examples/

Cortexdb Examples

This directory contains comprehensive examples demonstrating the full capabilities of the cortexdb SQLite vector database library.

🚀 Quick Start

All examples are self-contained and runnable. To run any example:

# Run directly
go run examples/simple_usage/main.go

# Or build and run
go build -o simple_example examples/simple_usage/main.go
./simple_example

📚 Examples Overview

1. simple_usage - Getting Started

• Basic vector operations (add, search, delete)
• Collections management
• Similarity search fundamentals
• Perfect for: First-time users

2. semantic_search - Text Search with Embeddings

• Document indexing with metadata
• Semantic similarity search
• Collection-based filtering
• Similarity threshold filtering
• Performance metrics
• Perfect for: Search applications, document retrieval

3. document_clustering - K-means Clustering

• K-means clustering implementation
• Intra-cluster similarity analysis
• Outlier detection
• Cross-cluster analysis
• Cluster models for classification
• Perfect for: Data analysis, content organization

4. hybrid_search - Vector + Graph Search

• Knowledge graph construction
• Citation network analysis with PageRank
• Research path finding
• Combined vector and graph search
• Performance comparison
• Perfect for: Research papers, knowledge bases

5. multi_collection - Multi-tenant Data Management

• Multiple collections with different dimensions
• E-commerce products, users, reviews
• Cross-collection search
• Personalized recommendations
• Collaborative filtering
• Perfect for: E-commerce, multi-tenant applications

6. image_search - Multi-modal Search

• CLIP-like image embeddings
• Text-to-image search
• Image-to-image similarity
• Tag-based filtering
• Duplicate detection
• Perfect for: Image galleries, visual search

7. knowledge_graph - Graph-based Knowledge Management

• Entity nodes with embeddings
• Relationship edges
• Graph traversal algorithms
• Community detection
• PageRank for importance
• Perfect for: Knowledge graphs, recommendation systems

8. rag_system - Retrieval-Augmented Generation

• Document graph construction
• Hybrid retrieval strategies
• Context-aware search
• Graph-enhanced retrieval
• Perfect for: RAG applications, Q&A systems

9. benchmark - Performance Testing

• Insert performance testing
• Search performance benchmarking
• Batch operations
• Large-scale testing
• Perfect for: Performance optimization, capacity planning

10. chat_memory - AI Chat Memory

• Session management
• Message storage
• Context retrieval
• Semantic memory search
• Perfect for: Chatbots, AI Agents

11. benchmark_ivf - Index Comparison

• Compare HNSW vs IVF performance
• Index training demo
• Perfect for: Performance tuning

💻 API Usage Patterns

All examples use the latest cortexdb API:

import (
    "github.com/liliang-cn/cortexdb/v2/pkg/cortexdb"
    "github.com/liliang-cn/cortexdb/v2/pkg/core"
    "github.com/liliang-cn/cortexdb/v2/pkg/graph"
)

// Initialize database
config := cortexdb.DefaultConfig("mydb.db")
config.Dimensions = 384 // or 0 for auto-detect

db, err := cortexdb.Open(config)
defer db.Close()

// Use Quick API for simple operations
quick := db.Quick()
id, err := quick.Add(ctx, vector, content)
results, err := quick.Search(ctx, queryVector, topK)

// Use Vector store for advanced operations
vectorStore := db.Vector()
// Creating collections
vectorStore.CreateCollection(ctx, "products", 256)

// Advanced search
results, err := vectorStore.Search(ctx, query, core.SearchOptions{
    Collection: "products",
    TopK:       10,
    Threshold:  0.7,
})

// Use Graph store for graph operations
graphStore := db.Graph()
// Add nodes and edges...
err := graphStore.UpsertNode(ctx, &graph.GraphNode{...})
err := graphStore.UpsertEdge(ctx, &graph.GraphEdge{...})

🎯 Key Features Demonstrated

• Vector Operations: Add, search, update, delete embeddings
• Collections: Multi-tenant support with isolated namespaces
• Similarity Metrics: Cosine, dot product, Euclidean distance
• Graph Operations: Nodes, edges, traversal, PageRank
• Hybrid Search: Combine vector similarity with graph relationships
• Performance: Benchmarking and optimization techniques
• Real-world Scenarios: E-commerce, documents, images, knowledge graphs

📊 Performance Tips

1. Batch Operations: Use batch inserts for better performance
2. Collection Isolation: Use collections to separate different data types
3. Dimension Consistency: Keep vector dimensions consistent within collections
4. Index Optimization: SQLite indexes are automatically managed
5. Connection Pooling: Reuse database connections

🔧 Requirements

• Go 1.19 or higher
• Pure Go implementation (no CGO required!)
• SQLite driver included (pure Go)
• No external dependencies

📝 Notes

• All examples create temporary databases that are cleaned up after execution
• Examples use simulated embeddings for demonstration (in production, use real embedding models)
• Each example is self-contained and can be run independently
• Database files are created in the current directory and removed after execution

🤝 Contributing

Feel free to add more examples! Make sure to:

1. Use the latest cortexdb API
2. Include comprehensive comments
3. Provide realistic use cases
4. Clean up resources after execution
5. Test your example thoroughly

📖 Further Reading

• Cortexdb Documentation
• API Reference
• Feature Comparison