# Hello world
## Setup environment
```bash
uv venv
source .venv/bin/activate
```
## 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`.
## Create a index to be able to retrieve
```
gcloud firestore indexes composite create \
--project=<FIREBASE-PROJECT>\
--collection-group=films \
--query-scope=COLLECTION \
--field-config=vector-config='{"dimension":"768","flat": "{}"}',field-path=embedding
```
## Run the sample
TODO
```bash
genkit start -- uv run src/main.py
```
## Testing This Demo
1. **Prerequisites**:
```bash
# Set GCP project
export GCLOUD_PROJECT=your_project_id
# Authenticate with GCP
gcloud auth application-default login
```
Or the demo will prompt for the project interactively.
2. **Firestore Setup**:
- Enable Firestore in your GCP project
- Create a Firestore database (if not exists)
- Enable Vector Search extension (if required)
3. **Run the demo**:
```bash
cd py/samples/firestore-retreiver
./run.sh
```
4. **Open DevUI** at http://localhost:4000
5. **Test the flows**:
- [ ] `index_documents` - Index sample documents
- [ ] `retrieve_documents` - Query for similar documents
- [ ] Verify retrieved documents match query semantics
6. **Expected behavior**:
- Documents are embedded and stored in Firestore
- Vector similarity search returns relevant documents
- Firebase telemetry captures traces/metrics
