Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Marko Lohert, profile picture
Uploaded byMarko Lohert
PPTX, PDF173 views

Vector Databases and Why Are They Used in Modern AI - Marko Lohert - ATD 2024

Vector databases store and manage high-dimensional vector data, enabling similarity searches based on semantic meaning rather than exact matches. They play a vital role in modern AI applications, such as semantic search and recommendation systems, by quickly retrieving complex data. Popular vector databases include Chroma, Pinecone, Qdrant, and Weaviate, each with unique advantages and use cases.

Software◦
Related topics:
Vector Database Insights• Large Language Model• Retrieval-Augmented-Generation•
Downloaded 15 times
Vector Databases and Why Are They Used in Modern AI Marko Lohert Span
Agenda What is a vector database? Popular vector databases Why are vector databases used in modern AI?
What is a vector database? Popular vector databases Why are vector databases used in modern AI?
Why do we need a vector database? • In some applications we need • To find similar data (not the exact match) • Semantic search – content aware search, search based on meaning • … And we need to find data fast
Vector database stores and manages high-dimensional vector data and allows similarity search.
Basic idea behind a vector database • For every piece of data (text, image, ... ) a vector is stored in vector database • „Apple” -> [0.52, 0.56, 0.80, … 0.83] • These vectors have a fixed number of dimensions • Similar data is clustered together • Find similar data in database == find similar vectors
Data -> vector apple Apple orange Raspberry Pi 5 is_fruit 1 0 1 0 round 0.86 0 0.93 0 expensive 0.01 9.97 0.02 0.74 use_electricity 0 0.97 0 0.12 company_type 0 0.9 0 0.75 related_to_computers 0 1 0 1 related_to_phone 0 1 0 0 vectors
Data represented as vectors • Grouping by similarity • Hundreds / thousands of dimensions Image source: https://weaviate.io/blog/what-is-a-vector-database
Embedding • Embedding == an array of numbers that is used to describe the data • Data can be text (word, sentence), image .. • Often words „embedding” and „vector” are used interchangeable • Note: • word „vector” puts focus on numerical representation itself (mathematics), and word „embedding” puts focus on representing data stored in database
Embedding • A vector database stores embeddings (+ data) • Embeddings are used to search for (similar) data • Embeddings capture semantic meaning and context  similar data have embeddings that are near each other in high- dimensional vector space
How to calculate an embedding from an input data? • Embedding model uses machine learning models to turn input text, image… into vector / embedding Image source: https://medium.com/@junxie2/vector-in-machine-learning-a24880e3b96a Embedding model
Embedding models • OpenAI • text-embedding-ada-002 (1536 dimensions) • text-embedding-3-small (1536 dimensions) • text-embedding-3-large (3072 dimensions) • Google Gemini • Multiple embedding models • Ollama Embeddings • Multiple embedding models • Hugging Face • …
Types of embeddings • Word embeddings • Individual words as vectors • Used techniques: Word2Vec, GloVe, FastText, … • Sentence embeddings • Entire sentences as vectors • Used models: Universal Sentence Encoder (USE), SkipThought, … • Document embeddings • Document as vector (from articles to books) • Used techniques: Doc2Vec and Paragraph Vectors
Types of embeddings • Image embeddings • Images as vectors (capture different visual features) • Used techniques convolutional neural networks (CNNs), pre- trained models like ResNet and VGG generate image embeddings • Used for image classification, object detection, and finding similar images • …
How to find similar vectors? • Once we have vectors, generated by embedding models, we can search for similar vectors • Similar vectors  data with similar meaning Image source: https://weaviate.io/blog/what-is-a-vector-database
How to find similar vectors • Multiple methods for calculating distances • Distance between vectors vs similarity between vectors • Let’s take a look at 3 of those methods for calculating similarity …
Euclidean Distance • Squared Euclidean distance (L2-Squared distance) measures the distance between two vectors x and y: distance =
Manhattan distance • Manhattan distance (L1 norm or Taxicab Distance) measures the distance between two vectors x and y distance =
Cosine similarity • Measures the angle between two vectors =
