MojoVec 🔥

A Vector Database (HNSW) written entirely in Mojo.

MojoVec is an Approximate Nearest Neighbor (ANN) search library built from scratch in pure Mojo — no C++ dependencies. HNSW, IVF, and Product Quantization (PQ) are implemented.

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Why

FAISS and hnswlib are C++ with Python bindings. MojoVec exists to answer a narrower question: can a pure-Mojo implementation get close to hand-tuned C++ SIMD performance without dropping into C/C++/assembly, using only what the language and its SIMD type give you today. This was prompted by curiosity about Mojo's SIMD codegen and a desire to build a zero-dependency, bare-metal alternative to FAISS for the Mojo ecosystem.

Features

• Flat and SQ8 HNSW collections behind one Chroma-style API;
• squared L2, cosine, and inner-product distance metrics;
• managed add, upsert, update, delete, and batched query;
• typed String, Int, Float64, and Bool record metadata;
• record documents returned directly with query results;
• native BM25 full-text search over collection documents;
• hybrid vector + BM25 search with reciprocal rank fusion (RRF);
• typed where filtering backed by automatic sparse bitmap indexes;
• compound and_, or_, not_, in_, and not_in expressions;
• atomic save/load, optional WAL recovery, collection statistics, and graph compaction;
• IVF-Flat and IVF-PQ lower-level indexes implemented in pure Mojo.

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Performance

Dataset: SIFT1M (1,000,000 base vectors, 10,000 query vectors, 128 dimensions). MojoVec/FAISS parameters: M=32, efConstruction=200, efSearch=96, k=10. L2 Distance. SQ8 results use exact reranking of 20 candidates (k_factor=2).

Apple Silicon (ARM64)

Index	Build Time	QPS	Recall@10
MojoVec Flat	~97.9 s	23,783	99.160%
MojoVec Quantized (SQ8)	~59.3 s	36,353	99.144%
FAISS Flat	~100.8 s	17,330	99.201%
FAISS Quantized (SQ8)	~104.9 s	14,712	99.185%
ChromaDB (hnswlib, Python)	~105.6 s	~1,990	99.22%

x86_64 (4 Cores VM)

Index	Build Time	QPS	Recall@10
MojoVec (Pure Mojo)	~367.1 s	~8,912	94.64%
FAISS (HNSW, C++ via Python)	~693.2 s	~4,773	95.88%
ChromaDB (hnswlib, Python)	~658.3 s	~1,610	99.20%

Methodology: FAISS uses 10 OpenMP threads. MojoVec uses the Mojo AsyncRT worker pool for construction and small query batches; large Flat/SQ8 HNSW batches use native OS threads so that all logical CPU cores participate. Apple Silicon QPS and recall are isolated cold search-only measurements over ten samples; build times come from separate end-to-end runs and are more sensitive to thermal state. Recall is the exact intersection against SIFT1M's provided ground truth (sift_groundtruth.ivecs).

On Apple Silicon, MojoVec Flat delivers about 1.37x the QPS of FAISS Flat, while MojoVec SQ8 delivers about 2.47x the QPS of FAISS SQ8, with no C/C++ dependency or handwritten assembly.

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Installation

# Download the latest mojovec.mojoc
curl -LO https://github.com/bewaffnete/MojoVec/releases/latest/download/mojovec.mojoc

Place the mojovec.mojoc file in your project directory. You can now import it directly in your code. (If you place it elsewhere, pass the include path to the compiler: mojo run -I /path/to/dir your_script.mojo).

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Quick Start

1. Initialize the Client

from mojovec import Client, Metadata, Where
from std.collections import List

def main() raises:
    var client = Client()
    # quantized=True selects SQ8; False selects exact Flat storage.
    # metric accepts "l2", "cosine", or "ip".
    var collection = client.create_collection(
        "my_docs", 
        dimension=128,
        M=32, 
        ef_construction=40, 
        ef_search=16,
        quantized=True,
        metric="cosine",
    )

Metric and storage are independent. Public vector distances are always smaller-is-better: L2 returns squared Euclidean distance, cosine returns 1 - cosine_similarity, and IP returns 1 - inner_product. Cosine vectors and queries are normalized automatically without modifying caller-owned Lists; zero or non-finite cosine vectors are rejected. The selected metric is preserved by save/load, mmap, compaction, filtering, hybrid search, and WAL recovery.

