Store feature vectors from any model for every detection, indexed for the ways they are read

[mihow]

Summary

Processing services can describe what each detection looks like as a feature vector (an embedding), and several parts of Antenna need those vectors: tracking compares detections across neighbouring captures, retraining builds on verified detections, and similarity search and clustering compare everything in a project. Until now Antenna had nowhere to keep them, and each of those efforts was starting to add its own column.

This PR adds one place for them. When a pipeline returns vectors with its detections, Antenna stores them for every detection, including the crops the moth/non-moth filter rejected, from any number of models side by side (for example a classifier backbone's 2,048 values and BioCLIP's 1,024). The table and its indexes are laid out for the ways we already know vectors will be read, each of which has a small helper function, tests and a measured query plan, and a reference document records the conventions for what comes next (logits, reduced dimensions, nearest-neighbour indexes).

Sorting occurrences by visual similarity, with a "Show similar occurrences" link, is included as an example use of the stored vectors and a way to try the feature from the interface.

List of Changes

#	Change (what it does for users and developers)	How (implementation)
1	Vectors sent with each detection are kept, for every detection, without adding any prediction that could change an identification	DetectionResponse.embeddings (matches ami-data-companion #175); create_detection_embeddings() in ami/ml/embeddings/writer.py, called from save_results
2	One table holds vectors from any number of models per detection	DetectionEmbedding in ami/ml/models/embedding.py: detection, algorithm, key (one model may return several outputs), job (set null on delete), project (copied from the capture), unsized halfvec, STORAGE EXTERNAL; unique on (detection, algorithm, key)
3	Every model output keeps one vector length, so its vectors can always be compared and indexed	Length fixed per (algorithm, key) on the first write and checked on every batch with one indexed lookup (EmbeddingDimensionMismatch)
4	Saving the same results twice changes nothing; a new vector replaces the old one	DetectionEmbeddingQuerySet.store() skips identical vectors and upserts the rest
5	Vectors land on the right detection even when some detections already existed	matched by capture and box, not by position
6	The known read patterns are fast and have a function each	ami/ml/embeddings/reader.py: vectors_for_detections, project_vectors (bounded chunks), vector_counts_by_algorithm, detections_missing_vectors; indexes (project, algorithm, key, detection) and (algorithm, key, detection); measured below
7	Conventions for future vector and model-output storage are written down	docs/claude/reference/feature-vectors.md: query patterns, anti-patterns, where logits and reduced-dimension vectors should go, when to add a nearest-neighbour index
8	Example use: sort the occurrence list by visual similarity to one occurrence	GET /occurrences/?ordering=visual_similarity&similar_to=<id>[&similarity_algorithm=<id>]; invalid values return 400
9	Example use in the interface: "Show similar occurrences" on an occurrence, and a sortable snapshots column	link on the details page, a read-only "Similar to occurrence" filter chip, i18n strings
10	A job's "View occurrences" list includes occurrences the job only added vectors to	a third Exists branch in OccurrenceQuerySet.created_or_updated_by_job(), served by a partial (job, detection) index
11	First install of pgvector, with a guard that explains itself	ml/0029_enable_pgvector checks for the 0.8 package before CREATE EXTENSION; the local and CI Postgres image installs the 0.8 series

Related Issues

• Part of Tracking usable & available behind feature flag #1412 (tracking behind a per-project flag): tracking's appearance term will read its vectors from this table. Related to Store, show and review what post-processing methods decide #1457 (storing what post-processing methods decide).
• Retrain a classifier head from verified identifications, and score what it produces #1407 (classifier retraining) will adopt this table and its readers in place of its own.
• Follow-up: Add feature vectors to existing detections, and reprocess detections without re-detecting #1464, adding vectors to existing detections. An admin-only post-processing task for it is planned as a separate pull request stacked on this one.
• Processing-service side: Give every detection a BioCLIP feature vector, and embed existing detections without re-detecting ami-data-companion#175.

