Semantic Vector Embeddings & Similarity Studio
Generate 384-dimensional dense text embeddings and visualize semantic cosine similarity rankings 100% locally in browser with WebGPU.
In-Browser Vector Inference Notice
Text tokenization, neural network forward passes, and dense vector normalizations execute 100% locally inside your browser sandbox via WebGPU and WebAssembly (Xenova/all-MiniLM-L6-v2). Zero document text or vector embeddings are transmitted across the network.
Comprehensive Technical Guide & Reference
Enter a reference query and multiple target candidate sentences to compute dense vector embeddings, analyze cosine angles, and evaluate semantic relevance for RAG pipelines and search engines.
11. Step-by-Step: How to Test Semantic Similarity
Evaluate dense semantic vector retrieval in three straightforward steps:
- •Enter Reference Query: Input your baseline search prompt, question, or reference sentence.
- •Add Target Candidate Phrases: Enter multiple candidate sentences or answers to rank against the query.
- •Inspect Real-Time Cosine Scores: Review normalized similarity percentages (0.00 to 1.00) and inspect 384-dimensional dense vector embeddings.
- •Export Embeddings: Download raw embedding vectors as JSON arrays for integration into vector databases.
22. Technical Explanation: Dense Embeddings & Cosine Geometry
Unlike keyword search (BM25) which matches exact tokens, transformer embedding models project sentences into a high-dimensional continuous semantic vector space:
- •Score ~1.0: Near-identical semantic intent (e.g. 'wireless headphones' vs 'bluetooth headset').
- •Score 0.5 - 0.7: Related domain context (e.g. 'headphones' vs 'audio speakers').
- •Score <0.3: Unrelated or out of context concepts.
33. Pure Client-Side WebGPU & Transformers.js Architecture
Powered by Transformers.js v3, the Xenova/all-MiniLM-L6-v2 model weights download once directly into your browser's IndexedDB. Subsequent runs execute on your local GPU via WebGPU or WebAssembly with zero network latency.
- •Zero Cloud Dependencies: Embeddings are computed on your local device without sending text to OpenAI or third-party APIs.
- •Deterministic Reproducibility: Yields standardized 384-dimensional dense representations compatible with all major vector database indexes.
Frequently Asked Questions
This tool runs Xenova/all-MiniLM-L6-v2, a quantized 384-dimensional sentence transformer model optimized for blazing-fast in-browser inference.
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