2. Add Vectors

    var ids = List[Int]()
    var embeddings = List[Float32]()
    
    # ... fill ids and embeddings ...
    
    # No pointers, no alloc/free!
    # add inserts new records and rejects IDs that already exist.
    collection.add(ids, embeddings)

3. Insert or Replace with Upsert

Use upsert when the same batch may contain both new and existing IDs. New IDs are inserted; existing active records are replaced:

    collection.upsert(ids, embeddings)

A single add or upsert batch must not contain duplicate IDs.

4. Add Metadata and Documents

Metadata is an owned, typed record supporting String, Int, Float64, and Bool values. Documents are ordinary owned String values:

    var document = Metadata()
    document.set("title", "Mojo vector search")
    document.set("year", 2026)
    document.set("score", Float64(0.97))
    document.set("published", True)

    var document_embedding = List[Float32]()
    # ... append exactly collection.dimension() components ...

    var metadatas = List[Metadata]()
    metadatas.append(document.copy())
    var documents = [String("A guide to fast vector search in Mojo.")]
    collection.add([42], document_embedding, metadatas, documents)

    var stored = collection.get_metadata(42)
    print(stored.get_string("title"))
    print(stored.keys())
    print(collection.get_document(42))

When supplied, there must be one metadata object and one document per ID. add, upsert, and update accept vector-only, metadata-only, document-only, or metadata-plus-document batches. Vector-only operations preserve both payloads. Supplying one payload kind replaces that kind while preserving the other. An empty metadata object or document string removes that payload. Metadata and documents are owned values preserved by save/load and compaction.

5. Search

    var query_embeddings = List[Float32]()
    # ... fill query ...
    
    # Optionally increase precision before search
    collection.set_ef_search(100)
    var results = collection.query(query_embeddings, n_results=5)
    
    for i in range(len(results.ids)):
        print("Query", i)
        for j in range(len(results.ids[i])):
            print("ID:", results.ids[i][j], "Dist:", results.distances[i][j])
            if len(results.metadatas) > 0:
                print("Metadata:", results.metadatas[i][j])
            if len(results.documents) > 0:
                print("Document:", results.documents[i][j])

query() returns managed QueryResults. Application code never allocates output buffers, handles pointers, or calls free; result memory is released automatically when it is no longer used. Metadata and document rows align with ids by query and rank. Missing per-record values use empty placeholders. If the collection has no metadata or no documents at all, the corresponding outer List is empty and no payload matrix is allocated.

6. Filter by Metadata

Where overloads every predicate by metadata type. Ordered comparisons accept Int or Float64:

    var conditions = List[Where]()
    conditions.append(Where.eq("published", True))
    conditions.append(Where.gte("year", 2024))

    var filtered = collection.query(
        query_embeddings,
        where=Where.and_(conditions),
        n_results=5,
    )

Available predicates are eq, ne, gt, gte, lt, lte, in_, and not_in. Combine them with and_, or_, and not_. MojoVec automatically maintains typed bitmap indexes for metadata fields and rebuilds them on load or compaction; no index-management API is required.

Constructor	Accepted values
eq, ne	String, Int, Float64, or Bool
gt, gte, lt, lte	Int or Float64
in_, not_in	typed List[String/Int/Float64/Bool]
and_, or_	List[Where]
not_	one Where expression

Scalar predicates require the field to exist. Consequently, ne and not_in do not match records missing that field. not_(...) negates the complete expression and can therefore include records with missing fields.

