Let each vectorizer choose its own provider and model by dpage · Pull Request #74 · pgEdge/pgedge-vectorizer

dpage · 2026-09-09T12:43:25Z

Summary

pgedge_vectorizer.model was one GUC applied to every table, so a database could not embed a table of short product titles differently from a table of long technical documents, nor send one table to Ollama locally whilst another went to a hosted provider.

A vectorizer records its own provider and model in pgedge_vectorizer.vectorizers, both nullable, NULL meaning inherit. Inheritance resolves when the work runs rather than being copied at creation, so an installation that sets neither is unaffected. Pin them at enable_vectorization(), or change them later with the new set_embedding_model().
The provider interface takes the model explicitly and the four implementations stop reading the GUC. Setting the GUC around each request would have been a smaller diff, but it means mutating global state inside a loop whose error paths are routine.
The worker resolves inheritance in the query that fetches a batch, so the rule lives in one place and an item whose vectorizer has since been disabled falls back through the same expression. A batch is selected by age across every vectorizer at once, so it can hold several models, and a request carries one: the batch is grouped by (provider, model) before the existing loop and batch_extent() breaks on the same key. Sorting rather than merely breaking the run matters, since two tables' items alternating in time would otherwise give requests of one item each.

Changing the model on a populated vectorizer

set_embedding_model() refuses unless force_reembed => true. The refusal keys on the model changing, not the dimension changing, which is the case worth guarding: a dimension change is already caught before any write by worker.c:1512, whilst swapping between two models of the same width, say text-embedding-3-small and text-embedding-ada-002 at 1536 each, would leave the old vectors in place, correctly shaped and meaningless beside the new ones, with nothing reporting a problem.

With the flag: embeddings cleared, column rewidened if needed, queue cleared, every chunk requeued, one transaction. Chunk rows, token counts, sparse embeddings and BM25 statistics are untouched, because none of them depends on the embedding model, which is also why this does not go near recreate_chunks().

Three things that fell out

generate_embedding() and detect_embedding_dimension() take an optional provider and model, and can no longer be STRICT, since NULL has to reach the function to mean "use the GUC". Behaviour change: generate_embedding(NULL) now raises instead of returning NULL, which is what the function always meant to do and said so in its own code, unreachably. Changelogged.
The array of chunk tables disable_vectorization() drops was collected with no ORDER BY, so its notices came out in scan order; adding registry columns changed it. Ordered now, in both scripts.
An upgrade bug pg_regress structurally cannot catch. Adding defaulted parameters to enable_vectorization() with CREATE OR REPLACE defines a second function rather than replacing the old one, so an upgraded install had two overloads, an eight-argument call could reach a body that knew nothing of the new columns, and COMMENT ON FUNCTION failed as ambiguous. A fresh install was perfect throughout, because pg_regress installs whatever default_version says and never runs the upgrade script.

Test plan

per_table_model regression test: provider resolution by name, the new registry columns, enable_vectorization() with and without an override and via named notation, set_embedding_model()'s error for an unregistered vectorizer, the no-op when effective values do not move, reverting to the GUC with NULL, the refusal on a populated table, and force_reembed clearing embeddings, rewidening the column to vector(768), requeuing, and leaving token_count and sparse_embedding intact.
011_per_table_model.pl: a fake provider records every request; two vectorizers, one pinned and one inheriting, populated in a single transaction so one poll sees both. Asserts the batch is split into one request per model, each carrying its own table's three chunks. Without the change this is a single request of six. A third registry row names the inheriting table's chunk table, since chunk_table is not a key, and the same assertions catch the duplicate rows a plain join would return.
012_upgrade_1_1_to_1_2.pl: builds a 1.1 installation with a real vectorizer and chunks, upgrades it, and compares functions, table columns and views against a fresh 1.2. Verified to fail hard with the DROP FUNCTION removed, and to pass with it, so it guards the class rather than the instance.
Full suite green on PostgreSQL 18.4: 22 pg_regress tests, 83 TAP tests.

Known limitations, deliberate

A vectorizer that inherits still follows the GUC, so changing pgedge_vectorizer.model globally re-points every inheriting table with no guard and no re-embed. That is today's behaviour, unchanged; documented in configuration.md with the advice to pin the model where embeddings matter. Logged as Changing pgedge_vectorizer.model silently re-points every inheriting vectorizer #75.
The provider rate-limit cooldown is still global, so one provider limiting us holds off requests to another. Pre-existing, and only reachable now that two providers can be in play at once. Logged as The worker treats a pull of queue items as belonging to one provider #76.
A model wider than 2000 dimensions cannot be used at all, because the HNSW index enable_vectorization() creates does not support one. Pre-existing and unrelated to this change, but it surfaced while testing and is now written down in best_practices.md.

