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When pgvector works on a developer’s machine but a FastAPI RAG backend fails in CI, first verify which PostgreSQL server the tests actually reach. Then check that pgvector is installed for that server and enabled in the specific database used by the tests. Those are separate setup conditions, and a local success does not prove either is true in CI.
Why can pgvector work locally but fail in CI?
pgvector is a PostgreSQL extension, so its availability depends on the PostgreSQL instance used by the test process. A CI workflow may connect to a service container, a PostgreSQL server installed on the runner, or another endpoint. Installing pgvector for one instance does not make it available to a different one.
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There are two distinct states to verify:
- Installed on the server: the extension files are present for the PostgreSQL instance the application connects to.
- Enabled in the test database: that database has run
CREATE EXTENSION vector;.
The pgvector installation instructions describe source installation for Linux and Mac, including support for PostgreSQL 13 and later in that installation path, and advise selecting the correct pg_config when multiple PostgreSQL installations exist. The project also documents PostgreSQL-major-specific packages and Docker image tags. Check the current instructions before relying on a particular runner package or image tag: pgvector installation documentation.
Trace the test connection before changing the workflow
Read the CI service definition, connection URL or environment variables, and test configuration together. The goal is to identify the database endpoint the tests use—not merely the PostgreSQL version installed on the runner or available to a developer locally.
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- Find the test database URL. Check the variables and test settings used by the FastAPI application and test suite.
- Identify the database service. Determine whether the job connects to a GitHub Actions service container, a runner-installed server, or another endpoint.
- Query that connection for its server version. Compare the connected server’s PostgreSQL major with the package or image used to install pgvector.
GitHub’s PostgreSQL service-container guidance distinguishes networking for jobs that run directly on a runner from jobs that run inside a container. Use the hostname and port appropriate to the workflow’s topology rather than assuming one hostname works in both arrangements. GitHub also documents that service containers require Linux runners: GitHub Actions: creating PostgreSQL service containers.
Choose an installation method that matches the CI database
The pgvector Actions guide documents two common approaches. Neither is universally better; choose based on which PostgreSQL instance the tests use and how the workflow manages its version and initialization.
| CI setup | What to align | Useful when |
|---|---|---|
| Install pgvector on the runner | Use the package for the PostgreSQL major of the runner’s actual server. | The tests connect to a PostgreSQL instance provided by the runner. |
| Use a pgvector PostgreSQL service image | Choose a pgvector/pgvector image tag for the intended PostgreSQL major. |
The tests connect to a PostgreSQL service container. |
The official guide includes example package choices for particular Ubuntu runner releases and service-image tags, but those details can change. Check its current instructions rather than copying an old package name or tag: pgvector GitHub Actions guidance.
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Enable pgvector in the database the tests use
Having extension files installed on the server is not the same as enabling the extension in a database. pgvector’s README says to enable it once in each database where it is needed. Connect using the same endpoint and database name as the tests, then inspect the extension state:
SELECT extversion FROM pg_extension WHERE extname = 'vector';If the extension is not enabled in that database, ensure the test database initialization runs:
CREATE EXTENSION vector;Put this in the initialization or migration path appropriate to the project so a newly created CI database receives the extension. Checking another database—or running the query against a local development connection—does not establish the state of the CI test database.
Separate service readiness from extension problems
If CI fails before a query reaches PostgreSQL, investigate startup and connection details before changing pgvector installation. A service that is not ready, a wrong endpoint, or incorrect job-to-service networking can prevent SQL from executing and mimic a database setup problem.
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GitHub’s PostgreSQL service-container guide and pgvector’s Actions example show a pg_isready health check. Use a readiness check suited to the service and workflow, and confirm the database is accepting connections before tests start: GitHub PostgreSQL service-container guidance and pgvector Actions guidance.
Use the error to narrow the next check
- Missing
vectortype, extension, or operator: check whether pgvector is installed for the connected server, then whether it is enabled in the test database. - Extension fails to load after installation: compare the server’s PostgreSQL major with the package or image, and check
PG_CONFIGif source builds or multiple installations are involved. - Connection fails before SQL runs: check service health, connection endpoint, and runner/container networking.
- Local passes while CI fails: compare the actual connection target, PostgreSQL major, extension installation method, and database initialization across environments.
These checks cover common pgvector and CI setup mismatches, but they do not prove a particular cause without the workflow and failing log. A migration, configuration, or other database issue may produce a failure that is not caused by PostgreSQL major-version mismatch.
Verify the fix through the same CI path
After changing the package, service image, connection settings, or database initialization, rerun the failing test in CI. Confirm that the test process reaches the intended database and that the extension query reports an installed version there. A successful local test alone does not verify the corrected CI connection.
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For a workflow that supports more than one PostgreSQL major, apply the same checks to each test target: the server version, matching pgvector installation, database-level extension setup, and service readiness. Keep the server/image choice and extension initialization aligned for every target.
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