AI & RAG

Nightly: build a searchable config-rationale index from Git history and ADRs

On a schedule, this workflow walks every config key, links it to the commit that set it and the ADR that justified it.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionEnumerate config files and blame keys to commits in GitLabGitLabGitLab
  • ActionFetch and match ADRs from ConfluenceConfluenceConfluence
  • ActionSummarize and embed each key's rationaleOpenAI
  • OutputUpsert rationale index into PostgresPostgreSQLPostgres

What it does

Builds and refreshes a backing index that maps each config key to its current value, the commit SHA that introduced that value, the commit author and message, and any linked ADR. Storing this in Postgres means downstream "why" lookups are instant instead of re-walking Git every time.

When to use it

When ad-hoc rationale lookups are too slow or you want one canonical, auditable table of why every setting is what it is. Run it nightly so the index stays current as config and ADRs evolve.

How it works

  1. 1A scheduled trigger fires nightly.
  2. 2The workflow enumerates config files in GitLab and runs blame to attribute each key to its introducing commit.
  3. 3It fetches the ADR space from Confluence and matches ADRs to keys by reference and semantic similarity.
  4. 4An embedding/summarization step distills each key's rationale.
  5. 5It upserts key, value, SHA, author, ADR ID, and rationale into Postgres.
  6. 6It writes a run summary noting keys with no discoverable rationale (gaps to document).

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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