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 lays the foundation for storing 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. A shared base model lets captures and taxa get their own vector tables later, and a reference document records the conventions for what comes next (logits, reduced dimensions, nearest-neighbour indexes).

What users and admins see is small on purpose: the occurrence Fields tab says which models have vectors for an occurrence, the algorithm filter can find occurrences by a model that only produces vectors (such as BioCLIP), and admins can inspect stored vectors. Sorting occurrences by visual similarity, the first feature built on these vectors, is a separate pull request stacked on this one.

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	Captures and taxa can get their own vector tables with little work	abstract BaseEmbedding and BaseEmbeddingQuerySet hold the shared columns, saving and the length check; DetectionEmbedding is the first table; no schema change
4	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); the first write takes a transaction-scoped advisory lock so two workers cannot store different lengths
5	Saving the same results twice changes nothing; a new vector replaces the old one and records the job that produced it	BaseEmbeddingQuerySet.store() skips identical vectors and upserts the rest
6	Vectors land on the right detection even when some detections already existed	matched by capture and exact box coordinates (the identity detections are reused by), not by position; a box stored twice on one capture is skipped and logged
7	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
8	The occurrence Fields tab shows which models have feature vectors for the occurrence	embedding_algorithms on the occurrence detail response (one query; not in list or export responses); a "Feature vectors" row
9	The occurrence algorithm filter finds occurrences by models that only produce vectors, and offers those models as choices	a third Exists branch in the occurrence algorithm filter; Algorithm.objects.used_in_project() also reads the vector table
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	Admins can inspect stored vectors	read-only DetectionEmbedding admin: model, output key, vector length computed in SQL, job, project; the vector itself is never loaded
12	Conventions for future vector and model-output storage are written down	docs/claude/reference/feature-vectors.md: query patterns, anti-patterns, adding a sibling table, where logits and reduced-dimension vectors should go, when to add a nearest-neighbour index
13	First install of pgvector, with a guard that explains itself. Split out to #1517, merged into this branch	ml/0030_enable_pgvector checks for the 0.8 package before CREATE EXTENSION; the local and CI Postgres image installs pgvector from the PostgreSQL apt repository
14	Adding a feature extractor to a pipeline no longer makes every capture look unprocessed	filter_processed_images leaves algorithms with task type embedding out of the classifiers it waits for; before, a service listing an extractor in /info sent every capture through the whole pipeline again on each run
15	The job, algorithm and "used in project" filters all know every table where models leave output. The shared list is split out to #1518, merged into this branch; this PR adds the vector table to it	ami/ml/model_outputs.py (#1518) lists detections, classifications and algorithm results; this PR adds one DetectionEmbedding entry, and the registry's tests then cover vectors too

Related Issues

• Split for review: Enable the pgvector database extension for storing feature vectors #1517 (enable pgvector) and Make the occurrence algorithm and job filters search every model output table from one list #1518 (one list of model output tables) carry two parts of this PR on their own, each based on main. Both are merged into this branch, so its diff shrinks once they land; review those two first.
• 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); this PR follows the plan in Store, show and review what post-processing methods decide #1457 (comment).
• 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 open as draft Add feature vectors to existing detections from the admin, without detecting again #1479, stacked on this branch.
• Stacked on this branch: the similarity sort, draft Sort occurrences by visual similarity to one occurrence #1509.
• Declare each model's vector length and add nearest-neighbour indexes when exact search gets slow #1500 outlines a declared vector length per model output and nearest-neighbour indexes. Stop saving a detection with another detection's classification #1436 covers pairing results with detections by response order, which would let this writer pair by index too.
• Processing-service side: Give every detection a BioCLIP feature vector, and embed existing detections without re-detecting ami-data-companion#175.

