rat-embed 0.2.0

Relative Anchor Translation: zero-shot embedding space translation via similarity profiles

RAT — Relative Anchor Translation

Zero-shot embedding space translation using relative distances to shared anchors. No additional training required.

Install

pip install rat-embed            # core (numpy only)
pip install "rat-embed[models]"  # + sentence-transformers for fit()
pip install "rat-embed[dev]"     # + pytest, ruff

Quick Start

import numpy as np
from rat import RATranslator

# 1. Prepare anchor embeddings from both models (K anchors, L2-normalized)
anchor_a = ...  # (K, D_a) from model A
anchor_b = ...  # (K, D_b) from model B

# 2. Fit the translator
translator = RATranslator(kernel="poly").fit_embeddings(anchor_a, anchor_b)

# 3. Transform & retrieve
query_emb = ...  # (N, D_a) from model A
db_emb = ...     # (M, D_b) from model B
results = translator.retrieve(query_emb, db_emb, top_k=10)
# results["indices"]  → (N, 10) nearest neighbor indices
# results["scores"]   → (N, 10) cosine similarity scores

Or transform individually for more control:

q_rel = translator.transform(query_emb, "a")              # query side (no z-score)
d_rel = translator.transform(db_emb, "b")                 # db side (z-score applied)
d_rel = translator.transform(db_emb, "b", role="query")   # override: skip z-score

Note: For cross-family model pairs (e.g., MiniLM → BGE), use normalize="always" when constructing the translator. The default "auto" mode may skip z-score normalization for some models where it would actually help in cross-model scenarios.

Advanced: RATHub (multi-model)

from rat import RATHub

hub = RATHub(kernel="poly")
hub.set_anchors("minilm", anchor_minilm)      # (K, 384)
hub.set_anchors("e5", anchor_e5)              # (K, 1024)
hub.set_anchors("bge", anchor_bge)            # (K, 384)

# Transform from any model
q = hub.transform("minilm", query_emb, role="query")
d = hub.transform("e5", db_emb, role="db")

# Or use retrieve directly
results = hub.retrieve(query_emb, db_emb, "minilm", "e5", top_k=10)

Important: All models must use anchors from the same texts in the same order. Select anchor indices once (e.g., via FPS on one model), then use those indices for every model.

Multi-DB search

Search across multiple databases built with different models:

results = hub.retrieve_multi(
    query_emb,
    databases=[
        (db1_emb, "bge"),       # BGE database
        (db2_emb, "minilm"),    # MiniLM database
        (db3_emb, "e5"),        # E5 database
    ],
    query_model="bge",
    top_k=10,
)
# results["indices"]   → (N, 10) global indices
# results["scores"]    → (N, 10) normalized scores
# results["db_labels"] → (N, 10) which DB each result came from (0, 1, 2)

retrieve_multi uses per-database score normalization internally to make scores comparable across databases. Do not vstack relative representations from different models — their score scales differ.

Paper

See the Zenodo record for the full experiment report.

Experiment reproduction code is in experiments/ (unchanged from the original research).

License

MIT