embeddings
Semantic grep tool for use by AI and humans!
ck (seek) finds code by meaning, not just keywords. It's a drop-in replacement for grep that understands what you're looking for — search for "error handling" and find try/catch blocks, error returns, and exception handling code even when those exact words aren't present.
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
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Scalable, Interactive Visualization. Compute & interactively visualize large embeddings.
Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.
Embedditor is the open-source MS Word equivalent for embedding that helps you get the most out of your vector search.
⚡ GUI for editing LLM vector embeddings. No more blind chunking. Upload content in any file extension, join and split chunks, edit metadata and embedding tokens + remove stop-words and punctuation with one click, add images, and download in .veml to share it with your team.
the AI-native open-source embedding database. The fastest way to build Python or JavaScript LLM apps with memory! Chroma is the open-source AI application database. Batteries included.
Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. All in one place. Retrieval that just works. As it should be.
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