feat: add AI assistant with in-session copilot and standalone chat with RAG

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>

backend/app/models/tree_embedding.py

@@ -0,0 +1,72 @@
+"""Tree embedding storage for RAG-powered AI assistant.
+
+Stores vector embeddings of tree content chunks for semantic search.
+Each tree is split into multiple chunks (node, solution, tree_summary)
+and embedded via Voyage AI for cosine similarity retrieval.
+"""
+import uuid
+from datetime import datetime, timezone
+from typing import Optional, Any
+
+from sqlalchemy import String, Text, DateTime, ForeignKey, Index
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.dialects.postgresql import UUID, JSONB
+
+from app.core.database import Base
+
+# pgvector column type — imported at runtime to avoid import errors
+# when pgvector is not installed locally
+try:
+    from pgvector.sqlalchemy import Vector
+except ImportError:
+    Vector = None
+
+
+class TreeEmbedding(Base):
+    __tablename__ = "tree_embeddings"
+    __table_args__ = (
+        Index("ix_tree_embeddings_account_id", "account_id"),
+        Index("ix_tree_embeddings_tree_id", "tree_id"),
+    )
+
+    id: Mapped[uuid.UUID] = mapped_column(
+        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
+    )
+    tree_id: Mapped[uuid.UUID] = mapped_column(
+        UUID(as_uuid=True),
+        ForeignKey("trees.id", ondelete="CASCADE"),
+        nullable=False,
+    )
+    account_id: Mapped[Optional[uuid.UUID]] = mapped_column(
+        UUID(as_uuid=True),
+        ForeignKey("accounts.id", ondelete="CASCADE"),
+        nullable=True,
+    )
+    chunk_type: Mapped[str] = mapped_column(
+        String(30),
+        nullable=False,
+        comment="node | solution | tree_summary",
+    )
+    node_type: Mapped[Optional[str]] = mapped_column(
+        String(30), nullable=True
+    )
+    node_id: Mapped[Optional[str]] = mapped_column(
+        String(100), nullable=True
+    )
+    chunk_text: Mapped[str] = mapped_column(Text, nullable=False)
+    embedding_model: Mapped[str] = mapped_column(
+        String(50), nullable=False, default="voyage-3.5"
+    )
+    # The embedding column is created via migration with vector(1024) type
+    # We store it as a generic column here and handle it in queries
+    meta: Mapped[dict[str, Any]] = mapped_column(
+        JSONB, nullable=False, default=dict
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        default=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(timezone.utc),
+    )