EmbedAnything
ActiveDescription
EmbedAnything is a highly performant, modular, and memory-safe embedding inference and indexing framework built in Rust, providing production-ready RAG ingestion and indexing pipelines for local and cloud deployment.
Key Features
- High-performance embedding framework built in Rust — no PyTorch dependency, minimal memory footprint
- Vector streaming: separates file preprocessing from model inference, dramatically reducing pipeline latency
- Multi-modal support: text (PDF/TXT/MD), images (JPG), audio (WAV)
- Multiple embedding types: Dense, Sparse, ONNX, Model2Vec, Late-interaction
- Modular adapter architecture — switch vector databases with a single line of code
- GPU acceleration support with direct AWS S3 bucket file import
Use Cases
💡 Semantic search and RAG embedding pipelines for large-scale document collections
💡 Unified vectorization of multi-modal content (text + images + audio)
💡 Production-grade high-performance embedding inference replacing PyTorch-based solutions
💡 Building retrieval systems with Weaviate, Milvus, and other vector databases
💡 GPU-accelerated embedding and indexing for massive datasets
Categories
Quick Start
1. Install: pip install embed-anything
2. Choose an embedding model (HuggingFace / ONNX)
3. Configure TextEmbedConfig (chunk_size, batch_size)
4. Call embed_file() to embed documents and stream them to a vector database