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retriever neu

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Lars 2025-12-18 16:59:37 +01:00
parent cbfdd96152
commit cc12dcf993
1 changed files with 82 additions and 33 deletions
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app/core/retriever.py
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@ -3,7 +3,7 @@ FILE: app/core/retriever.py
DESCRIPTION: Implementiert die Hybrid-Suche (Vektor + Graph-Expansion) und das Scoring-Modell (Explainability).
WP-22 Update: Dynamic Edge Boosting, Lifecycle Scoring & Provenance Awareness.
Enthält detaillierte Debug-Informationen für die mathematische Verifizierung.
VERSION: 0.6.10 (WP-22 Full, Debug & Stable)
VERSION: 0.6.11 (WP-22 Full, Debug & Stable)
STATUS: Active
DEPENDENCIES: app.config, app.models.dto, app.core.qdrant*, app.services.embeddings_client, app.core.graph_adapter
LAST_ANALYSIS: 2025-12-18
@ -46,7 +46,8 @@ logger = logging.getLogger(__name__)
def _get_scoring_weights() -> Tuple[float, float, float]:
"""
Liefert die Basis-Gewichtung (semantic_weight, edge_weight, centrality_weight).
Prio: 1. retriever.yaml -> 2. Environment -> 3. Hardcoded Defaults
Priorität: 1. retriever.yaml -> 2. Environment/Settings -> 3. Hardcoded Defaults
"""
settings = get_settings()
sem = float(getattr(settings, "RETRIEVER_W_SEM", 1.0))
@ -118,6 +119,7 @@ def _get_status_multiplier(payload: Dict[str, Any]) -> float:
WP-22 A: Lifecycle-Scoring.
- stable: 1.2 (Validiertes Wissen fördern)
- draft: 0.5 (Entwürfe de-priorisieren)
"""
status = str(payload.get("status", "active")).lower().strip()
if status == "stable":
@ -137,6 +139,7 @@ def _compute_total_score(
"""
Die zentrale mathematische Scoring-Formel von WP-22.
Score = (Similarity * StatusMult) * (1 + (Weight-1) + DynamicBoost)
"""
_sem_w, edge_w_cfg, cent_w_cfg = _get_scoring_weights()
status_mult = _get_status_multiplier(payload)
@ -156,8 +159,11 @@ def _compute_total_score(
# 4. Final Merge
total = base_val * (1.0 + (node_weight - 1.0) + dynamic_graph_impact)
# Floor-Schutz
final_score = max(0.001, float(total))
return {
"total": max(0.001, float(total)),
"total": final_score,
"edge_bonus": float(edge_bonus_raw),
"cent_bonus": float(cent_bonus_raw),
"status_multiplier": status_mult,
@ -168,6 +174,8 @@ def _compute_total_score(
"cent_impact_final": cent_impact
}
# ==============================================================================
# 3. EXPLANATION LAYER (DEBUG & VERIFIABILITY)
# ==============================================================================
@ -180,7 +188,7 @@ def _build_explanation(
target_note_id: Optional[str],
applied_boosts: Optional[Dict[str, float]] = None
) -> Explanation:
"""Erstellt ein detailliertes Explanation-Objekt mit WP-22 Metriken."""
"""Erstellt ein detailliertes Explanation-Objekt inkl. WP-22 Metriken."""
