[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 one per detection and algorithm, from any number of ML models side by side (for example a classifier backbone's 2,048 values and BioCLIP's 1,024). Each algorithm declares the length of its vectors when its processing service registers, so one algorithm's vectors can always be compared. Vectors follow the same rules as detections and classifications: the first one stored is kept, and results with a vector that cannot be stored are refused before anything is saved. The table and its indexes are laid out for the ways we already know vectors will be read, with measured query plans; a shared base model lets captures and taxa get their own vector tables later, and a reference document records the conventions.

Nothing changes for users yet: admins can inspect stored vectors, and that is all. Showing which ML models have vectors on an occurrence and filtering occurrences by them is the follow-up #1525, and sorting occurrences by visual similarity is #1509. Both are stacked on this branch.

List of Changes

#	Change (what it does for users and developers)	How (implementation)
1	Vectors sent with each detection are kept, without adding any prediction that could change an identification	DetectionResponse.embeddings (matches ami-data-companion #178); create_detection_embeddings() in ami/ml/embeddings/writer.py, called from save_results
2	One table holds vectors from any number of ML models per detection	DetectionEmbedding in ami/ml/models/embedding.py: detection, algorithm, job (set null on delete), project (the capture's), unsized halfvec, STORAGE EXTERNAL; unique on (detection, algorithm). An ML model with several outputs registers one algorithm per output; processing_services/README.md explains how
3	Every algorithm has one declared vector length, so its vectors can always be compared and indexed	new Algorithm.embedding_dimensions, declared in /info (or a pull-mode service's registration request) and recorded at registration. If a service later declares a different length, the registered length stays and a warning suggests a new algorithm key; vectors of the new length are then refused when saved. Mixed lengths would make similarity reads fail loudly (pgvector raises on distances between different lengths) rather than return wrong results, so one check before saving is the guard; a database-level variant was explored and left out. The recorded length is read-only in the admin and the API
4	Results with a vector that cannot be stored are refused before anything is saved, so a retry starts clean	validate_detection_embeddings() runs before create_detections and raises EmbeddingRefused: an algorithm the pipeline did not register, one that declares no length, a different length, values that are not finite or beyond half precision, two vectors from one algorithm on a detection, a vector on a detection without a box, or a capture without a project. Such a vector means the processing service and the pipeline's registration disagree, so every image would fail the same way: the job fails with the reason, and on the async path the message is acked so NATS does not redeliver it. Run a pipeline without the feature extractor, or fix the registration
5	Saving results again never overwrites a vector	the first vector for a (detection, algorithm) is kept: bulk_create(ignore_conflicts=True). To store new vectors, delete the detections or register the new ML model as a new algorithm. Rows are inserted in (detection, algorithm) order so concurrent workers cannot deadlock
6	Vectors land on the right detection even when some detections already existed	each vector is stored on the detection its response was saved as, from the (detection, response) pairs create_detections returns since #1528, never by position
7	Captures and taxa can get their own vector tables with little work	abstract BaseEmbedding (shared columns, reverse names algorithm.<table>s) and embedding_table_options() (the unique constraint and indexes for a target field); DetectionEmbedding is the first table; commented-out SourceImageEmbedding and TaxonEmbedding show the pattern
8	Admins can inspect stored vectors	read-only DetectionEmbedding admin: algorithm, vector length, job, project; the vector itself is never loaded
9	A normal processing job fills in missing vectors, the way it fills in missing classifications, and there is no separate vectors-only pipeline	filter_processed_images counts an algorithm that declares a vector length (Algorithm.writes_embeddings) like a classifier: a capture is sent again while one of its detections lacks that algorithm's vector, and a pipeline of a detector and feature extractors skips captures that already have every vector. The whole pipeline runs again on those captures; see the end-to-end runs for what that costs
10	Conventions for future vector storage are written down	docs/claude/reference/feature-vectors.md: storage, writing rules, the measured read patterns, precision, the query cache, adding a table for another target
11	One error for "a result came from an algorithm the service never declared"	get_known_algorithm() in ami/ml/models/algorithm.py, used for detections, classifications and vectors; BoundingBox.as_coords() is the one place that writes the [x1, y1, x2, y2] order
12	First install of pgvector, with a guard that explains itself. Split out to #1517, merged into main	ml/0030_enable_pgvector
13	Reading vectors: showing them on the occurrence page, filtering by them, the similarity sort. In the stacked PRs	each reader moves to the PR that first calls it: vector counts per algorithm and the representative vector of an occurrence to #1509, detections_missing_vectors to #1479. Reading one algorithm's vectors for a set of detections has no caller yet, so it waits for tracking or retraining. TestReadingVectors here shows the read pattern to copy

