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David Myriel
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---
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title: "How to Build a Hotel Search App with Multimodal Vector Embeddings"
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draft: false
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short_description: "Transform hotel search with multi-modal vector embeddings, combining text, price, ratings, and amenities for intelligent results."
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description: "Discover how multimodal vector search revolutionizes hotel discovery by understanding complex queries with text, numerical, and categorical data."
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preview_image: /blog/superlinked-multimodal-search/social_preview.png
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social_preview_image: /blog/superlinked-multimodal-search/social_preview.png
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date: 2025-04-03T00:00:00-08:00
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author: Filip Makraduli, David Myriel
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featured: true
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tags:
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- vector search
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- vector database
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- multi-modal search
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- semantic search
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- hybrid search
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- machine learning
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- AI
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- search engine
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- technology
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- innovation
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---
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## Introduction
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In the age of AI, search has undergone a radical transformation. Users no longer want to meticulously craft keyword combinations; they want to express their desires in **natural language** and receive precisely tailored results.
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*"Family-friendly beachfront resorts in Miami under $300 with good reviews"* isn't just a search query; it's a complex set of interrelated preferences spanning multiple data types.
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In this blog, we'll show you how we built [**The Hotel Search Demo**](https://hotel-search-recipe.superlinked.io/).
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This app demonstrates how Qdrant and Superlinked can solve real-world challenges in search. We'll teach you how to combine **textual understanding**, **numerical reasoning**, and **categorical filtering** into a seamless experienc that modern users expect.
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## Core Components
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### 1. Vector Spaces: The Building Blocks of Intelligent Search
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At the heart of Superlinked's innovation are [Spaces](https://docs.superlinked.com/concepts/overview) - specialized vector embedding environments designed for different data types. Unlike conventional approaches that force all data into a single embedding format, these spaces respect the inherent characteristics of different data types.
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In the Hotel Search Demo, four distinct spaces work together: **Description**, **Rating**, **Price** and **Rating Count**.
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```python
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# Text data is embedded using a specialized language model
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description_space = sl.TextSimilaritySpace(
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text=hotel_schema.description,
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model=settings.text_embedder_name # all-mpnet-base-v2
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)
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# Numerical data uses dedicated numerical embeddings with appropriate scaling
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rating_space = sl.NumberSpace(
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hotel_schema.rating,
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min_value=0,
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max_value=10,
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mode=sl.Mode.MAXIMUM # Linear scale for bounded ratings
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)
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price_space = sl.NumberSpace(
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hotel_schema.price,
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min_value=0,
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max_value=1000,
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mode=sl.Mode.MAXIMUM,
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scale=sl.LogarithmicScale() # Log scale for prices that vary widely
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)
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rating_count_space = sl.NumberSpace(
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hotel_schema.rating_count,
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min_value=0,
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max_value=22500,
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mode=sl.Mode.MAXIMUM,
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scale=sl.LogarithmicScale() # Log scale for wide-ranging review counts
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)
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```
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What makes this powerful is that each space properly preserves the semantic relationships within its domain. Prices are embedded to maintain their proportional relationships, text embeddings capture semantic meanings, and ratings preserve their relative quality indicators - all while allowing these different spaces to be combined into a cohesive search experience.
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### 2. Multimodal Vector Search: The Full Picture
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Traditional vector search is single-dimensional - typically just text. Superlinked transcends this limitation by creating a rich multi-modal search environment where different data types collaborate rather than compete.
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In our hotel demo, this means:
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- **Text descriptions** are embedded using state-of-the-art language models that understand semantics
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- **Prices use** logarithmic scaling to properly handle wide ranges of values
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- **Ratings** are embedded linearly to preserve their quality indicators
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- **Review counts** use logarithmic scaling to account for the diminishing returns of additional reviews
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Unlike approaches that stringify all data into text before embedding (resulting in unpredictable non-monotonic relationships between numbers), or systems that maintain separate indices for different attributes, Superlinked creates a unified search space where multiple attributes can be considered simultaneously with appropriate semantic relationships preserved.
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The declaration of this unified index is remarkably straightforward:
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```python
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index = sl.Index(
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spaces=[
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description_space,
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price_space,
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rating_space,
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rating_count_space,
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],
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# Additional fields for hard filtering
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fields=[hotel_schema.city, hotel_schema.amenities, ...]
