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Ai Typesense Store Laravel Package
symfony/ai-typesense-store
Typesense vector store integration for Symfony AI Store, adding a fast, typo-tolerant search backend for storing and querying embeddings. Supports vector search via the Typesense API within your Symfony AI applications.
Symfony AI Typesense Store integrates Typesense as a vector store for the Symfony AI Store component, enabling fast semantic search over embedded documents.
- Seamless Typesense backend for Symfony AI Store
- Store and query vector embeddings with document metadata
- Supports vector search with filtering
- Configurable through Symfony’s service container
- Built on the Typesense vector search API
Frequently asked questions about Ai Typesense Store
- How do I install symfony/ai-typesense-store in a Laravel application?
- Use Composer: composer require symfony/ai-typesense-store. Ensure symfony/ai and typesense/typesense are installed. There is no native Laravel service provider, so register bindings manually. Pin exact versions in composer.json due to 0.x interface changes.
- Does symfony/ai-typesense-store work with Laravel without Symfony?
- It depends on Symfony AI components but can be used in Laravel via container binding. You do not need the full Symfony framework; just the symfony/ai package and related dependencies. You will manually configure the Typesense client and bind StoreInterface.
- How do I configure Typesense for the Symfony AI vector store in Laravel?
- Create config/typesense.php with your Typesense nodes and API key. Instantiate the Typesense client and pass it to TypesenseStore along with the collection name. Bind the store to StoreInterface in a service provider. Use environment variables for credentials.
- What Laravel versions are supported by symfony/ai-typesense-store?
- The package itself does not declare Laravel version constraints, but it requires PHP and Symfony AI compatibility. Check composer.json for symfony/ai and typesense/typesense constraints. Test with your Laravel version in a proof-of-concept. Since there is no native integration, Laravel version support depends on the underlying dependencies.
- Can I use symfony/ai-typesense-store for semantic search and RAG in Laravel?
- Yes, it implements StoreInterface for vector search. You can add documents, query vectors, and filter metadata. Use it for semantic search, RAG pipelines, and recommendations. Ensure your Typesense server supports vector search.
- How do I test symfony/ai-typesense-store in a Laravel app?
- Use a test Typesense server or mock the Typesense client. Since StoreInterface is an interface, you can bind a fake store for unit tests. Integration tests can use a local Typesense instance via Docker. Pin versions to avoid interface changes in 0.x.
- What is VectorDocumentInterface in symfony/ai-typesense-store v0.14.0?
- It is a new first-class document abstraction introduced in v0.14.0. It improves type safety and decouples business logic from raw array payloads. Use it to future-proof integrations against further interface evolution. Adopt it in place of loose array payloads.
- Is symfony/ai-typesense-store production-ready?
- It is early-stage with 0.x versioning, low adoption, and sparse documentation. Conduct a proof-of-concept with a non-critical feature before production. Pin exact versions and isolate document construction behind an app-level factory. Monitor changelogs for interface changes.
- How does symfony/ai-typesense-store compare to other Symfony AI vector stores?
- It provides a Typesense backend for StoreInterface, similar to Redis or PostgreSQL stores. The abstraction lets you swap backends without rewriting business logic. Typesense offers built-in vector search and metadata filtering. Choose based on your existing infrastructure and search requirements.
- How do I handle count() and document counting with symfony/ai-typesense-store?
- As of v0.14.0, the store supports count() for document counting. Use it for dashboards, pagination, and index-health checks. Call the method on the bound StoreInterface. Ensure your Typesense collection is configured correctly.
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