Related Issues

• Merge order: Enable the pgvector database extension for storing feature vectors #1517 (enable pgvector) and Save each species label on the box it was returned with when a batch mixes new and stored boxes #1528 (save each classification on the box it was returned with, fixing Stop saving a detection with another detection's classification #1436) are merged into main. Show post-processing results in the occurrence algorithm filter, and keep the occurrence result queries side by side #1518 (the occurrence algorithm and job filter queries) is independent, and Show which models have feature vectors on an occurrence, and filter occurrences by them #1525 (vectors on the occurrence page and in those filters) needs this PR and Show post-processing results in the occurrence algorithm filter, and keep the occurrence result queries side by side #1518 and merges last.
• Part of Occurrence Tracking is usable & available behind feature flag #1412 (tracking behind a per-project flag): tracking's appearance term will read its vectors from this table; Revive occurrence tracking as a post-processing task, behind a per-project opt-in flag #1272 currently stores its own copy on classifications and will switch before it merges. 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).
• Add feature vectors to existing detections, and reprocess detections without re-detecting #1464, adding vectors to existing detections: a normal processing job now does this (row 9), so draft Add feature vectors to existing detections from the admin, without detecting again #1479 (stacked here) shrinks to what that does not cover. The similarity sort is draft Sort occurrences by visual similarity to one occurrence #1509 (stacked here).
• Declare each model's vector length and add nearest-neighbour indexes when exact search gets slow #1500: nearest-neighbour indexes; the declared length it outlined is now in this PR.
• Found while testing this branch, not changed by it: Parallel result saving fails when two workers create the same new taxon at once #1529 (two workers creating the same new taxon at once).
• Processing-service side: Give every detection a feature vector from the pipeline's own feature extractor, in the API and the Antenna worker ami-data-companion#178 (stacked on Setup interface for jobs #177), which replaces Bump react-admin from 4.8.4 to 4.11.4 in /frontend #175.

Detailed Description

Data model

DetectionEmbedding is the only new table, and Algorithm gains one field. A vector hangs off a detection and names the algorithm that produced it, the job that stored it, and the project of its 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.

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)"
    Algorithm ||--o{ DetectionEmbedding : "produced by"
    Job |o--o{ DetectionEmbedding : "stored by (set null on delete)"
    Project ||--o{ DetectionEmbedding : "the capture's project"
    Algorithm ||--o{ Classification : "produced by"
    Algorithm ||--o{ Detection : "detected by"

    DetectionEmbedding {
        bigint id PK
        bigint detection_id FK "unique with algorithm"
        bigint algorithm_id FK
        halfvec vector "unsized, STORAGE EXTERNAL"
        bigint job_id FK "nullable"
        bigint project_id FK
    }
    Algorithm {
        bigint id PK
        string key
        string task_type "embedding for vector-only models such as BioCLIP"
        int embedding_dimensions "NEW: declared vector length, nullable"
    }

Loading

Indexes on DetectionEmbedding, built by embedding_table_options(): unique (detection, algorithm), for reads by detection and the cascade from detections; (project, algorithm, detection), for project-wide reads in detection order; (algorithm, detection), for one algorithm's vectors across projects and the cascade when an algorithm is deleted; and (job, detection) where a job is set, for a job's vectors and for clearing the job when it is deleted. The foreign keys have no extra single-column indexes, because these lead with them.

