plagiarism-engine

Detect paraphrased plagiarism by using HNSW vector indexing with sentence embeddings to find semantically similar content. This approach identifies rewritten text by comparing meaning rather than exact string matching against existing document archives.

The best way to identify verbatim copied content is using Winnowing and Rabin-Karp fingerprinting algorithms. These methods create document fingerprints to efficiently match exact text strings across large archives of existing submissions.

Yes, you can use ChromaDB or Qdrant for document similarity analysis. They provide HNSW vector indexing to handle high-performance semantic comparisons across large-scale document archives asynchronously via BullMQ or Celery task queues.

Check document originality by submitting text to scan against your project archive. The engine uses vector search and fingerprinting to flag both verbatim and paraphrased matches, generating detailed reports with highlighted similarities.

Yes, plagiarism detection works with sentence-transformers to generate sentence embeddings for semantic matching. These embeddings power the HNSW vector indexing required to accurately identify paraphrased content within submitted documents.

Vector search limitations for plagiarism detection include missing exact verbatim matches if used alone. It must be combined with Winnowing fingerprinting to ensure both direct copy-pasted and semantically paraphrased text are accurately identified.