agentskillexchange Prepare local retrieval embeddings with FastEmbed
Generate dense, sparse, image, and reranking embeddings locally before writing vectors into Qdrant or another retrieval stack for agent memory and RAG workflows.
Prerequisites
Python, FastEmbed, optional qdrant-client and Qdrant vector database
Installation
Use the upstream install or setup path that matches your environment:
- pip install fastembed
- pip install fastembed-gpu
- pip install qdrant-client[fastembed]
- pip install qdrant-client[fastembed-gpu]
Requirements and caveats from upstream:
- FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models. Please [open a GitHub issue](https://gi...
- Light: FastEmbed is a lightweight library with few external dependencies. We don't require a GPU and don't download GBs of PyTorch dependencies, and instead use the ONNX Runtime. This makes it a great candidate for se...
- python
Basic usage or getting-started notes:
The default text embedding (TextEmbedding) model is Flag Embedding, presented in the MTEB leaderboard. It supports "query" and "passage" prefixes for the input text. H...
Check our example for detailed instructions, CUDA 12.x support and troubleshooting of the common issues.
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Extracted from upstream docs: https://raw.githubusercontent.com/qdrant/fastembed/HEAD/README.md