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)
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```
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### 3. Intelligent Query Processing: From Natural Language to Results
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The query processing system in Superlinked simplifies the way search queries are built and executed. This system allows users to interact using natural language, which is then converted into multi-dimensional vector operations, thereby moving away from rigid query structures.
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The query construction in the hotel demo demonstrates this power:
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```python
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query = (
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sl.Query(
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index,
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weights={
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price_space: sl.Param("price_weight", description=price_description),
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rating_space: sl.Param("rating_weight", description=rating_description),
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# Additional space weights...
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},
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)
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.find(hotel_schema)
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.similar(description_space.text, sl.Param("description"))
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.filter(hotel_schema.city.in_(sl.Param("city")))
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# Additional filters...
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.with_natural_query(natural_query=sl.Param("natural_query"))
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)
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```
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This enables queries like "affordable luxury hotels in Paris with spa" to be automatically translated into:
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- A text similarity search for "luxury" and "spa" concepts
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- Appropriate weighting for price (lower range)
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- Hard filtering for "Paris" location
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- Potential filtering for spa amenities
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Unlike systems that rely on reranking after retrieval (which can miss relevant results if the initial retrieval is too restrictive) or metadata filters (which convert fuzzy preferences like "affordable" to rigid boundaries), this approach maintains the nuance of the search throughout the entire process.
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### 4. Hybrid Search Reimagined: Solving the Modern Search Problem
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Today's search landscape is dominated by discussions of hybrid search - the combination of keyword matching for precision with vector search for semantic understanding. The Hotel Search Demo takes this concept further by implementing a multimodal hybrid search method that spans not just text retrieval methods but entire data domains.
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In the hotel search demo, we see hybrid search reimagined across multiple dimensions:
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- Text hybrid search: Combining exact matching (for city names, amenity keywords) with semantic similarity (for concepts like "luxury" or "family-friendly")
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- Numerical hybrid search: Blending exact range filters (minimum/maximum price) with preference-based vector similarity (for concepts like "affordable" or "high-rated")
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- Categorical hybrid search: Integrating hard categorical constraints (must be in Paris) with soft preferences (prefer hotels with specific amenities)
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This multi-dimensional hybrid approach solves critical problems that plague conventional search systems:
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1. Single-modal vector search fails when queries span multiple data types
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2. Traditional hybrid search still separates keyword and vector components, making it difficult to weight them appropriately
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3. Separate storage per attribute forces complex result reconciliation that loses semantic nuance
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4. Pure filtering approaches convert preferences into binary decisions, losing the "strength" of preference
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5. Re-ranking strategies suffer from poor initial retrieval, especially with broad queries
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This unified approach maintains the semantic relationships of all attributes in a multi-dimensional search space, where preferences become weights rather than filters, and where hard constraints and soft preferences seamlessly coexist in the same query.
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The result is a search experience that feels intuitive and "just works" - whether users are looking for "pet-friendly boutique hotels with good reviews near the city center" or "affordable family suites with pool access in resort areas" - because the system understands both the semantics and the relationships between different attributes of what users are asking for.
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### Results
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As search expectations continue to evolve, this multimodal and multidimensional hybrid search approach represents not just an incremental improvement but a fundamental rethinking of how search should work. The Hotel Search Demo showcases this vision in action, a glimpse into a future where search understands not just the words we use, but the complex, nuanced preferences they represent.
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## Watch the Video
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As usual, we prepared a video recording of the talk, so you can watch it at your convenience:
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<iframe width="560" height="315" src="https://www.youtube.com/embed/WIBtZa7mcCs?si=7PrL0B74kRZK_BFC" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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## Materials
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- [Superlinked GitHub Repository](https://github.com/superlinked/superlinked)
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- [Superlinked Documentation](https://docs.superlinked.com)
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- [Qdrant Vector Database](https://qdrant.tech)
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- [Qdrant Documentation](https://qdrant.tech/documentation)
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- [Qdrant Cloud](https://cloud.qdrant.io)
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- [Qdrant Discord Community](https://discord.gg/qdrant)
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