qdrant/fastembed

Health v2 · maintenance only

Adoption not part of the score

3166 stars · 234 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

FastEmbed is a lightweight Python library for generating text embeddings using ONNX Runtime instead of PyTorch, requiring no GPU and minimal dependencies. It supports a growing set of dense, multilingual, and sparse embedding models and integrates closely with Qdrant for vector search and RAG pipelines.

Use cases

• generate text embeddings for vector search
• build a RAG pipeline without GPU or heavy PyTorch dependencies
• run embedding generation in serverless functions like AWS Lambda
• replace OpenAI Ada-002 embeddings with a local model
• embed documents and queries for retrieval with Qdrant
• compute multilingual text embeddings on CPU

When to choose

• you need fast, accurate embeddings on CPU with a small dependency footprint
• you want to avoid downloading gigabytes of PyTorch for embedding generation
• you are deploying to serverless or constrained environments
• you use Qdrant and want built-in embedding support

When to avoid

• you need custom model training or fine-tuning of embedding models
• you require modalities or models not in the supported list and cannot extend them
• you already have a heavy ML stack with PyTorch and GPU inference available

Facets

library · maturity active

machine-learning rag llm-inference search-engine machine-learning python cross-platform serverless embeddings onnx-runtime vector-search text-embedding cpu-inference qdrant retrieval-augmented-generation search natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="qdrant/fastembed")