7. Search Documents with BM25

The same query name accepts List[String] for native full-text search. No embedding model or second collection is required:

    var text_results = collection.query(
        [String("fast vector search"), String("HNSW graph")],
        n_results=5,
    )

    for query_index in range(len(text_results.ids)):
        for rank in range(len(text_results.ids[query_index])):
            print(
                text_results.ids[query_index][rank],
                text_results.scores[query_index][rank],
                text_results.documents[query_index][rank],
            )

BM25 results are ordered by descending scores—larger is better—and leave distances empty. Vector results populate distances and leave scores empty. Text queries also accept the same typed where filter:

    var filtered_text = collection.query(
        [String("vector database")],
        where=Where.eq("published", True),
        n_results=5,
    )

BM25 uses the same analyzer while indexing documents and parsing queries:

• Unicode-aware lowercase normalization;
• word boundaries at whitespace, ASCII/Unicode punctuation, symbols, and emoji;
• bundled English and Russian stopwords.

ASCII apostrophes and underscores are preserved inside a term, so values such as rock'n'roll and model_v2 stay intact. Stemming is intentionally not applied: run and running remain different terms. A query containing only stopwords returns the normal padded no-match result.

8. Hybrid Search with RRF

query_hybrid combines vector and BM25 rankings without comparing their incompatible raw distance and relevance scales:

    var hybrid = collection.query_hybrid(
        query_embeddings,
        [String("fast vector search")],
        n_results=5,
    )

The embedding batch and text batch must contain the same number of queries. For each pair, MojoVec retrieves candidates independently from HNSW and BM25, then adds an equal-weight reciprocal-rank contribution from each list:

RRF score(document) = Σ 1 / (rrf_k + one_based_rank)

Defaults are rrf_k=60 and candidate_multiplier=4, so each source supplies up to 4 * n_results candidates before fusion. The candidate pool is capped at 2048. Larger scores are better; distances is empty because raw vector distances are not meaningful after rank fusion.

The same typed metadata filter is applied to both candidate sources:

    var filtered_hybrid = collection.query_hybrid(
        query_embeddings,
        [String("vector database")],
        where=Where.eq("published", True),
        n_results=5,
        rrf_k=60,
        candidate_multiplier=4,
    )

Results that occur in both lists receive both contributions. A text query with no indexed terms naturally falls back to the vector ranking; an empty vector candidate list naturally falls back to BM25.

9. Disk Persistence

    # Save to disk
    collection.save("my_database.bin")
    
    # Files of at least 64 MiB use read-only memory-mapped vector and graph
    # arrays by default. Smaller files are copied to owned heap memory.
    from mojovec import Collection
    var loaded = Collection.load("my_database.bin")
    print("memory mapped:", loaded.is_memory_mapped())

    # Force mmap regardless of file size, or disable it explicitly.
    var forced = Collection.load(
        "my_database.bin",
        mmap_threshold_bytes=0,
    )
    var copied = Collection.load(
        "my_database.bin",
        memory_mapped=False,
    )

save() uses a synchronized temporary file and atomic rename. A crash cannot expose a half-written collection at the destination path, and existing mmap readers keep using the previous complete file while the new snapshot is published. Every snapshot includes a checksum trailer. load() validates the complete payload before parsing metadata or exposing memory-mapped arrays, so truncation and on-disk corruption fail closed.

For one-writer/many-reader workloads, snapshot() publishes the writer's current state and returns an independent point-in-time collection:

    var reader_v1 = collection.snapshot(
        "my_database.bin",
        mmap_threshold_bytes=0,
    )

    collection.upsert(ids, embeddings)

    # Still observes the state captured before upsert.
    var old_results = reader_v1.query(query, n_results=10)

    # Newly acquired readers observe the new atomic publication.
    var reader_v2 = collection.snapshot(
        "my_database.bin",
        mmap_threshold_bytes=0,
    )

Readers do not block the single writer and do not need locks or manual cleanup. Without a WAL, writes made after the latest save() or snapshot() are not crash-durable. This is the simplest and fastest mode when another datastore is the source of truth.

