barrel_rerank

Cross-encoder reranking for Erlang.

A gen_server that manages communication with a Python cross-encoder server. Supports concurrent requests by tracking request IDs and routing responses back to the correct callers.

Cross-encoders score query-document pairs directly, providing more accurate relevance scores than bi-encoder similarity for reranking candidate results.

Installation

Add to your rebar.config:

{deps, [
    {barrel_rerank, "0.1.0"}
]}.

Requirements

Python 3.8+ with the following packages:

pip install transformers torch

Or let barrel_rerank manage the venv automatically:

{ok, _} = barrel_rerank_venv:ensure_venv().

Usage

%% Start the reranker
{ok, Server} = barrel_rerank:start_link(#{}).

%% Rerank documents by relevance to query
Query = <<"What is machine learning?">>,
Documents = [
    <<"Machine learning is a subset of AI.">>,
    <<"Python is a programming language.">>,
    <<"Deep learning uses neural networks.">>
],

{ok, Results} = barrel_rerank:rerank(Server, Query, Documents).
%% Results = [{0, 0.95}, {2, 0.82}, {1, 0.12}]
%% Sorted by score descending, tuples are {Index, Score}

%% Stop when done
barrel_rerank:stop(Server).

Configuration

Config = #{
    python => "python3",                               %% Python executable (fallback)
    model => "cross-encoder/ms-marco-MiniLM-L-6-v2",   %% Model name
    timeout => 120000                                  %% Timeout in ms
}.
{ok, Server} = barrel_rerank:start_link(Config).

Supported Models

• cross-encoder/ms-marco-MiniLM-L-6-v2 - Default, fast, good quality
• cross-encoder/ms-marco-MiniLM-L-12-v2 - Better quality, slower
• BAAI/bge-reranker-base - Good quality
• BAAI/bge-reranker-large - Best quality, slowest

Integration with Search

Typical two-stage retrieval:

%% Stage 1: Fast vector search (top 100)
{ok, Candidates} = barrel_vectordb:search(Store, Query, #{k => 100}),

%% Stage 2: Rerank top candidates
Docs = [maps:get(text, C) || C <- Candidates],
{ok, Ranked} = barrel_rerank:rerank(Server, Query, Docs),

%% Get top 10 after reranking
Top10Indices = [Idx || {Idx, _Score} <- lists:sublist(Ranked, 10)],
Top10 = [lists:nth(Idx + 1, Candidates) || Idx <- Top10Indices].

License

Apache-2.0