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118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
"""
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app/core/retriever.py — Semantischer/Edge-Aware/Hybrid Retriever (WP-04)
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Zweck:
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Kandidatenfindung via Vektorsuche in *_chunks, optionale Edge-Expansion und
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kombiniertes Ranking zur Rückgabe von Top-K Treffern.
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Kompatibilität:
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Python 3.12+, qdrant-client 1.x
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Version:
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0.1.0 (Erstanlage)
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Stand:
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2025-10-07
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Bezug:
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- app/core/graph_adapter.py (expand)
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- app/core/ranking.py (combine_scores)
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- app/core/qdrant_points.py (search_chunks_by_vector)
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Nutzung:
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from app.core.retriever import hybrid_retrieve
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Änderungsverlauf:
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0.1.0 (2025-10-07) – Erstanlage.
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"""
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from __future__ import annotations
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import time
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from typing import Dict, List, Optional, Tuple
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from qdrant_client import QdrantClient
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from app.models.dto import QueryRequest, QueryResponse, QueryHit
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from app.core.ranking import combine_scores
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from app.core.graph_adapter import expand
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from app.core import qdrant_points as qp
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from app.config import get_settings
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def _require_query_vector(req: QueryRequest) -> List[float]:
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"""
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Für den Schnelltest ohne eingebundene Embeddings muss query_vector gesetzt sein.
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Später kann hier der Embed-Aufruf (Text → 384d) angebunden werden.
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"""
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if not req.query_vector:
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raise ValueError(
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"query_vector fehlt. Für den Quick-Test ohne Embeddings bitte einen 384d-Vektor übergeben."
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)
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return req.query_vector
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def semantic_retrieve(req: QueryRequest) -> QueryResponse:
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"""Nur semantische Kandidaten, keine Edge-Expansion (depth=0)."""
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t0 = time.time()
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s = get_settings()
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client = QdrantClient(url=s.QDRANT_URL, api_key=s.QDRANT_API_KEY)
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q_vec = _require_query_vector(req)
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raw_hits = qp.search_chunks_by_vector(client, s.COLLECTION_PREFIX, q_vec, top=req.top_k, filters=req.filters)
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id2payload = {pid: payload for (pid, score, payload) in raw_hits}
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results: List[QueryHit] = []
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for pid, s_score, payload in raw_hits:
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results.append(QueryHit(
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node_id=pid,
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note_id=payload.get("note_id"),
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semantic_score=float(s_score),
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edge_bonus=0.0,
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centrality_bonus=0.0,
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total_score=float(s_score), # hier un-normalisiert; ok für schnelle Prüfung
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paths=None,
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source={"path": payload.get("path"), "section": payload.get("section_title")}
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))
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dt = int((time.time() - t0) * 1000)
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return QueryResponse(results=results, used_mode="semantic", latency_ms=dt)
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def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
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"""Semantik + Edge-Expansion + kombiniertes Ranking."""
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t0 = time.time()
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s = get_settings()
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client = QdrantClient(url=s.QDRANT_URL, api_key=s.QDRANT_API_KEY)
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q_vec = _require_query_vector(req)
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# 1) Semantische Seeds (top_k * 3 für breitere Basis)
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raw_hits = qp.search_chunks_by_vector(client, s.COLLECTION_PREFIX, q_vec, top=req.top_k * 3, filters=req.filters)
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id2payload = {pid: payload for (pid, score, payload) in raw_hits}
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seeds = [pid for (pid, _, _) in raw_hits]
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# 2) Edge-Expansion
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edge_types = req.expand.get("edge_types") if req.expand else None
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depth = req.expand.get("depth", 1) if req.expand else 1
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sg = expand(client, s.COLLECTION_PREFIX, seeds, depth=depth, edge_types=edge_types)
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edge_bonus_map = {pid: sg.aggregate_edge_bonus(pid) for pid in seeds}
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centrality_map = {pid: sg.centrality_bonus(pid) for pid in seeds}
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# 3) Combined Ranking
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scored = combine_scores(raw_hits, edge_bonus_map, centrality_map,
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w_sem=s.RETRIEVER_W_SEM,
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w_edge=s.RETRIEVER_W_EDGE,
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w_cent=s.RETRIEVER_W_CENT)
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# 4) Antwortobjekte (Chunk-Ebene)
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results: List[QueryHit] = []
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for pid, total, e, c, s_score in scored[: req.top_k]:
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payload = id2payload[pid]
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results.append(QueryHit(
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node_id=pid,
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note_id=payload.get("note_id"),
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semantic_score=float(s_score),
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edge_bonus=float(e),
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centrality_bonus=float(c),
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total_score=float(total),
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paths=None,
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source={"path": payload.get("path"), "section": payload.get("section_title")}
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))
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dt = int((time.time() - t0) * 1000)
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return QueryResponse(results=results, used_mode="hybrid", latency_ms=dt)
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