utc),
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)
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