DAG generation · Intelligent pipeline builder

Generate pipelines, then run them in Airflow

Call POST /pipeline-builder/pipelines/generate or the CLI. After generation, open Airflow to trigger runs. Manifest below refreshes on each load.

Step 1

Generate

Create DAGs from your pipeline definitions.

Step 2

Deploy

Artifacts land under AI_Pipelines and dags/.

Step 3

Operate

Reload the scheduler; monitor in Airflow UI.

Generated pipelines

Files under intelligent_pipeline_builder_2/AI_Pipelines/. Run triggers Airflow DAGs on the server.

File	pipeline_id	dag_id	Actions
No pipeline files found. Generate from opportunities or the form below.

GET /pipeline-builder/pipelines · DELETE /pipeline-builder/pipelines/{filename} · POST /pipeline-builder/pipelines/{filename}/trigger

Run pipeline builder (from pipeline_opportunities.csv)

Generates pipelines sequentially and streams logs from the server.

limit (optional)schedulemodelOverwrite if file existsTrigger DAG after create (Airflow CLI on server)

Live log

—

Generate Airflow pipeline

POST /pipelines/generate — writes under intelligent_pipeline_builder_2/AI_Pipelines/ on the server.

pipeline_namepurpose

scheduleOllama model

Overwrite if file existsTrigger DAG after create (Airflow CLI on server)

Set NEXT_PUBLIC_ADMIN_API_URL or Settings if the API is not on 127.0.0.1:8090.

API — generate pipelines

From repository root (server must be running).

cd /path/to/kinefinai/intelligent_pipeline_builder_2
./scripts/run_api.sh

# Then POST /pipelines/generate (see Pipeline Builder README for curl example).

Manifest snapshot

Read from disk on each load.

ENOENT: no such file or directory, open '/app/intelligent_pipeline_builder_2/AI_Pipelines/_manifest.json'

Output locations

Where files land.

intelligent_pipeline_builder_2/AI_Pipelines/
intelligent_pipeline_builder_2/airflow/dags/ (framework DAG)
intelligent_pipeline_builder_2/AI_Pipelines/_manifest.json