For standalone durability, enable the optional write-ahead log:

    from mojovec import Collection, WAL_ASYNC, WAL_SYNC

    # Create the recovery base once, then log later mutations.
    collection.save("my_database.bin")
    collection.enable_wal(
        "my_database.wal",
        durability=WAL_ASYNC,
    )

    # One public add/upsert/update/delete batch becomes one committed frame.
    collection.add(ids, embeddings, metadatas, documents)

    # WAL_ASYNC avoids per-batch fsync. Group any number of batches behind one
    # durability barrier chosen by the application.
    collection.flush_wal()

    # After restart, replay committed frames and continue using the same WAL.
    var recovered = Collection.recover(
        "my_database.bin",
        "my_database.wal",
        durability=WAL_ASYNC,
    )

    # Publish a durable snapshot first, then safely rotate the covered WAL.
    recovered.checkpoint("my_database.bin")

WAL_ASYNC is the high-throughput default: writes are appended immediately, but only flush_wal() or checkpoint() establishes an explicit durability boundary. A process or machine failure can lose the unflushed tail. WAL_SYNC calls fsync once after every complete public mutation batch and is intended for applications that prefer the smallest loss window over ingestion throughput.

WAL is absent from the query path and stores no second in-memory vector copy. The writer streams IDs and embeddings directly from the managed input Lists, adds a checksum and commit marker, and ignores an incomplete final frame during recovery. Checkpoint sequence numbers make recovery idempotent even if a crash happens after the new snapshot is published but before WAL rotation. A non-empty WAL cannot be silently replaced by enable_wal(); use recover() so committed mutations are not skipped.

snapshot() performs the same safe checkpoint when WAL is enabled, then returns an independent reader. The returned reader does not append to the writer's WAL.

Saved collections omit unused reserved capacity and store populated vector rows plus occupied HNSW links in 64-byte-aligned regions. Large Flat and SQ8 indexes can therefore start without copying those arrays into a second heap allocation. IDs, metadata, documents, deletion flags, and the BM25 index remain managed by the collection.

Mappings are read-only and owned by the loaded collection; the caller does not manage file handles or release memory. Queries, filtering, BM25, hybrid search, soft deletion, and set_ef_search() keep the mapping active. The first operation that extends or rebuilds the graph (add, update, upsert, or compaction) transparently materializes owned writable arrays, after which is_memory_mapped() returns False.

10. Inspect and Compact

update, upsert, and delete use soft deletion so searches can continue without mutating HNSW links in place. Inspect accumulated historical rows and rebuild the graph when they occupy a meaningful fraction of the collection:

    var snapshot = collection.stats()
    print("active:", snapshot.active_count)
    print("deleted:", snapshot.deleted_count)
    print("deleted ratio:", snapshot.deleted_ratio)

    # Rebuild only when at least 20% of stored rows are deleted.
    var report = collection.compact_if_needed(deleted_ratio=0.20)
    print("compacted:", report.performed)
    print("reclaimed rows:", report.reclaimed_records)

    # Use collection.compact() to rebuild immediately when garbage exists.

Compaction builds the replacement independently and installs it only after a successful rebuild. Collection name, storage kind, metric, M, ef_construction, and ef_search are preserved.

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Python API

MojoVec includes a managed Python API backed by the same native Mojo collection implementation. Install it from PyPI (Linux x86-64 and macOS Apple Silicon are supported):

pip install mojovec

To build the Python package from a source checkout with the PEP 517 backend:

python -m pip install --upgrade build
python -m build --wheel
python -m pip install dist/mojovec-*.whl

The build backend invokes Mojo only during build_ext; reading package metadata and creating the source distribution do not compile native code.

Linux wheels contain both AVX2 (x86-64-v3) and AVX-512 (x86-64-v4) native backends. MojoVec checks the CPU before loading native code and selects AVX-512 when its complete ISA level is available; otherwise it uses AVX2. There is no dispatch branch in the search hot path. For diagnostics or repeatable benchmarks, set MOJOVEC_FORCE_BACKEND=avx2 to select AVX2 even on an AVX-512 machine. Forcing AVX-512 on an unsupported CPU is rejected before the native module is loaded.

import mojovec

# Public wrappers expose ordinary Python signatures, type annotations, and
# NumPy-style runtime documentation.
help(mojovec.Collection.query)
print("native backend:", mojovec.native_backend())

collection = mojovec.Collection(
    dimension=3,
    M=32,
    ef_construction=96,
    ef_search=96,
    quantized=True,
    name="articles",
    metric="cosine",  # "l2", "cosine", or "ip"
)