Note on the base branch

Cut from token-count-consistency (#72), which creates the 1.2 scripts this also lands in, so the diff below carries that commit too. It targets main because CI only runs on PRs based on main, master or develop. The diff reduces to this feature once #72 merges. Independent of #73.

Closes #27

codacy-production · 2026-09-09T12:44:18Z

Up to standards ✅

🟢 Issues 5 medium

Results:
5 new issues

Category Results

Complexity 5 medium

View in Codacy

🟢 Metrics 29 complexity · 0 duplication

Metric Results

Complexity 29

Duplication 0

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Makefile
docs/api_reference.md
docs/changelog.md
docs/monitoring.md
sql/pgedge_vectorizer--1.1--1.2.sql
sql/pgedge_vectorizer--1.2.sql
src/worker.c
test/sql/delete_truncate.sql
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📝 Walkthrough

Walkthrough

The extension version changes to 1.2. Vectorizers can store provider and model overrides or inherit global settings. Embedding generation and dimension detection accept optional overrides. Model changes support validation, dimension changes, clearing, and re-queueing. Queue workers group requests by provider and model. A shared count_tokens() function supports chunking and BM25 statistics. New SQL, Perl, and upgrade tests cover these behaviors.

Fixed issue severity: Medium

Priority: ➖ Normal

Severity of issue fixed: Medium

Merge Risk: ⚪ Minimal · up to 0c154

This change adds per-vectorizer provider and model overrides with a forced re-embedding path for populated vectorizers. No concrete merge-blocking risk remains in the supplied change context.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Out of Scope Changes check	⚠️ Warning	The changes include unrelated token-counting functionality and associated documentation and tests, including count_tokens() and token-count consistency coverage. Ordered disable notices are also unrel…	Move the token-counting and unrelated notice-ordering changes to issue `#72` or link `#72` as an accepted scope for this pull request. Keep only changes required for per-vectorizer provider/model selection, populated-table model changes, compat…

✅ Passed checks (4 passed)

Check name	Status	Explanation
Linked Issues check	✅ Passed	The changes satisfy issue `#27`. Vectorizers store nullable provider and model overrides, workers group requests by provider and model, and populated vectorizers require forced re-embedding with dimensi…
Docstring Coverage	✅ Passed	Docstring coverage is 90.91% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 22 functions across 10 files. (1 skipped: 1…
Title check	✅ Passed	The title clearly and concisely describes the main change: allowing each vectorizer to select its own provider and model.
Description check	✅ Passed	The description directly explains per-vectorizer provider and model selection, model changes, re-embedding behavior, upgrade handling, and test coverage.

Full details: Out of Scope Changes check

Explanation

The changes include unrelated token-counting functionality and associated documentation and tests, including count_tokens() and token-count consistency coverage. Ordered disable notices are also unrelated to issue #27. The description states these changes come from base work in #72, but #72 is not linked here.

Resolution

Move the token-counting and unrelated notice-ordering changes to issue #72 or link #72 as an accepted scope for this pull request. Keep only changes required for per-vectorizer provider/model selection, populated-table model changes, compatible upgrades, and their tests.

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⚠️ Outside diff range comments (1)

src/worker.c (1)
1892-1896: 🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win

Update this warning text for per-vectorizer models.

The message tells the operator to reconfigure pgedge_vectorizer.model. With this change, the effective model can come from the vectorizer's own model column, so changing the GUC has no effect for that table. Name pgedge_vectorizer.set_embedding_model(..., force_reembed => true) as the corrective action, and report the model that was actually used, which is available as models[batch_start].
🤖 Prompt for AI Agents
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In `@src/worker.c` around lines 1892 - 1896, Update the dimension-mismatch warning
in the worker’s embedding path to report the actual model from
models[batch_start] and direct operators to use
pgedge_vectorizer.set_embedding_model(..., force_reembed => true) rather than
reconfiguring the GUC. Preserve the existing table, returned-dimension, and
expected-dimension details.

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only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.

Inline comments:
In `@docs/best_practices.md`:
- Around line 17-19: Update the later Data Management dimension-change guidance
to align with force_reembed: direct users to use the per-vectorizer re-embedding
operation that preserves chunk state, or explicitly limit the drop-and-recreate
instructions to global GUC changes without such an operation. Remove any
conflicting recommendation to drop the chunk table for force_reembed-based
changes.

In `@docs/configuration.md`:
- Around line 59-65: Update the configuration documentation around
set_embedding_model to explain that NULL resets only the specified override;
show provider => NULL when resetting the provider as well, and include
force_reembed => true when resetting the model changes the effective model for a
populated vectorizer.

