PgVector vs PgVectorStore: What’s the Difference? A Clear Guide for AI & RAG Developers

As AI-powered applications and Retrieval-Augmented Generation (RAG) systems become more widespread, PostgreSQL has emerged as a powerful database option for storing and searching vector embeddings. One key reason: the growing popularity of pgvector, a PostgreSQL extension purpose-built for vector similarity search.

But if you are working with frameworks like LangChain, LlamaIndex, or other LLM-based app builders, you’ll often also see something called pgvectorstore (or PGVector). Many developers wonder:

“What is the difference between pgvector and pgvectorstore? Are they the same or do I need both?”

This comprehensive guide explains it in simple terms.

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🚀 Quick Answer

Feature	pgvector	pgvectorstore
What it is	A PostgreSQL extension that adds a vector data type	A library wrapper used by LLM frameworks
Purpose	Store and search embedding vectors	Store documents + embeddings + metadata easily
Requires SQL?	Yes	Not necessarily
Index support	Native: HNSW, IVFFlat	Uses pgvector indexes internally
Best for	Database-level vector search	RAG & AI app development

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🧩 Understanding pgvector

What is pgvector?

pgvector is an open-source PostgreSQL extension that adds first-class support for vector embeddings. Embeddings are just lists of numbers that represent text, images, audio, etc. With pgvector, you can store them directly in Postgres.

Example:

CREATE TABLE items (

  id serial primary key,

  content text,

  embedding vector(768)

);

Key Features of pgvector

✔ A new vector(n) column type
✔ Similarity search using operators:

• <-> Euclidean distance
  
  

• <#> Inner product
  
  

• <=> Cosine distance
  
  

✔ Indexes for fast search (IVFFlat, HNSW)
✔ Fully integrated with SQL and PostgreSQL tooling

When should you use pgvector?

Use pgvector if you want:

• Full SQL control
  
  

• To integrate vector search into existing Postgres apps
  
  

• Maximum database-level flexibility
  
  

• No dependency on AI frameworks
  
  

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🧩 Understanding pgvectorstore (PGVector in LangChain)

What is pgvectorstore?

pgvectorstore (often called PGVector in LangChain) is not a database extension. It is a high-level wrapper that uses pgvector under the hood.

It is designed for LLM / RAG developers who want to store:

• Document text
  
  

• Embeddings
  
  

• Metadata (source, URL, tags, etc.)
  
  

…without writing SQL manually.

Sample usage in Python:

from langchain_postgres import PGVector from langchain.embeddings import OpenAIEmbeddings vectorstore = PGVector.from_texts(     texts=["AI is transforming business.", "Cats are cute."],     embedding=OpenAIEmbeddings(),     connection="postgresql://user:pass@localhost/db" ) results = vectorstore.similarity_search("What is AI?")

Key Features of pgvectorstore

 ✔ Easy document and embedding storage
✔ Auto-schema creation
✔ Fits neatly into LLM pipelines
✔ Supports similarity search & filtering
✔ Good for RAG applications

When should you use pgvectorstore?

Use pgvectorstore if you want:

• Fast RAG development
  
  

• Minimal SQL
  
  

• Seamless LangChain or LlamaIndex integration
  
  

• Ability to store rich metadata and text
  
  

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🧠 Analogy to Make It Crystal Clear

Concept	Analogy
pgvector	A high-performance engine
pgvectorstore	A car built using that engine so you can drive easily

pgvector is the powerful backend component. Pgvectorstore makes it usable for everyday LLM application developers.

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🛠 Example RAG Architecture Using Both

User Question

      │

      ▼

LLM Embedding Model (OpenAI, Cohere, NVIDIA, etc.)

      │

      ▼

pgvectorstore (RAG logic)

      │

      ▼

pgvector Table in PostgreSQL

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🔍 Which One Should You Choose?

Goal	Recommendation
You want full SQL control	Use pgvector directly
You are building a RAG chatbot/search app	Use pgvectorstore
You need best performance & indexing control	Use pgvector
You want fast LLM development and abstractions	Use pgvectorstore
You’re replacing Pinecone, Chroma, Weaviate	pgvector + pgvectorstore

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🔗 Comparison with Other Vector Databases

Database	Type	Notes
pgvector	PostgreSQL extension	Great if you already use Postgres
Pinecone	Cloud Vector DB	Fully managed, high-performance
Weaviate	Vector DB	Graph + hybrid search
Chroma	Local or hosted	Simple and lightweight
Milvus	Distributed vector DB	Scales to billions of vectors

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🧩 Conclusion

Both pgvector and pgvectorstore are valuable—but they serve different purposes:

• pgvector is the core PostgreSQL technology that enables vector storage and similarity search.
  
  

• pgvectorstore (e.g., LangChain PGVector) is a developer-friendly tool that simplifies building LLM and RAG applications on top of pgvector.
  
  

If you are building an AI application with embeddings and you want the power of PostgreSQL along with easy integration into LLM toolchains, you will usually use both together.