How can I retrieve original text from an embedding-based similarity search in PHP?

Why You Can't Convert Embeddings Back to Natural Language

Embeddings are numerical representations of text—dense vectors that capture semantic information. However, they are a lossy transformation of the data. This means:

• Non-Invertibility: The function that creates an embedding (e.g., via the text-embedding-ada-002 model) is not reversible. It compresses the text into a set of numbers without retaining all the detailed information required to reconstruct the original text.

• Mapping Instead of Decoding: When you perform similarity searches, you’re comparing these vectors in a high-dimensional space. To deliver natural language results, you need to maintain a mapping between each vector and its corresponding source text.

Next Steps in Your Application

To effectively respond to user queries with natural language, follow these steps:

1. Store the Original Text: When you generate an embedding for a piece of text, save the source text together with its embedding. This is typically done in a database or an associative array in your application.

2. Perform the Similarity Search: Use your existing method to compare the query embedding against stored embeddings and determine the most similar one(s).

3. Retrieve the Associated Text: Once you identify the most similar embedding, look up the original text that was stored alongside it. This is the natural language output you will deliver to the user.

Example Concept in PHP

Below is a conceptual snippet showing the idea:

PHPCopied
// Example mapping: an associative array where keys are IDs and values are arrays
// containing both the embedding and the original text.
$documentStore = [
    1 => [
        'embedding' => [/* vector values */],
        'text' => "The food was delicious and the waiter was friendly."
    ],
    2 => [
        'embedding' => [/* vector values */],
        'text' => "The service was slow, but the desserts were outstanding."
    ],
    // ... more documents
];

// Assume $queryVector is your query's embedding and calculateSimilarity() is defined.
$mostSimilarId = null;
$highestSimilarity = -1;
foreach ($documentStore as $id => $doc) {
    $similarity = calculateSimilarity($queryVector, $doc['embedding']);
    if ($similarity > $highestSimilarity) {
        $highestSimilarity = $similarity;
        $mostSimilarId = $id;
    }
}

// Retrieve the corresponding original text using the stored ID.
$resultText = $documentStore[$mostSimilarId]['text'];
echo "Most similar text: " . $resultText;

Summary

You won't be "converting" the embedding back to natural language per se—rather, you retrieve the natural language text that was originally converted into that embedding. Maintaining the association between the text and its embedding is key for building effective semantic search applications.