Detailed Description

Data model

DetectionEmbedding is the only new table. It hangs off a detection and names the model that produced the vector, the job that stored it, and the project (copied from the capture, so project-wide reads need no join). Occurrences are reached through the detection rather than stored, because tracking merges and splits occurrences while the detection stays put. The shared columns live on the abstract BaseEmbedding, so later capture or taxon vector tables are siblings of the same shape.

erDiagram
    Project ||--o{ Deployment : has
    Deployment ||--o{ SourceImage : captures
    SourceImage ||--o{ Detection : "has boxes"
    Occurrence ||--o{ Detection : groups
    Detection ||--o{ Classification : "labelled by"
    Detection ||--o{ DetectionEmbedding : "NEW: vectors (one per algorithm and key)"
    Algorithm ||--o{ DetectionEmbedding : "produced by"
    Job |o--o{ DetectionEmbedding : "stored by (set null on delete)"
    Project ||--o{ DetectionEmbedding : "copied from the capture"
    Algorithm ||--o{ Classification : "produced by"
    Algorithm ||--o{ Detection : "detected by"
    Algorithm ||--o{ AlgorithmResult : "produced by"
    Occurrence ||--o{ AlgorithmResult : "recorded on"

    DetectionEmbedding {
        bigint id PK
        bigint detection_id FK "unique with algorithm and key"
        bigint algorithm_id FK
        string key "model output name, default 'embedding'"
        halfvec vector "unsized, STORAGE EXTERNAL; one length per algorithm and key"
        bigint job_id FK "nullable"
        bigint project_id FK
        datetime timestamp
    }
    Detection {
        bigint id PK
        bigint source_image_id FK
        bigint occurrence_id FK
        bigint detection_algorithm_id FK
        json bbox "vectors are matched to detections by capture and exact box"
    }
    Algorithm {
        bigint id PK
        string key
        string task_type "embedding for vector-only models such as BioCLIP"
    }

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Classification and AlgorithmResult are unchanged and shown for context: together with detections and vectors they are the four output tables listed in ami/ml/model_outputs.py, which the occurrence algorithm filter, the job filter and the "used in project" list read. Indexes on DetectionEmbedding: unique (detection, algorithm, key); (project, algorithm, key, detection); (algorithm, key, detection); and (job, detection) where a job is set.

Proposed: vectors for captures, occurrences and taxa (not in this PR)

How sibling tables for other targets could look, following the conventions in docs/claude/reference/feature-vectors.md. Each is a subclass of the abstract BaseEmbedding with its own target foreign key, its own unique (target, algorithm, key) constraint and its own indexes, rather than one table with a polymorphic target column, so foreign keys and cascades stay exact. Nothing here is built; the dotted lines mark the shared base columns, not database relations.

erDiagram
    BaseEmbedding {
        bigint algorithm_id FK "the model; vectors are only compared within one algorithm and key"
        string key "model output name"
        halfvec vector "unsized; one length per algorithm and key"
        bigint job_id FK "nullable"
        bigint project_id FK
        datetime timestamp
    }
    BaseEmbedding ||..o{ DetectionEmbedding : "shared columns (this PR)"
    BaseEmbedding ||..o{ SourceImageEmbedding : "shared columns (proposed)"
    BaseEmbedding ||..o{ OccurrenceEmbedding : "shared columns (proposed)"
    BaseEmbedding ||..o{ TaxonEmbedding : "shared columns (proposed)"

    Detection ||--o{ DetectionEmbedding : "unique (detection, algorithm, key)"
    SourceImage ||--o{ SourceImageEmbedding : "unique (source_image, algorithm, key)"
    Occurrence ||--o{ OccurrenceEmbedding : "unique (occurrence, algorithm, key)"
    Taxon ||--o{ TaxonEmbedding : "unique (taxon, algorithm, key, project)"
    SourceImage ||--o{ Detection : "has boxes"
    Occurrence ||--o{ Detection : groups

    SourceImageEmbedding {
        bigint source_image_id FK
        string key "e.g. whole-frame image vector"
    }
    OccurrenceEmbedding {
        bigint occurrence_id FK
        string key "how it was built, e.g. mean of detection vectors"
        int detection_count "detections it was built from"
    }
    TaxonEmbedding {
        bigint taxon_id FK
        bigint project_id FK "nullable: a prototype may be shared across projects"
        string key "e.g. text prompt vector or prototype from verified detections"
        int detection_count "nullable; verified detections it was built from"
    }

