embedding-dedupe 0.1.0

Deduplicate near-identical embedding records by cosine similarity. Pure Python, zero runtime deps. Python port of @mukundakatta/embedding-dedupe.

embedding-dedupe

Deduplicate near-identical embedding records by cosine similarity. Pure Python, zero runtime dependencies.

Python port of @mukundakatta/embedding-dedupe. Same algorithm, ergonomic Python API.

Install

pip install embedding-dedupe
# Optional: faster cosine via numpy
pip install "embedding-dedupe[numpy]"

Usage

from embedding_dedupe import dedupe

records = [
    {"id": "a", "embedding": [0.10, 0.20, 0.30], "text": "Cats are great."},
    {"id": "b", "embedding": [0.10, 0.20, 0.31], "text": "Cats are great pets."},   # near-dup of a
    {"id": "c", "embedding": [0.90, 0.10, 0.05], "text": "Stock prices fell."},
]

# Default: keep the lowest-id record from each cluster (deterministic).
dedupe(records, threshold=0.95)
# -> [
#      {"id": "a", "embedding": [0.10, 0.20, 0.30], "text": "Cats are great."},
#      {"id": "c", "embedding": [0.90, 0.10, 0.05], "text": "Stock prices fell."},
#    ]

# Or: keep the record with the longest text (ties -> lowest id).
dedupe(records, threshold=0.95, keep="longest")
# -> [
#      {"id": "b", "embedding": [0.10, 0.20, 0.31], "text": "Cats are great pets."},
#      {"id": "c", "embedding": [0.90, 0.10, 0.05], "text": "Stock prices fell."},
#    ]

API

dedupe(
    records,                  # list[dict]
    threshold=0.95,           # cosine sim above which two records are duplicates
    key="id",                 # record id field name
    vector="embedding",       # embedding field name
    keep="first",             # "first" (lowest-id) or "longest" (longest .text)
) -> list[dict]

cosine(a, b) is also exported for ad-hoc use.

Algorithm

Greedy single-link clustering: scan records in input order, place each into the first existing cluster whose anchor vector is within threshold, else start a new cluster. From each cluster, return one survivor according to keep. Cluster output order matches input order (= order in which clusters were created).

O(n * k) where k is the cluster count -- fine up to ~100k records. For larger sets, plug in an ANN index upstream and dedupe within candidates.

API differences from the JS sibling

• JS: dedupeEmbeddings(items, { threshold }) returning { kept, duplicates }. Python: dedupe(records, threshold=0.95, key=, vector=, keep=) returning a flat list[dict] of survivors.
• The default threshold is 0.95 (was 0.98 in JS) -- tuned for typical OpenAI/Anthropic embedding noise.
• New keep="longest" strategy mirrors a common request -- prefer the most informative chunk in each cluster.

See the JS sibling for the original heuristics and design notes.