How Do I Interpret High Cosine Similarity Scores from text-embedding-ada-002?

Below is some guidance on how to interpret and work with the embeddings from the text-embedding-ada-002 model:

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1. Different Models, Different Scales

Keep in mind that the raw cosine similarity scores you get from different embedding models aren’t directly comparable. The text-embedding-ada-002 model is designed for semantic search tasks, and its cosine similarity values come out on a different scale than, for example, the HuggingFace model all-MiniLM-L6-v2. Rather than looking at an absolute threshold (e.g., "should be near 0 for unrelated sentences"), you should use the similarities for ranking or nearest-neighbor queries within a corpus.

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2. Relative Comparison is Key

For semantic search, you are typically comparing many embeddings against a query rather than trying to interpret the raw score between any two sentences. A higher score means the documents are more similar relative to each other. If you see a score of about 0.75 between two sentences, that score is only meaningful in context—for example, by comparing it to other pairwise similarities in your dataset.

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3. Ensure Correct Extraction of Embeddings

Make sure that the embedding vectors are extracted correctly. In your code snippet you have:

PYTHONCopied
emb1 = np.asarray(emb.data[0]["embedding”])
emb2 = np.asarray(emb.data[1]["embedding”])

Double-check that the quotation marks around "embedding" are the standard ASCII quotes (") rather than “smart quotes.” In Python, non-standard quotes can cause unexpected behavior.

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4. Use Cosine Similarity as Intended for Ranking

The cosine similarity function you’re using is fine. The primary point to remember for retrieval is that you should rank documents by their cosine similarity rather than trying to set a fixed threshold for “relatedness.” For example:

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def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

When performing search operations, make sure that you normalize and compare among a set of document embeddings to find the most relevant results.

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5. Adjusting for Your Business Case

If the scores from text-embedding-ada-002 appear too “high” compared to your expectations from another model, you might consider:

• Relative Ranking: Use the scores to order candidate documents relative to each other.
• Calibration: Experiment with thresholds that work for your specific search scenario.
• Preprocessing: Ensure consistency in text preprocessing (e.g., case, punctuation) for both your query and document texts.

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Summary

The text-embedding-ada-002 model’s embeddings are built primarily for search and retrieval tasks, so while two semantically different sentences may produce a higher cosine similarity than you expect, what matters is how they rank in relation to your full collection of documents. Use these scores in a comparative manner instead of treating them as standalone measures of similarity.

If you have any further questions strictly related to the OpenAI API or how the embeddings are generated, feel free to ask!