Barrel VectorDB¶

Embeddable vector database for Erlang with HNSW indexing and semantic search.

Build AI-powered search into your Erlang applications. Barrel VectorDB provides a production-ready vector store with pluggable embedding providers, automatic batching, and sub-millisecond search latency.

Quick Start

Get up and running in 5 minutes with vector search

Quick Start
Embedding Models

Local, Ollama, OpenAI, and more - choose your embedder

Embeddings
Clustering

Scale out with sharding and automatic rebalancing

Clustering
API Reference

HTTP and Erlang APIs for full control

HTTP API

What is Barrel VectorDB?¶

Barrel VectorDB is an embeddable vector database designed for Erlang/OTP applications:

HNSW Indexing: Pure Erlang HNSW with O(log N) search, or optional FAISS backend for high throughput
Pluggable Embeddings: Local (sentence-transformers), Ollama, OpenAI, FastEmbed, with fallback chains
Sub-millisecond Search: P50 ~1ms, P99 ~5ms typical latency
Production Ready: Built on RocksDB for persistence, gen_batch_server for write coalescing

Quick Example¶

%% Start a store with local embeddings
{ok, _} = barrel_vectordb:start_link(#{
    name => my_store,
    path => "/tmp/vectors",
    embedder => {local, #{}}  %% requires Python + sentence-transformers
}).

%% Add documents (text is embedded automatically)
ok = barrel_vectordb:add(my_store, <<"doc1">>, <<"Hello world">>, #{}).
ok = barrel_vectordb:add(my_store, <<"doc2">>, <<"Goodbye world">>, #{}).

%% Search with text query
{ok, Results} = barrel_vectordb:search(my_store, <<"hi there">>, #{k => 5}).
%% => [#{key => <<"doc1">>, text => <<"Hello world">>, score => 0.89, ...}, ...]

Core Features¶

Vector Indexing¶

Feature	HNSW (Default)	FAISS (Optional)
Dependencies	None	barrel_faiss NIF
Insert speed	Baseline	1.6-3x faster
Search speed	Baseline	2x faster
Delete speed	Fast (native)	Slower (soft delete)
Memory	Higher	Lower

Embedding Providers¶

Provider	Description
`local`	Python + sentence-transformers (CPU)
`ollama`	Local Ollama server
`openai`	OpenAI Embeddings API
`fastembed`	ONNX-based embeddings
Provider chain	Try providers in order

Advanced Features¶

SPLADE: Neural sparse embeddings for hybrid search
ColBERT: Multi-vector late interaction
CLIP: Cross-modal image-text search
Reranking: Cross-encoder for improved relevance
BM25: Pure Erlang lexical search

Get Started¶

5 minutes

Install and start searching

{deps, [
    {barrel_vectordb, {git,
      "https://github.com/barrel-db/barrel_vectordb.git",
      {branch, "main"}}}
]}.

Vector-only mode

Bring your own embeddings

{ok, _} = barrel_vectordb:start_link(#{
    name => store,
    path => "/tmp/vectors",
    dimensions => 768
}).

barrel_vectordb:add_vector(store,
    <<"id">>, <<"text">>, #{}, Vector).