Recommended Tooling & Stack¶
| Layer | Technology / GCP Product |
|---|---|
| Language & Runtime | Python 3.11+, Asyncio |
| LLM & Embeddings | Vertex AI Gemini 1.5 Pro / Flash, text-embedding-004 (or current equivalent) |
| Vector & Hybrid Storage | Vertex AI Vector Search, AlloyDB (pgvector), BigQuery |
| Graph Storage (Optional) | Neo4j on GCP / Cloud Spanner Graph |
| Frameworks | LangChain / LlamaIndex / Vertex AI SDK, Pydantic |
| Evaluation Frameworks | Ragas, Vertex AI Evaluation SDK, pytest |
| CI/CD & DevOps | GCP Cloud Build, Artifact Registry, Secret Manager, GitHub Actions |
Local / Cloud Development Tips¶
- Prefer Vertex AI Workbench or Colab Enterprise for notebooks that need direct GCP authentication.
- Use Application Default Credentials (ADC) everywhere possible.
- Keep secrets in Secret Manager; never hard-code keys.
Student Jupyter Environment¶
For a zero-install notebook experience, use the course Docker image:
Details: Docker / JupyterLab Environment.
-
VS Code + Container Workflow — run labs from VS Code on the host while Python runs in Docker.
-
Google Colab — open any lab notebook in the browser with one click.