🧬🔍 Vecgo

Vecgo is a pure Go, embeddable, hybrid vector database designed for high-performance production workloads. It combines commit-oriented durability with HNSW + DiskANN indexing for best-in-class performance.

⚠️ This is experimental and subject to breaking changes.

✨ Key Differentiators

• ⚡ Faster & lighter than external services — no network overhead, no sidecar, 15MB binary
• 🔧 More capable than simple libraries — durability, MVCC, hybrid search, cloud storage
• 🎯 Simpler than CGO wrappers — pure Go toolchain, static binaries, cross-compilation
• 🏗️ Modern architecture — commit-oriented durability (append-only versioned commits), no WAL complexity

📊 Performance

Vecgo is optimized for high-throughput, low-latency vector search with:

• FilterCursor — zero-allocation push-based iteration
• Zero-Copy Vectors — direct access to mmap'd memory
• SIMD Distance — AVX-512/AVX2/NEON/SVE2 runtime detection

Run benchmarks locally to see performance on your hardware:

cd benchmark_test && go test -bench=. -benchmem -timeout=15m

See benchmark_test/baseline.txt for reference results.

🎯 Features

📊 Index Types

🗜️ Quantization

Quantization reduces in-memory index size for DiskANN segments. Full vectors remain on disk for reranking.

📖 See Performance Tuning Guide for detailed quantization configuration.

🏢 Enterprise Features

• ☁️ Cloud-Native Storage — S3/GCS/Azure via pluggable BlobStore interface
• 🔒 Commit-Oriented Durability — Atomic commits with immutable segments
• 🔀 Hybrid Search — BM25 + vector similarity with RRF fusion
• 📸 Snapshot Isolation — Lock-free reads via MVCC
• ⏰ Time-Travel Queries — WithTimestamp() / WithVersion() to query historical state
• 🏷️ Typed Metadata — Schema-enforced metadata with filtering
• 📊 Query Statistics — WithStats() + Explain() for debugging
• 🎯 Segment Pruning — Triangle inequality, Bloom filters, numeric range stats
• 🚀 SIMD Optimized — AVX-512/AVX2/NEON/SVE2 runtime detection

🚀 Quick Start

📦 Installation

go get github.com/hupe1980/vecgo

Platform Requirements: Vecgo requires a 64-bit architecture (amd64 or arm64). SIMD optimizations use AVX-512/AVX2 on x86-64 and NEON/SVE2 on ARM64.

💻 Basic Usage

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/hupe1980/vecgo"
    "github.com/hupe1980/vecgo/metadata"
)

func main() {
    ctx := context.Background()

    // Create a new index (128 dimensions, L2 distance)
    db, err := vecgo.Open(ctx, vecgo.Local("./data"), vecgo.Create(128, vecgo.MetricL2))
    if err != nil {
        log.Fatal(err)
    }
    defer db.Close()

    // Insert with fluent builder API
    vector := make([]float32, 128)
    rec := vecgo.NewRecord(vector).
        WithMetadata("category", metadata.String("electronics")).
        WithMetadata("price", metadata.Float(99.99)).
        WithPayload([]byte(`{"desc": "Product description"}`)).
        Build()
    
    id, err := db.InsertRecord(ctx, rec)
    if err != nil {
        log.Fatal(err)
    }
    fmt.Printf("Inserted ID: %d\n", id)

    // Or use the simple API
    id, err = db.Insert(ctx, vector, nil, nil)

    // Commit to disk (data is durable after this)
    if err := db.Commit(ctx); err != nil {
        log.Fatal(err)
    }

    // Search — returns IDs, scores, metadata, and payload by default
    query := make([]float32, 128)
    results, err := db.Search(ctx, query, 10)
    if err != nil {
        log.Fatal(err)
    }

    for _, r := range results {
        fmt.Printf("ID: %d, Score: %.4f\n", r.ID, r.Score)
    }

    // High-throughput mode (IDs + scores only)
    results, _ = db.Search(ctx, query, 10, vecgo.WithoutData())
}

🔄 Re-open Existing Index

// Dimension and metric are auto-loaded from manifest
db, err := vecgo.Open(ctx, vecgo.Local("./data"))

