a673e641b8
- Search now runs semantic + keyword in parallel, merges and deduplicates - Keyword results always included with match_context explaining WHY matched - Semantic results filtered by minimum score threshold (0.45) - match_context shows 'Creator: X', 'Tag: Y', 'Title match', 'Content: ...' - Qdrant points use deterministic uuid5 IDs (no more duplicates on reindex) - Embedding timeout raised from 300ms to 2s (Ollama needs it) - _enrich_qdrant_results reads creator_name from payload before DB fallback - Frontend displays match_context as highlighted bar on search result cards
603 lines
23 KiB
Python
603 lines
23 KiB
Python
"""Async search service for the public search endpoint.
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Orchestrates semantic search (embedding + Qdrant) with keyword search.
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Both run in parallel — results are merged and deduplicated. Keyword matches
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get a match_context explaining WHY they matched. Semantic-only results get
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a "Semantic match" context and are filtered by a minimum score threshold.
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import time
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from typing import Any
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import openai
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from qdrant_client import AsyncQdrantClient
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from qdrant_client.http import exceptions as qdrant_exceptions
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from qdrant_client.models import FieldCondition, Filter, MatchValue
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from sqlalchemy import and_, func, or_, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from config import Settings
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from models import Creator, KeyMoment, SourceVideo, TechniquePage
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logger = logging.getLogger("chrysopedia.search")
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# Timeout for external calls (embedding API, Qdrant) in seconds
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_EXTERNAL_TIMEOUT = 2.0 # 2s — Ollama local embedding needs more than 300ms
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# Minimum cosine similarity score for semantic results to be included
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_SEMANTIC_MIN_SCORE = 0.45
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class SearchService:
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"""Async search service with parallel semantic + keyword search.
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Parameters
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----------
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settings:
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Application settings containing embedding and Qdrant config.
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"""
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def __init__(self, settings: Settings) -> None:
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self.settings = settings
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self._openai = openai.AsyncOpenAI(
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base_url=settings.embedding_api_url,
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api_key=settings.llm_api_key,
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)
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self._qdrant = AsyncQdrantClient(url=settings.qdrant_url)
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self._collection = settings.qdrant_collection
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# ── Embedding ────────────────────────────────────────────────────────
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async def embed_query(self, text: str) -> list[float] | None:
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"""Embed a query string into a vector.
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Returns None on any failure (timeout, connection, malformed response)
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so the caller can fall back to keyword search.
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"""
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try:
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response = await asyncio.wait_for(
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self._openai.embeddings.create(
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model=self.settings.embedding_model,
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input=text,
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),
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timeout=_EXTERNAL_TIMEOUT,
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)
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except asyncio.TimeoutError:
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logger.warning("Embedding API timeout (%.0fms limit) for query: %.50s…", _EXTERNAL_TIMEOUT * 1000, text)
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return None
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except (openai.APIConnectionError, openai.APITimeoutError) as exc:
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logger.warning("Embedding API connection error (%s: %s)", type(exc).__name__, exc)
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return None
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except openai.APIError as exc:
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logger.warning("Embedding API error (%s: %s)", type(exc).__name__, exc)
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return None
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if not response.data:
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logger.warning("Embedding API returned empty data for query: %.50s…", text)
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return None
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vector = response.data[0].embedding
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if len(vector) != self.settings.embedding_dimensions:
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logger.warning(
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"Embedding dimension mismatch: expected %d, got %d",
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self.settings.embedding_dimensions,
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len(vector),
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)
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return None
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return vector
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# ── Qdrant vector search ─────────────────────────────────────────────
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async def search_qdrant(
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self,
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vector: list[float],
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limit: int = 20,
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type_filter: str | None = None,
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) -> list[dict[str, Any]]:
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"""Search Qdrant for nearest neighbours.
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Returns a list of dicts with 'score' and 'payload' keys.
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Returns empty list on failure.