In `@sql/pgedge_vectorizer--1.2.sql`:
- Around line 753-757: Update set_embedding_model in both
sql/pgedge_vectorizer--1.2.sql (lines 753-757) and
sql/pgedge_vectorizer--1.1--1.2.sql (lines 935-939): move new_dim resolution,
current_dim lookup, and the embedding ALTER COLUMN outside the chunk_count > 0
block, while keeping embedding clearing and requeue logic inside it.
- Around line 786-790: Update both set_embedding_model() requeue INSERT
statements to include the queue’s max_attempts column and populate it from
current_setting('pgedge_vectorizer.max_retries')::INT, ensuring model-change
jobs use the configured retry limit.

In `@src/provider_gemini.c`:
- Around line 153-156: Update gemini_generate_batch() to escape model with
provider_escape_json_string() before inserting it into the JSON request body,
and percent-encode model as a single URL path segment before constructing the
Gemini request URL. Preserve the existing model value for input while ensuring
quotes, control characters, slashes, and query characters cannot alter the JSON
or request path.

In `@src/provider_ollama.c`:
- Line 106: Update the Ollama JSON body construction near model insertion to
pass the effective model through provider_escape_json_string(), use the escaped
value when appending the JSON, and free it after use. Preserve the existing
model source precedence and ensure quotes, backslashes, and control characters
produce valid JSON.

In `@src/provider_openai.c`:
- Line 154: Update provider_build_openai_request() to pass the model value
through provider_escape_json_string() before appendStringInfo() constructs the
request body, while preserving the existing escaping of other inputs and
avoiding direct insertion of the per-vectorizer model override.

In `@src/worker.c`:
- Around line 1768-1770: Update the worker’s rate-limit handling after
sort_batch_by_model so rate_limit_deferrals and failure scheduling apply only to
rows belonging to the provider that was actually rate limited. Requeue rows for
unrelated provider groups safely, restoring their queued state without
incrementing their deferral counters or setting next_retry_at to NULL.
- Around line 1768-1770: Update process_queue_batch and provider_begin_cooldown
to track cooldown deadlines per provider rather than using the shared
provider_cooldown_until value. When selecting or polling work, consult only the
cooldown associated with that work’s provider so a rate-limited provider does
not delay runnable work for unrelated providers.
- Around line 1647-1652: Update the worker query’s provider and model
expressions to convert empty-string values to NULL with NULLIF before applying
COALESCE, matching the inheritance behavior of resolve_provider() and
resolve_model(). Preserve the existing GUC fallbacks for both provider and
model.

---

Outside diff comments:
In `@src/worker.c`:
- Around line 1892-1896: Update the dimension-mismatch warning in the worker’s
embedding path to report the actual model from models[batch_start] and direct
operators to use pgedge_vectorizer.set_embedding_model(..., force_reembed =>
true) rather than reconfiguring the GUC. Preserve the existing table,
returned-dimension, and expected-dimension details.

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Makefile
docs/api_reference.md
docs/best_practices.md
docs/changelog.md
docs/configuration.md
pgedge_vectorizer.control
sql/pgedge_vectorizer--1.1--1.2.sql
sql/pgedge_vectorizer--1.2.sql
src/embed.c
src/pgedge_vectorizer.h
src/provider_gemini.c
src/provider_ollama.c
src/provider_openai.c
src/provider_voyage.c
src/tokenizer.c
src/worker.c
test/sql/count_tokens.sql
test/sql/embedding.sql
test/sql/per_table_model.sql
test/t/011_per_table_model.pl
test/t/012_upgrade_1_1_to_1_2.pl

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Widening where the model comes from, from one GUC to a registry column and a SQL argument, broke assumptions in several places that were safe whilst it was a single trusted setting. The model was interpolated raw into every provider's request body, and into Gemini's URL path. Escaped now in all four, once in the shared OpenAI-format builder that OpenAI and Voyage use and once each in Ollama and Gemini, with a percent-encoding helper for the URL segment, where a '/' or '?' would have sent the request to a different endpoint. A rate limit deferred every remaining item in the pull and charged each one a deferral, which was right whilst a pull could only carry one provider's work. Now that a batch can span providers, that spent deferrals belonging to a provider that had not refused anything and, once they ran out, failed its items outright. Only the refused provider's items are deferred; the rest go back to pending uncharged for the next pull. set_embedding_model() computed the new dimension and altered the column only when the vectorizer had chunks, so an empty one kept the width it was created with and failed every embedding written after the change, which is precisely the failure the function exists to prevent. The dimension is resolved and the column altered whichever it is, whilst clearing embeddings and requeuing stay conditional. That does mean a call without an explicit dimension always probes the provider, as enable_vectorization() does, which the reference now states. The requeue omitted max_attempts, taking the column default of 3 rather than pgedge_vectorizer.max_retries as every other queue insert does. The worker's inheritance expression handled NULL but not an empty string, which resolve_provider() and resolve_model() in embed.c both treat as inherit; NULLIF puts them on the same footing. Two documentation corrections. The data-management guidance still told users to drop the chunk table and enable vectorization again after a dimension change, which throws away chunks, sparse embeddings and BM25 statistics that were never wrong. And the configuration example passed NULL as the model without saying that the provider defaults to NULL too, so it reverts alongside unless named again. Raised by CodeRabbit on #74.

