Practical overview
When it fits
Use it when embedding generation is the main technical requirement and the application benefits from a Python library using ONNX Runtime rather than large PyTorch dependencies.
Who it is for
- Python developers building semantic search or retrieval systems.
- Teams creating retrieval-augmented generation workflows.
- Developers integrating embedding generation with vector search.
- Applications needing documented dense, sparse, late-interaction or image embedding classes.
Documented uses
- Generate vector representations of documents and queries.
- Prepare retrieval inputs using query and passage prefixes.
- Create embeddings for semantic search and RAG workflows.
- Register and use documented custom embedding models.
- Rerank documents with a text cross-encoder.
Requirements & limits
The repository is public and distributed under the Apache-2.0 license. The documented installation uses pip; standard and GPU package names are provided.
- The library uses ONNX Runtime and is documented as not requiring a GPU for its standard approach.
- Model creation can trigger model download and initialization.
- The repository documents multiple embedding classes, including dense, sparse, late-interaction, image and multimodal options.
- Custom model registration is documented for supported model extensions.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Fastembed and generate dense text embeddings
Install the library, initialize a text embedding model and turn documents into embedding vectors.
Before you start
- Python environment with pip available.
- A supported Fastembed model; the documented example uses BAAI/bge-small-en-v1.5.
Steps
Install the library with the command below, or use the command below for the documented GPU package.
pip install fastembedpip install fastembed-gpuImport
TextEmbeddingand initialize the model.Pass a list of documents to
model.embed(documents).Convert the returned generator to a list of embeddings.
Expected outcome
A list of dense text embedding vectors is produced; the documented example shows vectors with 384 dimensions.
Guide limitations
- Model download and initialization are triggered when the model is created.
- This procedure generates embeddings but does not document a complete vector database or retrieval implementation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- qdrant/fastembed · Official homepage title · retained in its source language