datalex-cli 1.11.0

Git-native data modeling for dbt users

DataLex

AI-first dbt adoption, contracts, diagrams, and manifest publishing.

DataLex is a local-first OSS workflow for teams that already use dbt. It scans your existing dbt project, lets AI propose business domains and contracts from dbt evidence, and writes reviewed DataLex artifacts back to Git.

DataLex does not replace dbt. dbt remains the source of truth for SQL, model YAML, semantic metrics, tests, exposures, and enforced physical contracts. DataLex adds the business/domain layer above dbt.

Install from PyPI

Use this path when you want DataLex on your machine or inside an existing dbt repo.

python3 -m pip install -U 'datalex-cli[serve]'
datalex --version
datalex serve

Open http://localhost:3030.

To open DataLex directly on an existing dbt repo:

cd ~/path/to/your-dbt-project
datalex serve --project-dir .

For warehouse drivers, add the matching extra:

python3 -m pip install -U 'datalex-cli[serve,duckdb]'
python3 -m pip install -U 'datalex-cli[serve,postgres]'
python3 -m pip install -U 'datalex-cli[serve,snowflake]'
python3 -m pip install -U 'datalex-cli[serve,all]'

Requirements: Python 3.9+ and Git. The [serve] extra includes a portable Node runtime for the local UI.

Run with Docker

Use Docker when you do not want to install Python packages on the host.

git clone https://github.com/duckcode-ai/DataLex.git
cd DataLex
docker build -t datalex:local .
docker run --rm -p 3030:3001 datalex:local

To use Docker with an existing dbt repo:

cd ~/path/to/your-dbt-project
docker run --rm -p 3030:3001 \
  -v "$PWD":/workspace \
  -e REPO_ROOT=/workspace \
  -e DM_CLI=/app/datalex \
  datalex:local

In the UI, choose /workspace as the dbt project path.

Core workflow

Connect dbt repo -> AI Setup -> Readiness -> Generate -> Review -> Contracts -> Publish

1. Connect your dbt repo.
2. Set up AI with OpenAI, Claude, or Ollama.
3. Scan readiness from dbt manifest, YAML, metrics, tests, exposures, owners, and contracts.
4. Generate focused proposal packs for one domain, model group, or metric family.
5. Review and certify proposals before anything becomes trusted.
6. Publish datalex-manifest.json from certified contracts.

Generation requires a tested AI provider. Readiness works without AI, but DataLex will not create fake domains or placeholder contracts.

AI setup

DataLex uses your dbt evidence to generate proposals:

• target/manifest.json
• dbt model YAML
• semantic models and metrics
• tests and relationships
• exposures
• owners and descriptions
• existing dbt contracts
• existing DataLex artifacts

Provider settings are project-private and stored under:

<your-dbt-project>/.datalex/agent/provider-settings.json

They are not written under versioned DataLex/, and API responses redact secrets.

Ollama example

ollama pull gemma4:12b
ollama serve

In DataLex, open AI Setup, choose Ollama, set:

Base URL: http://localhost:11434
Model: gemma4:12b

Then click Save and Test.

What DataLex writes

New OSS artifacts use this domain-first layout:

DataLex/
  datalex.yaml
  domains/
    commerce.yaml
  commerce/
    conceptual/
    logical/
    physical/
    contracts/
    proposals/
    glossary/
    semantic/
  imported/
    dbt/
  generated/
    dbt/
  generated-sql/
  Skills/

DataLex still reads older layouts for compatibility, but new UI actions write lowercase canonical paths.

Only certified contracts and metric contracts enter datalex-manifest.json. Draft, reviewed, and rejected proposals stay out of the publish manifest.

Publish a manifest

datalex datalex manifest build DataLex --out DataLex/datalex-manifest.json

The manifest is the stable OSS handoff for downstream tools and future cloud flows. DQL is not required in the OSS repo. DataLex only shows DQL readiness when a project explicitly enables that integration.

Tutorials

Start here:

1. Install and run DataLex
2. Connect an existing dbt repo
3. Configure AI with OpenAI, Claude, or Ollama
4. Generate, review, and certify a proposal pack
5. Publish the DataLex manifest
6. Run DataLex with Docker

For the full flow in one place, read Getting started.

For contributors

git clone https://github.com/duckcode-ai/DataLex.git
cd DataLex
python3 -m venv .venv
source .venv/bin/activate
pip install -e '.[serve,duckdb]'
npm --prefix packages/api-server install
npm --prefix packages/web-app install
datalex serve

Useful checks:

npm --prefix packages/api-server test
npm --prefix packages/web-app run build
python3 -m pytest tests/datalex packages/readiness_engine/tests

Links

• Docs: docs/index.md
• CLI reference: docs/cli.md
• Enterprise OSS workflow: docs/enterprise-oss-workflow.md
• DataLex layout: docs/datalex-layout.md
• Issues: GitHub Issues
• Community: Discord