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Inline comments:
In `@test/sql/per_table_model.sql`:
- Around line 134-136: Update the set_embedding_model call for ptm_named to
explicitly restore the provider override by passing provider => 'openai'
alongside the existing model and embedding_dimension arguments, preserving the
intended pinned-provider state for the subsequent reset case.

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docs/configuration.md
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sql/pgedge_vectorizer--1.2.sql
src/provider_common.c
src/provider_common.h
src/provider_gemini.c
src/provider_ollama.c
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The call restoring ptm_named's model left provider at NULL, so the reset case that follows started from a vectorizer with nothing pinned and could not show that the provider clears alongside the model. Pinning both first makes the assertion mean something. The provider named matches the GUC, so the effective values still do not move and the case remains the no-op it is there to cover. Raised by CodeRabbit on #74.

Widening where the model comes from, from one GUC to a registry column and a SQL argument, broke assumptions in several places that were safe whilst it was a single trusted setting. The model was interpolated raw into every provider's request body, and into Gemini's URL path. Escaped now in all four, once in the shared OpenAI-format builder that OpenAI and Voyage use and once each in Ollama and Gemini, with a percent-encoding helper for the URL segment, where a '/' or '?' would have sent the request to a different endpoint. A rate limit deferred every remaining item in the pull and charged each one a deferral, which was right whilst a pull could only carry one provider's work. Now that a batch can span providers, that spent deferrals belonging to a provider that had not refused anything and, once they ran out, failed its items outright. Only the refused provider's items are deferred; the rest go back to pending uncharged for the next pull. set_embedding_model() computed the new dimension and altered the column only when the vectorizer had chunks, so an empty one kept the width it was created with and failed every embedding written after the change, which is precisely the failure the function exists to prevent. The dimension is resolved and the column altered whichever it is, whilst clearing embeddings and requeuing stay conditional. That does mean a call without an explicit dimension always probes the provider, as enable_vectorization() does, which the reference now states. The requeue omitted max_attempts, taking the column default of 3 rather than pgedge_vectorizer.max_retries as every other queue insert does. The worker's inheritance expression handled NULL but not an empty string, which resolve_provider() and resolve_model() in embed.c both treat as inherit; NULLIF puts them on the same footing. Two documentation corrections. The data-management guidance still told users to drop the chunk table and enable vectorization again after a dimension change, which throws away chunks, sparse embeddings and BM25 statistics that were never wrong. And the configuration example passed NULL as the model without saying that the provider defaults to NULL too, so it reverts alongside unless named again. Raised by CodeRabbit on #74.

The call restoring ptm_named's model left provider at NULL, so the reset case that follows started from a vectorizer with nothing pinned and could not show that the provider clears alongside the model. Pinning both first makes the assertion mean something. The provider named matches the GUC, so the effective values still do not move and the case remains the no-op it is there to cover. Raised by CodeRabbit on #74.

@ibrarahmad

The worker resolved each item's provider and model through a plain left join from the queue to pgedge_vectorizer.vectorizers on chunk_table, which is not a key: the column carries no unique constraint, enable_vectorization() accepts an explicit chunk_table_name, and the generated default can collide of its own accord. Two registry rows naming one chunk table therefore returned a row per match, so a single queued item was embedded once per model and the later write won, with nothing reporting a problem. The lookup is a LATERAL ... LIMIT 1 now, ordered by id so that the vectorizer which claimed the name first wins, and which cannot multiply rows whatever the registry holds. 011_per_table_model adds a second registry row against the inheriting table's chunk table; without this change the batch is split three ways rather than two. Raised by @ibrarahmad on #74.

Widening where the model comes from, from one GUC to a registry column and a SQL argument, broke assumptions in several places that were safe whilst it was a single trusted setting. The model was interpolated raw into every provider's request body, and into Gemini's URL path. Escaped now in all four, once in the shared OpenAI-format builder that OpenAI and Voyage use and once each in Ollama and Gemini, with a percent-encoding helper for the URL segment, where a '/' or '?' would have sent the request to a different endpoint. A rate limit deferred every remaining item in the pull and charged each one a deferral, which was right whilst a pull could only carry one provider's work. Now that a batch can span providers, that spent deferrals belonging to a provider that had not refused anything and, once they ran out, failed its items outright. Only the refused provider's items are deferred; the rest go back to pending uncharged for the next pull. set_embedding_model() computed the new dimension and altered the column only when the vectorizer had chunks, so an empty one kept the width it was created with and failed every embedding written after the change, which is precisely the failure the function exists to prevent. The dimension is resolved and the column altered whichever it is, whilst clearing embeddings and requeuing stay conditional. That does mean a call without an explicit dimension always probes the provider, as enable_vectorization() does, which the reference now states. The requeue omitted max_attempts, taking the column default of 3 rather than pgedge_vectorizer.max_retries as every other queue insert does. The worker's inheritance expression handled NULL but not an empty string, which resolve_provider() and resolve_model() in embed.c both treat as inherit; NULLIF puts them on the same footing. Two documentation corrections. The data-management guidance still told users to drop the chunk table and enable vectorization again after a dimension change, which throws away chunks, sparse embeddings and BM25 statistics that were never wrong. And the configuration example passed NULL as the model without saying that the provider defaults to NULL too, so it reverts alongside unless named again. Raised by CodeRabbit on #74.

