Quick start · Search · APIs

Zeppelin Embed is the fastest and most accurate in-process search engine built for macOS and Apple silicon.

Search performance

Vector graph-search p95 latency is plotted against nDCG@10. Lower latency and higher nDCG@10 are better. Measured on an Apple M3 Max using warm indexes and k=10. The SQLite TREC-COVID result (82.36 ms p95) is omitted to keep the remaining results legible on a linear scale.

BEIR is a benchmark suite for evaluating information retrieval across different domains.

• SciFact retrieves scientific evidence for factual claims.
• TREC-COVID retrieves biomedical literature for COVID-19 research questions.
• Natural Questions retrieves Wikipedia evidence for real search-engine questions.

Compared with: Chroma, hnswlib, LanceDB, sqlite-vec, and USearch.

Why Zeppelin Embed

• Vector, lexical, and hybrid retrieval. Use one store and one document ID space for vector similarity, BM25, or fused results.
• Local and persistent. The engine runs inside your process, recovers from its write-ahead log, and publishes immutable searchable generations.
• Fast native execution. Runtime-dispatched SIMD kernels, quantized graph traversal, exact rescoring, memory-mapped segments, and bounded worker pools.
• Production controls. Typed filters, idempotent document revisions, cancellation, deadlines, retention, physical purge, memory budgets, health, and query diagnostics.
• Bring your own vectors. Use embeddings from any model that produces compatible document and query vectors.
• One native engine, several languages. Rust, a versioned C ABI, Python, Swift, Node.js, and TypeScript call the same storage and retrieval implementation.

One store, three ways to search

Mode	Input	Result
Vector	A precomputed float32 query vector	Approximate graph retrieval by default, with exact and scan tiers available
Lexical	Raw query text or a structured lexical query	BM25 ranking with term, phrase, prefix, and phonetic operators
Hybrid	A query vector plus text	Vector and lexical candidates fused over one pinned store generation

Vector and hybrid graph queries use quantized traversal to select candidates and full-precision vectors to score the retained rows. Exact search remains available when exhaustive membership is required. Every result reports the generation it observed, and diagnostics report the path and work that actually ran.

Quick start

Install the Python package and run the five-vector example:

python -m pip install zeppelin-embed
python bindings/python/examples/five_vectors_search.py

The example supplies its own document and query vectors. Zeppelin Embed v0.3.0 does not bundle or download an embedding model.

The directory is the database. Reopen the same path to recover its committed state and continue ingesting or searching.

For Rust, add the native core to an application:

cargo add zeppelin-embed

Language APIs

API	Distribution	Example
Python	PyPI wheel with the native library included	five_vectors_search.py
Rust	zeppelin-embed on crates.io	five_vectors_search.rs
Swift	Swift Package Manager with a downloadable XCFramework	five_vectors_search.swift
C/C++	GitHub release archive with the header and .a/.dylib libraries	five_vectors_search.c
Node.js / TypeScript	@zepdb/zeppelin-embed with the native addon included	javascript.cjs / typescript.ts

The C ABI uses size-versioned requests and responses, typed error codes, and matching free functions for every callee-owned result. Python wheels include the native library. Swift consumes the same ABI through an actor-based interface. Only the Rust core is published as a crate; the other packages embed or link the C ABI.

The macOS SDK archive is language-neutral. Any runtime with C-compatible foreign functions can use its header and static or dynamic library.

From a source checkout, run each example with:

# Python
cargo build --release -p zeppelin-embed-ffi
PYTHONPATH=bindings/python ZEPPELIN_EMBED_LIBRARY=target/release/libzeppelin_embed_ffi.dylib \
  python bindings/python/examples/five_vectors_search.py

# Rust
cargo run --release -p zeppelin-embed --example five_vectors_search

# Node.js
cd bindings/node
npm ci
npm run build:native
node examples/javascript.cjs
cd ../..

# Swift
cargo build --release -p zeppelin-embed-ffi
ZE_USE_LOCAL_FFI=1 swift run --package-path bindings/swift/Examples/FiveVectorsSearch

# C
cargo build --release -p zeppelin-embed-ffi
cc -std=c11 crates/zeppelin-embed-ffi/examples/five_vectors_search.c \
  -I crates/zeppelin-embed-ffi/include -L target/release -lzeppelin_embed_ffi \
  -o /tmp/zeppelin-c-example
DYLD_LIBRARY_PATH=target/release /tmp/zeppelin-c-example /tmp/zeppelin-c-index

Building from source

Zeppelin Embed uses stable Rust 1.93 or newer.

git clone https://github.com/zepdb/zeppelin-embed.git
cd zeppelin-embed
cargo test --workspace

Build the core C ABI library:

cargo build --release -p zeppelin-embed-ffi

Build a Python wheel:

python -m build --wheel bindings/python

License

Zeppelin Embed is free software licensed under the GNU General Public License v3.0.