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

Each would be a subclass of BaseEmbedding with its own target foreign key, using embedding_table_options() for its constraint and indexes, rather than one table with a polymorphic target column, so foreign keys and cascades stay exact.

• SourceImageEmbedding: a vector for the whole capture, for finding similar frames (empty frames, lighting changes, a moved camera) or grouping captures. The project comes from the capture.
• OccurrenceEmbedding: one vector per occurrence for occurrence-level similarity without picking a representative detection at query time. It is derived data: tracking merges and splits occurrences, so these rows would be rebuilt from the detection vectors, which is why the source vectors stay on detections.
• 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). Open question: taxa belong to no project, so decide what its project column means before building it.

How vectors are stored, and why

• One table for every ML model, with one row per (detection, algorithm). Every read takes one algorithm, because vectors from different algorithms cannot be compared.
• halfvec, unsized, with a declared length per algorithm. Two bytes per value (4 KB for 2,048 values). Because each algorithm has one length, a per-algorithm index on vector::halfvec(N) is valid whenever we add one. 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 the capture's, with no fallback to the station's. Moving a station to another project does not move its vectors yet; that belongs with the move command (Method to move stations and data to another project #1192) and a project-consistency check (Add a direct project relationship on more models to aid scaling and portability  #1453).
• Writes take no lock of their own. One bulk_create per batch inserts rows in (detection, algorithm) order, which is what keeps two workers saving overlapping rows from deadlocking: TestConcurrentWriters fails with "deadlock detected" when the ordering is removed.

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 ML models (2,048 and 1,024 values): 449,160 rows. Median of 5 runs after a warm-up, measured at d98c022 on an earlier version of the table with the same indexes. The readers now ship with the PRs that use them.

Read pattern (who needs it)	Reader (PR)	Plan	Time
Vectors for the detections of two adjacent captures (tracking)	not shipped yet	index scan, unique index	< 0.1 ms
Vectors for 5,000 detections (retraining)	not shipped yet	sequential scan at this table size; forced onto (algorithm, detection), 12 ms	17-19 ms
A project's vectors in chunks of 2,000 (exports, clustering)	not shipped yet	index scan on (project, algorithm, detection), no sort	0.5 ms per chunk
Which algorithms have vectors in a project, and how many	vector_counts_by_algorithm (#1509)	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 an algorithm	detections_missing_vectors (#1479)	anti-join, index-only scan on (algorithm, detection), 0 heap fetches	22.5 ms
The same over a whole 179k-detection project	detections_missing_vectors (#1479)	parallel hash anti-join	138 ms

The length check before saving compares each vector with Algorithm.embedding_dimensions, which save_results has already loaded, so it costs no query. Nearest-neighbour (HNSW) indexes are not created; #1500 outlines when to add them.

End-to-end runs against the processing service

Async (NATS, parallel workers), at this head before the review fixes (b310d4c): a throwaway local stack with Celery at 8 worker processes (the production default) and two ami-data-companion #175 workers, one per GPU, with ee8c248 (which declares the vector lengths) and vectors switched on. 104 captures from one night; vectors from the species classifier's backbone (2,048 values). Registration recorded the declared length.

Run	Result (measured)
1. First write, in parallel	job SUCCESS, 104/104 captures; 1,001 real detections and 1,001 vectors, all 2,048 values, no duplicate rows, no deadlocks; no advisory or waiting locks in 432 samples of pg_locks taken every 0.5 s. Eight result tasks failed on a race creating taxa (#1529, not this PR) and NATS redelivered them; the retries stored no duplicate vectors
2. The same captures again, reprocessing on	job SUCCESS; 1,001 vectors sent and none written: a checksum over every row's vector, job and updated_at is identical before and after
3. Vectors deleted, then the Celery worker killed (SIGKILL) at 306 vectors, mid-save, and restarted	job SUCCESS, 1,001 vectors, none missing, no errors after the restart

Async (NATS), at e391785 with ami-data-companion #178 (one worker, BioCLIP 2.5 and the classifier backbone on), 2026-10-21: the same 104 captures, which already had 1,001 backbone vectors and no BioCLIP vectors.