Loading

What each would be for, and the questions to settle first:

• SourceImageEmbedding: a vector for the whole capture, for finding similar frames (empty frames, lighting changes, a moved camera) or for grouping captures. It follows the detection pattern directly: the project comes from the capture.
• OccurrenceEmbedding: one vector per occurrence for fast occurrence-level similarity and clustering, without picking a representative detection at query time. It is derived data: tracking merges and splits occurrences, so these rows must be rebuilt from the detection vectors whenever an occurrence's detections change. That is why the source vectors stay on detections, and why this table records how it was built.
• TaxonEmbedding: prototypes for each species, for zero-shot prediction and for checking an identification against the species' typical appearance (Suggest species from image-text models by storing a vector for each taxon #1510). Either a text vector from an image-text model such as BioCLIP, or an average of verified detections' vectors. Open question: taxa are not project-scoped, so the project column may need to be nullable, which means overriding or relaxing the base field (noted in the reference doc).

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. Measured at d98c022, after the move to the ml app and the index changes; the later commits change no index used below. Two reads added later are not measured: the algorithm filter's choices (one project's range of the (project, algorithm, key, detection) index) and the occurrence filter's third EXISTS branch (a probe of the unique index per occurrence).

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; forced onto (algorithm, key, detection), 12 ms	17-19 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)

The occurrence algorithm filter, measured. Medians of 5 EXPLAIN (ANALYZE, BUFFERS) runs, read-only. They used a developer machine's copy of production data (projects of 179k and 45k occurrences, main tables only) and a small integration database that has the vector and result tables (21k detections, 30k vectors). Selecting one classifier on the 45k-occurrence project: page 1 takes about 0.8 ms on main, about 240 ms with one EXISTS per output table joined by OR, and about 0.8 ms now. The OR figure is approximated, because the production copy has no vector table. The counts are 223 ms, 461 ms and 138 ms. On the 179k-occurrence project, the OR shape took about 700 ms to count and the current shape takes 187 ms. A selection that mixes a classifier and an embedding model still needs two OR terms, and page 1 takes 19.5 ms on the small database. Timings for the vector and result tables at production scale are not measured, because neither table exists in the production copy yet.

Nearest-neighbour (HNSW) indexes are not created; in an experiment one 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 they are not worth it at today's sizes. #1500 outlines when to add them, together with a declared length per model output.

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. This PR adds the shared abstract base (BaseEmbedding, BaseEmbeddingQuerySet) with DetectionEmbedding as its first table, so a capture or taxon vector table is a new model plus a migration; the doc lists the steps.
• Where model outputs go: anything that is searched, filtered or indexed gets a typed table (vectors here, labels in Classification); figures that are only shown or kept for the record go in AlgorithmResult (New home for algorithm results that are not species classifications #1461). Vectors never go in a result's data field. This follows the plan on Store, show and review what post-processing methods decide #1457: Store, show and review what post-processing methods decide #1457 (comment)

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 an earlier head of this branch (3930783); the save path is the same apart from where the code lives, the per-output length rule, and box matching, which was then by rounded coordinates and is now exact.

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

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 installs pgvector from the PostgreSQL apt repository (0.8 today), and run migrations. ml/0030_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.ml.tests ami.main.tests.TestOccurrenceJobFilter, 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 open one of its occurrences: the Fields tab lists the models under "Feature vectors".
4. In the occurrence list, filter by an embedding-only model (for example BioCLIP): only occurrences with its vectors remain, and the model appears in the filter's choices.
5. Open /admin/ml/detectionembedding/: rows show the model, key, vector length and job, and cannot be added or edited.

Screenshots

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/0030 stops before any SQL with a message that says so. If an administrator has already created the extension at 0.8 or later, the migration leaves it alone; it upgrades an existing extension in place (ALTER EXTENSION vector UPDATE) only when the installed version is older than 0.8, because that statement requires owning the extension.

No data is backfilled: the table starts empty and fills as pipelines that return vectors run. The occurrence algorithm filter and its list of choices now also match and list models that stored vectors.