Detailed Description

How vectors are stored, and why

• One table for every model, keyed (detection, algorithm, key). A detection can carry vectors from several models without schema changes. Every reader that returns vectors takes an algorithm id, because vectors from different models cannot be compared.
• halfvec, unsized. Two bytes per value (4 KB for 2,048 values). Each (algorithm, key) keeps one length, so a per-model index on vector::halfvec(N) is always valid later. On 600 real 2,048-value vectors, half precision changed cosine similarity by at most 1.5e-4.
• STORAGE EXTERNAL. Vectors do not compress, so they are stored out of line without compression attempts, and the table rows stay small.
• The project is copied onto each row, so project-wide reads need no join through detections and captures.
• No occurrence column. Occurrences change when tracking merges them; the detection is the stable key.
• Four indexes, each with a different leading column: the unique (detection, algorithm, key) for reads by detection and for deletes that cascade from detections; (project, algorithm, key, detection) for project-wide reads in detection order; (algorithm, key, detection) for the length check and the "missing vectors" filter; and (job, detection) where a job is set, for a job's list of occurrences. The foreign keys have no extra single-column indexes, because these already lead with them.

The known read patterns, measured

Measured with EXPLAIN (ANALYZE, BUFFERS) on a throwaway database holding the real layout of two projects from a production copy (179,466 and 45,114 detections), with synthetic clustered vectors for every detection under two models (2,048 and 1,024 values): 449,160 rows. Median of 5 runs after a warm-up.

Read pattern (who needs it)	Function	Plan	Time
Vectors for the detections of two adjacent captures (tracking)	vectors_for_detections	index scan, unique index	< 0.1 ms
Vectors for 5,000 detections (retraining)	vectors_for_detections	sequential scan at this table size (17-19 ms); forced onto (algorithm, key, detection), 12 ms	12-19 ms
A project's vectors in chunks of 2,000, first and middle chunk (exports, clustering)	project_vectors	index scan on (project, algorithm, key, detection), no sort	0.5 ms per chunk
Which models have vectors in a project, and how many	vector_counts_by_algorithm	sequential scan when one project holds most of the table (an index-only scan is 20.6 ms when forced)	36 ms (large), 10.7 ms (medium)
Detections of one session still missing vectors from a model	detections_missing_vectors	anti-join, index-only scan on (algorithm, key, detection), 0 heap fetches	22.5 ms
The same over a whole 179k-detection project	detections_missing_vectors	parallel hash anti-join	138 ms
Length check before saving (per model output, per batch)	writer	index scan, LIMIT 1	0.01-0.02 ms (a sequential scan of 13-18 ms before the index)
Example: occurrence list sorted by similarity, page 1, 36,253 eligible occurrences	with_visual_similarity	per-occurrence probe of (algorithm, key, detection), then a sort	about 1.5 s, of which about 1.0 s is Postgres JIT compilation

The similarity sort is not tuned in this PR. Earlier measurements put it at 16-19 ms per 1,000 eligible occurrences with JIT off and about 36 ms with JIT on; a station or taxon filter brings it to 0.1-0.2 s. Turning JIT off for that query, capping very large sorts, and a nearest-neighbour (HNSW) index are follow-up tickets; in an experiment an HNSW index cost 0.9-2.7 GB and minutes to build per model at a few hundred thousand rows, with top-20 recall of 0.68-0.98, so it is not worth it at today's sizes.

Guidance for what comes next

docs/claude/reference/feature-vectors.md records the conventions. In short:

• Logits are not vectors to search, and do not go in this table. On a production copy, 308k classifications carry logits (4.3 GB), one classifier has 29,176 classes (above pgvector's 16,000-value storage limit), and 37,776 duplicate (detection, algorithm) groups disagree, so logits stay keyed to their classification. If they move, a side table keyed by classification (real[], STORAGE EXTERNAL) or files in object storage for bulk exports.
• Reduced dimensions (PCA, UMAP, random projection) are stored here as their own Algorithm, recording the source model and fit settings, so each has its own length and index.
• Captures and taxon prototypes get sibling tables of the same shape rather than a polymorphic target column.