The call restoring ptm_named's model left provider at NULL, so the reset case that follows started from a vectorizer with nothing pinned and could not show that the provider clears alongside the model. Pinning both first makes the assertion mean something. The provider named matches the GUC, so the effective values still do not move and the case remains the no-op it is there to cover. Raised by CodeRabbit on #74.

Widening where the model comes from, from one GUC to a registry column and a SQL argument, broke assumptions in several places that were safe whilst it was a single trusted setting. The model was interpolated raw into every provider's request body, and into Gemini's URL path. Escaped now in all four, once in the shared OpenAI-format builder that OpenAI and Voyage use and once each in Ollama and Gemini, with a percent-encoding helper for the URL segment, where a '/' or '?' would have sent the request to a different endpoint. A rate limit deferred every remaining item in the pull and charged each one a deferral, which was right whilst a pull could only carry one provider's work. Now that a batch can span providers, that spent deferrals belonging to a provider that had not refused anything and, once they ran out, failed its items outright. Only the refused provider's items are deferred; the rest go back to pending uncharged for the next pull. set_embedding_model() computed the new dimension and altered the column only when the vectorizer had chunks, so an empty one kept the width it was created with and failed every embedding written after the change, which is precisely the failure the function exists to prevent. The dimension is resolved and the column altered whichever it is, whilst clearing embeddings and requeuing stay conditional. That does mean a call without an explicit dimension always probes the provider, as enable_vectorization() does, which the reference now states. The requeue omitted max_attempts, taking the column default of 3 rather than pgedge_vectorizer.max_retries as every other queue insert does. The worker's inheritance expression handled NULL but not an empty string, which resolve_provider() and resolve_model() in embed.c both treat as inherit; NULLIF puts them on the same footing. Two documentation corrections. The data-management guidance still told users to drop the chunk table and enable vectorization again after a dimension change, which throws away chunks, sparse embeddings and BM25 statistics that were never wrong. And the configuration example passed NULL as the model without saying that the provider defaults to NULL too, so it reverts alongside unless named again. Raised by CodeRabbit on #74.

The call restoring ptm_named's model left provider at NULL, so the reset case that follows started from a vectorizer with nothing pinned and could not show that the provider clears alongside the model. Pinning both first makes the assertion mean something. The provider named matches the GUC, so the effective values still do not move and the case remains the no-op it is there to cover. Raised by CodeRabbit on #74.

The four provider implementations read pgedge_vectorizer.model straight from the GUC, and the provider interface had nowhere to put a model, so nothing could ask for an embedding from anything other than whatever the database was globally configured to use. That is the obstacle to a per-vectorizer model, and it is removed here rather than worked around by setting the GUC around each request: a provider that silently depends on ambient global state cannot be asked to embed with anything else, and mutating that state inside the worker's batch loop would have made the error paths considerably harder to reason about. generate() and generate_batch() therefore take the model explicitly, and the providers use the argument. Every existing call site passes the GUC, so behaviour is unchanged. generate_embedding() and detect_embedding_dimension() gain optional provider and model arguments on the same rule that will apply to the registry: NULL means fall back to the GUC. Neither is STRICT any more, since a STRICT function would return NULL before that argument could reach the C. The dimension probe needs this in particular, because a vectorizer created with an override needs the dimension of the model it names, not of whatever the GUCs happen to say.

pgedge_vectorizer.vectorizers gains nullable provider and model columns, and enable_vectorization() gains matching parameters in ninth and tenth position so that existing positional calls are untouched. NULL means inherit the GUC at the time the work runs rather than a copy taken at creation, so an installation that never sets either behaves exactly as it did. Where the dimension is not given, the probe now asks about the model this vectorizer will actually use rather than the one the GUCs name, which would otherwise size the vector column against the wrong model whenever an override was passed. Nothing reads the columns yet; the worker does that in the next commit.