Run	Result (measured)
A. A normal processing job fills in missing vectors	job SUCCESS in 52 s; 91 captures sent (the other 13 have no real detections); 1,001 BioCLIP vectors stored, all 1,024 values; no new detections. It also stored 744 classifications that repeat an existing one with the same taxon, with scores differing by about 0.00005 (GPU results vary slightly between runs, and the duplicate check compares exact scores)
C. The same job again	job SUCCESS, 0 captures sent, nothing written
B. A wrong length recorded (512), with 30 vectors missing on 3 captures	job FAILURE on the first result: "The results do not match the registered pipeline: Algorithm bioclip_2_5_embeddings is registered with 512-dimension vectors but sent one of length 1024." The message was acked, NATS reported no redeliveries, and nothing was written
Length restored, the same job again	28 of the 30 vectors stored. Two detections came back with boxes 1 pixel off, so they were saved as new detections with vectors and the originals stayed without one. The next job sent those 2 captures again, the boxes matched, and every detection then had its vector. The 2 near-duplicate detections remain

The repeat classifications and the near-duplicate boxes come from running the whole pipeline again on a fill-in, not from the vector storage; docs/claude/reference/feature-vectors.md records both. Sending the stored detections and skipping the detector on a fill-in would avoid them, and is open for discussion.

BioCLIP 2.5 (ViT-H/14) image encoder alone on one RTX 3090, random 224 x 224 inputs (measured): weights 2.4 GiB; fp32 about 51 crops/s at batch sizes 32 and 64, fp16 autocast 118-160 crops/s; a ResNet50 at the same size about 1,600 crops/s. So BioCLIP in fp32 adds about 20 ms per detection. Whether fp16 vectors agree closely enough with fp32 ones is not yet checked.

Synchronous /process, on an earlier head (3930783): 7 real test captures, a classifier backbone and BioCLIP side by side: every detection got one vector per ML model, reprocessing left identical checksums, and with the config flag removed no vectors were stored.

How to Test

1. Run migrations; ml/0031_detection_embedding creates the vector table and adds Algorithm.embedding_dimensions.
2. Backend tests: docker compose -f docker-compose.ci.yml run --rm django python manage.py test ami.ml.test_detection_embeddings ami.ml.tests, or the full suite.
3. Register a processing service that declares embedding_dimensions (ami-data-companion Update to new installation method for Poetry #178, with AMI_EMBEDDING_EXTRACTOR and optionally AMI_INCLUDE_FEATURES=true) and run a normal processing job. Every detection gets one row per algorithm in /admin/ml/detectionembedding/, which shows the algorithm, vector length and job, and cannot be edited.

Deployment Notes

• One migration, ml/0031_detection_embedding: creates the vector table and its indexes on an empty table, and adds a nullable column to ml_algorithm.
• Deploy the processing service first and register its pipelines again. Antenna refuses vectors from an algorithm that has not declared its length, which fails the job. Algorithms registered before this change have no length until their service registers again with ami-data-companion Update to new installation method for Poetry #178 or later. Services on ami-data-companion main send no vectors, so they are unaffected.