☁️ Cloud Storage (Writer/Reader Separation)

import (
    "github.com/hupe1980/vecgo"
    "github.com/hupe1980/vecgo/blobstore/s3"
)

// === Writer Node (build index locally, then sync to S3) ===
db, _ := vecgo.Open(ctx, vecgo.Local("/data/vecgo"), vecgo.Create(128, vecgo.MetricL2))
db.Insert(ctx, vector, nil, nil)
db.Close()
// Sync: aws s3 sync /data/vecgo s3://my-bucket/vecgo/

// === Reader Nodes (stateless, horizontally scalable) ===
store, _ := s3.New(ctx, "my-bucket", s3.WithPrefix("vecgo/"))

// Remote() is automatically read-only
db, err := vecgo.Open(ctx, vecgo.Remote(store))

// Writes return ErrReadOnly
_, err = db.Insert(ctx, vec, nil, nil)  // err == vecgo.ErrReadOnly

// With explicit cache directory for faster repeated queries
db, err := vecgo.Open(ctx, vecgo.Remote(store),
    vecgo.WithCacheDir("/fast/nvme"),
    vecgo.WithBlockCacheSize(4 << 30),  // 4GB
)

🔍 Filtered Search

// Define schema for type safety
schema := metadata.Schema{
    "category": metadata.FieldTypeString,
    "price":    metadata.FieldTypeFloat,
}

db, _ := vecgo.Open(ctx, vecgo.Local("./data"),
    vecgo.Create(128, vecgo.MetricL2),
    vecgo.WithSchema(schema),
)

// Search with filter
filter := metadata.NewFilterSet(
    metadata.Filter{Key: "category", Operator: metadata.OpEqual, Value: metadata.String("electronics")},
    metadata.Filter{Key: "price", Operator: metadata.OpLessThan, Value: metadata.Float(100.0)},
)

results, _ := db.Search(ctx, query, 10, vecgo.WithFilter(filter))

🔀 Hybrid Search (Vector + BM25)

// Insert with text for BM25 indexing
doc := metadata.Document{
    "text": metadata.String("machine learning neural networks"),
}
db.Insert(ctx, vector, doc, nil)

// Hybrid search with RRF fusion
results, _ := db.HybridSearch(ctx, vector, "neural networks", 10)

⏰ Time-Travel Queries

Query historical snapshots without affecting the current state:

// Open at a specific point in time
yesterday := time.Now().Add(-24 * time.Hour)
db, _ := vecgo.Open(ctx, vecgo.Local("./data"), vecgo.WithTimestamp(yesterday))

// Or open at a specific version ID
db, _ := vecgo.Open(ctx, vecgo.Local("./data"), vecgo.WithVersion(42))

// Query as if it were that moment in time
results, _ := db.Search(ctx, query, 10)

How it works:

• Old manifests are preserved (each points to immutable segments)
• Compaction still runs — creates NEW optimized segments
• Old segments retained until Vacuum() removes expired manifests
• Storage: ~current_data × (1 + retained_versions × churn_rate)

Use cases:

• 🔍 Debug production issues: "What did the index look like before the bad deployment?"
• 📊 A/B testing: Compare recall against historical versions
• 🔄 Recovery: Roll back to a known-good state

Managing retention:

// Configure retention policy
policy := vecgo.RetentionPolicy{KeepVersions: 10}
db, _ := vecgo.Open(ctx, vecgo.Local("./data"), vecgo.WithRetentionPolicy(policy))

// Reclaim disk space from expired versions
db.Vacuum(ctx)

📊 Query Statistics & Explain

Understand query execution for debugging and optimization:

var stats vecgo.QueryStats
results, _ := db.Search(ctx, query, 10, vecgo.WithStats(&stats))

// Summary explanation
fmt.Println(stats.Explain())
// Output: "searched 3 segments (1 pruned by stats, 0 by bloom), 
//          scanned 1200 vectors in 2.1ms, recalled 847 candidates (0.7 hit rate)"

