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Retrieval Quality Gate (CONCEPT:KG-2.6)

Overview

Systematic retrieval quality measurement with 5-mode failure taxonomy (drift, truncation, staleness, low-relevance, inter-agent), configurable per-SchemaPack relevance thresholds, and temporal freshness scoring. Based on Ambekar (2026) research.

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/retrieval_quality.py
  • Pillar: KG

Documentation Coverage

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Cross-Agent Context Provenance (CONCEPT:KG-2.6)

Overview

Tracks retrieval quality scores and failure modes across agent boundaries via ContextProvenanceRecord. Detects cascading retrieval degradation in multi-agent pipelines.

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/retrieval_quality.py
  • Pillar: KG

Documentation Coverage

This is an auto-generated dedicated concept page to ensure 100% documentation coverage across the ecosystem.

Hybrid Search Index (CONCEPT:KG-2.3)

Overview

Weighted semantic+keyword search scoring (72%/28% default) with CamelCase/snake_case token splitting, phrase boost, and symbol-specific matching. Uses existing create_embedding_model() infrastructure. Adapted from contextplus's embedding.ts.

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/semantic_retrieval_engine.py
  • Pillar: KG

Documentation Coverage

This is an auto-generated dedicated concept page to ensure 100% documentation coverage across the ecosystem.

RAG-KG Unification (CONCEPT:KG-2.3)

Overview

Collapses separate RAG vector index into KG-native retrieval using three acceleration layers: similarity-edge shortcuts (O(degree) vs O(N)), spectral cluster scoping (search space reduction), and hybrid semantic+keyword scoring. Drop-in enhancement for HybridRetriever via retrieve_unified().

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/semantic_retrieval_engine.py
  • Pillar: KG

Documentation Coverage

This is an auto-generated dedicated concept page to ensure 100% documentation coverage across the ecosystem.

Graph Distillation Migration (CONCEPT:KG-2.6)

Overview

Migrates standard RAG retrieval to pre-computed SimilarityEdgeNode shortcuts for O(degree) retrieval. Manages distillation index lifecycle: batch creation, incremental updates, stale edge pruning, and coverage health monitoring. Includes migrate_existing() for batch migration of legacy nodes.

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/semantic_retrieval_engine.py
  • Pillar: KG

Documentation Coverage

This is an auto-generated dedicated concept page to ensure 100% documentation coverage across the ecosystem.

Evaluation Corpus (CONCEPT:KG-2.3)

Overview

Fixed corpus evaluation mode for reproducible deep-research benchmarking. Inspired by BrowseComp-Plus (arXiv:2508.06600). Stores named, versioned document sets with optional query-answer pairs. Supports freeze semantics for immutable benchmarks. Integrated into HybridRetriever via corpus_id parameter for constrained search.

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/evaluation_corpus.py
  • Hot Path: HybridRetriever.retrieve_hybrid(corpus_id=...) → Cypher WHERE n.id IN $corpus_ids
  • Python API: CorpusManager.create_corpus(), CorpusManager.list_corpora(), CorpusManager.freeze_corpus() (in evaluation_corpus.py)
  • Pillar: KG

Hard Negative Mining (CONCEPT:KG-2.3)

Overview

Mines challenging distractors from query decomposition to calibrate retriever precision. Uses existing _decompose_query() to break complex queries into sub-queries, retrieves per sub-query, and identifies documents that match sub-queries but not the full query. Gated behind KG_ENABLE_HARD_NEGATIVE_MINING env var. From BrowseComp-Plus (arXiv:2508.06600).

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/hard_negative_miner.py
  • Hot Path: HybridRetriever.retrieve_hybrid(hard_negatives=...) → score × 0.5 penalty
  • Pillar: KG

nDCG Retrieval Scoring (CONCEPT:KG-2.3)

Overview

Normalized Discounted Cumulative Gain computation for retrieval quality assessment. Uses binary relevance against gold document sets. Aligns with BrowseComp-Plus evaluation methodology (Section 4.1). Integrated into RetrievalQualityGate.compute_ndcg() and consumed by EvaluationEngine.evaluate_disentangled().

Implementation Details

  • Source Code: agent_utilities/knowledge_graph/retrieval/retrieval_quality.py
  • Hot Path: RetrievalQualityGate.compute_ndcg(results, gold_doc_ids, k=10)
  • Pillar: KG