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"""
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query_filter = None
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if type_filter:
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query_filter = Filter(
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must=[FieldCondition(key="type", match=MatchValue(value=type_filter))]
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)
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try:
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results = await asyncio.wait_for(
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self._qdrant.query_points(
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collection_name=self._collection,
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query=vector,
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query_filter=query_filter,
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limit=limit,
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with_payload=True,
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),
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timeout=_EXTERNAL_TIMEOUT,
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)
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except asyncio.TimeoutError:
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logger.warning("Qdrant search timeout (%.0fms limit)", _EXTERNAL_TIMEOUT * 1000)
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return []
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except qdrant_exceptions.UnexpectedResponse as exc:
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logger.warning("Qdrant search error: %s", exc)
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return []
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except Exception as exc:
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logger.warning("Qdrant connection error (%s: %s)", type(exc).__name__, exc)
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return []
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return [
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{"score": point.score, "payload": point.payload}
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for point in results.points
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]
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# ── Token helpers ───────────────────────────────────────────────────
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@staticmethod
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def _tokenize(query: str) -> list[str]:
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"""Split query into non-empty lowercase tokens."""
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return [t.lower() for t in query.split() if t]
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@staticmethod
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def _tp_token_condition(token: str):
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"""Build an OR condition for a single token across TechniquePage + Creator fields."""
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pat = f"%{token}%"
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return or_(
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TechniquePage.title.ilike(pat),
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TechniquePage.summary.ilike(pat),
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TechniquePage.topic_category.ilike(pat),
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func.array_to_string(TechniquePage.topic_tags, " ").ilike(pat),
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Creator.name.ilike(pat),
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)
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@staticmethod
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def _km_token_condition(token: str):
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"""Build an OR condition for a single token across KeyMoment + Creator fields."""
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pat = f"%{token}%"
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return or_(
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KeyMoment.title.ilike(pat),
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KeyMoment.summary.ilike(pat),
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Creator.name.ilike(pat),
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)
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@staticmethod
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def _cr_token_condition(token: str):
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"""Build an OR condition for a single token across Creator fields."""
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pat = f"%{token}%"
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return or_(
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Creator.name.ilike(pat),
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func.array_to_string(Creator.genres, " ").ilike(pat),
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)
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# ── Match context generation ────────────────────────────────────────
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@staticmethod
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def _build_match_context(item: dict[str, Any], tokens: list[str]) -> str:
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"""Generate a human-readable string explaining which fields matched.
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Checks each token against each field and returns a compact summary
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like "Creator: Keota · Tag: snare" or "Title: ...snare drum...".
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"""
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if not tokens:
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return ""
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matches: list[str] = []
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seen: set[str] = set()
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for token in tokens:
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t = token.lower()
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# Check creator name
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creator = item.get("creator_name", "")
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if creator and t in creator.lower() and "creator" not in seen:
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matches.append(f"Creator: {creator}")
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seen.add("creator")
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continue
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# Check title
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title = item.get("title", "")
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if title and t in title.lower() and "title" not in seen:
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matches.append(f"Title match")
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seen.add("title")
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continue
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# Check topic_category
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cat = item.get("topic_category", "")
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if cat and t in cat.lower() and "category" not in seen:
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matches.append(f"Category: {cat}")
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seen.add("category")
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continue
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# Check topic_tags
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tags = item.get("topic_tags", [])
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matched_tag = next((tag for tag in tags if t in tag.lower()), None)
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if matched_tag and f"tag:{matched_tag.lower()}" not in seen:
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matches.append(f"Tag: {matched_tag}")
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seen.add(f"tag:{matched_tag.lower()}")
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continue
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# Check summary
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summary = item.get("summary", "")
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if summary and t in summary.lower() and "summary" not in seen:
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# Extract a small context window around the match
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idx = summary.lower().find(t)
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start = max(0, idx - 20)
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end = min(len(summary), idx + len(t) + 20)
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snippet = summary[start:end].strip()
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if start > 0:
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snippet = "…" + snippet
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if end < len(summary):
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snippet = snippet + "…"
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matches.append(f"Content: {snippet}")
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seen.add("summary")
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continue
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return " · ".join(matches) if matches else ""
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# ── Keyword search (multi-token AND) ─────────────────────────────────
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async def keyword_search(
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self,
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query: str,
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scope: str,
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limit: int,
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db: AsyncSession,
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sort: str = "relevance",
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) -> dict[str, list[dict[str, Any]]]:
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"""Multi-token AND keyword search across technique pages, key moments, and creators.
