Enable Postgres as the primary embeddings store via pg_vector support

Many engineering and ML teams are wrestling with system sprawl and high operational overhead as they bolt dedicated vector stores onto existing relational databases for semantic search, recommendations, and RAG workflows; this affects a broad addressable base of roughly 6 million developer organizations estimated to spend about $5,000 per year on tooling and DB infra for embedding workloads. The practical problems are predictable: inconsistent tooling, duplicated data pipelines, and difficulty meeting low-latency SLAs while maintaining transactional consistency and familiar SQL workflows. A focused product that enables Postgres to be the primary embeddings store—by hardening pg_vector support with production-grade indexing, tunable ANN algorithms (e.g., HNSW/IVF with controlled recall/latency tradeoffs), robust WAL/replication semantics for vectors, SQL-first APIs, migration tooling, observability, and managed/operator distributions—would let teams consolidate infra and reuse existing Postgres expertise. This is a timely opportunity: the embeddings-first wave, Postgres consolidation trend, and mature OSS projects like pg_vector and LangChain lower integration cost and create a $30.0B market opportunity; our Market Score of 92/100 and Revenue Potential of 88/100 reflect strong demand and monetization paths. The way to stand out is pragmatic: deliver measurable latency and recall guarantees for common workloads, seamless SQL ergonomics, and an easy migration path from competing vector DBs, while offering enterprise features (auth, backup, monitoring) and clear cost comparisons. The honest trade-offs are that specialized vector databases may outperform Postgres at extreme scale and that convincing conservative teams to rely on Postgres for high-throughput ANN requires rigorous benchmarks and strong operational experience, but the consolidation benefits and reduced TCO make this a compelling, actionable play given medium competition and clear market pull.