# Hello world
## Setup environment
Use `gcloud auth application-default login` to connect to the VertexAI.
```bash
uv venv
source .venv/bin/activate
```
## Run the sample
### Monitoring and Running
For an enhanced development experience, use the provided `run.sh` script to start the sample with automatic reloading:
```bash
./run.sh
```
This script uses `watchmedo` to monitor changes in:
- `src/` (Python logic)
- `../../packages` (Genkit core)
- `../../plugins` (Genkit plugins)
- File patterns: `*.py`, `*.prompt`, `*.json`
Changes will automatically trigger a restart of the sample. You can also pass command-line arguments directly to the script, e.g., `./run.sh --some-flag`.
```bash
genkit start -- uv run src/main.py
```
## Testing This Demo
1. **Prerequisites**:
```bash
# Set GCP project (for Vertex AI embeddings)
export GCLOUD_PROJECT=your_project_id
# Authenticate with GCP
gcloud auth application-default login
```
Or the demo will prompt for the project interactively.
2. **Run the demo**:
```bash
cd py/samples/dev-local-vectorstore-hello
./run.sh
```
3. **Open DevUI** at http://localhost:4000
4. **Test the flows**:
- [ ] `index_documents` - Index sample film documents
- [ ] `retreive_documents` - Query for similar films
- [ ] Try different query terms
5. **Expected behavior**:
- Documents are embedded using Vertex AI
- Vector store persists locally (in-memory for dev)
- Retrieval returns semantically similar documents
- No external vector database required
- Sample documents include film descriptions with genres and plots
