overview.md repeated the landing page: the same install-and-ask block and
five of six identical links. It was positioning prose, where the docs had no
page describing how the system works.
Rewrite it as Architecture, following the data through: source adapter,
converter, chunker, embedder, transaction; then storage and its versioning;
then retrieval, with the 10x rerank fetch and section-bounded expansion; then
the two capabilities; then laptop versus ingester. Retitled in the nav and on
the landing page, filename kept so existing links resolve.
Extras were listed in three places and none was complete.
docs/installation.md now carries a table of all fifteen slim extras, what each
provides, and which the full package already includes.
haiku_rag_slim/README.md names them and links there. The claim that other
providers need their own pydantic-ai extra was wrong: haiku.rag-slim defines
anthropic, google, groq, mistral, bedrock and vertexai itself.
configuration/storage.md opens with the four operational constraints, which
were either buried in an S3 section or undocumented: one writer per URI,
reader lag by read_consistency_interval_seconds, migrate after a
schema-changing upgrade, and the fixed embedding dimension with what
ConfigMismatchError means and which rebuild mode resolves it.
The one-writer rule is stated as a haiku.rag constraint, which is what it is:
the multi-table lock, version snapshot and rollback are process-local, so a
second writer can commit inside another's transaction and be reverted by its
rollback. storage.md and ingester.md both claimed it was a LanceDB property
that corrupts manifests. The S3 deployment section now links to the
constraint instead of restating it.
Get started reads index, Quickstart, Installation, Architecture. The landing
page's list was missing Installation.
The README feature list and the overview stopped at the analysis capability.
Both examples and the app backend composed agents without the capabilities the
documentation recommends alongside an evidence capability.
custom_agent.py ran each input as an independent agent run, so it needed a state
dict and a carried history before compaction could mean anything there: without
state the evidence record is empty, and earlier evidence would reduce to
receipts retaining nothing.
