Renames "How to Fuse" to "Which Fusion to Use" and expands it to
cover RRF (default), Weighted RRF, DBSF, and custom formulas as four
named options, with the FormulaQuery code block moved in from what
was the boosting section. Softens RRF framing from "stick with RRF
unless..." to "reasonable starting point; variants often do better
once you have an eval set." Adds the distribution-alignment
explanation in the custom-formula paragraph and links the in-repo
Decay Functions and Score Boosting references along with the RRF vs
DBSF FAQ entry.
The "When to Boost, When to Rerank" section now only covers true
boosting (recency, authority, decay) and reranking, and is reordered
to sit immediately after fusion so the ranking decisions stay
together. The "When to Group, When Not To" section moves to the end
as a presentation concern.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Calibrates BM25 length normalization for the short title+categories
sparse field with a comment on why. Removes redundant k1/b/avg_len
prose from the tutorial Open Ends section and the cross-link paragraph
in text-search.md, since the BM25 Parameters subsection above already
documents calibration with a working example.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The two lookup_from mentions were misleading: the feature is for
querying by ID across collections, not for splitting representation
storage. The line-107 paragraph now points readers to the documented
with_lookup pattern for the payload-split case and stays silent on
vector splits, which are a separate design problem (multiple queries
plus client-side fusion) that doesn't fit this tutorial's scope.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The arxiv data has abstracts, not summaries. Renaming the named
vector and prose throughout removes the ambiguity flagged on the PR.
Adds a short paragraph to the Dataset section explaining that
abstracts fit any embedding model's context window, so chunking is
included to mirror the pipeline shape you'd use on full bodies.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Drops FastEmbed in favor of server-side embedding via Cloud Inference
for dense vectors and core BM25 (in Qdrant since 1.15) for sparse.
Simplifies ingestion and query code; adds an aside covering the
self-host path. Also clears two em dashes from the tutorial prose.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the FastEmbed tutorial link with the hybrid search section
of the core Text Search guide, since FastEmbed is a satellite library.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- hybrid-queries: see-also link at end of grouping section
- vectors: clarify MaxSim returns one combined score and point to named vectors + tutorial
- text-search: BM25 short-field calibration note plus BM25F workaround pointer
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Clicking a language tab now switches all other code-snippet widgets on
the page to the same language, and the choice is saved to localStorage
so it is auto-applied on future page loads.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* Add stronger wording about wait=true
* Position indexed_only and prevent_unoptimized as alternatives
* Review feedback
* Apply suggestions from code review
Co-authored-by: Tim Visée <tim+github@visee.me>
* Small edit
---------
Co-authored-by: Tim Visée <tim+github@visee.me>
Adds a documentation page for Superlinked (SIE) as a Qdrant embedding
provider. The sie-qdrant package provides SIEVectorizer for dense
embeddings and SIENamedVectorizer for multi-type (dense, sparse, and
multivector/ColBERT) embeddings, enabling hybrid search via Qdrant's
Reciprocal Rank Fusion and native MaxSim retrieval via MultiVectorConfig.
Python-only.
