Integrate Typesense and Hugging Face
Connect Typesense and Hugging Face nodes in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!
Node stack
Supported Typesense and Hugging Face Nodes
Add any other tools or your preferred database nodes. If an integration is not available generate your own using AI
Add Document
Creates or uses an existing Typesense collection to insert a document, utilizing provided schema and authentication details. [TypeSense API](https://typesense.org/docs/latest/api/)
Add Documents
Creates or uses an existing Typesense collection to insert documents, utilizing provided schema and authentication details. [TypeSense API](https://typesense.org/docs/latest/api/)
Create Collection
Create a new Typesense collection with the specified schema
Delete Document
Delete a document from a Typesense collection.
Full Text Search
Performs a full text search on a specified collection in Typesense.
Geo Search
Performs a geo-location based search in Typesense using specified parameters.
Get Document
Retrieve a document from a Typesense collection using the given Document ID.
Update Document
Creates or uses an existing Typesense collection to update documents, utilizing provided schema and authentication details. [TypeSense API](https://typesense.org/docs/latest/api/)
Vector Search
Perform a vector search in a Typesense collection and return the results.
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).
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.
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.
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.
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.
Quick start
How to integrate Typesense and Hugging Face
Step 1 — Add the nodes to your workflow
Create a new workflow in BuildShip, click “Add node”, and select the Typesense and Hugging Face actions you want to use.
Step 2 — Configure each node
Go to each node to authenticate (or add your API key) and fill in the required parameters.
Step 3 — Connect the nodes
Each node in BuildShip can connect to others by using their output variables. When you reference a variable from one node in another, BuildShip automatically links them in the workflow.
Step 4 — Test your workflow
Define your starting data in the Inputs node and choose what to do with the result in the Flow Output node. Finally, run a test to see your workflow in action.
blog posts & tutorials
Recommended Reads
Below are recommneded blogs that will help in your journey
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