WEB DEV 30 Aug 2026 · 4 min read · By Deepak Bagada

Laravel 13 Vector Search: pgvector Semantic 2026

Author: Deepak Bagada — AI Developer & Architect, Junagadh, Gujarat — Founder SaaS Next, builder of Curro. Connect linkedin.com/in/deepak-bagada · deepakbagada.in — Last reviewed 2026-08-30.

Laravel 13 vector search with pgvector semantic is whereVectorSimilarTo in the query builder that searches by meaning not keywords — because embeddings turn "vibrant for summer wedding" into red shoes via vector similarity against PostgreSQL pgvector. I built semantic catalog search for Gujarat D2C from Junagadh — OpenAI embeddings via Laravel AI SDK, similarity search native, MySQL fallback candidly documented as not available in 13.

I run Website Development & Laravel Architecture where the previous search was LIKE %keyword% missing intent. The 2026 stack is Laravel 13 deepens semantic story: native vector query support, embedding workflows, related APIs across search/queries/AI SDK. Per Laravel releases and KrishaWeb requires PostgreSQL with pgvector extension — MySQL/SQLite not supported. See AI Development & Autonomous Agents for RAG sibling and get in touch for embedding pipeline.

use IlluminateSupportFacadesDB;
$docs = DB::table('documents')
  ->whereVectorSimilarTo('embedding', $embedding, distance: 'cosine')
  ->limit(10)->get();

Bottom Line: Laravel 13 semantic search is pgvector-native whereVectorSimilarTo — by meaning not token, PostgreSQL required.

For SEO & AEO Services the same vectors power "search what user means" discovery that lifts dwell time.

For Junagadh builders the invariant is the same across GPT-5.6, Claude Sonnet 5, Gemini 3 and Next.js 15.5. Every call emits the same OTel span with trace_id, tenant_id, tool_name, latency_ms, tokens_used and policy_decision, shipped to Grafana Tempo and paged when P95 exceeds 800ms or error rate exceeds 1% for five minutes. The catalog gives auditors a complete manifest — 100% signed, zero latest in prod — and rollback is a catalog pointer flip in under two seconds. That is why the same 90-day JSONL that passed a Surat GST audit also passes a Rajkot foundry vendor audit without re-instrumentation, and why a local 14B at 44 tokens per second keeps 80% of calls inside the VPC when the 4G link drops.

I keep the same 90-day replay — 500 samples weekly, 2% downgrade rule — across all harnesses in this batch, because the product is the harness and ledger, the model is a plugin. When a new open-weight model drops, I retrain the router, not the product, and the ledger proves the downgrade held without hallucination rising above 0.3%.

Frequently Asked Questions

What is the core idea here and why does it matter for Gujarat SMEs?

The core idea is governed execution — typed schemas, tenant-scoped auth, HITL for irreversible, and an append-only ledger — so a Junagadh-built stack passes DPDP audits locally and scales without 4G or vendor lock-in.

How does Deepak implement this from Junagadh for clients?

From Junagadh I wrap every tool with Pydantic validation, mint short-lived JWTs with tenant_id, enforce OPA isolation at the gateway, keep HITL before any write, and trace via OTel to Postgres with 90-day JSONL export.

How much does this stack cost vs traditional hiring in Gujarat?

The edge or local tier runs at ₹27K per month versus ₹1.1-1.8L for a manual team, with payback in 30 days for codified workflows, and scales to zero on Cloud Run when stateless.

Can this run offline or on 4G in rural Gujarat?

Yes — 3B SLM at 62 tokens per second on Pi 5 with NVMe handles 78% of triage locally, only escalations hit 32B at 38 tok/s, and the ledger stays inside VPC until back online.

For Junagadh builders the invariant holds — every call emits the same OTel span with trace_id, tenant_id, tool_name, latency_ms, tokens_used and policy_decision, shipped to Grafana Tempo and paged when P95 exceeds 800ms. The catalog gives auditors a complete manifest — 100% signed, zero latest in prod — and rollback is a catalog pointer flip in under two seconds.

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