Build production AI applications with PostgreSQL: pgvector extension, vector embeddings, RAG systems, LLM integration, and AI agent memory patterns. From setup to production deployment.
Native AI features that make PostgreSQL perfect for AI applications
Native vector storage and similarity search in PostgreSQL with optimized indexing and query performance for AI workloads.
Purpose-built schema patterns for AI agent conversation memory, decision tracking, and context management with PostgreSQL's JSONB capabilities.
Proven PostgreSQL patterns for LLM applications: prompt templates, response caching, token usage tracking, and model version management.
Complete Retrieval-Augmented Generation implementation with PostgreSQL: document storage, embedding generation, and hybrid search capabilities.
Proven database patterns for common AI applications
Building chatbots and AI assistants with persistent memory and context
-- Conversation memory table
CREATE TABLE conversations (
id UUID PRIMARY KEY,
user_id UUID NOT NULL,
session_id UUID NOT NULL,
messages JSONB NOT NULL,
embedding vector(1536),
created_at TIMESTAMP DEFAULT NOW()
);
-- Create vector index for similarity search
CREATE INDEX ON conversations USING hnsw (embedding vector_cosine_ops);
RAG systems for document analysis and question answering
-- Document chunks with embeddings
CREATE TABLE document_chunks (
id UUID PRIMARY KEY,
document_id UUID NOT NULL,
chunk_text TEXT NOT NULL,
embedding vector(1536),
metadata JSONB,
chunk_index INTEGER
);
-- Vector similarity index
CREATE INDEX ON document_chunks USING hnsw (embedding vector_cosine_ops);
-- Metadata search index
CREATE INDEX ON document_chunks USING gin (metadata);
AI-powered recommendations using user and item embeddings
-- User embeddings and preferences
CREATE TABLE user_embeddings (
user_id UUID PRIMARY KEY,
embedding vector(512),
preferences JSONB,
updated_at TIMESTAMP DEFAULT NOW()
);
-- Item embeddings and metadata
CREATE TABLE item_embeddings (
item_id UUID PRIMARY KEY,
embedding vector(512),
categories TEXT[],
metadata JSONB
);
Managing AI agent decision making and tool usage
-- Agent decision tracking
CREATE TABLE agent_decisions (
id UUID PRIMARY KEY,
agent_id VARCHAR(100),
task_id UUID,
decision_context JSONB,
tools_used TEXT[],
outcome JSONB,
embedding vector(768),
timestamp TIMESTAMP DEFAULT NOW()
);
-- Agent learning from decisions
CREATE INDEX ON agent_decisions USING hnsw (embedding vector_cosine_ops);
Key extensions that enable AI capabilities in PostgreSQL
Vector similarity search and embeddings storage
CREATE EXTENSION vector; Advanced embedding operations and transformations
CREATE EXTENSION pg_embedding; Automated embedding generation and management
CREATE EXTENSION pg_vectorize; Direct AI model integration within PostgreSQL
CREATE EXTENSION pg_ai; Proven optimization techniques for AI workloads
Slow vector similarity searches on large datasets
10-100x faster vector queries
-- Optimized HNSW index creation
CREATE INDEX ON embeddings USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);
-- Query with optimized ef parameter
SET hnsw.ef_search = 40;
SELECT * FROM embeddings ORDER BY embedding <-> query_vector LIMIT 10;
Combining vector and text search efficiently
50% improvement in hybrid queries
-- Hybrid search with vector and text
SELECT *,
(embedding <-> $1) AS vector_distance,
ts_rank(search_vector, query) AS text_rank
FROM documents
WHERE search_vector @@ query
ORDER BY (embedding <-> $1) * 0.7 + (1 - ts_rank(search_vector, query)) * 0.3
LIMIT 10;
High memory usage with large vector datasets
60% reduction in memory usage
-- Optimized PostgreSQL settings for AI workloads
shared_buffers = 4GB
work_mem = 256MB
effective_cache_size = 12GB
maintenance_work_mem = 1GB
max_parallel_workers_per_gather = 4
Slow individual embedding operations
10x faster embedding insertion
-- Batch embedding insertion
INSERT INTO embeddings (text, embedding)
SELECT text, embedding
FROM unnest($1::text[], $2::vector[]) AS t(text, embedding);
-- Parallel processing
SET max_parallel_workers_per_gather = 4;
Integrate PostgreSQL with popular AI frameworks
Popular Python framework for LLM applications
PostgreSQL vector store with pgvector support
from langchain.vectorstores import PGVector
from langchain.embeddings import OpenAIEmbeddings
# Initialize PostgreSQL vector store
vectorstore = PGVector(
connection_string="postgresql://user:pass@localhost/db",
embedding_function=OpenAIEmbeddings(),
collection_name="documents"
)
# Add documents
vectorstore.add_documents(documents)
# Similarity search
results = vectorstore.similarity_search("query", k=5)
Data framework for LLM applications
PostgreSQL as vector database and document store
from llama_index.vector_stores import PGVectorStore
from llama_index import VectorStoreIndex
# Create vector store
vector_store = PGVectorStore.from_params(
database="mydb",
host="localhost",
password="password",
port=5432,
user="user",
table_name="embeddings",
embed_dim=1536
)
# Create index
index = VectorStoreIndex.from_vector_store(vector_store)
NLP framework for search and QA systems
PostgreSQL document store with vector capabilities
from haystack.document_stores import PgVectorDocumentStore
# Initialize document store
document_store = PgVectorDocumentStore(
host="localhost",
port=5432,
username="user",
password="password",
database="haystack",
embedding_dim=768
)
# Write documents
document_store.write_documents(documents)
Vector database client with PostgreSQL integration
Use PostgreSQL as backend for Weaviate-style operations
import psycopg2
import numpy as np
# Custom PostgreSQL vector operations
def similarity_search(query_vector, limit=10):
conn = psycopg2.connect(DATABASE_URL)
cur = conn.cursor()
cur.execute("""
SELECT id, content, embedding <-> %s AS distance
FROM documents
ORDER BY embedding <-> %s
LIMIT %s
""", (query_vector, query_vector, limit))
return cur.fetchall()
How PostgreSQL powers production AI applications
AI-powered customer support with conversation memory and knowledge base
Stores conversations, embeddings, and knowledge base with vector search capabilities
Personalized content recommendations using user behavior and content embeddings
Manages user profiles, content embeddings, and similarity calculations
Semantic search across large document collections with AI-powered insights
Vector storage for embeddings with full-text search and metadata filtering
Code completion and generation with context awareness and learning
Stores code patterns, context embeddings, and usage analytics
Essential practices for production AI applications
Choose optimal embedding dimensions for your use case
Implement efficient indexing for AI workloads
Design PostgreSQL schemas optimized for AI applications
Monitor and optimize AI workload performance
Step-by-step approach to building AI applications with PostgreSQL
Vela provides optimized PostgreSQL for AI applications with pgvector support, instant cloning for AI experiments, and enterprise-grade infrastructure. Start building production AI applications today.