Integrate Meilisearch and Hugging Face to automate workflows with scalable backend
Connect Meilisearch and Hugging Face nodes to in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!
Getting Started
How To Connect Meilisearch and Hugging Face
Popular Templates With Meilisearch and Hugging Face
Explore our popular Google Sheets & templates below. Click. Remix. Ship!
Node stack
Supported Triggers & Actions
Meilisearch NODES
Add Document
Add a single document to MeiliSearch. Accepts a JSON object representing the document and adds it to the index
Add Documents
Add a list of documents to MeiliSearch index.
Delete Document
Delete a document from a Meilisearch index.
Full Text Search
Performs a full text search on a Meilisearch index
Get Document
Retrieve a document from a Meilisearch index using the given Document ID.
Hybrid Search
Performs a hybrid search combining full-text and attribute-based filtering, returning a JSON object or an array of them. For more details on using vector search in MeiliSearch, visit the [official documentation](https://www.meilisearch.com/docs/learn/experimental/vector_search#using-vector-search).
Update Document
Update a single document in MeiliSearch. Accepts a JSON object representing the document and updates it.
Update Documents
Update multiple documents in MeiliSearch by sending an array of JSON objects.
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Update Synonyms
Update the synonyms for any index in your MeiliSearch instance
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Hugging Face NODES
Caption Image
Generate caption for the image using Hugging Face's [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) model for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).
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Image Classification
Get classification labels for your image using Hugging Face's [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) model which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
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Text Summarization
Summarize long text using Hugging Face's [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model which is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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Text-To-Image
Generate image from text, using Hugging Face's [openskyml/dalle-3-xl](https://huggingface.co/openskyml/dalle-3-xl) test model very similar to Dall•E 3.
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Text-To-Music
Generate music from text using Hugging Face's [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.
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Blog posts & Tutorials
Recommended
Reads
Below are recommneded blogs that will help in your journey
Support
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