The model was one GUC applied to every table in the database, which is the wrong granularity: a table of short product titles and a table of long technical documents are rarely well served by the same model, and there was no way to embed one table locally through Ollama whilst another went to a hosted provider. A vectorizer now records its own provider and model, both nullable, NULL meaning inherit. Inheritance resolves when the work runs rather than being copied at creation, so an installation that sets neither carries on exactly as before. They can be pinned at enable_vectorization() or changed afterwards with the new set_embedding_model(). The worker resolves inheritance in the query that fetches a batch, with a left join against the registry and COALESCE against the GUCs, so the rule lives in one place and an item whose vectorizer has since been disabled falls back through the same expression rather than needing a special case. A batch is selected by age across every vectorizer at once, so it can hold items for several models, and a request carries one; the batch is therefore grouped by (provider, model) before the existing loop runs and batch_extent() breaks on the same key. Sorting rather than merely breaking the run matters, because two tables' items alternating in time would otherwise give requests of one item each. The provider is resolved per request instead of once per batch. set_embedding_model() refuses to change a vectorizer that already has embeddings unless force_reembed is passed. The refusal keys on the model changing rather than the dimension changing, which is the case worth guarding: a dimension change is caught before any write by the existing check in the worker, whilst a change between two models of the same width, say text-embedding-3-small and text-embedding-ada-002 at 1536 each, would leave the old vectors in place, correctly shaped and meaningless beside the new ones, with nothing reporting a problem. With force_reembed the embeddings are cleared, the column rewidened if needed, the queue cleared and every chunk requeued, all in one transaction. Chunk rows, token counts, sparse embeddings and the BM25 statistics are untouched, since none of them depends on the embedding model. Three things fell out along the way. generate_embedding() and detect_embedding_dimension() take an optional provider and model, and can no longer be STRICT, because NULL has to reach the function to mean "use the GUC". That makes generate_embedding(NULL) raise rather than return NULL, which is what the function always meant to do and said so in its own code, unreachably. The array of chunk tables that disable_vectorization() drops was collected with no ORDER BY, so the notices came out in whatever order the scan returned, and adding registry columns changed it. It is ordered now. Adding defaulted parameters to enable_vectorization() with CREATE OR REPLACE defined a second function rather than replacing the old one, leaving two overloads on any upgraded installation, an eight-argument call reaching a body that knew nothing of the new columns, and COMMENT ON FUNCTION failing as ambiguous, whilst a fresh install was perfect throughout. pg_regress installs whatever default_version says, so it can never see this; 012_upgrade_1_1_to_1_2 builds a 1.1 installation, upgrades it, and compares its functions, columns and views against a fresh 1.2, which is the shape of check that catches the whole class rather than this one instance. Closes #27

test/expected/ carries numbered variants of embedding.out for the cases where provider API keys are actually available, and pg_regress passes if the output matches any of them. Only the default one was updated for the new generate_embedding() signature and for NULL input now raising, so every platform without keys was happy and the macOS runner, which has them, failed against embedding_4.out. The two changed lines are common to all five, so all five now carry them.

Widening where the model comes from, from one GUC to a registry column and a SQL argument, broke assumptions in several places that were safe whilst it was a single trusted setting. The model was interpolated raw into every provider's request body, and into Gemini's URL path. Escaped now in all four, once in the shared OpenAI-format builder that OpenAI and Voyage use and once each in Ollama and Gemini, with a percent-encoding helper for the URL segment, where a '/' or '?' would have sent the request to a different endpoint. A rate limit deferred every remaining item in the pull and charged each one a deferral, which was right whilst a pull could only carry one provider's work. Now that a batch can span providers, that spent deferrals belonging to a provider that had not refused anything and, once they ran out, failed its items outright. Only the refused provider's items are deferred; the rest go back to pending uncharged for the next pull. set_embedding_model() computed the new dimension and altered the column only when the vectorizer had chunks, so an empty one kept the width it was created with and failed every embedding written after the change, which is precisely the failure the function exists to prevent. The dimension is resolved and the column altered whichever it is, whilst clearing embeddings and requeuing stay conditional. That does mean a call without an explicit dimension always probes the provider, as enable_vectorization() does, which the reference now states. The requeue omitted max_attempts, taking the column default of 3 rather than pgedge_vectorizer.max_retries as every other queue insert does. The worker's inheritance expression handled NULL but not an empty string, which resolve_provider() and resolve_model() in embed.c both treat as inherit; NULLIF puts them on the same footing. Two documentation corrections. The data-management guidance still told users to drop the chunk table and enable vectorization again after a dimension change, which throws away chunks, sparse embeddings and BM25 statistics that were never wrong. And the configuration example passed NULL as the model without saying that the provider defaults to NULL too, so it reverts alongside unless named again. Raised by CodeRabbit on #74.

