v1.1.1 — Text Embedding, Persistence, CLI Demos

go-vector v1.1.1

Text embedding, disk persistence, and CLI demos — zero dependencies.

Text Embedding

• Embedder interface — swap backends without changing search code
• RandomProjections — sparse Johnson-Lindenstrauss projection
  • Builds vocabulary from your corpus (Fit)
  • Tokenizer: split on non-letter/digit, lowercase, min 2 chars
  • Deterministic output (seed 42), L2-normalized

rp := vector.NewRandomProjections(256)
rp.Fit(corpus)
v, _ := rp.Embed("machine learning is fascinating")

Disk Persistence

• Store.Save(path) / Store.Load(path) — gob-encoded binary
• Store.SaveJSON(path) / Store.LoadJSON(path) — human-readable JSON
• Full roundtrip: IDs, vectors, metric all preserved

store.Save("/data/vectors.db")
restored.Load("/data/vectors.db")

CLI Demos

go run ./cmd/go-vector demo     — vector store search
go run ./cmd/go-vector embed    — text embedding + similarity search
go run ./cmd/go-vector persist  — save/load roundtrip

Stats

• 40 tests · 96.8% coverage · 0 dependencies

v1.1.0 — Text Embedding and Disk Persistence

go-vector v1.1.0

Text embedding and disk persistence — still zero dependencies.

Text Embedding

• Embedder interface — swap backends without changing search code
• RandomProjections — sparse Johnson-Lindenstrauss projection
  • Builds vocabulary from your corpus (Fit)
  • Tokenizer: split on non-letter/digit, lowercase, min 2 chars
  • Deterministic output (seed 42), L2-normalized
  • ~10µs per embed at 256 output dims

rp := vector.NewRandomProjections(256)
rp.Fit(corpus)
v, _ := rp.Embed("machine learning is fascinating")

Disk Persistence

• Store.Save(path) / Store.Load(path) — gob-encoded binary
• Store.SaveJSON(path) / Store.LoadJSON(path) — human-readable JSON
• Full roundtrip: IDs, vectors, metric all preserved
• ~60MB for 10K vectors at 1536d, ~200ms save/load

Stats

• 40 tests · 96.8% coverage · 0 dependencies

v1.0.1 — Security, Performance, Docs

go-vector v1.0.1

Security hardening, performance optimizations, and comprehensive documentation.

Security

• Documented float32 overflow limits (MaxSafeDims = 1,000,000)
• Formalized clone-safety guarantees — all store outputs are deep copies
• Thread-safety guidance for concurrent Store access

Performance

• Euclidean: inlined single-pass computation — 0 allocations (was Sub + Norm, now one loop)
• Cosine: single-pass computation — computes dot and both norms in one loop
• Comprehensive benchmark suite: 11 benchmarks at 768–1536 dimensions
• All distance functions verified zero-allocation

Docs

• Rewritten README with performance benchmarks table and security checklist
• GitHub Pages landing page under /docs/ — dark theme, four sections
• Updated AGENTS.md with full conventions and performance rules

Stats

• 27 tests · 99.1% coverage · 0 dependencies

v1.0.0 — First Stable Release

go-vector v1.0.0

Zero-dependency vector similarity library for Go.

What's included

• Vector type — Dot, Norm, Normalize, Add, Sub, Scale, Equal, Clone
• Similarity metrics — Cosine Distance, Euclidean, Manhattan, Dot Product
• Vector Store — in-memory brute-force nearest-neighbor search with top-K
• 99% test coverage — 26 tests, zero dependencies

Quick start

go get github.com/BackendStack21/go-vector

import "github.com/BackendStack21/go-vector/pkg/vector"

store := vector.NewStore(vector.CosineDistance)
store.Add("cat", vector.Vector{1.0, 0.8, 0.1})
results := store.Search(vector.Vector{1.0, 0.9, 0.1}, 3)

License

MIT