Updated AI & Embedding Tool

Text Similarity Calculator

Measure text similarity using embedding vectors and cosine similarity. Paste embeddings from models like text-embedding-3-small, text-embedding-3-large, or any custom model and get similarity scores, vector angles, distances, and pairwise similarity matrices directly in your browser.

Embedding Vectors Cosine Similarity Angle & Distance Similarity Matrix

Calculate Text Similarity from Embeddings

This Text Similarity Calculator is designed for embedding-based workflows. Generate embeddings with your preferred model (for example text-embedding-3-small or text-embedding-3-large), paste the numeric vectors here, and compute cosine similarity, angle between vectors, and distances. For multiple texts, build a full similarity matrix to see how each textates to the others.

This calculator is client-side only. It does not call any external API or model. Generate embeddings in your own environment (for example with OpenAI or another provider), then paste the resulting vectors to analyze similarity.

Used only for labels and summaries. It does not affect the math.
Accepts comma-separated, space-separated, or JSON-style lists. Example: [0.12, -0.03, 0.85] or 0.12 -0.03 0.85.
Vectors A and B must have the same length (same embedding dimension).

The calculator computes cosine similarity, which measures the angle between embedding vectors and is commonly used for semantic similarity. You will also see the raw dot product, Euclidean distance, and the angle in degrees.

Each non-empty line should contain either label: numbers... or just numbers. All vectors must share the same dimension. For readability, limit to 2–20 vectors.

The matrix view computes cosine similarity for every pair of vectors, producing a symmetric similarity matrix with 1.0 on the diagonal. This is useful for clustering, nearest-neighbor inspection, and sanity-checking embedding behavior.

Text Similarity Calculator – Measure Semantic Similarity from Embeddings

The Text Similarity Calculator on MyTimeCalculator helps you quantify how similar two pieces of text are by working directly with embedding vectors. Instead of comparing raw words or characters, embeddings represent meaning in a high-dimensional space, where cosine similarity becomes a powerful proxy for semantic similarity.

Many modern applications—such as semantic search, retrieval-augmented generation (RAG), recommendation systems, and clustering—rely on embedding-based similarity. This calculator provides a convenient, private way to analyze similarity scores using embeddings you generate with your own tools or APIs.

1. Embedding-Based Text Similarity in a Nutshell

An embedding model maps text to a vector of real numbers, often with hundreds or thousands of dimensions. Texts with similar meaning should end up with vectors that point in roughly the same direction. The calculator focuses on cosine similarity, defined as:

cosine(A, B) = (A · B) / (||A|| ||B||)

where A and B are embedding vectors, A · B is the dot product, and ||A||, ||B|| are their Euclidean norms. Cosine similarity ranges from −1 to 1, but for typical embedding models most values fall between 0 and 1. Higher values indicate more similar texts.

2. Working with Two Texts vs. Many Texts

The calculator has two main modes:

  • Two-text similarity: Paste embedding vectors A and B and compute a detailed similarity report, including cosine similarity, vector angle, Euclidean distance, dot product, and norms.
  • Similarity matrix: Paste multiple embedding vectors (for example one per document or per sentence) and generate a pairwise cosine similarity matrix. This view is helpful for spotting clusters, outliers and near-duplicates.

In both modes, all math runs locally in your browser. You can use embeddings from any provider, including OpenAI’s text-embedding-3-small, text-embedding-3-large, or your own custom model.

3. How to Use the Text Similarity Calculator

  1. Generate embeddings externally. Use your preferred API or library to turn text into embedding vectors. Copy the numeric vectors, not the original text, into the calculator.
  2. Label the model (optional). Select one of the preset model names or type your own custom embedding model name. This is used for summaries only and does not affect the calculation.
  3. For two texts: Go to the Two-Text Similarity tab and paste vectors A and B. Ensure they have the same dimension and use a consistent format (commas, spaces, or JSON-style lists are all accepted).
  4. For multiple texts: Go to the Similarity Matrix tab and paste one vector per line. Optionally add labels before a colon (for example Doc1: 0.1 0.2 0.3). All vectors must share the same length.
  5. Click the calculate button. The calculator will compute cosine similarity andated metrics, then display them in cards or in a similarity matrix table.
  6. Interpret the scores. Higher cosine similarity means more similar embeddings (and usually more similar text), while lower scores suggest lessated content.

4. Interpreting Cosine Similarity and Angle

Cosine similarity is directlyated to the angle between two vectors. If the vectors are normalized, a similarity of 1 corresponds to an angle of 0° (identical direction), 0 corresponds to 90° (orthogonal), and −1 corresponds to 180° (opposite direction). The calculator reports both the cosine value and an approximate angle in degrees:

angle(A, B) = arccos(cosine(A, B)) (in degrees)

This geometric view can be easier to reason when comparing multiple similarity scores across a dataset.

5. Use Cases for Embedding-Based Text Similarity

  • Semantic search and RAG: Check thatevant documents truly have high similarity to the query embeddings and analyze borderline cases.
  • Deduplication: Identify near-duplicate texts by scanning for pairs whose similarity exceeds a chosen threshold.
  • Clustering and topic analysis: Inspect similarities within and across clusters to validate grouping quality.
  • Model comparison: Compare similarity patterns when using different embedding models or parameter settings.

6. Best Practices and Caveats

While cosine similarity is a popular and effective metric, it is not the only way to compare embeddings. Some tasks may benefit from additional normalization, distance metrics, or learned similarity functions. Also, similarity scores are model-dependent: the same pair of texts can produce slightly different scores across providers or model versions.

The Text Similarity Calculator focuses on clear, transparent math over embedding vectors you provide. For production systems, you may want to combine similarity thresholds with task-specific evaluation, user feedback, and monitoring of edge cases.

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Text Similarity Calculator FAQs

Frequently Asked Questions

Quick answers to common questions embedding-based text similarity, cosine similarity, and how to use this calculator with your own models and APIs.

No. The calculator is purely client-side and only works with the numeric embedding vectors you paste into it. It does not send your data to any external service. To use it with models such as text-embedding-3-small or text-embedding-3-large, generate the embeddings in your own environment, then paste the vectors here for analysis.

The calculator accepts comma-separated values, space-separated values, or JSON-style lists. For example, [0.12, -0.03, 0.85], 0.12 -0.03 0.85, and 0.12, -0.03, 0.85 are all valid. Internally, the tool extracts numeric tokens from the text line and converts them into a vector of floating-point numbers.

Cosine similarity is only defined when both vectors have the same dimension. If the calculator detects a length mismatch, it will show an error and ask you to check your embeddings. This often indicates that the vectors were generated by different models or that one of them was truncated or copied incorrectly.

For readability and performance in the browser, the tool is designed for small to medium sets of vectors, such as 2–20 items. Larger matrices are still mathematically valid, but the table can become difficult to interpret and slower to render. For very large datasets, you may want to compute similarity matrices in a separate script or notebook instead of in the browser.

Higher cosine similarity values indicate more similar embeddings. For many embedding models, scores above 0.7–0.8 often correspond to clearlyated or near-duplicate texts, while scores near 0 indicate low similarity. The exact thresholds depend on your model, task, and tolerance for false positives or false negatives, so it is best to experiment with real examples from your own data.

Yes, as long as you can paste the numeric embedding vectors, the calculator does not care whether they come from text, image, audio, or multimodal models. Cosine similarity and Euclidean distance are defined for any numeric vectors, so you can use the tool to analyze similarity between images, captions, or cross-modal representations as well.

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