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FASTEMBED
Fast, lightweight text embeddings with ONNX Runtime
Apache-2.0
ABOUT
Generating text embeddings for production RAG pipelines typically requires loading heavy deep learning frameworks like PyTorch or TensorFlow, which are overkill for the simple inference task of producing embeddings. FastEmbed provides a lean alternative using ONNX Runtime — no GPU required, sub-millisecond inference on CPU, minimal memory footprint. It bundles the most popular embedding models (BGE, Instructor, Jina) as quantized ONNX files, so developers get high-quality embeddings with a single pip install and no GPU dependency.
INSTALL
pip install fastembedINTEGRATION GUIDE
1. Generate text embeddings for RAG document ingestion without PyTorch or GPU requirements
2. Build production embedding pipelines for semantic search across millions of documents on CPU
3. Create lightweight embedding services for edge devices and resource-constrained environments
4. Batch-embed text corpora for vector database indexing with Qdrant, Pinecone, or Weaviate
5. Replace slow or heavy embedding libraries with a fast, memory-efficient alternative
TAGS
embeddingspythonvector-searchtext-encodingnlponnxqdrant