Redis OM .NET has evolved to embrace the transformative world of vector database technology, now supporting Redis vector search and seamless integration with OpenAI, Azure OpenAI, Hugging Face, and ML.NET. This talk highlights the latest advancements in Redis OM .NET, focusing on how it simplifies the complex process of vector indexing, data modeling, and querying for AI-powered applications. Vector databases are redefining data handling, enabling semantic searches across text, images, and audio encoded as vectors. Redis OM .NET simplifies this innovative approach, making it accessible even for those new to vector data. We will explore the new capabilities of Redis OM .NET, including intuitive vector search interfaces and semantic caching, which reduce the overhead of large language model (LLM) calls.

1. Our Sponsors
2024

2. Unleashing the Power of
Vector Search and
Semantic Caching in .NET
Luigi Fugaro

3. Agenda
WeBuild 2023 2024
Unleashing the Power of Vector Search and Semantic Caching in .NET
The Data Balance
Turning Data Into Vectors
Enter ML Embeddings
Redis as Vector Database
Redis OM .NET for Vector and more
Demo – Live Coding... dotnet run

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The Data Balance
Structured vs. Unstructured

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Growth
IDC Report 2023 - https://www.box.com/resources/unstructured-data-paper
Around 80%
of the data generated by organizations is
Unstructured

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Growth
Unstructured
Quasi-Structured
Semi-Structured
Structured
No inherent structure/many degrees of
freedom ~ Docs, PDFs, images, audio, video
Erratic patterns/formats ~ Clickstreams
There's a discernible pattern ~ Spreadsheets /
XML / JSON
Schema/defined data model ~ Database
Data type

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How to deal with unstructured data?
Common approaches were labeling and tagging
There are labor intensive, subjective, and error-prone

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Turning Data into Vectors
The Old Fashion Way

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Vectors…
What is a Vector?
Numeric representation of something in N-dimensional space
Can represent anything... entire documents, images, video, audio
Quantifies features or characteristics of the item
More importantly... they are comparable

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Vector generating Techniques

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Image Vector Generation example with HOG
Pre-Processing Gradient Calculation
Our Vector!

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Enter ML Embeddings
Using Machine Learning for Feature Extraction

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Machine Learning/Deep Learning has leaped forward in the last decade
ML model outperform humans in many tasks nowadays
CV (Computer Vision) models excel at detection/classification
LLMs (LArge Language Models) have advanced exponentially

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Feature Engineering

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T-Shirt Jacket

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Automated Feature Engineering
ML model extract latent features
ML embeddings catch the gray areas between features
The process of generating the embeddings is vectorizing

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Vectors
Storing and creating Vectors

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Visually
Semantic Relationship Syntactic Relationship

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Visually
https://jalammar.github.io/illustrated-word2vec

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https://jalammar.github.io/illustrated-word2vec
Vectors can be operated upon

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Vectorizing
Generating Vector Embeddings for your Data

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Vectorizing
1. Choose an Embedding Method
2. Clean and preprocess the data as needed
3. Train/Refine the embedding model
4. Generate Embeddings

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Vectorizing
Better Models, better Vectors
Embeddings can capture the semantics of complex data
Option #1: Use a pre-trained model
Option #2: train your models with custom data
Vector similarity is a downline tool to analyze embeddings

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Similarity
Finding similar vectors

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Similarity Metrics
➢ Measure “closeness” between vectors in multi-dimensional space
➢ Enable efficient similarity search in vector databases
➢ Improve relevance and precision of search results

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Similarity/Distance Metrics

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Cosine Similarity

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Vector Similarity in Redis
Redis as Vector Database

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Vector Similarity in Redis
Index and query vector data stored as BLOBs in Redis Hashes/JSON
3 distance metrics: Euclidean, Internal Product and Cosine
2 indexing methods: HNSW and Flat
Pre-filtering queries with GEO, TAG, TEXT or NUMERIC fields

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Demo with a Fashion Product Finder
Image & Text - Hybrid Similarity Search

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eCommerce Searches
There's a growing expectation for effective search functionality
Unstructured Data is "high-dimensional"
Needs to capture "meaning and context" in unstructured data
Encompasses Recommendation Engines and Similarity Search

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Natural Language Search
Visual Search
Recommenders

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How to implement the solution?
Framework and libraries

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A Redis Framework
It’s more than Object Mapping
Redis OM stands for Redis Object Mapping, a suite of libraries designed to
facilitate object-oriented interaction with Redis. It simplifies working with
Redis by abstracting direct commands into higher-level operations.
https://github.com/redis/redis-om-dotnet

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More to come...