_, edge_w_cfg, cent_w_cfg = _get_scoring_weights()
type_weight = float(payload.get("retriever_weight", 1.0))
@ -189,7 +197,7 @@ def _build_explanation(
note_type = payload.get("type", "unknown")
base_val = scoring_debug["base_val"]
# 1. Score Breakdown
# 1. Score Breakdown Objekt
breakdown = ScoreBreakdown(
semantic_contribution=base_val,
edge_contribution=base_val * scoring_debug["edge_impact_final"],
@ -205,7 +213,7 @@ def _build_explanation(
reasons: List[Reason] = []
edges_dto: List[EdgeDTO] = []
# 2. Reasons generieren
# 2. Gründe generieren
if semantic_score > 0.70:
reasons.append(Reason(kind="semantic", message="Textuelle Übereinstimmung.", score_impact=base_val))
@ -217,7 +225,7 @@ def _build_explanation(
txt = "Bonus" if status_mult > 1.0 else "Malus"
reasons.append(Reason(kind="lifecycle", message=f"Status-{txt} ({payload.get('status')}).", score_impact=0.0))
# 3. Kanten-Details (WP-22 B)
# 3. Kanten-Details (WP-22 B) - Beachtet eingehende UND ausgehende Kanten
if subgraph and target_note_id and scoring_debug["edge_bonus"] > 0:
raw_edges = []
if hasattr(subgraph, "get_incoming_edges"):
@ -226,7 +234,8 @@ def _build_explanation(
raw_edges.extend(subgraph.get_outgoing_edges(target_note_id) or [])
for edge in raw_edges:
src, tgt = edge.get("source"), edge.get("target")
src = edge.get("source")
tgt = edge.get("target")
k = edge.get("kind", "edge")
prov = edge.get("provenance", "rule")
conf = float(edge.get("confidence", 1.0))
@ -234,7 +243,7 @@ def _build_explanation(
is_incoming = (tgt == target_note_id)
direction = "in" if is_incoming else "out"
# neighbor_id Scope-Fix
# neighbor_id FIX: Variable sicher innerhalb der Schleife definieren
neighbor_id = src if is_incoming else tgt
edge_obj = EdgeDTO(
@ -244,18 +253,19 @@ def _build_explanation(
)
edges_dto.append(edge_obj)
# Die 3 stärksten Signale als Gründe formulieren
top_edges = sorted(edges_dto, key=lambda e: e.confidence, reverse=True)
for e in top_edges[:3]:
prov_txt = "Explizite" if e.provenance == "explicit" else "Heuristische"
boost_txt = f" [Boost x{applied_boosts.get(e.kind)}]" if applied_boosts and e.kind in applied_boosts else ""
# Nachbar-ID innerhalb der Loop sicherstellen
target_name = e.source if e.direction == "in" else e.target
msg = f"{prov_txt} Kante '{e.kind}'{boost_txt} von/zu '{target_name}'."
# e.source/e.target sind durch e.direction eindeutig zugeordnet
peer_name = e.source if e.direction == "in" else e.target
msg = f"{prov_txt} Kante '{e.kind}'{boost_txt} von/zu '{peer_name}'."
reasons.append(Reason(kind="edge", message=msg, score_impact=edge_w_cfg * e.confidence))
if scoring_debug["cent_bonus"] > 0.01:
reasons.append(Reason(kind="centrality", message="Knoten liegt zentral im Kontext.", score_impact=breakdown.centrality_contribution))
reasons.append(Reason(kind="centrality", message="Knoten liegt zentral im aktuellen Kontext.", score_impact=breakdown.centrality_contribution))
return Explanation(
breakdown=breakdown,
@ -266,13 +276,17 @@ def _build_explanation(
def _extract_expand_options(req: QueryRequest) -> Tuple[int, List[str] | None]:
"""Extrahiert Expansion-Tiefe und Kanten-Filter."""
"""Extrahiert Expansion-Tiefe und Kanten-Filter aus dem Request."""
expand = getattr(req, "expand", None)
if not expand: return 0, None
if not expand:
return 0, None
if isinstance(expand, dict):
return int(expand.get("depth", 1)), expand.get("edge_types")
if hasattr(expand, "depth"):
return int(getattr(expand, "depth", 1)), getattr(expand, "edge_types", None)
return 1, None
@ -284,26 +298,33 @@ def _build_hits_from_semantic(
explain: bool = False,
dynamic_edge_boosts: Dict[str, float] = None
) -> QueryResponse:
"""Wandelt semantische Roh-Treffer in strukturierte QueryHits um."""
"""Wandelt semantische Roh-Treffer in strukturierte QueryHits um und berechnet WP-22 Scores."""
