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Documentation Index

Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

This guide is aimed at experienced users working with a self-hosted Meilisearch instance. It shows you how to compile a Meilisearch binary that generates Hugging Face embeddings with an Nvidia GPU.

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Prerequisites

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Install CUDA

Follow Nvidia’s CUDA installation instructions.

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Verify your CUDA install

After you have installed CUDA in your machine, run the following command in your command-line terminal: 
nvcc --version | head -1
If CUDA is working correctly, you will see the following response: 
nvcc: NVIDIA (R) Cuda compiler driver

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Compile Meilisearch

First, clone Meilisearch: 
git clone https://github.com/meilisearch/meilisearch.git
Then, compile the Meilisearch binary with cuda enabled: 
cargo build --release --features cuda
This might take a few moments. Once the compiler is done, you should have a CUDA-compatible Meilisearch binary.

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Configure the Hugging Face embedder

Run your freshly compiled binary: 
./meilisearch
Then add the Hugging Face embedder to your index settings: 
curl \
  -X PATCH 'MEILISEARCH_URL/indexes/INDEX_NAME/settings/embedders' \
  -H 'Content-Type: application/json' \
  --data-binary '{ "default": { "source": "huggingFace" } }'
Meilisearch will return a summarized task object and place your request on the task queue: 
{
  "taskUid": 1,
  "indexUid": "INDEX_NAME",
  "status": "enqueued",
  "type": "settingsUpdate",
  "enqueuedAt": "2024-03-04T15:05:43.383955Z"
}
Use the task object’s taskUid to monitor the task status. The Hugging Face embedder will be ready to use once the task is completed.

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Conclusion

You have seen how to compile a Meilisearch binary that uses your Nvidia GPU to compute vector embeddings. Doing this should significantly speed up indexing when using Hugging Face.