* Increase maximum side navigation depth by one
* Break up Text Search guide into multiple pages
* Update links to Text Search guide
* Broken link
* One more broken link
* One more broken link
* Shorten title
* Break Inference page into several pages
* Make all inference code snippets testable and clean up
* Make more snippets testable
* Edits
* Document automatic query and passage prefix injection in Cloud Inference
Qdrant Cloud Inference silently applies model-specific prefixes (e.g.
"query: "/"passage: " for E5, BGE-style instruction prefix for BGE/mxbai/
Snowflake arctic-embed) so users don't need to manage them manually.
Add a section explaining this behavior, the idempotency guarantee, and
the scope (Qdrant-hosted models only; external providers handle their own).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Document short query optimization in Cloud Inference
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Update links
* Expand on external provider API key usage
* Add section about external provider API keys
* Default to header for external API keys
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Reconcile every step to the parents-only ancestry model (branch passed
separately), add docstrings with simplified filter results, hide full
filter code under <details>, and show storage state at the end of each
step. Also fix step 6 typos (models., Selector, result, "A") and the
shadowed loop variable.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds the missing keyword index on document_id so grouping works under
strict mode (Cloud default), and tunes the upload_points call to
batch_size=256, parallel=2 for faster ingestion. Mirrors the notebook
in qdrant/examples#103.
Switches the two body references to the accompanying notebook from
GitHub URLs to githubtocolab so readers can run it without cloning.
The header table's GitHub link stays for readers who want the source
view.
Tightens awkward and stale phrasing across the intro and Dataset
section (per a full review pass), repositions FormulaQuery in Wrapping
Up as an alternative rather than part of the default pipeline, drops
the redundant 'document-side equivalent' closing line, and adds a
one-line pointer to the notebook right before the Setup section so
readers can pivot to runnable code.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds dense_abstract as a fourth prefetch in retrieve() since we
ingest it already. Removes the unactionable dense_abstract hedge
paragraph, the When to Group section (the prefetch-limit gotcha
folds into a code comment), the duplicate schema-section vector
bullets, and the awkward transition sentence between the code and
the design subsections. Replaces 'summary' with 'abstract as a
whole' in descriptions for consistency with the schema rename done
earlier.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a one-line comment above the query_points_groups call so readers
see what the function does without waiting for the When to Group
subsection (which now sits at the end of the design-decisions list).
Removes the entire 'Where This Pattern Doesn't Fit' section: the
short/homogeneous paragraph read as obvious (a reader who's deep into
this tutorial wouldn't try to multi-rep a tweet), and the
inconsistent-metadata paragraph was too vague to be actionable.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Renames sparse_keywords to sparse_title (the vector now indexes only
the title text), adds a keyword index on the tags payload field, and
gives retrieve() an optional tags parameter that builds a query_filter
when set. Pre-filtering on the tags payload is faster and more precise
than mixing categories into BM25 lexical matching.
Updates the schema description bullets, the prefetch justification,
the intro failure-mode list, and the dataset framing to match the
new design. Drops avg_len from 15 to 10 to reflect title-only word
counts.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Moves the standalone chunking-strategy sentence into the Dataset
paragraph that already explains why we chunk, and drops the dedicated
Open Ends section. The chunking-method link list (POMA-AI VST, Jina,
Chonkie) goes away with the section, and the BM25F note is also
removed since the workaround it describes is what the tutorial
already demonstrates. Cleans up two stale step-number references in
Wrapping Up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Tutorial slugs now match their content:
- retrieval-quality/ -> ann-recall/ (alias preserves the old published path)
- retrieval-quality-golden-set/ -> retrieval-relevance/
- retrieval-quality-pipeline-output/ -> pipeline-output-quality/
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Splits the three retrieval-evaluation tutorials across two sections.
ANN Recall (Web UI, language-agnostic) stays under Search Engineering.
The two Python ecosystem tutorials (ranx, ragas) move to a new top-level
Improve Search section under the Ecosystem partition.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Web UI is renaming the tab, card title, and column header to
"ANN Recall" alongside the precision-to-recall switch. Update the
prose and screenshot alt text to match. Button label
(Check Index Quality) stays as is.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Recall@k is the standard metric for ANN benchmarks. Rename the
tutorial title, headings, helper function, and prose; update the
recent inline edits and merge sentence; and update cross-links from
the relevance and pipeline-output tutorials and the tutorials index.
Generic Precision@k mentions in the ranx metric list are unrelated
and left alone.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The Search Quality tab uses the Query Points API, so it can only
tune search-time SearchParams. Replace the m/ef_construct walkthrough
with hnsw_ef and link to the Essentials course for the build-time
trade-offs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Keep a brief Layer 4 mention in the ANN guide and remove the dedicated section from the pipeline-output guide.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds an inline gloss for SingleTurnSample where readers first encounter it,
names EvaluationResult precisely as the return type of evaluate(), and
normalizes "golden query set" to "golden set" in the intro to match the
rest of the tutorial. Ends the tutorial with a Wrapping Up section that
closes the series.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Dylan Couzon