canonical_name dropped "es" from every -ches/-ses/-zes plural, so "caches"
became "cach" while "cache" stayed "cache" -- the singular and plural landed on
two nodes, which is the split the function exists to prevent. The same rule
split the words documentation uses most: databases/database, responses/
response, releases/release, sizes/size.
An "-es" plural cannot say whether it is cache + "s" or batch + "es", so
instead of guessing, both sides now meet at the stem: the singular endings
-che/-she/-se/-ze/-xe drop their "e" the way the plurals drop "es", and a
singular's -ie folds to -y as -ies already did (cookie/cookies). The key is a
merge key that is never shown, so it only has to agree, not be a word. alias,
canvas, atlas and bias join the words that only look plural.
Found by the naming tests CI was missing: the module had no direct tests.
Graph retrieval tied plain vector search at best and never beat it. Measured
across five corpora, the bottleneck was seeding, not the graph: the walk
started from nodes whose embeddings were computed from bare entity names, and
a whole question shares almost nothing with a name like "Quill".
Extraction now embeds each node from "name (type): description" and each
relationship as the fact it asserts ("Alder streams_to Quill: ..."), stored on
a new nullable graph_edges.fact_embedding column that ensure_vector_schema adds
in place. Entity names are canonicalised (case, punctuation, word breaks and a
cautious plural) so "VECTOR_STORE" and "vector stores" land on one node. Extraction calls run
concurrently (GRAPHRAG_EXTRACTION_WORKERS, default 8) while embedding and graph
writes stay serial on the task thread, so ordering and idempotency are
unchanged; that measured 8.4x faster with identical output.
Retrieval gains per-source options, stored under retrieval.graph and read live
at query time:
- seed_strategy: start from matching entities (default) or matching
relationships, which can reach an entity the question never names;
- passage_nodes (on): walk the source's passages alongside entities, with
PageRank damping 0.5 instead of 0.85;
- blend_vector (on): fuse the graph ranking with the source's vector ranking
by reciprocal rank.
The defaults are the measured-best configuration. Through GraphRAGRetriever,
the new seeding moved recall@4 from 0.41 to 0.68 on a multi-hop corpus and
from 0.50 to 1.00 on the docs corpus, and regressed none of the corpora
measured. Existing graphs keep name-only embeddings until rebuilt.
Alex