feat: Add Top-K and min co-occurrence filters to NLP edge extraction

[nahisaho]

Summary

The NLP-based graph extraction in build_noun_graph.py uses itertools.combinations() to create co-occurrence edges between all noun phrases in each text chunk. With entity-dense corpora (e.g., scientific/technical text), this O(N²) all-pairs algorithm produces a massive number of edges that overwhelm downstream processing and paradoxically make the "fast/lazy" NLP mode more expensive than the LLM-based "standard" mode.

Problem

Root Cause

_extract_edges() calls combinations(sorted(set(titles)), 2) on every text unit. With scientific text averaging ~60 noun-phrase entities per 800-token chunk, this produces C(60,2) = 1,770 pairs per chunk.

Impact (measured on a 20-paper materials-science corpus, 153 chunks)

Mode	Entities	Relationships	LLM Cost (gpt-4o-mini)
Standard (LLM)	605	1,858	$0.600
Fast/Lazy (NLP)	1,147	120,287	$0.929

The NLP mode generates 65× more relationships than LLM extraction, causing the "fast" pipeline to be 55% more expensive than Standard despite using no LLM for entity extraction itself. The cost comes from downstream community_reports generation, which must summarize the inflated graph.

Why prune_graph does not fix this

The existing prune_graph workflow step applies PMI-based pruning, but:

• PMI normalization makes edge weights very uniform (range 0.000006–0.000124 for 120K edges)
• Moderate pruning (min_edge_weight_pct=40) still leaves ~72K edges
• Aggressive pruning (min_edge_weight_pct=80, max_node_degree_std=2.0) over-prunes to just 6 edges

The problem must be addressed at the source — during edge construction, not after.

Solution

This PR adds two configurable parameters to _extract_edges():

1. max_entities_per_chunk (default: 0 = disabled)

When > 0, only the K most globally-frequent entities per text chunk are paired, capping edges at C(K,2) instead of C(N,2).

# settings.yaml
extract_graph_nlp:
  max_entities_per_chunk: 15  # C(15,2) = 105 max pairs/chunk

2. min_co_occurrence (default: 1 = no filtering)

When > 1, edges appearing in fewer text units are discarded as likely coincidental co-occurrences. In testing, ~57.5% of edges appeared in only 1 chunk.

extract_graph_nlp:
  min_co_occurrence: 2

Why these defaults?

Both defaults preserve exact backward compatibility — existing users see zero behavioral change. Users experiencing the edge explosion can opt in by setting these parameters in settings.yaml.

Results

Tested with 20 materials-science papers (153 text chunks), gpt-4o-mini + text-embedding-3-small:

With max_entities_per_chunk=15, min_co_occurrence=2

Metric	Before (NLP)	After (NLP)	Standard (LLM)
Relationships	120,287	2,660	1,858
Total cost	$0.929	$0.097	$0.600
Reduction	—	97.8%	—

Top-K parameter sweep

K	Relationships	Cost	Query Quality
10	1,060	$0.072	★☆☆ (too sparse)
15	2,660	$0.097	★★★ (best)
20	5,108	$0.149	★★★
30	12,172	$0.270	★★★

K=15 emerged as the Pareto optimal — best query quality at the lowest cost, outperforming even Standard mode on a local search benchmark.

Changes

File	Change
config/defaults.py	Add max_entities_per_chunk=0, min_co_occurrence=1 to ExtractGraphNLPDefaults
config/models/extract_graph_nlp_config.py	Add corresponding Pydantic fields to ExtractGraphNLPConfig
index/workflows/extract_graph_nlp.py	Thread new parameters from config → build_noun_graph()
index/operations/build_noun_graph/build_noun_graph.py	Implement Top-K selection and co-occurrence filtering in _extract_edges()
tests/unit/config/utils.py	Add assertions for new config fields
tests/unit/indexing/operations/test_build_noun_graph.py	New: 15 unit tests covering filtering logic, edge cases, backward compatibility
.semversioner/next-release/	Patch change document

Backward Compatibility

• ✅ Default behavior unchanged — max_entities_per_chunk=0 disables Top-K, min_co_occurrence=1 keeps all edges
• ✅ Existing integration test (test_extract_graph_nlp) continues to pass with 1,147 entities and 29,442 relationships
• ✅ No breaking API changes — new parameters are keyword-only with defaults
• ✅ No new dependencies

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