Overview
Vector databases enable AI applications by storing and searching high-dimensional embeddings for similarity-based retrieval.
Key Use Cases
Semantic Search
- Document search by meaning
- Question answering
- Knowledge base retrieval
- Similar content discovery
RAG Systems
- Grounding LLM responses
- Enterprise chatbots
- Customer support automation
- Internal documentation search
Recommendation Engines
- Product recommendations
- Content suggestions
- User-item matching
- Collaborative filtering
Image and Video Search
- Visual similarity search
- Reverse image search
- Video content discovery
- Facial recognition
Anomaly Detection
- Fraud detection
- Cybersecurity threats
- Quality control
- Outlier identification
Deduplication
- Finding duplicate content
- Similar document detection
- Data cleaning
- Content moderation
Personalization
- User profiling
- Behavioral targeting
- Adaptive interfaces
- Custom experiences
Industry Applications
E-Commerce
- Product search and discovery
- Visual search
- Personalized recommendations
- Similar item suggestions
Healthcare
- Medical literature search
- Drug discovery
- Patient similarity
- Diagnostic assistance
Finance
- Fraud detection
- Document analysis
- Risk assessment
- Regulatory compliance
- Content recommendations
- Copyright detection
- Archive search
- Playlist generation
Customer Service
- Automated support
- FAQ matching
- Ticket routing
- Knowledge retrieval
Pricing
Use case implementation costs vary by vector database, scale, and infrastructure.