End-to-end run against the processing service

Run on a throwaway local stack with ami-data-companion PR #175 (commit a8047bd) serving the synchronous /process route on a GPU: a regional moth pipeline over 7 real test captures, vectors switched on only from Antenna ({"features_for_all_detections": true} in the project's pipeline config). This run used the earlier head of this branch; the save path is the same apart from where the code lives and the per-output length rule.

Check	Result
Every detection gets a vector	69 detections, 69 vectors, including the 13 crops the moth/non-moth filter rejected, which have no species classification
Two models side by side	species classifier backbone (2,048 values) and BioCLIP (1,024 values) stored under their own algorithms
Reprocessing the same captures	still 69 rows per model, identical checksums, no errors
Control: config flag removed	no vectors stored
Similarity sort through the API	the top 10 matched a direct SQL cosine-distance query; the reverse order is exact; invalid values return 400

BioCLIP vectors also need the service started with AMI_EMBEDDING_EXTRACTOR set; that is a processing-service setting.

How to Test

1. Rebuild the local Postgres image (docker compose build postgres), which now installs pgvector 0.8, and run migrations. ml/0029_enable_pgvector should print nothing; on a server without the package it stops with "The pgvector extension is not installed...".
2. Backend tests: docker compose run --rm django python manage.py test ami.ml.test_detection_embeddings ami.main.test_visual_similarity (38 tests), or the full suite.
3. Run a pipeline whose processing service returns embeddings on each detection (ami-data-companion Bump react-admin from 4.8.4 to 4.11.4 in /frontend #175 with features_for_all_detections on), then in a shell: from ami.ml.embeddings.reader import vector_counts_by_algorithm; vector_counts_by_algorithm(<project id>).
4. Open an occurrence and click "Show similar occurrences": that occurrence comes first, similar ones next, occurrences without vectors last.
5. GET /api/v2/occurrences/?project_id=<id>&ordering=visual_similarity&similar_to=abc returns 400.

Screenshots

Taken on a throwaway stack with the demo project and seeded vectors (vectors of the same species placed close together so the grouping is visible).

What comes next

• New captures should get vectors in the main pass. Processing-service workers on the queued path attach vectors only when configured to; that should default on, so the stacked Add feature vectors to existing detections from the admin, without detecting again #1479 ("Add feature vectors") is only needed to backfill existing detections. This is a processing-service setting, tracked with Add feature vectors to existing detections, and reprocess detections without re-detecting #1464.
• After Record any algorithm's results on occurrences in a standard way, and show them in each occurrence's history #1461 merges, this PR's ml/0029 and ml/0030 are renumbered to follow its ml/0029_algorithm_result, and a "vectors added" entry joins the occurrence history.

Deployment Notes

This is the first install of pgvector. Before deploying, the pgvector 0.8 package (for example postgresql-16-pgvector) must be installed on every PostgreSQL server: production, staging and demo. The migration then creates the extension in the database. If the package is missing or older than 0.8, ml/0029 stops before any SQL with a message that says so.

No data is backfilled: the table starts empty and fills as pipelines that return vectors run. The occurrence list is unchanged unless ordering=visual_similarity is requested.

Checklist

• Migrations included (ml/0029, ml/0030); makemigrations --check passes
• 44 targeted tests pass. Full backend suite: 794 tests, 1 failure in test_tasks_fetch_zero_delivered_does_not_log_to_stdout, which passes alone and within its class; it reads tasks left on the shared NATS server by an earlier test, and this PR touches no jobs or NATS code
• Query plans measured with EXPLAIN (ANALYZE, BUFFERS) at a realistic size
• Run end to end against a processing service that returns vectors (ami-data-companion Bump react-admin from 4.8.4 to 4.11.4 in /frontend #175)
• Screenshots added
• Operations: install pgvector 0.8 on every Postgres server before deploy

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Summary by CodeRabbit

• New Features
  • Added visual similarity ordering for occurrences, using a selected occurrence or a default reference. Occurrences without similarity data appear last.
  • Added a “Show similar occurrences” action on occurrence details and a “Similar to” filter.
  • Detection results can now include feature vectors for similarity comparisons.
• Documentation
  • Added guidance on feature-vector storage and querying.