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Redis OM
Why?
● Redis OM simplifies application
development by abstracting Redis'
complexity, increasing productivity, and
enhancing code readability.
What?
◆ Redis OM enables building real-time
applications, supporting:
● Model Data
● Perform CRUD Operations
● Index and Search Data

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Fashion Product Finder
Breakdown
■ A Product domain mapped to Redis JSON
■ [Indexed] decorated field for text and numeric indexing
■ [ImageVectorizer] decorated field to generate embeddings
■ [SentenceVectorizer] decorated field to generate embeddings
■ Pre-filter the search if needed
■ Entity Streams to query for K nearest neighbors
■ Display results

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namespace ProductCatalog.Model;
[Document(StorageType = StorageType.Json)]
public class Product
{
// Other fields...
}
Model → Redis JSON
[Document]

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Make Product Searchable
[Indexed] decoration
namespace ProductCatalog.Model;
[Document(StorageType = StorageType.Json)]
public class Product
{
[RedisIdField] [Indexed] public int Id { get; set; }
// Other fields...
[Indexed] public string Gender { get; set; }
[Indexed] public int? Year { get; set; }
// Other fields...
}
Decorate what
you want to
make searchable

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Automatic Embedding Generation
[IVectorizer] decoration
namespace ProductCatalog.Model;
[Document(StorageType = StorageType.Json)]
public class Product
{
// Other fields...
[Indexed(Algorithm = VectorAlgorithm.HNSW, DistanceMetric = DistanceMetric.COSINE)]
[ImageVectorizer]public Vector<string> ImageUrl { get; set; }
[Indexed(Algorithm = VectorAlgorithm.FLAT, DistanceMetric = DistanceMetric.COSINE)]
[SentenceVectorizer] public Vector<string> ProductDisplayName { get; set; }

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Searching with Fluent API
[IVectorizer] decoration
[HttpGet("byImage")]
public IEnumerable<CatalogResponse > ByImage([FromQuery]string url)
{
var collection = _provider.RedisCollection <Product>();
var response = collection.NearestNeighbors(x => x.ImageUrl, 15, url);
return response.Select(CatalogResponse .Of);
}
[HttpGet("byDescription" )]
public IEnumerable<CatalogResponse > ByDescription([FromQuery] string description)
{
var collection = _provider.RedisCollection <Product>();
var response = collection.NearestNeighbors(x => x.ProductDisplayName, 15, description);
return response.Select(CatalogResponse .Of);
}

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dotnet run --project ProductCatalog

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The tools and techniques to unlock
the value in Unstructured Data have
evolved greatly...

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Databases like Redis and frameworks
like Redis OM can help!
2024

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INTEGRATIONS
FEATURES
Storage: HASH | JSON
Indexing: HNSW (ANN) | Flat (KNN)
Distance: L2 | Cosine | IP
Search Types: KNN/ANN | Hybrid |
Range | Full Text
Management: Realtime CRUD
operations, aliasing, temp indices, and
more
Ecosystem integrations
NEW REDIS ENTERPRISE 7.2
FEATURE
Scalable search and query for
improved performance, up to 16X
compared to previous versions
Redis as Vector Database

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Vector Similarity Search Use Cases

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Vector Similarity Search Use Cases
Question & Answering

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Vector Similarity Search Use Cases
Context retrieval for Retrieval Augmented Generation (RAG)
Pairing Redis Enterprise with Large Language
Models (LLM) such as OpenAI's ChatGPT, you can
give the LLM access to external contextual
knowledge.
➔ Enables more accurate answers and
prevents model 'hallucinations'.
➔ An LLM combines text fragments in a (most
often) semantically correct way.

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Vector Similarity Search Use Cases
LLM Conversion Memory
The idea is to improve the model quality and
personalization through an adaptive memory.
➔ Persist all conversation history (memories)
as embeddings in a vector database.
➔ A conversational agent checks for relevant
memories to aid or personalize the LLM
behaviour.
➔ Allows users to change topics without
misunderstandings seamlessly.

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Vector Similarity Search Use Cases
Semantic Caching
Because LLM completions are expensive, it helps
to reduce the overall costs of the ML-powered
application.
➔ Use vector database to cache input
prompts.
➔ Cache hits evaluated by semantic similarity.

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Additional resources for learning about Redis
Central place to find
example apps that are
built on Redis
launchpad.redis.com
Redis Launchpad
Free online courses
taught by Redis experts
university.redis.com
Redis University
Create a database
Code your application
Explore your data
developer.redis.com
Developers Portal
Professional certification
program for developers
university.redis.com/
certification
Redis Certification

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Redis Stack for Application Modernization

59. Questions
&
Answers