Checklist

• Migration included (ml/0031_detection_embedding); makemigrations --check passes
• Full backend suite at ce39e8d (with main): 982 tests pass, 2 skipped; makemigrations --check passes
• Concurrency: TestConcurrentWriters reproduces the deadlock without the row ordering and passes with it
• Run end to end in async mode with parallel workers, including a worker killed mid-save
• Run end to end in async mode after the review fixes: filling in missing vectors, a rerun that sends nothing, and a wrong recorded length that fails the job without redelivery
• Query plans measured with EXPLAIN (ANALYZE, BUFFERS) at a realistic size

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https://claude.ai/code/session_01RPrBDvE8e49rB4o811zHZi

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📒 Files selected for processing: 18

• ami/main/models.py
• ami/ml/admin.py
• ami/ml/embeddings/__init__.py
• ami/ml/embeddings/reader.py
• ami/ml/embeddings/writer.py
• ami/ml/migrations/0031_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/ml/tests.py
• ami/tests/fixtures/main.py
• config/settings/base.py
• docs/claude/INDEX.md
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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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[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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Actionable comments posted: 1

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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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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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[kra]

# We don't want to use `filter(~Exists(...))`, because django-cachalot 2.6 does not see the tables inside a
# negated Exists and would use stale data.

[kra]

Execption for when ... or Exception to be raised when... or To be raised when...

[kra]

eh, maybe I'm just unfamiliar with the code and it's obvious enough that this an exception and docstring is clearer as is.

[kra]

"refuse" is documenting code that isn't in this function.
We aren't seeing vectors here, we are seeing derived dat? Sets of the lengths of those vectors?
I use "given" for values that are passed in the arguments (ie we are assuming that the caller is giving us what they say they are, not looking them up ourselves), but this might be a personal idiom, so YMMV.

"""Raise if the given vector lengths for an algorithm differ from the given lengths already stored for that
algorithm, or from each other.
"""
# Vectors of different lengths can never be compared, so vectors produced by each algorithm must
# only be of one length.

[kra]

We keep on comparing members of a set to the first member and failing if they aren't the same? But it's a set?
Isn't the criterion

• set of lengths must have only one member
• single member of lengths must equal stored if stored is not None

if len(lengths) > 1:
    raise ...
stored = stored_by_algorithm[algorithm]
if stored is not None:
    if next(iter(lengths) != stored:
        raise...        

Semantically I would expect stored to not have a key for an item, instead of None for a value. This is a minor point and not something that this particular code location does.

[kra]

comment what you are expecting to happen here, raise if we don't like the vector lengths

[kra]

style: should this be called _validate...?

[mihow]

love it, _validate_embedding_dimensions it is

[kra]

minor, but these should be booleans, we never care how many there are

[kra]

it's good that we are emphasizing the reason/result of using a map, but this comment is describing the embedding structure and should be there - we are really documenting the keys (and the result of using a map)

# Map of (detection, algorithm) pairs to DetectionEmbedding
# (so a second vector added for a pair will replace the first)

[kra]

we could do these warnings outside of this block, after we discovered them, that would signal to the reader that the repeated/not_finite values were only needed for the warnings.

We could and should do the _check at the same time? Why set up the stack before checking? We are mixing format/validate with save?

[kra]

Text blob is hard to read, use linebreaks.

Document the arguments, what do they mean?

"from the pairs create_detections returns" - this is documenting remote code. Are you trying to say "saved_detections should be a list of ... pairs returned by create_detections"?

"saving the same results twice changes nothing, and a new vector from an algorithm replaces its old one" -> "Existing vectors for an algorithm are replaced."

"a vector can never land on another box" - I don't understand this, are you documenting something that is enforced elsewhere, either by how the arguments we get are created, or how we are stored? What is a "box"? Or are you describing the implication of "embeddings" being a map and what the keys are?

"as it does for classifications" - that documents remote code and will become incorrect if/when that changes

The logging seems weird to me, this may be just me being unfamiliar with the process, so if that's the correct response to the situation, OK, you don't have to explain to me here. Will users read these logs? In any case, if a vector is repeated or unstorable, we would know that when it was created. If vectors might be used for other reasons than being stored here, and repeated or unstorable vectors could be valid for those uses, then OK, but if vectors are created only to be stored (every set of vectors created is expected to end up here to be stored), should we perform the action (log or invalidate) at creation validation, rather than here when they are stored?

This function is two things, "format/validate input" and "store input"? Can these be split into two subfunctions so docstrings can describe what is happening? Otherwise, comment those blocks?