A search asking for more than 10 passages got at most 40 candidates from
HNSW, pgvector's default ef_search. The full-text arm scored every chunk
holding a word of the question before limiting, over the whole
organization's GIN index; it now takes candidates by ts_rank from the
collection's own partial index and scores those. New collections index
halfvec on pgvector 0.7 and later (up to 4000 dimensions).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A chat model of the cluster reads each chunk and writes questions a person
might ask of it with the answers it holds, streamed as NDJSON a chunk at a
time, for people to correct before an adapter is trained on it. A chunk that
gave no pairs says so on its own line.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
On an organization, team, project or key, the narrowest winning: llmgw ctl
kb default, POST /api/v1/kb/defaults, and the console's Knowledge screen. A
request's own X-Llmgw-Knowledge overrides it and none turns it off. A default
never refuses a request: a collection the caller cannot read is left out, and
a failed search answers without passages. Removing a collection takes it out
of every default.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The best text match took the whole text weight, however little of the
question it matched: one everyday word ("most") put the remote-work page
above the travel one for "the most I can spend on a hotel". Now a chunk
gains the weight in proportion to the share of the question's words it
holds, counted once whatever their case.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Fused by rank, a full-text match on a question's everyday words outvoted a
far nearer meaning: on the PoC, "how much leave do fathers get" found the
annual-leave page before the paternity one (cosine distance 0.44 against
0.25). Now a chunk scores its similarity to the question plus up to 0.3 for
a full-text match, each search brings four times k candidates, the text
search drops Turkish and English stop words, and an operator's search says
when the embedding model did not answer rather than quietly searching text.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Collections owned by an organization, team, project or person; grants that
add up down the tree and never cross an organization; chunks in PostgreSQL
with pgvector, partitioned by organization, an HNSW index per collection, and
row-level security as a second line behind each query's filter. Search fuses
nearest-neighbour and full-text ranks. Off on SQLite and without pgvector,
saying why. server init and the HA database setup install and enable it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