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• ui/src/pages/occurrences/occurrences.tsx
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📝 Walkthrough

Walkthrough

The change adds detection embedding storage and pipeline writes, vector reader helpers, and visual-similarity ordering for occurrences. It also adds API validation and documentation, tests, and UI controls for opening and filtering similar occurrences.

Changes

Detection embeddings and visual similarity

Layer / File(s)	Summary
Embedding schema and storage ami/ml/schemas.py, ami/ml/models/embedding.py, ami/ml/models/__init__.py, ami/ml/migrations/0029_enable_pgvector.py, ami/ml/migrations/0030_detection_embedding.py, requirements/base.txt, compose/local/postgres/Dockerfile, ami/ml/test_detection_embeddings.py	Adds an embedding response and DetectionEmbedding storage with half-precision vectors, uniqueness constraints, indexes, and project assignment. Adds pgvector setup and tests for schema and migration behavior.
Pipeline embedding writes ami/ml/embeddings/writer.py, ami/ml/models/pipeline.py, ami/ml/test_detection_embeddings.py, ami/tests/fixtures/main.py	Matches response vectors to detections and validates them before storing. Pipeline result saving writes embeddings before classifications. Tests cover storage, vector replacement, project assignment, and job attribution.
Vector readers and similarity ordering ami/ml/embeddings/reader.py, ami/main/models.py, ami/main/api/views.py, ami/main/test_visual_similarity.py, ami/main/tests.py, docs/claude/INDEX.md, docs/claude/reference/canonical-patterns.md, docs/claude/reference/feature-vectors.md	Adds vector lookup, project iteration, counts, and missing-vector helpers. The API supports ascending and descending visual-similarity ordering, with algorithm and seed selection, validation, and null vectors ordered last. Tests cover ordering, filtering, permissions, and job matching. The reference docs describe embedding storage and query patterns.
Occurrence similarity UI ui/src/pages/occurrence-details/occurrence-details.tsx, ui/src/pages/occurrences/*, ui/src/components/filtering/*, ui/src/utils/getAppRoute.ts, ui/src/utils/language.ts, ui/src/utils/useFilters.ts	Adds a link from occurrence details to visually similar occurrences, a read-only similar_to filter, and the visual-similarity sort field. Adds filter rendering, route typing, and translated labels and tooltip.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant OccurrenceDetails
  participant OccurrenceViewSet
  participant algorithm_with_most_vectors
  participant representative_embeddings
  participant OccurrenceQuerySet
  OccurrenceDetails->>OccurrenceViewSet: Request visual-similarity ordering with a seed occurrence
  OccurrenceViewSet->>algorithm_with_most_vectors: Select the project algorithm when none is specified
  OccurrenceViewSet->>representative_embeddings: Resolve the seed occurrence vector
  OccurrenceViewSet->>OccurrenceQuerySet: Annotate cosine distance and order occurrences

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Merge Risk: ⚪ Minimal · up to 8f79e

The reviewed changes have no identified issue requiring a fix before merge; proceed with normal checks and the stated pgvector deployment prerequisite.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 38.53% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 109 functions across 24 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly summarizes the primary change: storing feature vectors for every detection and indexing them for supported read patterns.
Description check	✅ Passed	The description is comprehensive and follows the repository template. It covers the summary, changes, related issues, implementation details, testing, screenshots, deployment requirements, risks, and …
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[mihow]

why is this pgvector 0.5? not 0.8 or above?

