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README.md
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task_categories:
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- question-answering
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- text-retrieval
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language:
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- en
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tags:
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- vector search
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- retrieval augmented generation
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size_categories:
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- 1K<n<10K
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## Overview
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This dataset consists of AirBnB listings with property descriptions, reviews and other metadata.
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## Dataset Structure
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- weekly_price: Discounted price for week
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- monthly_price: Discounted price for month
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- reviews_per_month: Average monthly review count
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## Usage
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This dataset can be useful for:
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- Building Hybrid Search applications. Use the embeddings provided for vector search and the metadata fields for pre-filtering and/or full-text search.
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- Building Multimodal Search applications. Some listings have images associated with them. Use a model like [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) to generate image and text emebeddings.
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- Building RAG applications
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## Ingest Data
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task_categories:
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- question-answering
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- text-retrieval
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- text-to-image
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language:
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- en
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tags:
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- vector search
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- multimodal
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- retrieval augmented generation
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size_categories:
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- 1K<n<10K
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## Overview
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This dataset consists of AirBnB listings with property descriptions, reviews, and other metadata.
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It also contains text embeddings of the property descriptions as well as image embeddings of the listing image. The text embeddings were created using OpenAI's **text-embedding-3-small** model and the image embeddings using OpenAI's [**clip-vit-base-patch32**](https://huggingface.co/openai/clip-vit-base-patch32 model) available on Hugging Face.
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## Dataset Structure
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- weekly_price: Discounted price for week
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- monthly_price: Discounted price for month
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- reviews_per_month: Average monthly review count
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- **text_embeddings**: Embeddings of the property description in the `space` field
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- **image_embeddings**: Embeddings of the `picture_url` in the `images` field
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## Usage
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This dataset can be useful for:
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- Building Multimodal Search applications. Embed text queries using the CLIP model, and retrieve relevant images using the image embeddings provided.
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- Building Hybrid Search applications. Use the embeddings provided for vector search and the metadata fields for pre-filtering and/or full-text search.
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- Building RAG applications
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## Ingest Data
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