pgvectorConsulting & AI Services
Scale your AI applications with PostgreSQL's vector search extension. Expert embedding optimization, HNSW index tuning, and 24/7 SRE support for production RAG systems and semantic search.
What is pgvector?
pgvector is an open-source PostgreSQL extension that adds vector similarity search capabilities to your existing database. Store embeddings from OpenAI, Cohere, or any ML model alongside your relational data with full ACID compliance.
pgvector Index Comparison
Hierarchical Navigable Small World - Best for query speed
Best for: Production queries, real-time search
Inverted File with Flat vectors - Best for memory efficiency
Best for: Large datasets, cost-sensitive deployments
JusDB pgvector Services
End-to-end support for production AI applications powered by pgvector
Index Optimization
Configure optimal vector indexes for your workload. Choose between HNSW for speed or IVFFlat for memory efficiency with expert tuning of ef_construction, m, and nlist parameters.
- HNSW parameter tuning
- IVFFlat optimization
- Index build strategies
- Memory vs speed tradeoffs
Query Performance
Achieve sub-millisecond vector similarity search at scale. Optimize query plans, parallel execution, and result set handling for production AI applications.
- Query plan optimization
- Parallel query tuning
- Distance function selection
- Batch query optimization
Embedding Management
Design efficient embedding storage strategies. Handle multiple embedding models, dimension reduction, and hybrid search combining vectors with traditional filters.
- Multi-model storage
- Dimension optimization
- Hybrid search design
- Embedding versioning
Scaling & Performance
Scale pgvector from thousands to billions of vectors. Expert guidance on partitioning strategies, read replicas, and distributed vector search architectures.
- Horizontal partitioning
- Read replica setup
- Sharding strategies
- Connection pooling
High Availability Setup
Production-grade HA for AI applications with streaming replication, automatic failover, and disaster recovery ensuring your vector search never goes down.
- Streaming replication
- Automatic failover
- Multi-region DR
- Zero-downtime upgrades
24/7 SRE Support
Round-the-clock monitoring and incident response for production AI workloads. Expert support for pgvector-specific issues and performance optimization.
- Proactive monitoring
- Incident response
- Performance alerts
- Expert escalation
How JusDB Helps You Scale pgvector
Production-proven strategies for scaling vector search workloads
HNSW Index Architecture
pgvector's HNSW (Hierarchical Navigable Small World) index provides approximate nearest neighbor search with 95-99% recall at sub-millisecond latency. We tune ef_construction and m parameters for your specific recall/speed requirements.
Hybrid Search
Combine vector similarity with traditional SQL filters. Search for similar products within a category, or find relevant documents from a specific date range - all in a single query.
Partitioning Strategies
Scale beyond single-node limits with intelligent partitioning. Partition by customer, time period, or embedding model while maintaining fast vector search across partitions.
Integration Expertise
Seamless integration with LangChain, LlamaIndex, OpenAI, Anthropic, and other AI frameworks. We help you build production RAG pipelines with proper embedding management.
AI Framework Expertise
We help you integrate pgvector with leading AI frameworks
pgvector Use Cases
AI applications where JusDB delivers pgvector excellence
RAG & Chatbots
Power Retrieval-Augmented Generation systems and AI chatbots with fast semantic search over knowledge bases, documents, and conversation history.
Semantic Search
Build intelligent search that understands meaning, not just keywords. Power product search, content discovery, and enterprise search applications.
Image Similarity
Find visually similar images, detect duplicates, and power reverse image search with CLIP embeddings and efficient vector indexing.
Recommendations
Build personalized recommendation systems using user and item embeddings. Power product recommendations, content suggestions, and discovery feeds.
Document Analysis
Semantic document search, similarity detection, and intelligent document clustering for legal, research, and enterprise content management.
Multi-Modal AI
Combine text, image, and audio embeddings for cross-modal search and retrieval. Build unified AI experiences across content types.
Frequently Asked Questions
Common questions about pgvector and our AI database services
Why choose pgvector over Pinecone, Weaviate, or Milvus?
pgvector runs inside PostgreSQL, giving you ACID transactions, joins with relational data, and the mature PostgreSQL ecosystem. You avoid the complexity of managing a separate vector database, reduce costs, and maintain data consistency. For many AI applications, pgvector offers sufficient performance while dramatically simplifying your architecture.
How many vectors can pgvector handle?
pgvector can handle billions of vectors with proper configuration. We've helped clients manage 10+ billion vectors with sub-millisecond query latency using partitioning, HNSW indexes, and read replicas. The limit is typically memory and storage, not pgvector itself.
What embedding dimensions does pgvector support?
pgvector supports vectors up to 2,000 dimensions, which covers most embedding models including OpenAI's text-embedding-3-large (3,072 dims with dimension reduction), Cohere, and open-source models. For higher dimensions, we can implement dimension reduction strategies.
Can pgvector handle real-time embedding updates?
Yes, pgvector supports concurrent inserts and updates while maintaining index consistency. We implement strategies for high-throughput embedding ingestion, including batch processing, async updates, and index maintenance scheduling.
Do you support pgvector on managed PostgreSQL services?
Yes, we support pgvector on AWS RDS, Aurora, Google Cloud SQL, Azure Database for PostgreSQL, and all major managed services that support the pgvector extension. We also support self-hosted deployments on any cloud or on-premises.