How sqlite-vec Works for Storing and Querying Vector Embeddings

Vector search has become a foundational tool for modern applications — from powering recommendation engines to enabling semantic search in LLM pipelines. Traditionally, developers reached for dedicated vector databases like FAISS, Annoy, or Pinecone. But what if you could bring vector search directly into your favorite embedded database?

Enter sqlite-vec: a powerful SQLite extension that lets you store, manipulate, and query vector data — right inside SQLite. It brings K-Nearest Neighbor (KNN) search, multiple distance metrics, and SIMD-accelerated performance into a portable, dependency-free package.

In this post, I'll explore how sqlite-vec enables efficient vector search, its supported formats and distance functions, and how it can be integrated into AI or semantic search pipelines without leaving the SQLite ecosystem.

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👉 New to sqlite-vec? Check out this step-by-step tutorial where I walk through how to store real embeddings and run semantic search using Node.js.

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What is sqlite-vec?

sqlite-vec extends SQLite with native vector support. It introduces a new vector data type and adds a suite of functions for working with vectors. Think of it as embedding a minimal vector database engine directly into your local .db file.

You get:

• Native vector types: float32, int8, and bit (binary vectors)

• Distance metrics: L2 (Euclidean), L1 (Manhattan), cosine similarity, and Hamming

• SQL functions for manipulating vectors

• KNN search via virtual tables

• SIMD acceleration with AVX and NEON

This makes sqlite-vec ideal for use cases like:

• Embedding-based semantic search

• Local AI-powered search engines

• Lightweight ML applications

• Offline recommender systems

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Core Feature: Vector Search

At its heart, sqlite-vec enables fast vector similarity search directly in SQLite. This is accomplished through three main pieces:

1. Vector Storage and Types

Vectors can be stored as:

• float32 blobs (REAL[]-like arrays)

• int8 blobs for quantized vectors (smaller footprint)

• Bit vectors (bit[]) for binary operations and Hamming distance

Each vector column tracks its type using SQLite's "subtype" feature, a low-level tagging system that sqlite-vec uses to enforce type safety. For example, if you try to compare a float32 vector with an int8 vector, you'll get a helpful error — not undefined behavior.

You can ingest vectors using:

• JSON arrays ([0.1, 0.3, 0.5])

• Raw blobs (BLOB)

• SQL functions like vec_f32() and vec_int8()

Behind the scenes, sqlite-vec performs custom JSON parsing with character-level control — it doesn't rely on SQLite's JSON functions. If your input is malformed (e.g., missing brackets or a non-numeric value), the parser returns clear SQL errors like "invalid JSON array" or "NaN is not allowed".

2. Distance Metrics

The magic of vector search lies in distance calculations. sqlite-vec provides fast, SIMD-accelerated implementations of key metrics:

• L2 (Euclidean Distance)
  For float32 and int8, with AVX/NEON support.

    SELECT vec_distance_l2(a.vector, b.vector) FROM ...
  

• L1 (Manhattan Distance)
  Fast for quantized int8 vectors and compact float vectors.

    SELECT vec_distance_l1(a.vector, b.vector)
  

• Cosine Similarity
  Normalize vectors and compute angular similarity.

    SELECT vec_distance_cosine(a.vector, b.vector)
  

• Hamming Distance
  Only available for bit vectors.

    SELECT vec_distance_hamming(a.vector, b.vector)
  

Each function dynamically dispatches the correct implementation based on the vector type and dimension — which are validated before the calculation begins. This enforcement prevents accidental misuse and guarantees consistent results.

3. K-Nearest Neighbor Search (KNN)

The core of vector search is KNN — finding the k closest vectors to a query vector. sqlite-vec provides this via virtual tables (vec0_vtab), which support SQL queries like:

SELECT id, vector, distance
FROM my_vectors
WHERE vector MATCH ?
ORDER BY distance
LIMIT 5;

This scans the vector column, computes distances to the query vector, and returns the 5 nearest rows, sorted by distance.

The implementation uses:

• Memory-efficient chunked vector storage

• Indexed rowid buffers for filtering and sorting

• Optimized metric dispatch (L2, cosine, etc.)

• Optional metadata filters (WHERE label = 'cat')

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Real World Example: Semantic Search

Let's say you're building a local semantic search tool. First, you embed text into vectors using OpenAI, Mistral, or a local model. Then you store the vectors in SQLite:

CREATE VIRTUAL TABLE embeddings USING vec0(id TEXT, vector ANY);
INSERT INTO embeddings (id, vector) VALUES
  ('doc1', vec_f32('[0.01, 0.42, 0.5, ...]')),
  ('doc2', vec_f32('[0.12, 0.21, 0.3, ...]'));

Now run semantic search like this:

SELECT id, vec_distance_cosine(vector, vec_f32('[0.05, 0.41, 0.49, ...]')) AS score
FROM embeddings
ORDER BY score ASC
LIMIT 5;

Or use KNN:

SELECT id, distance 
FROM embeddings 
WHERE vector MATCH vec_f32('[...]')
ORDER BY distance
LIMIT 5;

All of this runs in embedded SQLite — no server, no dependencies.