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Tokenizes the query by whitespace. Each token must match at least one
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indexed field (title, summary, topic_category, topic_tags, creator name,
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genres). All tokens must match for a row to be included.
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If AND matching returns zero results but individual tokens would match,
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returns up to 5 partial_matches scored by the number of tokens matched.
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Returns ``{"items": [...], "partial_matches": [...]}``.
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"""
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tokens = self._tokenize(query)
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if not tokens:
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return {"items": [], "partial_matches": []}
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items = await self._keyword_search_and(tokens, scope, limit, db)
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# Add match_context to each item
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for item in items:
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item["match_context"] = self._build_match_context(item, tokens)
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partial: list[dict[str, Any]] = []
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if not items and len(tokens) > 1:
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partial = await self._keyword_partial_matches(tokens, scope, db)
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for p in partial:
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p["match_context"] = self._build_match_context(p, tokens)
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return {"items": items, "partial_matches": partial}
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async def _keyword_search_and(
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self,
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tokens: list[str],
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scope: str,
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limit: int,
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db: AsyncSession,
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) -> list[dict[str, Any]]:
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"""Run AND-logic keyword search — every token must match at least one field."""
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results: list[dict[str, Any]] = []
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if scope in ("all", "topics"):
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tp_stmt = (
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select(TechniquePage, Creator)
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.join(Creator, TechniquePage.creator_id == Creator.id)
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.where(and_(*(self._tp_token_condition(t) for t in tokens)))
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.limit(limit)
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)
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tp_rows = await db.execute(tp_stmt)
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for tp, cr in tp_rows.all():
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results.append({
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"type": "technique_page",
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"title": tp.title,
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"slug": tp.slug,
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"technique_page_slug": tp.slug,
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"summary": tp.summary or "",
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"topic_category": tp.topic_category,
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"topic_tags": tp.topic_tags or [],
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"creator_id": str(tp.creator_id),
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"creator_name": cr.name,
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"creator_slug": cr.slug,
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"created_at": tp.created_at.isoformat() if tp.created_at else "",
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"score": 0.0,
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})
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if scope in ("all",):
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km_stmt = (
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select(KeyMoment, SourceVideo, Creator, TechniquePage)
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.join(SourceVideo, KeyMoment.source_video_id == SourceVideo.id)
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.join(Creator, SourceVideo.creator_id == Creator.id)
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.outerjoin(TechniquePage, KeyMoment.technique_page_id == TechniquePage.id)
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.where(and_(*(self._km_token_condition(t) for t in tokens)))
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.limit(limit)
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)
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km_rows = await db.execute(km_stmt)
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for km, sv, cr, tp in km_rows.all():
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results.append({
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"type": "key_moment",
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"title": km.title,
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"slug": "",
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"technique_page_slug": tp.slug if tp else "",
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"summary": km.summary or "",
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"topic_category": "",
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"topic_tags": [],
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"creator_id": str(cr.id),
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"creator_name": cr.name,
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"creator_slug": cr.slug,
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"created_at": km.created_at.isoformat() if hasattr(km, "created_at") and km.created_at else "",
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"score": 0.0,
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})
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if scope in ("all", "creators"):
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cr_stmt = (
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select(Creator)
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.where(and_(*(self._cr_token_condition(t) for t in tokens)))
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.limit(limit)
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)
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cr_rows = await db.execute(cr_stmt)
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for cr in cr_rows.scalars().all():
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results.append({
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"type": "creator",
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"title": cr.name,
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"slug": cr.slug,
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"technique_page_slug": "",
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"summary": "",
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"topic_category": "",
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"topic_tags": cr.genres or [],
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"creator_id": str(cr.id),
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"created_at": cr.created_at.isoformat() if hasattr(cr, "created_at") and cr.created_at else "",
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"score": 0.0,
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})
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return results[:limit]
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async def _keyword_partial_matches(
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self,
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tokens: list[str],
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scope: str,
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db: AsyncSession,
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) -> list[dict[str, Any]]:
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"""When AND produces zero results, score rows by how many tokens match.
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Returns the top 5 results ordered by match count descending.
