resolutionflow/backend/app/services/session_embedding_service.py

"""Generate and store embeddings for AI sessions for similar-session matching.

Uses Voyage AI (voyage-3.5, 1024 dims) via the shared embedding_service to
create vector representations of session content. Enables cosine similarity
search across sessions within the same account.
"""
import logging
from uuid import UUID

from sqlalchemy import select, text
from sqlalchemy.ext.asyncio import AsyncSession

from app.models.ai_session import AISession
from app.models.ai_session_embedding import AISessionEmbedding
from app.services.embedding_service import get_embedding

logger = logging.getLogger(__name__)


async def generate_session_embedding(session_id: UUID, db: AsyncSession) -> None:
    """Generate embedding for an AI session's content.

    Builds a text chunk from the session's problem summary, resolution,
    domain, and escalation reason, then embeds it via Voyage AI and
    upserts into ai_session_embeddings.
    """
    result = await db.execute(
        select(AISession).where(AISession.id == session_id)
    )
    session = result.scalar_one_or_none()
    if not session:
        return

    # Build text to embed — combine available session metadata
    parts = []
    if session.problem_summary:
        parts.append(session.problem_summary)
    if session.resolution_summary:
        parts.append(f"Resolution: {session.resolution_summary}")
    if session.problem_domain:
        parts.append(f"Domain: {session.problem_domain}")
    if session.escalation_reason:
        parts.append(f"Escalation: {session.escalation_reason}")

    if not parts:
        return

    chunk_text = " ".join(parts)

    try:
        embedding_vector = await get_embedding(chunk_text, input_type="document")
        if not embedding_vector:
            return

        embedding_str = "[" + ",".join(str(v) for v in embedding_vector) + "]"

        # Check for existing embedding
        existing = await db.execute(
            select(AISessionEmbedding).where(
                AISessionEmbedding.session_id == session_id
            )
        )
        embed_record = existing.scalar_one_or_none()

        if embed_record:
            # Update existing
            embed_record.chunk_text = chunk_text
            await db.execute(
                text(
                    "UPDATE ai_session_embeddings "
                    "SET embedding = :emb::vector, updated_at = now() "
                    "WHERE session_id = :sid"
                ),
                {"emb": embedding_str, "sid": str(session_id)},
            )
        else:
            # Insert new via raw SQL to include vector column
            await db.execute(
                text("""
                    INSERT INTO ai_session_embeddings
                        (id, session_id, account_id, chunk_text, embedding_model, embedding, created_at, updated_at)
                    VALUES
                        (gen_random_uuid(), :session_id, :account_id, :chunk_text, :model, :embedding::vector, now(), now())
                """),
                {
                    "session_id": str(session_id),
                    "account_id": str(session.account_id),
                    "chunk_text": chunk_text,
                    "model": "voyage-3.5",
                    "embedding": embedding_str,
                },
            )

        await db.flush()
    except Exception:
        logger.warning(
            "Failed to generate embedding for session %s", session_id, exc_info=True
        )


async def find_similar_sessions(
    session_id: UUID,
    account_id: UUID,
    db: AsyncSession,
    limit: int = 5,
) -> list[dict]:
    """Find sessions similar to the given session using cosine similarity.

    Returns a list of dicts with session metadata and similarity score,
    ordered by highest similarity first.
    """
    # Verify the source session has an embedding
    check = await db.execute(
        text(
            "SELECT 1 FROM ai_session_embeddings "
            "WHERE session_id = :sid AND embedding IS NOT NULL"
        ),
        {"sid": str(session_id)},
    )
    if not check.first():
        return []

    # Cosine similarity search across all sessions in the account
    result = await db.execute(
        text("""
            SELECT
                e.session_id,
                s.problem_summary,
                s.problem_domain,
                s.status,
                s.resolution_summary,
                s.created_at,
                1 - (e.embedding <=> (
                    SELECT embedding FROM ai_session_embeddings WHERE session_id = :sid
                )) as similarity
            FROM ai_session_embeddings e
            JOIN ai_sessions s ON s.id = e.session_id
            WHERE e.account_id = :account_id
              AND e.session_id != :sid
              AND e.embedding IS NOT NULL
            ORDER BY e.embedding <=> (
                SELECT embedding FROM ai_session_embeddings WHERE session_id = :sid
            )
            LIMIT :lim
        """),
        {
            "sid": str(session_id),
            "account_id": str(account_id),
            "lim": limit,
        },
    )

    rows = result.mappings().all()
    return [
        {
            "id": str(row["session_id"]),
            "problem_summary": row["problem_summary"],
            "problem_domain": row["problem_domain"],
            "status": row["status"],
            "resolution_summary": row["resolution_summary"],
            "created_at": row["created_at"].isoformat() if row["created_at"] else None,
            "similarity": round(float(row["similarity"]), 3) if row["similarity"] else 0,
        }
        for row in rows
    ]