Cosine similarity • Measures the similarity between documents regardless of the magnitude • One of the most used similarity measurements • Commonly used in Natural Language Processing (NLP)
Embeddings Index • Vector databases can contain a huge number of vectors • For faster search vectors need to be indexed • There are different ways for calculating indexes • Inverted File (IVF), Navigable Small World (NSW), …
Different approaches to implementing vector databases 1. Fully vectorized database (Stand-alone) • Example: Chroma, Pinecone … 2. Extensions for traditional databases • For example: pgvector, an open-source extension for PostgreSQL (https://github.com/pgvector)
Advantages of vector databases • Speed / performance • various indexing techniques for faster searching • Scalability • Flexibility • Vector databases can handle higher complexity of using images, videos or other multidimensional data. • Semantic search
Disadvantages of vector databases • Because vector databases provide approximate results, applications requiring greater accuracy will need a different database
Use cases for vector databases • Semantic search • Search based on the meaning or context • Recommendation engine • Web shop recommends similar items to items user has previously bought (in vector database find nearest neighbor of previously bought items) • Natural-language search • Artificial Intelligence / Enhancing LLM capabilities
Database comparison Relational database • Data is in tables – rows and columns • Looks for the exact match select productName from Product where productId = xxxxx • Highly versatile and can be used for many different applications Vector database • Data represented as vectors • Looks for similar data • Finds products similar to apple
What is a vector database? Popular vector databases Why are vector databases used in modern AI?
Chroma • https://www.trychroma.com • A lightweight vector database • Used in RAG applications • Open source - license: Apache 2.0 • GitHub: https://github.com/chroma-core/chroma
Chroma • Default embedding model: all-MiniLM-L6-v2 • 384 dimensions • Created by Sentence Transformers (https://www.sbert.net) • More info at Hugging Face: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
Install Chroma Python pip install chromadb chroma run JavaScript pip install chromadb chroma run # install the JS client and the (optional) default embedding function npm install --save chromadb chromadb-default- embed # set "type": "module" in your package.json
Chroma database structure
• https://www.pinecone.io • Used for recommendation systems and search engines • Multiple embedding model supported • https://docs.pinecone.io/models/overview
• Pinecone serverless on Azure, AWS, and Google Cloud Platform • Pricing plan: • Starter – for trying out and for small applications (free) • Standard – for production applications at any scale (paid) • Enterprise – for mission-critical production applications (paid)
• https://qdrant.tech • Multiple cloud providers: Azure, AWS, Google Cloud Platform • Pricing plan • Managed Cloud (free) • Hybrid Cloud (starting from $0.014 per hour) • Custom (price on request)
• https://weaviate.io • Open source • Integrates semantic search and knowledge graphs -> weaviate is used for natural language processing (NLP)
• Multiple cloud providers: Azure, AWS, Google Cloud Platform • Free trial • Pricing plan • Serverless Cloud (starting at $25 per month) • Enterprise Cloud • Bring Your Own Cloud
What is a vector database? Popular vector databases Why are vector databases used in modern AI?
Why are vector databases used in modern AI / LLM? • Vector databases deliver performance for GenAI • Vector databases can handle large amounts of complex data • Vector database speed up the training of models because they enable faster data retravel • Long-term memory for LLM-s
Vector Databases and RAG • RAG (Retrieval-Augmented Generation) • Enables LLMs to use external data (our company data) • Allows LLMs to access the current (up-to-date) data • RAG needs fast access to data, and vector databases are effective at storing, indexing and retrieving that data • Vector databases are essential for RAG applications
Vector search in Azure AI Search • More info: https://learn.microsoft.com/en-us/azure/search/vector-search-overvi ew
Key messages • Vector database stores and manages high-dimensional vector data and allows similarity search. • Vector databases: Chroma, Pinecone, qdrant, weaviate… • Vector databases + modern AI: • Performance • Vector databases are essential for RAG applications • Long-term memory for LLM-s • …
Vector Databases and Why Are They Used in Modern AI - Marko Lohert - ATD 2024