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"""
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seen: dict[tuple[str, str], dict[str, Any]] = {}
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match_counts: dict[tuple[str, str], int] = {}
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for token in tokens:
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single_results = await self._keyword_search_and([token], scope, 20, db)
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for r in single_results:
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key = (r["type"], r.get("slug") or r.get("title", ""))
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if key not in seen:
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seen[key] = r
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match_counts[key] = 0
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match_counts[key] += 1
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ranked = sorted(match_counts.keys(), key=lambda k: match_counts[k], reverse=True)
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partial: list[dict[str, Any]] = []
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for key in ranked[:5]:
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item = seen[key]
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item["score"] = match_counts[key] / len(tokens)
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partial.append(item)
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return partial
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# ── Orchestrator ─────────────────────────────────────────────────────
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async def search(
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self,
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query: str,
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scope: str,
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limit: int,
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db: AsyncSession,
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sort: str = "relevance",
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) -> dict[str, Any]:
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"""Run semantic and keyword search in parallel, merge and deduplicate.
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Both engines run concurrently. Keyword results are always included
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(with match_context). Semantic results above the score threshold are
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merged in, deduplicated by (type, slug/title). Keyword matches rank
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higher when they exist.
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"""
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start = time.monotonic()
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if not query or not query.strip():
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return {"items": [], "partial_matches": [], "total": 0, "query": query, "fallback_used": False}
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query = query.strip()[:500]
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if scope not in ("all", "topics", "creators"):
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scope = "all"
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# Map scope to Qdrant type filter
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type_filter_map = {
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"all": None,
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"topics": "technique_page",
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"creators": None,
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}
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qdrant_type_filter = type_filter_map.get(scope)
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# Run both searches in parallel
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async def _semantic():
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vector = await self.embed_query(query)
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if vector is None:
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return []
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results = await self.search_qdrant(vector, limit=limit, type_filter=qdrant_type_filter)
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enriched = await self._enrich_qdrant_results(results, db)
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# Filter by minimum score and add match_context
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filtered = []
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for item in enriched:
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if item.get("score", 0) >= _SEMANTIC_MIN_SCORE:
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if not item.get("match_context"):
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item["match_context"] = "Semantic match"
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filtered.append(item)
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return filtered
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async def _keyword():
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return await self.keyword_search(query, scope, limit, db, sort=sort)
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semantic_results, kw_result = await asyncio.gather(
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_semantic(),
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_keyword(),
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return_exceptions=True,
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)
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# Handle exceptions gracefully
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if isinstance(semantic_results, Exception):
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logger.warning("Semantic search failed: %s", semantic_results)
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semantic_results = []
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if isinstance(kw_result, Exception):
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logger.warning("Keyword search failed: %s", kw_result)
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kw_result = {"items": [], "partial_matches": []}
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kw_items = kw_result["items"]
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partial_matches = kw_result.get("partial_matches", [])
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# Merge: keyword results first (they have explicit match_context),
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# then semantic results that aren't already present
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seen_keys: set[str] = set()
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merged: list[dict[str, Any]] = []
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def _dedup_key(item: dict) -> str:
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t = item.get("type", "")
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s = item.get("slug") or item.get("technique_page_slug") or ""
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title = item.get("title", "")
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return f"{t}:{s}:{title}"
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for item in kw_items:
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key = _dedup_key(item)
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if key not in seen_keys:
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seen_keys.add(key)
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merged.append(item)
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for item in semantic_results:
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key = _dedup_key(item)
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if key not in seen_keys:
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seen_keys.add(key)
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merged.append(item)
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# Apply sort
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merged = self._apply_sort(merged, sort)
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fallback_used = len(kw_items) > 0 and len(semantic_results) == 0
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elapsed_ms = (time.monotonic() - start) * 1000
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logger.info(
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"Search query=%r scope=%s keyword=%d semantic=%d merged=%d partial=%d latency_ms=%.1f",
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query, scope, len(kw_items), len(semantic_results),
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len(merged), len(partial_matches), elapsed_ms,
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)
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return {
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"items": merged[:limit],
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"partial_matches": partial_matches,
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"total": len(merged),
|
|
"query": query,
|
|
"fallback_used": fallback_used,
|
|
}
|
|
|
|
# ── Sort helpers ────────────────────────────────────────────────────
|
|
|
|
@staticmethod
|
|
def _apply_sort(items: list[dict[str, Any]], sort: str) -> list[dict[str, Any]]:
|
|
"""Sort enriched result dicts by the requested criterion."""