[mihow]

Claude says: This line pins the Python client library (pgvector-python on PyPI), not the database extension. The two have separate version numbers, and 0.5.0 is the latest client release. The client has provided the HalfVectorField used here since 0.3.0.

The PostgreSQL extension is the one that has to be 0.8 or later. The local and CI Postgres image installs pgvector from the PostgreSQL apt repository (compose/local/postgres/Dockerfile), which ships the 0.8 series today. Migration ml/0029_enable_pgvector reads pg_available_extensions and stops with a message before running any SQL if the server offers less than 0.8. Production has no pgvector today, so the deployment note asks operations to install the 0.8 package on each Postgres server first.

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Claude says: #1471 merges first and adds job to detections and classifications, plus a ?job= occurrence filter. Three notes for this PR.

• Migrations. Record which job created each detection and classification, and filter occurrences by job #1471 takes main/0096 and main/0097, so this PR's 0096_enable_pgvector_extension and 0097_detection_embedding need renumbering after Record which job created each detection and classification, and filter occurrences by job #1471 (and after Record any algorithm's results on occurrences in a standard way, and show them in each occurrence's history #1461, if that lands first).
• The embedding table's job column is fine as it is. The table is new, so its default index builds on an empty table and there is no lock concern. The writer should set the job the same way Record which job created each detection and classification, and filter occurrences by job #1471 does for detections: only on rows it creates.
• The job filter. OccurrenceQuerySet.created_or_updated_by_job() does not look at embeddings. That is right while embeddings are written by the same pipeline job that writes the detections. When a job that only adds vectors to existing detections exists (Add feature vectors to existing detections, and reprocess detections without re-detecting #1464), it will need a branch in that method, with an index that starts with the job, or its "View occurrences" link will open an empty list.

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[mihow]

Claude says: All three notes are addressed at 9d545d3.

• Migrations: the vector table moved to the ml app (ml/0029_enable_pgvector, ml/0030_detection_embedding), so nothing collides with main/0096 and main/0097 from Record which job created each detection and classification, and filter occurrences by job #1471. If Record any algorithm's results on occurrences in a standard way, and show them in each occurrence's history #1461 lands first, these two are renumbered after its ml/0029_algorithm_result.
• Job on vectors: the writer sets job on the rows it creates. A row whose vector is unchanged on a re-run keeps the job that first stored it, as detections and classifications do under Record which job created each detection and classification, and filter occurrences by job #1471.
• Job filter: created_or_updated_by_job() has a third branch for vectors, served by a partial (job, detection) index, and TestOccurrenceJobFilter covers an occurrence that a job only added vectors to (d90d88b).

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Copilot review overview

🟡 Changes recommended

Concurrent writes can violate vector dimensions, bbox matching can misassign vectors, and some similarity UI paths reliably produce invalid requests.

Review effort: Balanced
Findings: 2 · 1 · 1

Open (4)

• Rounded bbox keys merge distinct detections · New
• Concurrent vector writes allow mixed dimensions · New
• Render similar-to links only for compatible embeddings · New
• Clear or hide similar-to when changing sort · New

What changed in this PR

Adds persistent pgvector-backed detection embeddings and visual-similarity ordering across the backend and UI.

Changes:

• Stores and retrieves model-specific detection embeddings.
• Adds similarity sorting and navigation for occurrences.
• Adds pgvector infrastructure, migrations, documentation, and tests.

