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LayaPHP is a PHP SDK for sending text-classification requests to a separate Laya inference service called laya-serve. It is not a classifier that runs inside your PHP process: a working application needs a reachable service endpoint, which you can run locally with the project’s Docker Compose setup or configure elsewhere. The documented setup requires PHP 8.4 or newer and a PSR-18 HTTP client.
What LayaPHP does—and what it does not
The LayaPHP project describes its library as an SDK for a multilingual decision engine. Its API lets an application ask typed questions about text and receive categorical choices, ordered scores, or yes/no decisions, along with probability or confidence information. In PHP, decisions can be represented with readonly classes and enums. Those are descriptions of the interface and intended use, not independent evidence that a particular classification will be correct.
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The SDK communicates over HTTP with laya-serve, an inference service running the engine in Python. A PHP package installation alone does not provide that service. The project documents Docker Compose for local operation and configuration for a different endpoint. Consequently, “self-hosted” describes where you choose to operate or connect to the service—not a guarantee that inference happens inside the PHP application. LayaPHP’s README documents the SDK and service relationship.
Check runtime and package requirements
The documented Composer command is:
composer require marcreichel/laya-phpThe package requires PHP 8.4 or newer and a PSR-18 HTTP client. It discovers an installed client; the README names Guzzle and Symfony HttpClient as examples. Packagist lists version 1.2.0, published on October 1, 2026, with PHP ^8.4 and PSR HTTP dependencies. These package details can change, so check the current Packagist package metadata when selecting a version.
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Make the inference service reachable
For a local setup, the repository supplies a Docker Compose configuration and documents the service at http://localhost:8000. The SDK can use LAYA_URL to select its endpoint and LAYA_API_KEY where an API key is used. Configure the URL to match the service address reachable from the PHP application; a container’s localhost may refer to that container rather than the host or another service.
For deployment, decide where the service will run, how the PHP process will reach it, and whether requests require credentials. The project presents keeping text within the operator’s infrastructure and avoiding per-token fees as benefits of its self-hosting approach. Those are deployment-positioning claims, not independent privacy or cost findings: data exposure depends on endpoint, network, logging, and hosting configuration, while total cost depends on infrastructure and operations.
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Use the API to express a classification task
The documented question factories cover three kinds of decision:
- Choice: select one label from options, optionally with descriptions—for example, route a support ticket to billing, technical support, or account access.
- Score: assign an ordered level—for example, rate urgency from low to high.
- Yes or no: evaluate a binary question, optionally with criteria—for example, whether a message explicitly threatens cancellation.
The result exposes answer values and confidence or probability information. For ticket routing, that can support a workflow in which clear cases go to the selected team and uncertain cases are checked by a person. The README recommends using its calibrated answerConfidence value to identify uncertain items for human review. A confidence value is a review signal, not proof that the answer is correct; choose and validate any review threshold against your own task and data.
Connect LayaPHP to Laravel
The repository documents Laravel 13 and newer integration. For those versions, it says Laravel auto-discovers the service provider, registers Laya as a singleton, and reads settings such as the service URL, API key, and optional cache configuration from environment settings. Application code can receive the service through dependency injection. The README also documents a laya:health Artisan command and a fake for tests. Follow the repository’s current configuration and usage examples because integration details can change with package and framework releases.
Laravel lists its own classification API in its 13.x AI SDK documentation and marks that API experimental, with the possibility of changes in minor releases. That framework API is separate from the LayaPHP repository’s Laravel service-provider integration; the existence of one does not establish that the other is a first-party provider. See Laravel’s 13.x AI SDK documentation for the framework API’s status.
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Check server compatibility before relying on optional features
The project says its default router chooses a checkpoint by language and documents an option to pin a model. It also describes controls for longer documents, including maxLen on recent laya-serve versions and a truncated result indicator. These options are not universal across every server release: the README ties some features to server versions 0.3.21, 0.3.22, or 0.3.23 and newer. Confirm the exact minimum version for the option you plan to use, and test how your application handles truncation.
Plan for request limits and concurrency
The README states that the server handles one inference request at a time. It recommends predictMany() or decideMany() for multiple inputs on supported server versions. Documented request limits are:
| Limit | Documented maximum |
|---|---|
| States per batch | 64 |
| Questions per batch | 64 |
| Characters of state | 50,000 |
| Options in a choice question | 100 |
| Levels in a score question | 32 |
| Answer options in total | 512 |
These are project-documented limits, not throughput measurements. A batch request fails if even one state is rejected, so validate inputs and handle a failed batch deliberately rather than assuming valid items will still be returned. If your application needs parallel inference, the documented single-request handling is an operational constraint to account for; the available sources do not establish a throughput or latency figure.
Decide whether the operational model fits
LayaPHP may fit a PHP application when you can operate or access a compatible laya-serve endpoint, meet the runtime requirement, and build a review path for uncertain decisions. Before adopting it, check these deployment questions:
- Can your PHP runtime use PHP 8.4 or newer and a PSR-18 client?
- Can the application reach the inference endpoint reliably, with credentials configured where needed?
- Does the server version support the features and batch methods you intend to use?
- Can your expected request volume work with the documented sequential request handling and batch limits?
- How will you validate classification quality for your own messages and route low-confidence decisions to a person?
- Do your network, logging, and hosting choices meet your data-handling requirements, and do your infrastructure and operating costs make sense?
The sources describe the interface and deployment options but do not establish independent accuracy benchmarks, latency figures, hardware sizing, privacy audits, or a total-cost comparison. The repository identifies the project license as Apache-2.0; verify current project and package details before adoption.
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