Plagiarism Dectector

A sample showing how Vector comparison can be used to easily and effectively detect plagiarismm using a simple in-memory vector store

The repository contains three main projects:

• Memory Vector Store project, which focuses on storing vectors in memory;
• Chunk Creator project, which extracts vectors from PDF files; -Plagiarism project, which demonstrates how to perform similarity searches using the stored vectors and use OpenAI . Each project has its own set of code and resources, allowing you to explore and understand the implementation details.

This code is based on the code in MemoryVectorStore

Code example

First we need to make chunks both PDFs and build the embedding vectors

//  OpenAI service that we are going to use for embedding
_openAiService = new OpenAIService(new OpenAiOptions()  {ApiKey = apiKey });

// Set up a MemoryVector database, to be filled with chunks of documents
// including an embedding vector of 1536 dimensions
// Also included is a callback that embeds any text item into a vector
_vectorCollection = new MemoryVectorDB.VectorDB<Chunk>(1536, ChunkEmbedingAsync);

// Get text fom pdf 
_document = PdfTextExtractor.GetText(documentPath);

// Generate sentences of max 50 words
_chunkGenerator = new ChunkGenerator(50,  _document);

// Loop through chunks
foreach (var chunk in _chunkGenerator.GetChunk())
{
    // Add the source reference to the chunk
    chunk.Source = documentPath;

    // Add the chunk to the vector store
    await _vectorCollection.AddAsync(chunk);

    // We remove the text from the chunk to safe memory:
    // we just need the vector, start index, length and source
    // so we can recover the the chunk from the original document later
    chunk.Text = null!;
}

Now we can find the best matching sentences between both documents

var vectorObjects1 = _embedding1.VectorCollection.VectorObjects;
var vectorObjects2 = _embedding2.VectorCollection.VectorObjects;

// Find the closest matching vectors between the 2 documents
var bestMatches    = FindNearestSorted(vectorObjects1, vectorObjects2, 100);

// And here they are
foreach (var item in bestMatches)
{
    ShowMatch(true, item.Value.Item1, item.Value.Item2);
}

Note that the FindNearestSorted is just a brute-force comparison of the (normalized) dot products between the query vector and all chunk vectors. For larger vector stores, a database should be used that implements an indexing system for efficient nearest neighbour searches using something like this library

However, not all closely matching vectors might constitute plagiarism. Luckily we have the LLM to compare the highest ranking vector in-porducts

// Format the query to post to the LLM:
foreach (var item in bestMatches)
{
	var isPlagiarism = await FormulateComparisonAsync(item.Value.Item1, item.Value.Item2);
}