duplicate-detection

Duplicate detection combines exact hash matching for identical content with vector similarity search to catch near-duplicates and paraphrases. This two-stage approach quickly identifies exact copies, then uses embeddings and Milvus-based storage to find semantically similar records you'd miss with hash alone.

Yes. Semantic similarity detects paraphrased and near-duplicate content across FAQs, articles, tickets, and product descriptions by comparing embeddings rather than exact text. This catches duplicates that differ in wording but convey the same meaning.

You need an embedding model to convert records into vectors and Milvus for fast vector storage and similarity search. The skill handles hash-based exact matching and vector-based semantic matching once these prerequisites are in place.

Hash-based detection finds exact duplicates instantly for quick filtering. Vector similarity then catches near-duplicates and rephrased content that hash alone would miss, resulting in cleaner, more concise knowledge bases and datasets.

Exact duplicate detection uses content hashing to spot identical records instantly. Semantic similarity uses embeddings to find paraphrases and conceptually equivalent content, catching duplicates that differ in wording but carry the same information.