Ai Weaviate Store Laravel Package

Product Decisions This Supports

• Roadmap: whether to ship semantic search / RAG at all. This bridge is the cheapest path from "idea on a roadmap" to a working vector retrieval loop in a PHP codebase. It lets you validate demand for semantic search, "similar items," and LLM-grounded answers behind one StoreInterface abstraction rather than a bespoke Weaviate integration. A PM can scope a 2–3 sprint discovery spike, not a quarter-long infrastructure project.
• Build vs. buy — with a nuance. The integration is a buy (MIT, ~free, abstracts Weaviate's REST + GraphQL, nearVector similarity search, document CRUD, and clear()/count() operations). The infrastructure is still yours: you own the Weaviate cluster, schema/collection lifecycle, embeddings pipeline, and cost. Frame the decision as "buy the adapter, operate the store."
• Feature-level use cases you can put on a roadmap with confidence:
  • Semantic search over docs, product catalogs, support tickets, or knowledge bases.
  • "More like this" / recommendation surfaces via similarity retrieval.
  • RAG context injection for chat and Q&A assistants.
  • Search UX and admin instrumentation — the new count() capability gives you result totals, collection-size displays, index-health dashboards, and coverage metrics without fetching full documents.
• Vendor-portability hedge. Because the package targets a Symfony StoreInterface abstraction, a Weaviate decision doesn't hard-lock your application code the way a raw Weaviate SDK would. That materially lowers the cost of being wrong, which is exactly the argument a PM needs to get a first vector store into the stack.
• Cost positioning. Open-source Weaviate (self-hosted or cloud) vs. proprietary vector DBs (Pinecone, Astra) is a real line-item difference in a roadmap review. Include it in the business case — but pair it with the ops headcount you're implicitly adding.
• Not a decision this supports: any commitment to vector search as a core, revenue-critical primitive today. At v0.14.x pre-1.0 with 1 star and 0 dependents, this is an evaluation-grade dependency. Treat it as a spike enabler, not a foundation.

When to Consider This Package

Adopt when:

• You're on Laravel/PHP and already (or willing to) pull in Symfony AI components; the interoperability story is credible and the abstraction layer is Laravel-wrappable behind a service provider and facade.
• You need semantic search or RAG now, and the alternative is a 4–8 week custom Weaviate client. The package removes the raw HTTP/GraphQL work — schema, auth scoping (ScopingHttpClient), and query abstraction are handled.
• You already run, or plan to run, Weaviate. If the answer is "we'd have to stand up Weaviate first," add that as a separate line item and a separate owner before committing.
• Your team can pin Composer constraints to a tested minor line and absorb pre-1.0 churn. Symfony AI packages are still 0.x; releases like v0.14.0 are additive but the ecosystem moves.
• You're building an internal or discovery-phase feature where a mid-flight dependency change is recoverable.

Look elsewhere when:

• You need a vendor SLA, long-term support guarantees, or a security-reviewed dependency. One star, zero dependents, and "no significant changes" release notes across five of the last eight versions is a real signal — the bridge is maintained as part of the Symfony AI monorepo, not as an independently hardened product.
• You need capabilities the abstraction doesn't expose: advanced GraphQL traversal, hybrid/BM25 tuning, multi-tenancy controls, or fine-grained schema management. Those still require direct Weaviate API calls, which erodes the "drop-in" value proposition.
• Your latency budget is tight and count/search queries will sit on hot request paths. Counts are not free at scale, and the package won't cache or degrade for you — that's your engineering work.
• You're on a pure-Laravel stack with no appetite for Symfony HTTP client alongside Guzzle. Dependency conflicts are a known friction point; isolate and pin, or don't adopt.
• You're building a durable, revenue-critical search product. At that point evaluate Weaviate's official clients, a managed service, or a Laravel-native vector solution with a support contract.

Threshold to graduate: if the spike succeeds, re-evaluate against a supported client or managed vector DB before the feature becomes customer-facing and load-bearing.

How to Pitch It (Stakeholders)

For executives (one paragraph): We can ship semantic search and AI-grounded answers without hiring a vector-database team. This is a free, MIT-licensed PHP adapter that connects our Laravel app to Weaviate — the open-source vector store — so we write product code instead of database plumbing. It's roughly a two-to-three sprint discovery spike, and because it sits behind an abstraction layer, we can swap out Weaviate later without rewriting the feature. The caveat is maturity: this is a pre-1.0 component with a small footprint, so I'm recommending it for a scoped pilot with a defined go/no-go gate, not as a platform commitment. We'd own the Weaviate infrastructure and its operating cost either way.

For engineering (the framing that gets a clean estimate):

• The adapter covers vector persistence, nearVector similarity search, GraphQL document retrieval, delete, clear(), and count(). It does not cover schema/collection lifecycle — that's ours, and it needs an owner and an Artisan-command story before we start.
• Wrap StoreInterface in a Laravel service provider and facade. Do not let Symfony store types reach controllers; map to our own DTOs at the adapter boundary. If we adopt VectorDocumentInterface, do it inside the adapter, not in domain models.
• Pin to the tested 0.x minor. Assign upgrade validation explicitly — nobody owns this by default, and that's how pre-1.0 dependencies rot.
• Known risks to size in the estimate: Weaviate-specific exceptions need Laravel-side mapping; count queries need caching (Redis keys per collection/query) before they touch high-traffic paths; Symfony HTTP client vs. Guzzle conflicts should be isolated to the AI module; heavy ingestion goes on queues so it doesn't compete with search or count latency.
• Go/no-go gate: if the spike proves retrieval quality and p95 latency on a realistic collection, we promote it and revisit the dependency against a supported client. If not, we've spent a bounded spike and kept the abstraction knowledge.