# Nested vectors, metadata, and documents can be inserted in one batch.
collection.add(
    ids=[1, 2, 3],
    embeddings=[
        [1.0, 0.0, 0.0],
        [0.0, 1.0, 0.0],
        [0.8, 0.2, 0.0],
    ],
    metadatas=[
        {"kind": "guide", "year": 2024},
        {"kind": "reference", "year": 2026},
        {"kind": "guide", "year": 2027},
    ],
    documents=[
        "Mojo vector search guide",
        "Python API reference",
        "Hybrid search with reciprocal rank fusion",
    ],
)

# add rejects an existing ID. upsert inserts or replaces; update requires the
# ID to exist. Omitting metadata/documents preserves the active payload.
collection.upsert(
    ids=[2],
    embeddings=[[0.1, 0.9, 0.0]],
    documents=["Updated Python API reference"],
)

# Vector query with a Chroma-style typed metadata filter.
vector = collection.query(
    query_embeddings=[[1.0, 0.0, 0.0]],
    n_results=2,
    where={
        "$and": [
            {"kind": {"$in": ["guide", "reference"]}},
            {"year": {"$gte": 2024}},
        ]
    },
)
print(vector["ids"])
print(vector["distances"])
print(vector["metadatas"])
print(vector["documents"])

# BM25 and hybrid RRF use the same result shape and populate scores instead
# of distances.
text = collection.query(query_texts=["python api"], n_results=2)
hybrid = collection.query_hybrid(
    query_embeddings=[[0.0, 1.0, 0.0]],
    query_texts=["python api"],
    n_results=2,
    rrf_k=60,
    candidate_multiplier=4,
)
print(text["scores"], hybrid["scores"])

Python metadata accepts dictionaries with string keys and scalar str, int, float, or bool values. where supports $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $and, $or, and $not. Every managed query returns the five aligned keys ids, distances, metadatas, documents, and scores.

Persistence, mmap loading, compaction, snapshots, and the optional WAL are available from Python as well:

collection.save("articles.mojovec")
loaded = mojovec.load(
    "articles.mojovec",
    memory_mapped=True,
    mmap_threshold_bytes=64 * 1024 * 1024,
)

print(loaded.stats())
print(loaded.is_memory_mapped())
loaded.set_ef_search(128)
loaded.compact_if_needed()  # default deleted ratio: 0.25

loaded.enable_wal("articles.wal", durability=mojovec.WAL_ASYNC)
loaded.upsert([4], [[0.0, 0.0, 1.0]], documents=["Durable record"])
loaded.flush_wal()

# Recover committed WAL records on top of an earlier snapshot.
recovered = mojovec.recover(
    "articles.mojovec",
    "articles.wal",
    durability=mojovec.WAL_SYNC,
)

# Publish an independent point-in-time view and rotate the active WAL.
snapshot = loaded.snapshot("checkpoint.mojovec")

For NumPy-heavy ingestion, upsert_numpy() and query_numpy() keep the zero-copy pointer fast path. They require contiguous int64 IDs and float32 embeddings; the managed list API accepts either flattened or nested vectors.

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Examples

Executable tutorials are organized by language:

• examples/mojo/ — native Mojo lifecycle, IVF-PQ, persistence, compaction, metadata, filters, BM25, hybrid RRF, WAL, and distance metrics;
• examples/python/ — managed Python CRUD, metadata/documents, Chroma-style filters, BM25, hybrid RRF, mmap, compaction, WAL recovery, NumPy fast paths, and distance metrics.

See examples/README.md for the recommended order and commands.

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Running Tests & Benchmarks

Requires Mojo (via Pixi/Magic).

# Run all tests
for f in tests/test_*.mojo; do mojo run -I . "$f"; done

# Run the SIFT1M HNSW benchmarks
mojo run -I . benchmarks/suite/mojovec_sq8.mojo
mojo run -I . benchmarks/suite/mojovec_flat.mojo

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License

MIT License.