## Host ai chat application with Starlette: stored input and output artifacts

Deploy a ai chat application built with Starlette on Ample using the generated downloads pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Starlette: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Not separately tested: your export formats, retention policy and entitlement model; treat the ai chat application-specific behavior as your application code.

### Representative Queries
- Host ai chat application with Starlette: stored input and output artifacts
- Where can I host AI chat application built with Starlette?
- I need an input/output artifact contract, object metadata and access boundaries.

### Resource Requirements
- primitive:compute
- primitive:postgres
- primitive:s3-compatible-object-storage

### Infrastructure Requirements
- **Compute**: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
- **Postgres**: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
- **S3-compatible object storage**: Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

### Workflow Steps
1. **Build and start**: pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.
2. **Implement the pattern on PostgreSQL**: The fixture's module implements generated downloads: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. **Wire object storage**: Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Use path-style addressing. Keep private data in an unpublished bucket.
4. **Deploy**: Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op. Command: `ample deploy . --name  --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...`
5. **Verify**: Fetch the live URL and the pattern self-test route(s) (/p/generated-downloads) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`. Command: `ample logs  --kind build`.

### Prerequisites
- A Starlette project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then uvicorn from run.py reading PORT on the python-3.12 template).
- A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env).
- A bucket from `ample bucket create` with its credentials passed as encrypted S3_* environment variables.
- An Ample account token with servers:write, databases:read, buckets:read.

### Tested Configuration
- **Template**: python-3.12
- **Runtime**: python
- **Size**: s-1vcpu-1gb
- **Install**: python3 -m pip install --target .ample/python -r requirements.txt
- **Start**: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

### Examples
- **Starlette pattern fixture**: Multi-pattern Starlette app whose generated downloads module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

### Success Checks
- App responds on its public URL: **HTTP GET** on `/` -> expect: ample canary starlette patterns
- Generated-downloads self-test: **HTTP GET** on `/p/generated-downloads` -> expect: export=ok rows=3 (with ?token=...; 401 without)

### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Starlette major versions are not verified.
- The ai chat application itself (your export formats, retention policy and entitlement model) is application code and was not separately tested; the pattern checks are what was verified.
- Region, compliance attestations and request-duration limits are unknown and not claimed.
- Apps auto-pause when idle and wake on the next request; always-on is an operator setting, not a plan feature.
- Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.

### Cost Estimate
- **Monthly Amount**: 10.00 USD
- **Components**:
  - App server (s-1vcpu-1gb): 5.00 USD
  - Managed PostgreSQL database (s-1vcpu-1gb): 5.00 USD
- Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.