Embedanything
Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust 🦀
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What is Embedanything?
EmbedAnything is a minimalist, highly performant, lightning-fast, lightweight, multisource, multimodal, and local embedding pipeline built in Rust. It streamlines the process of generating embeddings from various sources including text, images, audio, PDFs, websites, and other media, then seamlessly streams them memory-efficiently to vector databases. The tool supports dense, sparse, ONNX, and late-interaction embeddings, offering flexibility for a wide range of use cases in generative AI and RAG workflows.
Key features include dual backend support (Candle and ONNX), cloud embedding models from OpenAI and Cohere, GPU acceleration via CUDA, semantic chunking for optimized RAG, vector streaming for low-resource environments, and no PyTorch dependency for easy cloud deployment. It provides a Python interface via PyO3 bindings, making it accessible without Rust knowledge. The library supports numerous models including BERT, Jina, ColPali, ColBERT, Splade, ModernBERT, CLIP, Whisper, and rerankers.
EmbedAnything is designed for AI engineers, developers building RAG systems, and anyone working with vector databases who needs fast, efficient embedding generation. It has collaborated with enterprises like Elastic, Weaviate, SingleStore, and Datahours. The tool supports vector database adapters for Elastic, Weaviate, Pinecone, Qdrant, and Milvus.
Embedanything pricing
Pricing model: Freemium
Free and open-source. Available via pip: 'pip install embed-anything' for CPU and 'pip install embed-anything-gpu' for GPU support. No paid tiers mentioned - the library is freely available for use and contribution.
Embedanything pros
- Lightning-fast execution powered by Rust
- No PyTorch dependency reduces memory footprint
- Dual backend support: Candle and ONNX
- GPU acceleration via CUDA out of the box
- Multimodal support: text, images, audio, PDFs, websites
- Vector streaming for memory-efficient indexing
- Supports dense, sparse, ONNX, and late-interaction embeddings
- Python interface via PyO3 bindings
- Works with any Hugging Face model on Candle
- Semantic chunking optimized for RAG workflows
- Chunkwise streaming instead of file-by-file
- Supports multiple vector databases: Elastic, Weaviate, Pinecone, Qdrant, Milvus
- Local embedding execution without cloud dependency
- Supports reranker models for improved relevance
- Low memory footprint easy for cloud deployment
Embedanything cons
- Requires learning new API compared to established libraries
- Candle backend trades some speed for flexibility
- Limited documentation compared to mature libraries
- No built-in fine-tuning yet (on roadmap)
- Video embedding not yet supported (coming soon)
- Graph embedding not yet implemented (coming soon)
- GGUF model support still in development
- Smaller community than established alternatives
Frequently asked questions about Embedanything
Do I need to know Rust to use or contribute to EmbedAnything?
No. EmbedAnything provides PyO3 bindings, so you can run any function in Python without any issues. To contribute, you should check out the contribution guidelines and the Python folder examples of adapters.
How is EmbedAnything different from fastembed?
EmbedAnything provides both Candle and ONNX backends, while fastembed is just an ONNX wrapper. EmbedAnything also gives an end-to-end pipeline that lets you ingest different data types, index to any vector database, and infer any model.
Why is EmbedAnything using Candle backend?
One of the main reasons is that Candle doesn't require any specific ONNX format models, meaning it can work seamlessly with any Hugging Face model. This flexibility has been a key factor, though there is some compromise on speed in favor of flexibility.
What vector databases does EmbedAnything support?
EmbedAnything supports Elastic (thanks to the Elastic team's contribution), Weaviate, Pinecone, Qdrant, and Milvus for streaming embeddings. They are actively working to expand this list and welcome contributions for new adapters.
What models are supported by EmbedAnything?
EmbedAnything supports Jina, BERT (all Bert-based models), CLIP (openai/clip-*), Whisper (OpenAI Whisper models), ColPali (starlight-ai/colpali-v1.2-merged-onnx), ColBERT (answerdotai/answerai-colbert-small-v1, jinaai/jina-colbert-v2), Splade models, and rerankers (Jina Reranker Models, Xenova/bge-reranker). Any Hugging Face model works on Candle.
What is Vector Streaming in EmbedAnything?
Vector Streaming enables processing and generating embeddings for files and streaming them chunk by chunk, which can be segmented semantically and stored in your vector database. This eliminates bulk embeddings storage on RAM at once. The embedding process happens separately from the main process to maintain high performance enabled by Rust MPSC.
Can I use custom Hugging Face models with EmbedAnything?
Yes. You can use any Hugging Face model on Candle using from_pretrained_hf, and you can also use custom ONNX models using from_pretrained_onnx with the model_id from Hugging Face.
How do I install EmbedAnything for GPU support?
For CPU: pip install embed-anything. For GPUs and using special models like ColPali: pip install embed-anything-gpu.
What modalities does EmbedAnything currently support?
EmbedAnything supports audio files, markdowns, websites, images, and currently has infrastructure for multimodality. Video embedding and graph embedding are coming soon on the roadmap.
What features are coming soon in EmbedAnything?
Coming soon: GGUF model support for significantly faster performance, video embedding, graph embedding (DeepWalks, depth-first, word-to-vec), Yolo Clip support, and fine-tuning capability to train models on your data like sentence transformers.