Reject missing relation endpoints#5
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…rrow-query strategy This is the second-leg of the recall overhaul (the first leg, d4b9288, fixed the silent embedding-zero bug and widened the scoring pool). Two new things land here, plus one prompt nudge. ## A.6 — fuzzy now goes through keywords too (symmetric retrieval) Before: the embedding pathway in assemble_context was keyword-mediated (after d4b9288), but the fuzzy pathway still ran pg_trgm + fulltext directly against entity content / title via fuzzy_search. The result was structurally unfair: a fact saved with keywords ["Petros", ...] got text_score ~0.06 against a multi-word query like "Petros person identity profile" because pg_trgm dilutes when a short query is compared against a long entity body. The keyword indexing was being bypassed by half the recall pipeline. After: a new helper find_fuzzy_keywords runs pg_trgm similarity(content, query) over entity_type='keyword' rows (short keyword content → no dilution), and assemble_context's text pathway fans out via the existing find_entities_for_keywords. Both pathways now produce a per-entity score equal to the best matched-keyword similarity over that entity's tagged_with neighbours. The geometric-mean merge and missing_signal_penalty are unchanged but become meaningful: they combine two signals about the SAME thing (how well the query matches this entity's keywords), one via trigrams and one via embeddings. fuzzy_search itself is intentionally left alone — it still serves the "arbitrary content matching" use-cases (quick_search agent tool, /memory/search). A discoverability backup in assemble_context still calls fuzzy_search and applies a heavy 0.2 discount as a pure fallback (only adds entities the keyword path didn't already cover; never overrides a keyword-path score). Design principle being restored (user-stated): keywords are the indexing hub. tagged_with relations are created automatically when an entity is saved, so the keyword graph alone is enough for retrieval connectivity. Explicit elaborates / refers_to edges are editorial nuance, not required for findability. ## A.7 — two-level diversity quota (per-search-term + per-keyword) When A.6 went live the top recall results for narrow-subject queries were dominated by a few popular hub keywords (CityFalcon ~42 entities, user-profile ~30, BrainDB ~12, ...). Each of those keywords was strongly matched by the broad multi-word queries the LLM was issuing, so their entities crowded top-N at near-identical scores; the narrow-subject fact (e.g. Petros, only 1 entity tagged) fell below the cut. Two complementary mechanisms, sharing ONE counter, fix this: L1 — per-search-term reservation: each query in queries[] gets ceil(max_results × per_query_share / num_queries) reserved slots filled from that query's OWN top-ranked entities. So a focused narrow query ALWAYS surfaces something in the result, no matter how broad the other queries are. L2 — per-keyword quota (geometric decay): walking the remaining (open) slots in final_rank-desc order, each new dominant matched keyword gets a halving allowance (50% / 25% / 12.5% ... of max_results, floor 1). Stops a popular keyword from monopolising the open portion. They share one bookkeeping dict (seen: kw_id -> remaining), so a keyword's allowance is decremented by BOTH L1 reservations and L2 walks — no double-spending, no conflict. The full coexistence rules are documented in the docstring of _apply_two_level_quota in braindb/services/context.py. Please read that block before touching the function; the no-conflict property depends on the shared counter. assemble_context now also tracks per-query scores (text_scores_by_q, embedding_scores_by_q) alongside the existing max-aggregated dicts, so L1 can rank entities by THAT query's own combined score (using the same geometric-mean / missing_signal_penalty merge per query). ## Prompt nudge — recall_memory docstring teaches narrow-query strategy A multi-word query like "Petros person identity profile" matches the short "Petros" keyword at only ~0.4 fuzzy (trigram dilution). The 1-word query "Petros" matches it at ~1.0 and surfaces the Petros fact at the top. To exploit this, the recall_memory tool's docstring (which the LLM reads as the tool description) now explicitly tells the model: - prefer 2-4 short focused queries over one long phrase - include bare subject names as standalone queries - example: ["Petros", "Selonda Saronikos fish farm", ...] - the per-search-term quota guarantees each angle gets representation, so adding the bare keyword is free The narrow strategy + L1 reservation together unlock the narrow-subject case: the LLM issues a single-keyword query for the subject, that query reserves slots in the result, the subject's fact tops those slots. Also bumped: agent recall_memory default max_results 15 → 30 (via new settings.recall_default_max_results). The /memory/context API schema default was already 30; this brings the agent tool in line. ## Verification (live, deepinfra/Gemma-4-31B) | Query | Petros position | final_rank | |--------------------------------------------------------|-----------------|------------| | ["Petros"] (narrow) | dimknaf#1 | 0.838 | | ["Petros", "Selonda Saronikos fish farm", "Dimitrios manager"] | dimknaf#1 | 0.839 | | ["Petros person identity profile", "Petros relation to Dimitris", "Petros CityFalcon"] (broad-only) | dimknaf#5 | (was: NOT in top-30) | Dimitrios Koutsoumpos /agent/query regression: 49.9s, 1362-char structured grounded answer. Tool sequence intact. ## Files braindb/agent/tools.py | 33 ++++- (docstring + default 30) braindb/config.py | 28 ++++ (3 new settings) braindb/services/context.py | 288 ++++++++++++ (the bulk: A.6 + A.7) braindb/services/keyword_service.py | 32 ++++ (find_fuzzy_keywords) 4 files changed, 342 insertions(+), 39 deletions(-) ## Knobs (all new settings, defaults are the shipping values) scoring_pool_keyword_neighbors: int = 500 Already shipped in d4b9288; unchanged here. scoring_pool_fuzzy: int = 500 Already shipped in d4b9288; unchanged here. The fuzzy scoring pool now applies to fuzzy_keyword matches (A.6). per_query_share: float = 0.5 L1 quota: fraction of max_results reserved across per-query slots. Set to 0 to disable L1. keyword_quota_halving: float = 0.5 L2 quota: each new dominant keyword's slot allowance shrinks geometrically. Set to 1.0 to disable L2. recall_default_max_results: int = 30 Default max_results the agent's recall_memory tool exposes to the LLM (and the /memory/context API). ## What is explicitly NOT touched - missing_signal_penalty (still 0.5) - effective_importance / temporal decay - graph_expand - the geometric-mean seed_score merge - fuzzy_search itself (still keyword-blind for quick_search / /memory/search consumers) - the agent loop, the typed final-answer contract, the wiki pipeline, the scheduler No IDF was added. The two-level quota plus the prompt nudge are sufficient for narrow-subject surfacing in our data; adding IDF on top would be bloat.
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Summary
Tests