Hi @kapa.ai I had install elementary, when I do dbt build it's end like this.

Exited because of keyboard interrupt
 Done. PASS=5 WARN=0 ERROR=0 SKIP=0 NO-OP=0 TOTAL=5
 Finished running 1 project hook, 1 table model, 3 data tests in 0 hours 0 minutes and 49.81 seconds (49.81s).

Encountered an error:
Database Error
  [UNRESOLVED_COLUMN.WITHOUT_SUGGESTION] A column, variable, or function parameter with name `artifacts_model` cannot be resolved.  SQLSTATE: 42703; line 6 pos 11

Did you have an explanation ? What should I do ?

The error you're seeing (

UNRESOLVED_COLUMN

for

artifacts_model

) is a known type of issue in Elementary, typically caused by a schema mismatch between your Elementary dbt package version and the Python

elementary-data

CLI version, or by stale/outdated Elementary tables in your warehouse. Likely Causes & Fixes 1. Version mismatch between dbt package and Python package The minor versions of both packages must be aligned. For example, if you're using

elementary-data==0.16.x

(Python CLI), you should also have

elementary-data/elementary version: 0.16.x

in your

packages.yml

. [Slack thread] Check your versions and align them, then follow the upgrade guide. 2. Stale Elementary tables with outdated schema If you recently upgraded Elementary, the tables in your warehouse may still have the old schema. The fix is to: 1. Drop the problematic table(s) (e.g., the table referenced in the error). 2. Re-run Elementary models to recreate them with the correct schema:

dbt run -s elementary

Or, if needed, force a full refresh:

dbt run -s elementary --full-refresh

[Slack thread; community discussion] 3. Macro conflicts with other dbt packages If you have other packages (e.g.,

dbt_artifacts

,

dbt_expectations

) installed alongside Elementary, macro name conflicts can sometimes cause unexpected errors. Checking your

on-run-end

hooks and ensuring there are no conflicting macros is worth investigating. [Slack thread] Recommended first step: Run

dbt run -s elementary

to refresh the Elementary models and their schemas. If the error persists, check that your dbt package version and Python CLI version are aligned. 👍 0 button 👎 0 button ℹ️ Tip: Mention @elementary.ai in the thread for follow-ups or DM the app for 1-to-1 help.

@kapa.ai Its is working on local but not on pipeline I used "uv" so it is the same version

The knowledge sources do not contain enough information to give a confident answer specific to your situation (working locally but failing in a pipeline when using

uv

for version management). That said, based on the knowledge sources, here are some possible explanations for environment-specific failures: Possible Causes 1. Elementary tables don't exist in the pipeline's target schema Even if versions match, the pipeline may be pointing to a different schema or database where Elementary models haven't been built yet. Make sure you run:

dbt run -s elementary

against the same target that your pipeline uses. [Slack thread] 2. Schema name mismatch in the pipeline One community member found that Elementary requires the full schema name as it exists in the warehouse, while dbt uses a prefix-based approach. Double-check that your pipeline's

profiles.yml

points to the exact correct schema. [community discussion] 3. CI/pipeline writes to a temporary schema If your pipeline is a CI job, dbt may be materializing Elementary models into a temporary PR schema instead of the expected Elementary schema. [GitHub issue] Could you share: • Your pipeline's

profiles.yml

or target configuration? • The full error log from the pipeline? This would help narrow down the root cause more precisely. 👍 0 button 👎 0 button ℹ️ Tip: Mention @elementary.ai in the thread for follow-ups or DM the app for 1-to-1 help.