|
|
if sort == "relevance" or not items:
|
|
return items
|
|
if sort == "newest":
|
|
return sorted(items, key=lambda r: r.get("created_at", ""), reverse=True)
|
|
elif sort == "oldest":
|
|
return sorted(items, key=lambda r: r.get("created_at") or "9999", reverse=False)
|
|
elif sort == "alpha":
|
|
return sorted(items, key=lambda r: (r.get("title") or "").lower())
|
|
elif sort == "creator":
|
|
return sorted(
|
|
items,
|
|
key=lambda r: ((r.get("creator_name") or "").lower(), (r.get("title") or "").lower()),
|
|
)
|
|
return items
|
|
|
|
# ── Result enrichment ────────────────────────────────────────────────
|
|
|
|
async def _enrich_qdrant_results(
|
|
self,
|
|
qdrant_results: list[dict[str, Any]],
|
|
db: AsyncSession,
|
|
) -> list[dict[str, Any]]:
|
|
"""Enrich Qdrant results with creator names and slugs from DB.
|
|
|
|
First reads creator_name from Qdrant payload; only hits DB for missing ones.
|
|
"""
|
|
enriched: list[dict[str, Any]] = []
|
|
|
|
# Collect creator_ids that need DB lookup
|
|
needs_db_lookup: set[str] = set()
|
|
for r in qdrant_results:
|
|
payload = r.get("payload", {})
|
|
if not payload.get("creator_name") and payload.get("creator_id"):
|
|
needs_db_lookup.add(payload["creator_id"])
|
|
|
|
# Batch fetch creators from DB
|
|
creator_map: dict[str, dict[str, str]] = {}
|
|
if needs_db_lookup:
|
|
import uuid as uuid_mod
|
|
valid_ids = []
|
|
for cid in needs_db_lookup:
|
|
try:
|
|
valid_ids.append(uuid_mod.UUID(cid))
|
|
except (ValueError, AttributeError):
|
|
pass
|
|
if valid_ids:
|
|
stmt = select(Creator).where(Creator.id.in_(valid_ids))
|
|
result = await db.execute(stmt)
|
|
for c in result.scalars().all():
|
|
creator_map[str(c.id)] = {"name": c.name, "slug": c.slug}
|
|
|
|
for r in qdrant_results:
|
|
payload = r.get("payload", {})
|
|
cid = payload.get("creator_id", "")
|
|
result_type = payload.get("type", "")
|
|
|
|
# Creator name: prefer payload, fall back to DB
|
|
creator_name = payload.get("creator_name", "")
|
|
creator_slug = ""
|
|
if not creator_name and cid:
|
|
info = creator_map.get(cid, {"name": "", "slug": ""})
|
|
creator_name = info["name"]
|
|
creator_slug = info["slug"]
|
|
elif creator_name and cid:
|
|
# We have the name from payload but need the slug from DB
|
|
info = creator_map.get(cid, {})
|
|
creator_slug = info.get("slug", "")
|
|
|
|
# Determine technique_page_slug based on result type
|
|
if result_type == "technique_page":
|
|
tp_slug = payload.get("slug", payload.get("title", "").lower().replace(" ", "-"))
|
|
else:
|
|
tp_slug = payload.get("technique_page_slug", "")
|
|
|
|
enriched.append({
|
|
"type": result_type,
|
|
"title": payload.get("title", ""),
|
|
"slug": payload.get("slug", payload.get("title", "").lower().replace(" ", "-")),
|
|
"technique_page_slug": tp_slug,
|
|
"summary": payload.get("summary", ""),
|
|
"topic_category": payload.get("topic_category", ""),
|
|
"topic_tags": payload.get("topic_tags", []),
|
|
"creator_id": cid,
|
|
"creator_name": creator_name,
|
|
"creator_slug": creator_slug,
|
|
"created_at": payload.get("created_at", ""),
|
|
"score": r.get("score", 0.0),
|
|
"match_context": "",
|
|
})
|
|
|
|
return enriched
|