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Ai Surreal Db Store Laravel Package
symfony/ai-surreal-db-store
SurrealDB vector store integration for Symfony AI Store. Use SurrealDB’s vector indexing and search (MTREE/HNSW) to store embeddings and perform similarity queries, leveraging SurrealQL vector functions for retrieval in Symfony AI applications.
SurrealDB vector store bridge for Symfony AI
Frequently asked questions about Ai Surreal Db Store
- Can I use this package directly in Laravel without Symfony AI?
- No, this package requires Symfony AI as a dependency. For Laravel, you’d need to either adopt Symfony AI or create a custom wrapper (e.g., a Laravel service provider) to abstract the Symfony-specific components. Alternatively, use the [surrealdb/surrealdb](https://github.com/surrealdb/surrealdb) client directly and implement a Laravel-compatible vector store interface.
- What Laravel versions are officially supported?
- This package doesn’t natively support Laravel—it’s built for Symfony AI. However, Laravel 9+ can integrate it via Composer if you abstract Symfony dependencies. Check the [Symfony AI requirements](https://github.com/symfony/ai) for PHP 8.1+ compatibility, which aligns with Laravel’s modern versions. Always test thoroughly in your Laravel environment.
- How do I configure SurrealDB’s vector indexes (MTREE/HNSW) for optimal performance?
- Use SurrealQL’s `DEFINE INDEX` to create MTREE or HNSW indexes on your vector fields. For example: `DEFINE INDEX vector_index ON table FOR vector FIELD $vector TYPE mtree`. Benchmark different index types and dimensions (e.g., 1536 for embeddings) in your SurrealDB instance. Refer to the [SurrealDB vector docs](https://surrealdb.com/docs/surrealdb/models/vector) for advanced tuning.
- Is SurrealDB’s vector search scalable for high-query workloads (e.g., 10K+ QPS)?
- SurrealDB’s vector search is less battle-tested than pgvector or Weaviate at scale. Start with benchmarks on your expected query volume, focusing on latency under load. Consider caching frequent queries (e.g., with Redis) or implementing a fallback to a secondary store if SurrealDB becomes a bottleneck. Monitor SurrealDB’s [performance metrics](https://surrealdb.com/docs/surrealdb/performance).
- How do I migrate existing vector data (e.g., from PostgreSQL or Elasticsearch) to SurrealDB?
- Use SurrealQL’s `INSERT` or `UPSERT` to bulk-load data, or write a custom ETL script leveraging the [SurrealDB PHP client](https://github.com/surrealdb/surrealdb). For large datasets, chunk migrations to avoid timeouts. Ensure your schema matches SurrealDB’s requirements (e.g., defining indexes post-migration). Test with a subset of data first.
- What’s the best way to handle authentication between Laravel and SurrealDB?
- SurrealDB uses its own [NSM (Namespace-Schema-Map) authentication](https://surrealdb.com/docs/surrealdb/security). For Laravel, configure the SurrealDB client with credentials (e.g., `new Client('http://localhost:8000', 'ns', 'db', 'root', 'password')`). Avoid hardcoding secrets—use Laravel’s `.env` or a secrets manager. For production, consider integrating with Laravel’s Passport or Sanctum via a custom middleware layer.
- Are there alternatives to this package for Laravel that avoid Symfony dependencies?
- Yes. For Laravel, consider [laravel-surreal](https://github.com/your-repo/laravel-surreal) (if available) or build a custom wrapper around the [surrealdb/surrealdb](https://github.com/surrealdb/surrealdb) client. For vector stores, alternatives include `pgvector` (PostgreSQL), `meilisearch/meilisearch`, or `weaviate/weaviate-client-php`. Evaluate based on your need for unified data models (SurrealDB) vs. specialized vector search.
- How do I test this package in a Laravel CI pipeline?
- Mock the SurrealDB client in your tests using Laravel’s `Mockery` or PHPUnit’s `createMock`. Test critical paths like vector insertion, similarity queries, and error handling (e.g., connection failures). Use Docker to spin up a SurrealDB instance for integration tests. Example: `docker run -p 8000:8000 surrealdb/surrealdb start --user root --password root`. Validate against your Laravel app’s service container bindings.
- What’s the production deployment strategy for SurrealDB in Laravel?
- Deploy SurrealDB as a separate service (e.g., Docker container, Kubernetes pod, or cloud instance). Use Laravel’s service container to manage the SurrealDB client connection pool. Implement retries with exponential backoff for transient failures (e.g., network issues). For high availability, replicate SurrealDB across regions and use Laravel’s queue workers to handle async vector operations.
- How do I handle schema changes in SurrealDB without downtime?
- SurrealDB’s schema-less design allows dynamic changes, but index modifications (e.g., `DEFINE INDEX`) may require downtime. For critical indexes, use blue-green deployments: create a new index on a replica, validate queries, then switch traffic. For non-critical changes, test in staging first. Monitor SurrealDB’s [admin API](https://surrealdb.com/docs/surrealdb/admin) for real-time status during changes.
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