Convert text to vector embeddings online free. Generate word embeddings, sentence vectors & cosine similarity scores — no signup, no API key, instant JSON export.
Uses state-of-the-art transformer models for accurate semantic understanding
Cosine, Euclidean, and Manhattan distance calculations
Secure processing with no data storage or tracking
The AI Text Embedding Generator converts text, phrases, and paragraphs into high-dimensional vector representations. It enables you to measure semantic similarity, evaluate cosine distance, and inspect vector geometry directly in your browser without requiring an OpenAI API key or backend environment.
Text embeddings transform words and sentences into coordinate points in a multidimensional mathematical space. Sentences with similar contextual meaning produce vector embeddings that are located closer together.
Measures the cosine angle between two vectors, regardless of document length. Ideal for search relevance.
Calculates the direct straight-line distance between two points in vector space.
Measures distance along grid axes (L1 norm), useful for sparse vector comparisons.
Choose between Single Generation for vector exploration or Compare & Analyze for pairwise similarity scoring.
Select 768D (lightweight), 1536D (standard OpenAI text-embedding-3-small dimension), or 2048D (dense).
Enter your sentences, queries, or document excerpts into the input field.
View raw vector coordinates, magnitude, and visual similarity charts in real time.
Download the raw vector arrays to ingest into vector databases like Pinecone, Qdrant, Weaviate, or pgvector.
Inspect embedding distances between user prompts and knowledge base chunks to optimize chunk size and search thresholds.
Test how well query variations map to target documents without standing up an entire cloud vector database.
Evaluate paraphrase similarity scores to identify duplicate support tickets or similar academic submissions.
Learn vector mathematics and transformer embeddings interactively without incurring API costs.
Your inputs remain strictly confidential. Calculations execute directly inside your browser using client-side algorithms. No text, prompts, or vectors are uploaded or stored on any external server.
No. This tool runs entirely free in your browser without requiring any API keys, tokens, or payment details.
You can choose between 768, 1536, and 2048 dimensions to match popular open-source and commercial embedding models.
Yes. Click the Export JSON button to download standardized float arrays formatted for immediate ingestion into any vector database.
Cosine similarity evaluates the cosine of the angle between two multi-dimensional vectors, producing a normalized score between -1 and 1 (or 0% to 100%).
Learn how vector embeddings power semantic search, RAG pipelines, and recommendation engines.
How client-side AI utilities accelerate everyday workflows without cloud subscriptions.
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