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# FastEmbed
-In the ever-changing landscape of Data Science and Machine Learning, practitioners often find themselves navigating through a labyrinth of models, libraries, and frameworks. Among the plethora of choices, the need for a specialized, efficient, and easy-to-implement solution for embedding generation is increasingly evident. This is where FastEmbed (docs: [https://qdrant.github.io/fastembed/](https://qdrant.github.io/fastembed/)) comes into play—a Python library engineered for speed, efficiency, and above all, usability.
+In the ever-changing landscape of Data Science and Machine Learning, practitioners often find themselves navigating through a labyrinth of models, libraries, and frameworks. Among the plethora of choices, the need for a specialized, efficient, and easy-to-implement solution for embedding generation is increasingly evident. This is where FastEmbed (docs: [https://qdrant.github.io/fastembed/](https://qdrant.github.io/fastembed/?utm_source=twitter&utm_medium=social&utm_campaign=fastembed&utm_term=fastembed)) comes into play—a Python library engineered for speed, efficiency, and above all, usability.
### Problem Statement
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On every metric that you care about: speed, accuracy and ease of use – we do better and intend to continue to do so!
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### Light
-FastEmbed sets itself apart by maintaining a lightweight footprint. It's designed to be agile and fast, a critical feature for businesses looking to integrate text embedding solutions without the cumbersome overhead typically associated with such libraries. \
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-For FastEmbed, the list of dependencies is refreshingly brief:
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+FastEmbed sets itself apart by maintaining a lightweight footprint. It's designed to be agile and fast, a critical feature for businesses looking to integrate text embedding solutions without the cumbersome overhead typically associated with such libraries. For FastEmbed, the list of dependencies is refreshingly brief:
* onnx: Version ^1.11 – We'll try to drop this also in the future if we can!
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Behind the scenes, Qdrant is using FastEmbed to make the text embedding, generate ids if they're missing and then adding them to the index with metadata.
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### Performing Queries
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Behind the scenes, we first convert the `query_text` to the embedding and use that to query the vector index.
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By following these steps, you effectively utilize the combined capabilities of FastEmbed and Qdrant, thereby streamlining your embedding generation and retrieval tasks.
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FastEmbed is a continually evolving platform that thrives on community contributions.
-If the utility of this library resonates with your organizational needs, please consider [starring the repository](https://github.com/qdrant/fastembed) as a sign of support and to stay abreast of our ongoing enhancements.
+If the utility of this library resonates with your organizational needs, please consider [starring the repository](https://github.com/qdrant/fastembed?utm_source=twitter&utm_medium=website&utm_campaign=fastembed) as a sign of support and to stay abreast of our ongoing enhancements.
If you'd like to request specific models or features, please consider opening an issue: [https://github.com/qdrant/fastembed/issues](https://github.com/qdrant/fastembed/issues) — that is what we use when starting our prioritization!
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The evidence is clear: FastEmbed's computational efficiency and accuracy are second to none, thanks to its quantized models and ONNX Runtime. When coupled with Qdrant's enterprise-grade vector storage capabilities, organizations can achieve an unprecedented level of performance, scalability, and operational excellence.
We invite you to experience the operational advantages of FastEmbed and Qdrant first-hand. Your organization can quickly capitalize on this integration through two simple options:
-1. Activate your Cloud trial today: [https://cloud.qdrant.io](https://cloud.qdrant.io)
-2. Download our docker container for instant deployment: [https://qdrant.tech/documentation/quick-start/](https://qdrant.tech/documentation/quick-start/)
+1. Activate your Cloud trial today: [https://cloud.qdrant.io](https://cloud.qdrant.io?utm_source=twitter&utm_medium=website&utm_campaign=fastembed)
+2. Download our docker container for instant deployment: [https://qdrant.tech/documentation/quick-start/](https://qdrant.tech/documentation/quick-start/?utm_source=twitter&utm_medium=website&utm_campaign=fastembed)