The call restoring ptm_named's model left provider at NULL, so the reset case that follows started from a vectorizer with nothing pinned and could not show that the provider clears alongside the model. Pinning both first makes the assertion mean something. The provider named matches the GUC, so the effective values still do not move and the case remains the no-op it is there to cover. Raised by CodeRabbit on #74.

@ibrarahmad

The worker resolved each item's provider and model through a plain left join from the queue to pgedge_vectorizer.vectorizers on chunk_table, which is not a key: the column carries no unique constraint, enable_vectorization() accepts an explicit chunk_table_name, and the generated default can collide of its own accord. Two registry rows naming one chunk table therefore returned a row per match, so a single queued item was embedded once per model and the later write won, with nothing reporting a problem. The lookup is a LATERAL ... LIMIT 1 now, ordered by id so that the vectorizer which claimed the name first wins, and which cannot multiply rows whatever the registry holds. 011_per_table_model adds a second registry row against the inheriting table's chunk table; without this change the batch is split three ways rather than two. Raised by @ibrarahmad on #74.

A vectorizer whose provider and model are NULL inherits the GUCs, and inheritance resolves when the work runs rather than being copied at creation. Changing pgedge_vectorizer.model therefore re-points every inheriting vectorizer at once, leaving a chunk table holding vectors from the old model beside new ones from the new. Similarity between two models' vectors is noise, so those rows become effectively invisible to search rather than merely stale, and where the widths match, as they do between text-embedding-3-small and text-embedding-ada-002, the dimension check in the worker cannot see it either. set_embedding_model() guards the per-vectorizer path and cannot guard this one: the extension does not own that setting and cannot intercept every way it changes. Rather than pretending otherwise, each chunk now records the provider and model that produced its vector, written by update_embedding() in the same statement as the embedding so the two cannot disagree, and embedding_model_status() reports where that differs from what the vectorizer would use now. That diagnoses instead of preventing, but it catches drift whatever its cause, including a setting changed months ago. Reporting alone would leave a number and nothing to do about it, and the obvious remedy does not work: set_embedding_model(force_reembed => true) takes its no-op branch here, because an inheriting vectorizer's effective model already is the new one. reembed() therefore clears and requeues every chunk not known to have come from the effective provider and model. Rows with nothing recorded are counted apart in the report, since they predate the columns and may well be current, but reembed() treats them as needing redoing, a row that cannot be shown to be current being one to do again; on a freshly upgraded installation that is every row, which the documentation says plainly. A change of embedding width takes every chunk with it, drifted or not, because a column cannot hold two widths. Two things found on the way. The columns had to be added to chunk tables by the upgrade script itself. enable_vectorization() adds them to a chunk table it finds without them, but nothing re-runs it on upgrade, and the worker writes both columns in the same statement as the embedding, so an existing installation would have failed every embedding write until someone happened to re-enable a vectorizer. 012 now compares an upgraded chunk table's columns against a freshly created one, which is the check that would have caught it. update_embedding() interpolated the chunk table's name into its UPDATE without quoting, so it would have failed for a schema-qualified source, where the generated name is one identifier with a dot in it rather than a qualified reference. Fixed in passing, since it is the statement being changed. Closes #75

hybrid_search() embedded the query with the GUCs, which was right whilst that was the only place a model could come from. Since a vectorizer can pin its own, a query embedded by one model was being compared against chunks embedded by another: meaningless distances where the widths match, and an outright error where they do not, which is the failure this whole line of work exists to prevent, arriving through the search path instead. It now looks the provider and model up alongside the chunk table and passes them through, NULL and all, so a vectorizer that has pinned nothing behaves as before. The 1.1 script never redefined the function, so the upgrade carries a full replacement. The worker's join to the registry could match a queue row twice, because only (source_table, source_column) is unique and two vectorizers can be pointed at one chunk table with an explicit chunk_table_name. The item would then be embedded twice and counted twice into the BM25 corpus statistics. A LATERAL with LIMIT 1 makes the join return one row whatever the registry holds; a unique constraint on chunk_table would be the stronger fix but needs a story for installations that already have duplicates, which is separate work. set_embedding_model() compared raw stored values where the worker, embedding_model_status() and reembed() all treat an empty string as inherit, so an empty override would have been seen as a change by one of the four and not by the other three, and would have reached the dimension probe as a model name of ''. NULLIF everywhere, and the probe now asks about the effective values. resolve_model() returned an unset model GUC unvalidated, where resolve_provider() already refuses one. The providers interpolate it straight into their request bodies, so an empty setting put "model":"" on the wire and a NULL one would have been dereferenced whilst escaping it. One finding declined. CodeRabbit proposed charging a provider that cannot be resolved to the items of its request, so that the rest of the pull proceeds. The observation behind it is right, and is now issue #78: since a vectorizer can name its own provider, one bad name stops every other vectorizer in the database. The prescription is not, because 005_batch_failure_backoff.pl exists to prevent exactly that and injects exactly this fault: charging a blameless item for a misconfigured provider works through the queue retiring one innocent row per max_attempts cycles, so a single mistyped provider name would mark the whole queue failed. Fixing it properly means skipping the group without charging it whilst still reaching the batch backoff, which needs a way to report a batch-level fault without an exception. That is more than this change should carry. Raised by CodeRabbit on #77.