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Performance Under the Hood

sqlite-vec uses smart low-level tricks to stay fast:

• SIMD: AVX (x86) and NEON (ARM) intrinsics for L2/L1

• Chunked Storage: Vectors are grouped into chunks, reducing memory fragmentation

• Bitmaps: Validity and metadata filters are bitmask-accelerated

• Memory Safety: Cleanup functions ensure no leaks in vector operations

For example, the AVX-accelerated L2 distance routine processes 16 float32 elements per loop using _mm256_loadu_ps, _mm256_sub_ps, and _mm256_mul_ps. Final results are summed and square-rooted for the true Euclidean distance.

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Other Goodies

Beyond vector search, sqlite-vec gives you:

• Vector math: Add, subtract, slice, normalize

    SELECT vec_add(vec1, vec2), vec_normalize(vec1)
  

• Type conversion: Quantize float32 → int8 or binary (bit)

    SELECT vec_quantize_int8(vec_f32(...), 'unit')
    SELECT vec_quantize_binary(vec_f32(...))
  

  This is useful when you want smaller vector footprints — e.g., for mobile apps or when storing millions of vectors. The 'unit' option scales vectors to the [-1, 1] range before quantization, preserving cosine relationships.

• JSON I/O: Convert vectors to/from JSON for logging and debugging

    SELECT vec_to_json(vector)
  

• Metadata filtering: Attach extra columns like tags, timestamps, or labels and filter before distance is computed

• Helpful Errors: If you pass mismatched types, malformed JSON, or vectors of different dimensions, sqlite-vec will return readable SQLite-native error messages — not just crash or return null.

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Advanced Architecture Highlights

While sqlite-vec feels simple to use at the SQL level, under the hood it's a robust system engineered for performance and modularity. Here are a few of the architectural systems that make it tick:

Virtual Table Engine (vec0_vtab)

All vector storage is handled via a custom SQLite virtual table module called vec0_vtab. This acts like a dynamic table interface that manages vector columns, metadata, partitions, and even auxiliary fields. It controls everything from row layout to how queries are planned and executed. When you write SELECT ... FROM vec0(...), you're using this engine.

It also enables dynamic query plans — like switching between full scans, point queries, or optimized KNN searches — based on what the SQL planner sees.

Chunk-Based Vector Storage

Vectors aren't just stored in rows — they're stored in chunks. These are memory-efficient, fixed-size blocks that group vectors by partition key or table layout. Chunking improves locality, reduces memory fragmentation, and allows fine-grained row tracking with bitmaps.

If you insert a new vector, it's placed into an available chunk; if a vector is deleted, the bitmap marks its slot invalid. This system scales surprisingly well and supports in-place updates and fast scans.

Metadata Filtering

Each table can include custom metadata columns (e.g., label, timestamp, language). These are indexed internally and can be used to filter KNN results before distance calculations happen — saving time and compute.

SELECT id FROM embeddings
WHERE label = 'cat' AND vector MATCH ?
ORDER BY distance
LIMIT 10;

Under the hood, a metadata filter bitmap intersects with the chunk validity bitmap to skip irrelevant rows early — like having a WHERE clause that's KNN-aware.

Transaction Handling and Consistency

The entire system is designed to respect SQLite's transactional semantics. Vector inserts, updates, and deletes are atomic and journaled properly. Virtual table methods hook into SQLite's transaction lifecycle (xBegin, xSync, xRollback, xCommit), ensuring consistency across both vector chunks and metadata.

This means you can use sqlite-vec safely in write-heavy or multi-threaded environments — rollbacks and savepoints just work.

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When to Use This

sqlite-vec is perfect when:

• You want lightweight, embedded vector search

• You already use SQLite and want to avoid external dependencies

• You're building AI-powered apps on the edge or mobile

• You want reproducible, portable vector pipelines

It may not be ideal for large-scale distributed search across millions of high-dimensional vectors — but for local, embedded, or small-medium applications, it's shockingly capable.

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Final Thoughts

With sqlite-vec, vector search becomes a SQL-native feature. You can run ANN-style queries, compute distances, manipulate vectors, and serialize them — all using familiar SQL. It's fast, flexible, and portable.

From embedded AI to offline semantic search to quantized mobile experiences, sqlite-vec unlocks a whole new layer of vector intelligence in SQLite. If you're building search interfaces, AI notebooks, or on-device intelligence, give it a spin — no external services required.