Stroma

Stroma is a neutral corpus and indexing substrate.

It owns the lowest-level operations needed to ingest text artifacts, chunk them, embed them, persist them in SQLite plus sqlite-vec, and retrieve semantically close sections. Callers consume Stroma through its APIs and treat the SQLite snapshot as an opaque local artifact. It does not own governance, specifications, compliance, drift analysis, MCP, or CLI workflows.

Scope

Stroma is for products that need a reusable text corpus layer with:

• canonical records with deterministic content fingerprints
• pluggable chunking strategies (chunk.Policy — MarkdownPolicy default, KindRouterPolicy for per-record-kind dispatch, LateChunkPolicy for parent/leaf hierarchy)
• pluggable embedders (Embedder / ContextualEmbedder) with a deterministic fixture and an OpenAI-compatible HTTP embedder
• hybrid retrieval: dense vector + FTS5, fused via a pluggable FusionStrategy (RRFFusion by default) with per-arm provenance surfaced to downstream rerankers
• quantization knobs: float32 (default), int8 (4× smaller), binary (1-bit sign-packed vec0 prefilter that is 32× smaller for the prefilter representation; full-precision vectors are retained in a companion table for cosine rescoring, so total snapshot size is not 32× smaller)
• optional Matryoshka prefilter at a truncated dimension with full-dim cosine rescore (SearchParams.SearchDimension)
• atomic rebuilds and incremental Update with embedding reuse at the section level, chaining schema migrations v2 → v3 → v4 → v5 in one transaction

Stroma is not for:

• spec governance
• source discovery or repository scanning
• code compliance or doc drift analysis
• product-specific adapters and transports

Packages

• corpus — canonical record model, NewRecord helper, Normalize, deterministic Fingerprint
• chunk — Policy interface with MarkdownPolicy, KindRouterPolicy, LateChunkPolicy; MarkdownWithOptions returns ErrTooManySections when a body exceeds the DoS cap
• embed — Embedder and ContextualEmbedder interfaces; deterministic Fixture; OpenAI-compatible HTTP embedder with MaxBatchSize batching, deadline scaling across batches, and APIToken redaction in String/GoString/LogValue
• store — SQLite readiness probes, sqlite-vec readiness, quantization blob helpers (QuantizationFloat32 / QuantizationInt8 / QuantizationBinary)
• index — atomic Rebuild with embedding reuse, incremental Update, long-lived Snapshot readers, Stats, hybrid Search with provenance, ExpandContext for parent/neighbor walks

Example

ctx := context.Background()

records := []corpus.Record{
    corpus.NewRecord(
        "widget-overview",
        "Widget Overview",
        "# Overview\n\nWidgets are synchronized in batches.",
    ),
}

fixture, err := embed.NewFixture("fixture-demo", 16)
if err != nil {
    log.Fatal(err)
}

if _, err := index.Rebuild(ctx, records, index.BuildOptions{
    Path:     "stroma.db",
    Embedder: fixture,
}); err != nil {
    log.Fatal(err)
}

hits, err := index.Search(ctx, index.SearchQuery{
    Path: "stroma.db",
    SearchParams: index.SearchParams{
        Text:     "synchronized batches",
        Limit:    5,
        Embedder: fixture,
        // Fusion / Reranker / SearchDimension are optional; zero values
        // give hybrid RRF over vector+FTS with the full stored dimension.
    },
})
if err != nil {
    log.Fatal(err)
}

fmt.Println(hits[0].Ref)

See the v2.0.0 release notes for the full API surface.

Status

v2.0.0 (current) ships the stable substrate: hybrid retrieval, pluggable fusion, quantization, matryoshka, contextual retrieval, adaptive chunking, and incremental update. Higher-order products should consume the library rather than re-embedding their own indexing substrate.