Hotel Search
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This project is a demonstration of a hotel search system built using Superlinked. It allows users to search for hotels based on various criteria such as description, price, rating, and more, all through natural language queries.
Natural Language Queries: Search for hotels using everyday language.
Multi-modal Semantic Search: Utilize different data types for comprehensive search results.
Cheap but highly rated hotels in Paris, no children
No pets, posh hotel in Berlin
Popular hotels in center of London with free breakfast
Text: Hotel descriptions.
Numbers: Price, rating, and number of reviews.
Location: City.
Numbers: Price, ratings.
Amenities: Options for property and room amenities; wellnes and spa; accessibility; children.
This section provides a step-by-step guide on how to run the whole system locally.
More details are provided below, in the Tutorial section.
It will take some time (depending on the network) to download the sentence-transformers model for the very first time.
To ingest the dataset, run this command in your terminal:
The superlinked cli
is a one-package solution to deploy the Superlinked cluster on your GCP cloud. Via superlinked cli
you will be able to run superlinked application at scale with additional important components such as batch engine, logging and more, utilizing the same superlinked configuration you used in your local setup!
It's needed just to make a python package, you can keep it empty.
This file defines three important things:
object schema: declares names and types of raw attributes
vector spaces: bind embedders to schema fields
index: combines spaces for multi-modal vector search
Attribues like city, hotel-type, and amenities are used for hard-filtering.
These two files define superlinked queries used for multi-modal semantic search with Natural Language Interface (NLI) on top. Our github contains many helpful notebooks that show how to configure superlinked queries:
This file sets the following components:
data loader: our data is ingested from gcp bucket
We publish our recipes as a starting point for your own projects. There are many things you might want to try:
Experiment with superlinked queries. Try to come up with more queries focused on different search scenarios fitting your use-case.
Once running, you can access the Redis browser at .
For more details on using Redis with Superlinked, refer to the .
Use as a template, create superlinked_app/.env
and set OPENAI_API_KEY
required for Natural Query Interface.
API docs will be available at .
The Streamlit UI will be available at .
Attach to VDB and experiment with different superlinked queries from the jupyter notebook: .
Want to try it now? Contact us at .
To configure your superlinked application you need to create a simple python package with few files, we will go though them one by one. All files contain necessary inline comments, check them out! Also, feel free to read our docs: .
Once you are happy with your local Superlinked setup, you can use config files without changes for your Cloud deployent. To make transition to the cloud smooth, we provide Superlinked CLI. if you want to try it now!
Settings of our application are read from .env
file. You can create one simply by copying and setting openai_api_key
which is needed for NLQ.
In our superlinked application, we will embed one textual field (hotel description
) and three numeric fields (price
, rating
, rating_count
). Description is embedded using . If you need faster model, you can try . Or if you are aiming for better retrieval quality, bigger models like are worth checking out.
Note. Apart from texts and numbers, out-of-the-box Superlinked can embed images, categories, recency. It also supports arbitrary embeddings via custom spaces. Learn more about Superlinked embeddings in !
and
vector database: in current application we are using Redis. We also support .
REST API: our app will provide endpoints for ingestion (bulk and one-by-one) and for querying. More information is in .
Bring your own dataset. Want to run Natural Language Query with your data? Define your schema, spaces, index, queries, and data-sources based on this recipe. In case of questions, don't hesitate to !
Try different VDBs. Depending on your needs you can choose one of the . More to come!
Try other text embedding models. There are a ton of different text embedding models out there. Discover , and select models that suit your use-case best.
Explore additional use-cases. Check out our and .