dsh-semantic-search

Local semantic code search for DeepSeek Harness (dsh) — and any Node script.

中文文档：README.zh.md

sema builds a fragment-level index of a workspace: source is tokenized by a language-aware tokenizer (camel/snake/kebab splitting, CJK n-grams), chunked with symbol-aware boundaries (functions/classes stay intact), and embedded into fixed dimension vectors — locally and dependency-free by default (feature-hashed lexical TF-IDF), or via any OpenAI-compatible embedding endpoint. Queries run a hybrid retrieval (vector cosine + BM25, fused with reciprocal-rank fusion) over the index, so meaning-based search works even when exact terms don't match.

Ships as a dsh plugin bundle — the sema_search, sema_reindex and sema_stats tools on the harness tool registry — plus a standalone sema CLI.

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Highlights

• Works offline by default — the built-in lexical provider needs no network, no model download, no API key. Use it as a fast BM25-plus code search.
• Symbol-aware chunking — boundaries from a conservative per-language table (16 languages) keep functions/classes intact; a missed boundary degrades to a plain chunk rather than breaking.
• CJK-aware tokenizer — n-gram tokenization (default bigrams) aligns Chinese queries and documents without a segmentation library; full-width punctuation is folded, not treated as a hard break.
• Hybrid retrieval with RRF — vector cosine + BM25 channels are fused by reciprocal-rank fusion, so a document found by one channel still ranks.
• Graceful degradation — if a configured remote provider is unreachable, the index falls back to the local lexical provider (configurable via allowFallback).
• Incremental refresh + file watching — sema_reindex diffs by size+mtime, and an optional watcher keeps the index live.
• Persistence — the index is saved to <root>/.sema atomically (JSON metadata
  • binary vectors), with staleness detection when the provider/dimension changes.
• Deterministic — the same workspace and options produce the same index and the same ranked answers.

Supported languages

TypeScript, JavaScript, Python, Go, Rust, Java, Kotlin, Scala, C, C++, C#, Objective-C, Ruby, PHP, Swift, Bash, plus common data/markup formats (JSON, YAML, TOML, Markdown, HTML, XML...).

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Installation

As a dsh bundle

The package declares "dsh": { "bundle": { "patch": "./cordis.patch.yml" } }. The patch inserts one plugin row that mounts the bundle and registers sema_search / sema_reindex / sema_stats on ctx.tools.

# from npm (name reserved; publish pending access setup)
npm install -g dsh-semantic-search

# straight from this repository
dsh plugin --profile demo add github:JohnXu22786/semantic-search

# or from a local checkout
dsh plugin --profile demo add /path/to/semantic-search

As a standalone CLI

npm install -g dsh-semantic-search   # or: npm run build && node bin/sema.mjs
sema --help

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CLI usage

sema index                build the full index from the workspace
sema reindex [--full]     incremental refresh (or full rebuild with --full)
sema search <query...>    hybrid vector + BM25 search, prints top hits
sema stats [--json]       index health, provider, and sizing numbers

Global options:

--root <dir>          workspace root (default: current directory)
--data-dir <dir>      index storage directory (default: <root>/.sema)
--provider <kind>     embedding provider: lexical | openai (default: lexical)
--dim <n>             embedding dimension (lexical default: 4096; openai 0 = auto)
--base-url <url>      OpenAI-compatible embeddings endpoint base URL
--model <name>        embeddings model name (openai only)
--api-key <key>       API key (openai only; env: SEMA_EMBEDDING_API_KEY)
--top <n>             hits to print for search (default: 20)
--json                machine-readable output where supported
--help                show this help

CLI exit codes

• 0 — success (including a search with zero hits and a --version/--help call).
• 1 — a runtime failure (config error, build/index/search error).
• 2 — a usage error: unknown command, unknown flag, or a missing query.

Configuration

The plugin is configurable through the bundle row's config (see cordis.patch.yml for an example), the CLI flags above, or defaults in code:

Option	Default	Meaning
root	cwd	workspace root to index
dataDir	.sema	index storage directory
provider.kind	lexical	lexical (offline) or openai
provider.dimension	0 (lexical: 4096)	embedding dimension; 0 = auto-infer from the endpoint
provider.baseUrl	https://api.openai.com/v1	OpenAI-compatible endpoint root
provider.model	text-embedding-3-small	embedding model name
provider.apiKeyEnv	SEMA_EMBEDDING_API_KEY	env var holding the API key
allowFallback	true	fall back to the lexical provider when a remote one fails
include / ignore	defaults	glob sets of files to index / skip
maxLinesPerChunk	80	hard chunk size upper bound
nGram	2	CJK n-gram size (1 disables n-gramming)
topK	20	hits returned by default
rrfK	60	RRF fusion constant
vectorK	300	candidates per channel before fusion
autosave	true	persist the index after builds
autoIndex	true	build lazily on first search
watch	true	watch the workspace for changes

Development

npm ci
npm test          # build + run the node:test suite (76 tests)
npm run typecheck
npm run build     # tsc -> lib/

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License

MIT — see LICENSE. © 2026 dsh-semantic-search contributors.