Local Embedding Similarity Calculator
Run client-side cosine similarity comparisons over text arrays safely.
| # | S1 | S2 | S3 | S4 | S5 |
|---|---|---|---|---|---|
| S1 | 100% | 0% | 0% | 0% | 12% |
| S2 | 0% | 100% | 0% | 11% | 0% |
| S3 | 0% | 0% | 100% | 0% | 0% |
| S4 | 0% | 11% | 0% | 100% | 0% |
| S5 | 12% | 0% | 0% | 0% | 100% |
About This Tool
The Local Embedding Similarity Calculator is a privacy-first vector search utility engineered for developers, data scientists, and AI researchers working with text embeddings and Retrieval-Augmented Generation (RAG) pipelines.
Comparing semantic similarity usually involves sending sensitive text data or custom embeddings to cloud services or setting up Python environments. This tool vectorizes text arrays, computes $N \times N$ cosine similarity heatmaps, ranks candidate RAG chunks against user query prompts, and evaluates raw floating-point vector arrays — 100% client-side inside your browser without network calls.
How to Use
- Select Engine Mode: Choose between N x N Text Matrix, Query vs RAG Candidate List, or Raw Vector Arrays.
- Input Data: Paste plain text sentences (one per line) or paste numerical vector arrays (e.g.
[0.15, -0.42, 0.88...]). - Adjust Similarity Threshold: Use the threshold slider filter to isolate top matches above your target similarity cutoff (e.g., 50%+).
- Inspect Heatmap Matrix: Click or hover over any cell in the similarity matrix to view cosine similarity %, dot product, and euclidean distance.
- Copy Code Snippets: Copy Python `scikit-learn` or `numpy` code snippets for production integration.
Common Use Cases & FAQ
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