Introduction
A customer types "can't log in" into your support portal. Instantly, they find your article titled "Password Recovery Guide" — even though none of the words match. How? Vector search.
In the era of Generative AI (GenAI), search has evolved far beyond simple keyword matching. Today’s users expect intelligent systems that understand meaning, context, and intent — not just literal words. Whether it’s retrieving the right answer from a knowledge base, recommending similar products, or powering Retrieval-Augmented Generation (RAG) pipelines for large language models (LLMs), the ability to search semantically has become fundamental to modern AI applications.
This is where vector search comes in.
Traditional search engines rely on exact keyword matches or structured queries. In contrast, vector search operates in a multidimensional space where each item — a document, image, audio clip, or product description — is represented by a numerical vector called an embedding. These embeddings capture semantic meaning, enabling searches based on similarity in meaning rather than exact text.
With the pgvector extension, EDB Postgres transforms into a full-fledged vector database — empowering developers and data scientists to store, index, and query embeddings directly within Postgres. This integration removes the need for external vector stores, allowing AI workloads to run seamlessly alongside transactional and analytical workloads, right where your enterprise data already resides.
What is Vector Search and Why Does It Matter?
At its core, vector search enables you to find data points that are similar in meaning or representation to a query, even if they don’t share exact words or values. Imagine asking a question like “How to reset my password?” — vector search can retrieve relevant documents such as “Steps to recover your login credentials” even though no words match exactly.
This ability to search by meaning rather than matching unlocks a new generation of intelligent applications.
Let’s explore some of the most common use cases:
1. Semantic Search
Move beyond keyword search. Semantic search allows your system to interpret the intent behind a query. For example, in a customer support portal, a user typing “I can’t sign in” should still find an article titled “Password Recovery Steps.” Vector search makes this possible by comparing the meaning of phrases rather than their literal wording.
2. Document Search and Retrieval
Organizations generate massive volumes of unstructured text — research papers, internal knowledge bases, legal documents, customer support tickets, etc. By converting these documents into embeddings and storing them in Postgres, vector search can instantly retrieve the most relevant content for a user query. This approach is central to RAG pipelines, where retrieved documents are fed into an LLM to provide accurate, context-aware answers.
3. Document Comparison
Banks, insurers, and auditors often need to compare large documents — contracts, regulatory reports, policy papers — to identify subtle differences. Vector similarity allows comparison even when documents are paraphrased or restructured. This helps detect duplicate, near-duplicate, or plagiarized content with high accuracy.
4. Image Similarity Search
Beyond text, embeddings can represent visual features too. An e-commerce site can use image embeddings to recommend visually similar products (“show me more like this dress”), or a media company can detect duplicate or near-duplicate images in massive libraries.
5. Recommendation Systems
By storing user and item embeddings in Postgres, you can recommend products, movies, or articles based on semantic similarity — similar preferences, purchase histories, or even emotional tone in user reviews.
How Does Vector Search Work?
To understand how vector search works inside Postgres, let’s break it down conceptually:
- Embedding Generation – A model (like OpenAI, BERT, or CLIP) converts raw data — text, image, or audio — into a dense vector representation. Each vector is a list of floating-point numbers (e.g., 768 dimensions for BERT).Think of these embeddings as GPS coordinates for meaning — just as similar locations cluster together on a map, similar concepts cluster together in this multidimensional space.
- Storage in Postgres – With pgvector, these embeddings are stored in a dedicated column of type vector. Each row in your table might represent a document, image, or record, with its corresponding vector embedding.
- Similarity Search – When a user submits a query (like a sentence or image), you convert it to an embedding using the same model. Then, you search for stored vectors that are closest in meaning — using similarity measures like cosine distance or Euclidean distance. Imagine finding which dots in a vast constellation are nearest to your query point.
- Ranking & Retrieval – The system ranks results by similarity score, returning the most relevant matches.
This architecture is simple yet powerful because it keeps everything — structured data, unstructured data, and embeddings — inside one unified Postgres ecosystem.
Similarity Metrics in Vector Search
The heart of vector search lies in comparing vectors — finding which stored embeddings are closest to the query vector. Different distance metrics determine how “similarity” is measured.
Two of the most widely used metrics are cosine similarity and Euclidean distance. Both are supported natively by pgvector.
Scaling Vector Search with Indexing
As your dataset grows — from thousands to millions of embeddings — searching through every vector (a brute-force approach) becomes computationally expensive. This is where vector indexing becomes essential.
What is a Vector Index?
A vector index structures the embeddings in a way that allows the system to find similar vectors quickly without scanning the entire dataset. It clusters and organizes vectors into partitions based on their characteristics.
Types of Vector Indexes
Several index types are used for vector search, including:
- IVF (Inverted File Index) – Divides vectors into clusters; search happens only within the most relevant clusters.
- HNSW (Hierarchical Navigable Small World Graphs) – A graph-based index that connects similar vectors, providing ultra-fast lookup.
- PQ (Product Quantization) – Compresses vectors into smaller representations, reducing storage and computation cost.
Approximate Nearest Neighbor (ANN) Search
ANN is the key to scaling vector search efficiently. Instead of finding the exact nearest neighbors, it finds almost the nearest ones — sacrificing a tiny bit of accuracy for massive performance gains.
For real-time AI use cases such as chatbots, recommendation engines, or semantic search, ANN indexing is the ideal balance between speed and precision.
Why Use EDB Postgres AI for Vector Search?
While many new vector databases have appeared in the AI ecosystem (like Pinecone, Milvus, Weaviate, etc.), enterprises often prefer EDB Postgres for several reasons:
1. Unified Platform
With pgvector, you don’t need a separate database just for embeddings. You can run your AI workloads directly inside the same Postgres instance that handles your transactional or analytical data. This drastically simplifies your architecture.
2. Enterprise-Grade Reliability
EDB Postgres builds upon decades of Postgres heritage — delivering enterprise-grade reliability, replication, and high availability. Unlike experimental vector stores, it’s trusted for mission-critical workloads.
3. Seamless Integration
EDB Postgres supports all major extensions, foreign data wrappers (FDWs), and integration frameworks — enabling hybrid analytics that combine structured data, embeddings, and external AI models.
4. Scalability
Combined with EDB Postgres Advanced Server, WarehousePG, or Postgres Distributed, organizations can scale their vector workloads across nodes, achieving both analytical performance and vector proximity search in one platform.
5. Governance and Security
Many industries — especially BFSI and public sector — demand strict governance and on-premise control. Running vector search on EDB Postgres ensures data sovereignty, auditability, and compliance.
Conclusion
Vector search represents a significant evolution in how we store and retrieve information. By bridging the gap between traditional databases and AI-driven semantics, EDB Postgres with pgvector empowers organizations to run intelligent, meaning-aware applications — all within a familiar, enterprise-grade database environment.
Whether you're building semantic search, recommendation engines, or RAG pipelines, vector search on EDB Postgres provides the scalability, reliability, and simplicity you need to bring AI closer to your data.
In this article, we explored the fundamentals of vector search, similarity metrics, and indexing techniques within EDB Postgres.
👉 In the next blog, we'll dive into hands-on implementation — walking through table creation, embedding storage, and running your first similarity queries with pgvector, complete with SQL examples and performance tuning tips.
Your journey into semantic intelligence with Postgres has just begun.
Stay tuned — your journey into semantic intelligence with Postgres has just begun.