mcp-server-elasticsearch VS pgvector

Yes, Postgres has full-text search. No, it’s not going to outshine Elasticsearch or Meilisearch for large-scale, search-heavy workloads. These tools are built for indexing, ranking, and lightning-fast queries across massive datasets.

That’s when Postgres turns into a real platform not just a database. You can go from analytics with TimescaleDB to AI-powered search with PGVector without juggling multiple systems.

GitHub: https://github.com/pgvector/pgvector

There aren't many options out there for sparse ANN search that supports txtai requirements, so IVFSparse was introduced. IVFSparse is an Inverted file (IVF) index with flat vector file storage and sparse array support. There is also support for storing sparse vectors in Postgres via pgvector.

Postgres with pgvector and ElasticSearch are Capable of storing vector values.

AI applications are expanding rapidly, and PostgreSQL is a popular choice among relational databases. The pgvector extension, a third-party add-on, enhances PostgreSQL by introducing a high-dimensional vector data type with similarity operations and search indexing. Integrating embeddings directly into general-purpose databases eliminates the need for a separate one. Typically, approximate searches on embeddings are performed alongside exact searches on various other attributes, SQL columns or document fields, such as metadata, dates, or other dimensions. PostgreSQL offers various index types, but it has notable limitations when combining them, as we have seen in PostgreSQL JSONB Indexing Limitations with B-Tree and GIN. Likewise, pgvector encounters similar issues. Some users have moved to MongoDB Atlas Vector Search because it offers pre-filtering capabilities. They had incomplete results with PostgreSQL pgvector when filtering with other predicates. To better understand the impact of lacking pre-filtering in such scenarios, I built this simple demo.

pgvector Documentation

pgvector Vector database. Approximate indexing HNSW: Hierarchical Navigable Small World IVFFlat: Inverted File Flat Supported by GCP Cloud SQL and AWS RDS. How does it compare to pg_search?

AI applications using RAG (retrieval-augmented generation) can help businesses unlock insights from mountains of unstructured data. Today, that unstructured data’s natural home is Amazon S3. On the other hand, Postgres has become the default vector database for developers, thanks to extensions like pgvector and pgvectorscale. These extensions enable them to build intelligent applications with vector search capabilities without needing to use a separate database just for vectors.

We’re releasing a new benchmark that challenges the assumption that you can only scale with a specialized vector database. We compared Postgres (with pgvector and pgvectorscale) to Qdrant on a massive dataset of 50 million embeddings. The results show that Postgres not only holds its own but also delivers standout throughput and latency, even at production scale.

Instead of using a separate vector database, you can store text embeddings inside PostgreSQL with pgvector. We recommend using pgai to simplify building RAG apps, as we have a handy interface called create_vectorizer() that automatically embeds raw text, chunks it, and continuously keeps it up-to-date.