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Ai Vektor Store Laravel Package
symfony/ai-vektor-store
Vektor vector store integration for Symfony AI Store, connecting your Symfony AI applications to the Vektor vector database for storage and retrieval.
Vektor Store provides a Vektor vector store integration for Symfony AI Store, letting you use the Vektor vector database as a backend for storing and retrieving embeddings in your Symfony AI applications.
- Drop-in store implementation for the Symfony AI
StoreInterface - Store, query, and manage vector embeddings through Vektor
- Works with Symfony AI's agent and RAG workflows
- Part of the modular Symfony AI component ecosystem
- Maintained in the main Symfony AI repository
Frequently asked questions about Ai Vektor Store
- Can I use symfony/ai-vektor-store in a Laravel application?
- Yes, but it is a Symfony AI store bridge rather than a native Laravel package. You can integrate it by wrapping the store in a Laravel service provider and binding the relevant Symfony AI store contract in the container. Expect to write custom boilerplate because the package does not ship Laravel facades, config publishing, or a service provider.
- What is symfony/ai-vektor-store used for?
- It integrates the Vektor vector store with Symfony AI Store so applications can perform Vektor-backed vector search. Typical uses include RAG, semantic search, chatbots, knowledge graphs, and recommendation systems. In Laravel you can pair it with Laravel Scout to combine keyword and vector search for hybrid retrieval.
- How do I install and configure symfony/ai-vektor-store in Laravel?
- Install it with Composer using composer require symfony/ai-vektor-store, then create a Laravel service provider that binds the Symfony AI store contract and maps your Vektor connection settings. Since the package ships no Laravel config publishing or facade, keep credentials in config/services.php or a dedicated config file. Configure Redis or PostgreSQL with pgvector as the Vektor backend and verify the container resolves the store.
- Does symfony/ai-vektor-store require Redis or PostgreSQL with pgvector?
- Yes, Vektor depends on Redis or PostgreSQL with the pgvector extension as its vector storage backend. This can conflict with existing Laravel infrastructure that already uses Redis for cache and queues or PostgreSQL as the primary database. Check your hosting environment because shared hosting and some PaaS tiers do not provide pgvector or dedicated Redis capacity.
- Does symfony/ai-vektor-store support Laravel 10 and PHP 8.2?
- The package requires PHP 8.2 or newer, which is satisfied by Laravel 10 and later. It is designed for Symfony, so Laravel compatibility comes through Symfony components and Laravel's container rather than an official Laravel integration. There is no Laravel-specific version matrix, so validate the bridge against your exact Laravel and PHP versions.
- What changed in symfony/ai-vektor-store 0.14.0?
- Version 0.14.0 adds a count capability so stores can report the number of stored or matching vectors without hydrating results. It also introduces VectorDocumentInterface, an explicit document abstraction that decouples payload and metadata handling from a concrete store class. The release is still 0.x, so revalidate any code that type-hinted concrete document classes because pre-1.0 changes may be breaking.
- How do I count vectors with symfony/ai-vektor-store?
- In v0.14.0 and later, the store can report a count without fetching full results, which is useful for N results found labels, LengthAwarePaginator-style UI, corpus dashboards, and pre-flight checks. You should confirm whether the count is exposed on the store contract used by your Laravel wrapper and whether it supports filtered counts or only whole-corpus counts. That distinction determines whether it is safe for user-facing pagination or better suited to admin dashboards.
- How should I test symfony/ai-vektor-store in a Laravel application?
- The package has no Laravel-specific test coverage, so add your own tests around the service provider binding, document mapping, and filtered queries. Cover Laravel idioms such as SoftDeletes, global scopes, queued re-indexing jobs, and multi-tenant filters, and use an integration test against Redis or pgvector for the real backend. Mock the store contract for fast unit tests and keep a smaller integration suite for vector behavior.
- What are alternatives to symfony/ai-vektor-store for Laravel vector search?
- Alternatives include other Symfony AI store bridges for Qdrant, Weaviate, or pgvector, or using a Laravel-native vector search package or direct pgvector queries. The VectorDocumentInterface in 0.14.0 keeps calling code backend-agnostic, reducing the cost of switching stores. Compare hosting requirements, Laravel integration boilerplate, and whether the alternative ships a Laravel service provider.
- Is symfony/ai-vektor-store production-ready and actively maintained?
- It is part of the actively maintained Symfony AI ecosystem, with a demo and recent 0.14.0 release, but it remains pre-1.0 with low adoption and no dependents. There are no Laravel-specific tests or official Laravel support, so production use requires careful version pinning and your own integration coverage. Treat it as viable for prototypes and controlled rollouts, not as a drop-in Laravel package.
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