Checklist

• Migrations included (ml/0030, ml/0031); makemigrations --check passes
• Full backend suite at dc28122, run serially: 899 tests pass, 2 skipped. UI lint and tsc are clean and jest passes (73 tests). CI passes at the current head.
• Integration run with the stacked pull requests (Sort occurrences by visual similarity to one occurrence #1509, Add feature vectors to existing detections from the admin, without detecting again #1479) merged on top of 7061d28: full backend suite 1,073 tests pass, 2 skipped; makemigrations is clean; the stacked task stores vectors once and a repeated run sends nothing.
• Query plans measured with EXPLAIN (ANALYZE, BUFFERS) at a realistic size (the two reads noted above are not)
• 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)
• Screenshot added
• Operations: install pgvector 0.8 on every Postgres server before deploy

🤖 Generated with Claude Code

https://claude.ai/code/session_0121zMVjnPsqeDFSBXRCPvMy

Summary by CodeRabbit

• New Features
  • Detection feature vectors are stored and can be retrieved by algorithm and vector key.
  • Occurrence details show which algorithms have feature vectors, or indicate when none are available.
  • Occurrence and job filters recognize records linked through feature vectors.
• Bug Fixes
  • Occurrence exports omit feature-vector algorithm labels.
  • Images with completed classifications are no longer treated as needing reprocessing just because embedding results are missing.
• Documentation
  • Added guidance on feature-vector storage, retrieval, and querying.

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📝 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: 🟡 Moderate · up to 8cd9a

Concurrent embedding jobs may fail, and invalid embedding results may leave detections without later processing results. Stabilize lock ordering and address partial saves before merging.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 39.58% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 144 functions across 33 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Title check		The title clearly and concisely summarizes the main change: storing feature vectors for detections and supporting indexed reads.
Description check		The description is complete and well aligned with the repository template. It covers the summary, changes, related issues, implementation details, testing, screenshots, deployment requirements, and ch…

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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/0030_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.

[mihow]

Claude says: This line now lives in #1517, which splits the pgvector setup out of this PR. The pin there is pgvector==0.5.1, the current release of the Python client (pgvector-python); the 0.8 minimum applies to the server extension and is enforced by the migration. #1517's description explains the two version numbers.

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[mihow]

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 New home for algorithm results that are not species classifications #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 New home for algorithm results that are not species classifications #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).

[Copilot]

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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[coderabbitai]

Actionable comments posted: 1

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• 🪄 Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @ui/src/pages/occurrences/occurrence-columns.tsx:
- Line 30: Update the `columns` configuration in `occurrence-columns.tsx` so the
Snapshots column is sortable only when a non-empty `similar_to` filter is
active. Pass that state from `occurrences.tsx` when calling `columns`, and leave
`sortField` undefined on ordinary occurrences pages.

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• 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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[mihow]

Claude says: On the outside-diff comment about making the writes in save_results (ami/ml/models/pipeline.py) atomic: I have left this as it is, for these reasons.

• This is the same shape as before this PR. An unregistered classification algorithm already makes create_classifications raise PipelineNotConfigured after create_detections has committed the detections. The vector writer is placed before classifications on purpose, so that an unregistered embedding algorithm stops the batch at the same point.
• A partly saved batch does not duplicate on retry. create_detections reuses a detection with the same capture and exact box, and the null-detection marker that records a capture as processed is written last (Fix captures are marked as processed with zero detections when they shouldn't be #1310), so a capture whose save failed is picked up again on the next run. Both failures this PR adds (an unregistered algorithm, a vector length that differs from the stored one) are configuration errors, so a retry fails the same way until the configuration is fixed, with or without a transaction.
• Wrapping the whole results save in one transaction would change how long every pipeline's results path holds locks on detection and occurrence rows. That decision is about the results path as a whole and is worth making on its own, separately from this PR.

[mihow]

make this a test for an "excluded field"

[mihow]

Claude says: Done in a7fdab2. The test is now test_json_export_leaves_out_the_excluded_fields. It checks that no exported row carries any field in EXPORT_EXCLUDED_FIELDS, and that the vector table is never queried, even when vectors exist.