// Detailed statistics
fmt.Printf("Segments searched: %d\n", stats.SegmentsSearched)
fmt.Printf("Segments pruned (stats): %d\n", stats.SegmentsPrunedByStats)
fmt.Printf("Segments pruned (bloom): %d\n", stats.SegmentsPrunedByBloom)
fmt.Printf("Vectors scanned: %d\n", stats.VectorsScanned)
fmt.Printf("Candidates recalled: %d\n", stats.CandidatesRecalled)
fmt.Printf("Latency: %v\n", stats.Latency)
fmt.Printf("Graph hops: %d\n", stats.GraphHops)
fmt.Printf("Cost estimate: %.2f\n", stats.CostEstimate())

🎯 Segment Pruning & Manifest Stats

Vecgo automatically prunes irrelevant segments using advanced statistics:

These statistics are automatically computed during Commit() and stored in the manifest (v3 format).

// Get current statistics
dbStats := db.Stats()
fmt.Printf("Manifest version: %d\n", dbStats.ManifestID)
fmt.Printf("Total vectors: %d\n", dbStats.TotalVectors)
fmt.Printf("Segment count: %d\n", dbStats.SegmentCount)

📦 Insert Modes

Vecgo offers three insert modes optimized for different workloads:

// 1. SINGLE INSERT — Real-time updates (HNSW-indexed immediately)
//    Use when: you need vectors searchable immediately
id, err := db.Insert(ctx, vector, metadata, payload)

// 2. BATCH INSERT — Indexed batch (HNSW-indexed immediately)
//    Use when: you have medium batches and need immediate search
ids, err := db.BatchInsert(ctx, vectors, metadatas, payloads)

// 3. DEFERRED INSERT — Bulk loading (NO HNSW indexing)
//    Use when: you're bulk loading and don't need immediate search
//    Vectors become searchable after Commit() triggers flush
ids, err := db.BatchInsertDeferred(ctx, vectors, metadatas, payloads)
db.Commit(ctx) // Flush to disk, now searchable via DiskANN

When to use Deferred mode:

• Initial data loading (embeddings from a corpus)
• Periodic bulk updates (nightly reindex)
• Migration from another database

When NOT to use Deferred mode:

• Real-time RAG (documents must be searchable immediately)
• Interactive applications with instant feedback

// Batch delete
err = db.BatchDelete(ctx, ids)

💾 Durability Model

Vecgo uses commit-oriented durability — append-only versioned commits:

sequenceDiagram
    participant App as Application
    participant MT as MemTable (RAM)
    participant Seg as Segment (Disk)
    participant Man as Manifest

    App->>MT: Insert(vector, metadata)
    Note over MT: Buffered in memory<br/>❌ NOT durable
    
    App->>MT: Insert(vector, metadata)
    
    App->>Seg: Commit()
    MT->>Seg: Write immutable segment
    Seg->>Man: Update manifest atomically
    Note over Seg,Man: ✅ DURABLE after Commit()

Why commit-oriented?

• 🧹 Simpler code — no WAL rotation, recovery, or checkpointing
• ⚡ Faster batch inserts — no fsync per insert
• ☁️ Cloud-native — pure segment writes, ideal for S3/GCS
• 🚀 Instant startup — no recovery/replay, just read manifest

📚 Documentation

• 📖 API Reference: pkg.go.dev/github.com/hupe1980/vecgo
• 🏗️ Architecture Guide — Engine internals, storage tiers, concurrency model
• ⚙️ Performance Tuning — HNSW parameters, compaction, caching
• 🔧 Operations Guide — Monitoring, troubleshooting
• 💾 Recovery & Durability — Crash safety, data guarantees
• 🚀 Deployment Guide — Local vs. cloud patterns

� Examples

�📄 Algorithm References

• HNSW: Malkov & Yashunin, "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs", IEEE TPAMI 2018
• DiskANN/Vamana: Subramanya et al., "DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node", NeurIPS 2019
• FreshDiskANN: Singh et al., "FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search", SIGMOD 2021
• Product Quantization: Jégou et al., "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011
• OPQ: Ge et al., "Optimized Product Quantization", IEEE CVPR 2013
• RaBitQ: Gao & Long, "RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search", SIGMOD 2024
• RRF: Cormack et al., "Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods", SIGIR 2009

🤝 Contributing

Contributions welcome! Please open an issue or pull request.

📜 License

Licensed under the Apache License 2.0. See LICENSE for details.