t0 = time.time()
enriched = []
for pid, semantic_score, payload in hits:
edge_bonus, cent_bonus = 0.0, 0.0
edge_bonus = 0.0
cent_bonus = 0.0
target_note_id = payload.get("note_id")
if subgraph is not None and target_note_id:
try:
edge_bonus = float(subgraph.edge_bonus(target_note_id))
cent_bonus = float(subgraph.centrality_bonus(target_note_id))
except Exception: pass
except Exception:
pass
# Messbare Scoring-Daten berechnen
debug_data = _compute_total_score(
semantic_score, payload, edge_bonus_raw=edge_bonus,
cent_bonus_raw=cent_bonus, dynamic_edge_boosts=dynamic_edge_boosts
semantic_score,
payload,
edge_bonus_raw=edge_bonus,
cent_bonus_raw=cent_bonus,
dynamic_edge_boosts=dynamic_edge_boosts
)
enriched.append((pid, float(semantic_score), payload, debug_data))
# Sortierung nach finalem Score
enriched_sorted = sorted(enriched, key=lambda h: h[3]["total"], reverse=True)
limited_hits = enriched_sorted[: max(1, top_k)]
@ -313,24 +334,33 @@ def _build_hits_from_semantic(
if explain:
explanation_obj = _build_explanation(
semantic_score=float(semantic_score),
payload=payload, scoring_debug=debug,
subgraph=subgraph, target_note_id=payload.get("note_id"),
payload=payload,
scoring_debug=debug,
subgraph=subgraph,
target_note_id=payload.get("note_id"),
applied_boosts=dynamic_edge_boosts
)
text_content = payload.get("page_content") or payload.get("text") or payload.get("content")
results.append(QueryHit(
node_id=str(pid),
node_id=str(pid),
note_id=payload.get("note_id", "unknown"),
semantic_score=float(semantic_score),
semantic_score=float(semantic_score),
edge_bonus=debug["edge_bonus"],
centrality_bonus=debug["cent_bonus"],
centrality_bonus=debug["cent_bonus"],
total_score=debug["total"],
source={"path": payload.get("path"), "text": payload.get("page_content") or payload.get("text")},
source={
"path": payload.get("path"),
"section": payload.get("section") or payload.get("section_title"),
"text": text_content
},
payload=payload,
explanation=explanation_obj
))
return QueryResponse(results=results, used_mode=used_mode, latency_ms=int((time.time() - t0) * 1000))
dt_ms = int((time.time() - t0) * 1000)
return QueryResponse(results=results, used_mode=used_mode, latency_ms=dt_ms)
# ==============================================================================
# 4. PUBLIC INTERFACE
@ -341,33 +371,52 @@ def hybrid_retrieve(req: QueryRequest) -> QueryResponse:
client, prefix = _get_client_and_prefix()
vector = list(req.query_vector) if req.query_vector else _get_query_vector(req)
top_k = req.top_k or 10
# 1. Semantische Suche
hits = _semantic_hits(client, prefix, vector, top_k=top_k, filters=req.filters)
# 2. Graph Expansion & Custom Weighting
expand_depth, edge_types = _extract_expand_options(req)
boost_edges = getattr(req, "boost_edges", {}) or {}
subgraph: ga.Subgraph | None = None
if expand_depth > 0 and hits:
seed_ids = list({h[2].get("note_id") for h in hits if h[2].get("note_id")})
if seed_ids:
try:
subgraph = ga.expand(client, prefix, seed_ids, depth=expand_depth, edge_types=edge_types)
# WP-22: Transformation der Gewichte im RAM-Graphen vor Bonus-Berechnung
if subgraph and hasattr(subgraph, "graph"):
for u, v, data in subgraph.graph.edges(data=True):
# Provenance Weighting (Concept 2.6)
prov = data.get("provenance", "rule")
prov_w = 1.0 if prov == "explicit" else (0.9 if prov == "smart" else 0.7)
# Intent Boost mapping
# Intent Boost Multiplikator
k = data.get("kind")
intent_b = boost_edges.get(k, 1.0)
data["weight"] = data.get("weight", 1.0) * prov_w * intent_b
except Exception: subgraph = None
intent_multiplier = boost_edges.get(k, 1.0)
# Finales Gewicht setzen
data["weight"] = data.get("weight", 1.0) * prov_w * intent_multiplier
return _build_hits_from_semantic(hits, top_k, "hybrid", subgraph, req.explain, boost_edges)
except Exception as e:
logger.error(f"Graph expansion failed: {e}")
subgraph = None
# 3. Scoring & Result Generation
return _build_hits_from_semantic(
hits,
top_k,
"hybrid",
subgraph,
req.explain,
boost_edges
)
class Retriever:
"""Asynchroner Wrapper für FastAPI-Integration."""
"""Wrapper-Klasse für FastAPI-Integration."""
async def search(self, request: QueryRequest) -> QueryResponse:
return hybrid_retrieve(request)