[mohamedelabbas1996]

Found an edge case in filter_processed_images: if a pipeline is just a detector + an embedding algorithm with no classifier, it never stops reprocessing. pipeline_classifier_ids ends up empty since embedding types are excluded now, so it hits the early-return and yields every image every time, even ones that already have a stored vector.

[mihow]

Claude says: Fixed in 394d0ef. A pipeline with a detector and feature extractors but no classifier now treats a capture as processed once every real detection on it has a vector from each extractor in the pipeline, so those captures stop being reprocessed. It costs one extra query per batch of captures, and only for pipelines without a classifier. test_filter_processed_images_with_only_a_detector_and_an_extractor_waits_for_vectors checks both sides: the capture is yielded while it has no vector and skipped once it has one.

For a pipeline that also has classifiers, an extractor is still not waited for (94bc8ad), so missing vectors there are backfilled by the "Add feature vectors" task (#1479) rather than by running the whole pipeline again.

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⚠️ Outside diff range comments (1)

🟡 Minor · Acquire first-write locks in a stable order. · writer.py:133-134

ami/ml/embeddings/writer.py:133-134
🩺 Stability & Availability | 🟡 Minor | ⚡ Quick win

Acquire first-write locks in a stable order.

If concurrent batches contain the same new pairs in different response orders, each transaction can hold one advisory lock while waiting for the other. PostgreSQL then aborts one transaction with a deadlock. Sort first_writes by (algorithm.pk, key) before acquiring locks.

Proposed fix

-            for algorithm, key in first_writes:
+            for algorithm, key in sorted(first_writes, key=lambda pair: (pair[0].pk, pair[1])):
                 _lock_first_write(algorithm, key)

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Review comment at @ami/ml/embeddings/writer.py around lines 133 - 134:
Sort first_writes by algorithm.pk and key before the lock-acquisition loop in
the writer flow, so _lock_first_write acquires advisory locks in a consistent
order.

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Outside diff comments:
Review comments at @ami/ml/embeddings/writer.py:
- Around line 133-134: Sort first_writes by algorithm.pk and key before the
lock-acquisition loop in the writer flow, so _lock_first_write acquires advisory
locks in a consistent order.

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[mohamedelabbas1996]

Noticed three places check "did this algorithm or job make a result" by listing detections, classifications, and vectors separately each time. Works fine now, but it's easy to update one and forget the others if a new result table shows up later.

Not blocking, just a thought: could split this into one method on the algorithm for "which algorithms have results," and one method on the job for "which occurrences did this job touch." Both would still need the same underlying list of tables underneath them, so worth keeping that in one shared spot rather than copying it twice.

[mihow]

Claude says: On the outside-diff note about the order in which first-write locks are taken (ami/ml/embeddings/writer.py): fixed in 8a0f9f7. The locks are now taken sorted by (algorithm id, key), so two batches that share new pairs cannot each hold one lock and wait for the other.

Also, a note on 394d0ef (in reply to an inline comment): a pipeline with a detector and feature extractors but no classifier now counts a capture as processed once each of its real detections has a vector from every extractor in the pipeline.

[mihow]

Claude says: Done in a6f8b79, 6fa77d0 and 0048796. The tables that record what an algorithm or job produced are now listed once, in ami/ml/model_outputs.py. The occurrence job filter, the occurrence algorithm filter and used_in_project are all built from that list. As a result, an algorithm that only wrote algorithm results now shows up in the algorithm filter; before, it did not. A test fails when a model with foreign keys to both an algorithm and a job is neither listed nor explicitly exempted.

While doing this we found that ORing one EXISTS per table made the algorithm filter slow: page 1 on a mid-sized project went from about 0.7 ms to about 240 ms on a copy of production data. The filter now first looks up which tables hold rows for the selected algorithms (one small cached query) and builds terms only for those. Selecting one classifier or detector is therefore again a single EXISTS, as on main. Classifications stay in the same EXISTS as detections for the same reason. The measurements are in the PR description.