File	Description
ui/​src/​utils/​useFilters.ts	Adds the similarity filter.
ui/​src/​utils/​language.ts	Adds translated similarity strings.
ui/​src/​utils/​getAppRoute.ts	Supports similarity route parameters.
ui/​src/​pages/​occurrences/​occurrences.tsx	Displays the similarity filter.
ui/​src/​pages/​occurrences/​occurrence-columns.tsx	Enables similarity sorting.
ui/​src/​pages/​occurrence-details/​occurrence-details.tsx	Adds the similar-occurrences link.
ui/​src/​components/​filtering/​filters/​occurrence-filter.tsx	Renders occurrence filter values.
ui/​src/​components/​filtering/​filter-control.tsx	Registers the occurrence filter.
requirements/​base.txt	Adds pgvector’s Python package.
docs/​claude/​reference/​feature-vectors.md	Documents vector storage and querying.
docs/​claude/​reference/​canonical-patterns.md	Records embedding patterns.
docs/​claude/​INDEX.md	Indexes the new documentation.
compose/​local/​postgres/​Dockerfile	Installs pgvector locally.
ami/​tests/​fixtures/​main.py	Adds an HTTP-free processing-service fixture.
ami/​ml/​test_detection_embeddings.py	Tests embedding storage and readers.
ami/​ml/​schemas.py	Adds embeddings to detection responses.
ami/​ml/​models/​pipeline.py	Stores embeddings from pipeline results.
ami/​ml/​models/​embedding.py	Defines the embedding model and storage logic.
ami/​ml/​models/​__init__.py	Exports the embedding model.
ami/​ml/​migrations/​0030_detection_embedding.py	Creates the embedding table and indexes.
ami/​ml/​migrations/​0029_enable_pgvector.py	Enables and validates pgvector.
ami/​ml/​embeddings/​writer.py	Matches and stores returned vectors.
ami/​ml/​embeddings/​reader.py	Adds bounded embedding query helpers.
ami/​ml/​embeddings/​__init__.py	Initializes the embeddings package.
ami/​main/​tests.py	Tests vector-only job filtering.
ami/​main/​test_visual_similarity.py	Tests similarity ordering and permissions.
ami/​main/​models.py	Adds similarity annotations and job matching.
ami/​main/​api/​views.py	Implements similarity-ordering API parameters.

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[mihow]

Claude says: Agreed that the link can lead to a 400 today, for an occurrence with no vector or with a vector only from a less common model. It is kept as it is on purpose: in this PR the link is the test surface for the stored vectors, and the planned follow-up on top of this PR adds which models have vectors to the occurrence details, with one "show similar" link per model that passes similarity_algorithm, so only routes that can succeed are shown. Leaving this thread open until that lands.

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Review comments at @ui/src/pages/occurrences/occurrence-columns.tsx:
- Line 30: Update the `columns` configuration in `occurrence-columns.tsx` so the
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⚙️ Run configuration

• Configuration used: defaults
• Review profile: CHILL
• Plan: Advanced
• Run ID: a75b1d0d-161d-49d9-89c0-82c1eaec6038

📥 Commits

Reviewing files that changed from the base of the PR and between aecbd8c and 9d545d3.

📒 Files selected for processing (28)

• ami/main/api/views.py
• ami/main/models.py
• ami/main/test_visual_similarity.py
• ami/main/tests.py
• ami/ml/embeddings/__init__.py
• ami/ml/embeddings/reader.py
• ami/ml/embeddings/writer.py
• ami/ml/migrations/0029_enable_pgvector.py
• ami/ml/migrations/0030_detection_embedding.py
• ami/ml/models/__init__.py
• ami/ml/models/embedding.py
• ami/ml/models/pipeline.py
• ami/ml/schemas.py
• ami/ml/test_detection_embeddings.py
• ami/tests/fixtures/main.py
• compose/local/postgres/Dockerfile
• docs/claude/INDEX.md
• docs/claude/reference/canonical-patterns.md
• docs/claude/reference/feature-vectors.md
• requirements/base.txt
• ui/src/components/filtering/filter-control.tsx
• ui/src/components/filtering/filters/occurrence-filter.tsx
• ui/src/pages/occurrence-details/occurrence-details.tsx
• ui/src/pages/occurrences/occurrence-columns.tsx
• ui/src/pages/occurrences/occurrences.tsx
• ui/src/utils/getAppRoute.ts
• ui/src/utils/language.ts
• ui/src/utils/useFilters.ts

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