How do you use ChromaDB to detect and remove near-duplicate or semantically similar documents?

import chromadb

client = chromadb.Client()
col = client.create_collection(
    "dedup_store",
    metadata={"hnsw:space": "cosine"},
)

# Similarity threshold — tune based on your use case
DUPLICATE_THRESHOLD = 0.95  # cosine similarity >= 0.95 → treat as duplicate

def cosine_dist_to_score(d: float) -> float:
    return 1 - d / 2

def add_if_unique(
    collection,
    document: str,
    doc_id: str,
    metadata: dict = None,
    threshold: float = DUPLICATE_THRESHOLD,
) -> bool:
    """Returns True if document was added, False if it was a duplicate."""
    if collection.count() == 0:
        collection.add(documents=[document], ids=[doc_id],
                       metadatas=[metadata or {}])
        return True

    # Query for the nearest existing document
    results = collection.query(
        query_texts=[document],
        n_results=1,
        include=["documents", "distances"],
    )
    nearest_dist  = results["distances"][0][0]
    nearest_score = cosine_dist_to_score(nearest_dist)
    nearest_doc   = results["documents"][0][0]

    if nearest_score >= threshold:
        print(f"DUPLICATE detected (score={nearest_score:.3f}):")
        print(f"  New:      {document[:60]}")
        print(f"  Existing: {nearest_doc[:60]}")
        return False  # skip insertion

    collection.add(documents=[document], ids=[doc_id],
                   metadatas=[metadata or {}])
    return True

# Test deduplication
phrases = [
    ("ChromaDB is a vector database for AI apps.", "p1"),
    ("Chroma DB is a vector store built for AI applications.", "p2"),  # near-dup of p1
    ("Python is great for machine learning.", "p3"),
]
for text, pid in phrases:
    added = add_if_unique(col, text, pid)
    print(f"Added: {added} — {text[:40]}")

print(f"\nFinal collection size: {col.count()}")  # 2 (p2 was duplicate of p1)