Fundamentals
Understand the core concepts and data structures in VectorAI DB.Data structures
Collections, points, vectors, and payloads that organize your data.
Search and filtering
Vector similarity search with metadata filtering using must, should, and must-not conditions.
Indexing
HNSW algorithm configuration for efficient approximate nearest neighbor search.
Distance metrics
Cosine similarity, Euclidean distance, and dot product for measuring similarity.
Guides
Configure, secure, and operate VectorAI DB in production environments.Configuration
Server settings, performance tuning, and environment configuration.
Security
Authentication, authorization, and secure deployment practices.
Monitoring and logging
Observability, metrics collection, and log management.
Troubleshooting
Diagnose and resolve common issues.
Installation and deployment
Set up VectorAI DB in your environment.Docker
Run VectorAI DB in containers for development and production.
Integrations
Connect VectorAI DB with embedding providers and AI frameworks.OpenAI
Generate embeddings using OpenAI models.
LangChain
Build RAG pipelines with the LangChain framework.
LlamaIndex
Build RAG applications with the LlamaIndex framework.
Quick reference
| Section | Content | Use case |
|---|---|---|
| Fundamentals | Core concepts and data structures | Understanding VectorAI DB architecture |
| Guides | Configuration and operations | Production deployment and maintenance |
| Installation | Setup procedures | Getting VectorAI DB running |
| Cloud platforms | AWS, Azure, GCP deployment | Cloud infrastructure setup |
| Integrations | Third-party connections | Connecting embedding providers and frameworks |