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Ai Pinecone Store Laravel Package
symfony/ai-pinecone-store
Symfony AI Store integration for Pinecone vector databases. Upsert, query, and delete embeddings, and work with Pinecone serverless indexes using Pinecone’s data/control plane APIs. Links to official Pinecone docs and Symfony AI contribution resources.
Pinecone vector store bridge for Symfony AI
Frequently asked questions about Ai Pinecone Store
- Can I use symfony/ai-pinecone-store in Laravel without adopting Symfony AI?
- Yes, but with trade-offs. The package requires Symfony’s StoreInterface, so you’ll need to manually bind it to Laravel’s service container or use Symfony’s HTTP Client directly. For minimal overhead, consider Pinecone’s official PHP SDK instead, which avoids Symfony dependencies entirely.
- What Laravel versions does this package support?
- The package itself doesn’t enforce Laravel version constraints, but it depends on Symfony AI (v1.0+). Ensure your Laravel app (8.0+) can resolve Symfony’s dependencies via Composer. Test compatibility if using older Laravel versions, as Symfony AI may introduce breaking changes.
- How do I configure Pinecone’s API key and index in Laravel?
- Store your Pinecone API key in Laravel’s `.env` (e.g., `PINECONE_API_KEY`) and pass it to the store’s constructor. For the index name, use environment variables or bind it dynamically in Laravel’s service provider. Example: `$store = new PineconeStore($apiKey, $indexName);`
- Does this package support Pinecone’s serverless indexes?
- Yes, the package fully supports Pinecone’s serverless indexes via the control plane API. You can create, manage, and query serverless indexes using the same StoreInterface methods (e.g., `upsert()`, `query()`). Refer to Pinecone’s [serverless docs](https://docs.pinecone.io/reference/api/2024-10/control-plane/create_index) for setup.
- What’s the performance impact of using this bridge vs. Pinecone’s PHP SDK?
- The performance impact is negligible—this package is a thin abstraction over Pinecone’s APIs. However, adding Symfony AI (~50MB) may increase bundle size. For pure performance, Pinecone’s PHP SDK is lighter, but this bridge offers Symfony AI’s consistency and future-proofing.
- How do I handle errors or debug issues with Pinecone queries?
- Symfony AI abstracts errors, so Pinecone-specific issues may require custom logging. Wrap the store in a try-catch block and log exceptions with Pinecone’s error codes. For debugging, enable Symfony’s debug mode or use Pinecone’s [API status page](https://status.pinecone.io/).
- Can I switch vector stores (e.g., Weaviate) without rewriting my Laravel app?
- No, this package is Pinecone-specific. To switch stores, you’d need to refactor code using Symfony’s StoreInterface. For multi-store flexibility, consider a wrapper like `spatie/laravel-ai` or Pinecone’s native SDK, which decouples logic from the vector DB.
- Is this package suitable for production AI applications like RAG?
- Yes, it’s production-ready for RAG pipelines. The package supports upsert/query/delete operations, metadata filtering, and hybrid search—key features for semantic search. Monitor Pinecone’s [SLA](https://www.pinecone.io/pricing/) and implement retries for transient failures.
- How do I test Pinecone interactions in Laravel’s testing environment?
- Use Laravel’s `MockHttpClient` to intercept Pinecone API calls in tests. Mock responses for `upsert`, `query`, and `delete` endpoints to avoid real API costs. Example: `$client->mock(...)->toReturn(...);` in your test setup.
- What are the alternatives to symfony/ai-pinecone-store for Laravel?
- For lightweight use, Pinecone’s [official PHP SDK](https://github.com/pinecone-io/php-client) avoids Symfony dependencies. For Laravel-native solutions, explore `spatie/laravel-ai` or build a custom wrapper using Guzzle HTTP client. Weaviate or Milvus may offer self-hosted alternatives with similar APIs.
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