embedding_model_status() narrowed by resolving every registry row's stored source_table back to a regclass and comparing that against the argument, which raises rather than returning nothing for a schema the caller has no USAGE on. A role with rights on one table and not on another vectorizer's schema therefore got a permission error from the narrowed call whilst the unnarrowed one worked, which is precisely backwards. The comparison is made as text now, as set_embedding_model(), reembed() and hybrid_search() all make it, and the regression test covers the case with a restricted role. The dimension probe in the worker interpolated the chunk table's name into '%s'::regclass unquoted, so it would have failed for a schema-qualified source where the generated name is one identifier with a dot in it. Same defect as the one already fixed in update_embedding(), and fixed the same way. Raised by ibrarahmad and CodeRabbit on #77.

Codacy's SQL rules read a GRANT SELECT to anything not ending in '_role' as a grant to a user account rather than to a role, which the new privilege case in model_drift tripped. The name carries no meaning in the test, so it follows the convention rather than an ignore being recorded against a security finding.

Three things, all raised by CodeRabbit on #77. The upgrade script resolved each registry entry's chunk table by name through whatever search_path the upgrade happened to run with, so a chunk table in a schema outside it resolved to NULL and was skipped without a word, leaving the provenance columns unadded and every later embedding write against that table failing. The registry records only a bare name, so the catalogue is searched instead, and a name found in more than one schema raises a warning rather than guessing which one was meant. 012 now upgrades an installation with a chunk table tucked into its own schema, which fails against the old lookup. reembed() cleared the whole queue for the chunk table, sparse-only rows included, and requeued only chunks with no embedding. A chunk whose dense vector was current and whose sparse vector was still outstanding therefore lost its queued work altogether and kept a NULL sparse_embedding until someone thought to run reprocess_chunks(). Sparse-only rows are left where they are: they carry no dense work to redo, and the probe only marks a chunk sparse-only whilst it still has an embedding. The regression test's check that reembed() rejects an unregistered column raised its own sentinel inside the block that caught it, so a reembed() that wrongly succeeded would have been reported as the expected failure. The sentinel is raised outside the handler now.

The reference described what reembed() does to the chunk table and said nothing about the queue rows, which it replaces, sparse-only rows excepted.

ibrarahmad

Checked the plumbing end to end rather than just the diff. Two vectorizers on one provider with different models, against a server that logs what it is asked for:

model=model-ALPHA n=3
model=model-BETA  n=3

Neither fell back to the GUC's model, both batched properly, and embedding_model is recorded correctly on each chunk table.

1.1 -> 1.2 upgrade applies, adds provider/model, and leaves an existing vectorizer NULL/NULL so it keeps inheriting. setup, per_table_model, embedding, pk_types and hybrid_test pass.

Escaping the model into the JSON body and percent-encoding it into the Gemini path are both worth having now that it is no longer only a GUC.

Nothing blocking from me.

Record and repair embedding model drift

coderabbitai Bot reviewed Sep 9, 2026

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Comment thread test/sql/per_table_model.sql Outdated

ibrarahmad reviewed Sep 15, 2026

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Comment thread src/worker.c Outdated

dpage force-pushed the fix/issue-27-per-table-model branch from 0c15499 to c0633ed Compare September 23, 2026 09:58

dpage requested a review from ibrarahmad September 23, 2026 10:03

dpage added 7 commits September 23, 2026 12:22

dpage changed the base branch from main to fix/issue-25-vectorizer-status September 23, 2026 11:25

dpage force-pushed the fix/issue-27-per-table-model branch from c0633ed to d05b5fc Compare September 23, 2026 11:25

dpage added 6 commits September 23, 2026 12:26

Say what reembed() does with the queue

4965a99

The reference described what reembed() does to the chunk table and said nothing about the queue rows, which it replaces, sparse-only rows excepted.

ibrarahmad approved these changes Sep 24, 2026

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Merge pull request #77 from pgEdge/fix/issue-75-model-drift

edaa28d

Record and repair embedding model drift

dpage merged commit 2caa1c4 into fix/issue-25-vectorizer-status Oct 1, 2026
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Let each vectorizer choose its own provider and model - #74

Let each vectorizer choose its own provider and model#74
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dpage commented Sep 9, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Summary

Changing the model on a populated vectorizer

Three things that fell out

Test plan

Known limitations, deliberate

Note on the base branch

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Up to standards ✅

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