1Panel-dev / laya-server

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 LAYA SERVER 
LAYA SERVER is a self-hosted API and web interface for Laya's structured decision models, compatible with the TypeSafe Jev API format. It packages the System One inference capabilities of upstream Laya as a standalone service, making them easier to deploy and use in your applications.
## Quick Start
Once the image is published, install Docker and run the following command. Replace `latest` with a version tag to pin a release.
 [code] 
Replace the sample password with your own, then sign in at `http://YOUR_SERVER_IP:8080`. Create a key on the **API Keys** page; the full key is shown only once. `/health/ready` checks the model files. Data is stored in the `laya-data` volume.
`YOUR_SERVER_IP` is your server's IP address. To use a domain name, configure HTTPS on a reverse proxy and forward requests to port 8080 on the server.
## Screenshot
## Call the API
 [code] 
Successful responses include `answers`, `model`, and actual token counts in `usage`. The published image includes the multilingual model; `model=auto` (the default) and `model=multilingual` are available. See **Documentation** in the console for more request examples.
## Usage statistics and storage
Inference responses enqueue usage statistics without waiting for SQLite writes. A single
background thread per process reuses a connection, writes batches of up to 100 events,
and coalesces `last_used_at` updates by API key. Partial batches flush after up to one
second when the writer is idle; slow storage can delay statistics further. The `usage`
field in each inference response is unchanged. `duration_ms` measures model prediction
and result validation, not queue time or database writes.
Statistics are best-effort, eventually consistent telemetry, not a billing or quota
ledger. Each process buffers up to 1,000 queued events plus one in-flight batch. A full
queue drops new statistics with rate-limited warnings. SQLite failures roll back where
possible, log and drop the affected batch, then reconnect for later batches; failed
batches are not retried because an uncertain commit could double-count usage. Neither
condition turns successful inference into an HTTP 503. API-key authentication and
revocation still use the database synchronously.
Graceful shutdown attempts to drain the queue for up to five seconds. An in-flight
SQLite call cannot be cancelled safely and may outlive that wait; forced termination,
a shutdown timeout, or storage errors can lose unflushed statistics. Keep `/data` on
low-latency persistent storage: background batching reduces write pressure but does
not fix slow authentication reads or database locking.
## License and Feedback
This project is licensed under Apache License 2.0. For issues and suggestions, visit GitHub Issues.