Weaviate Knowledge Cards
Unlock the power of vector search. Our guides will help you conquer vector embeddings and build better AI applications.
Categories
Intro to Vector Databases
Databases designed to store and search data using vector embeddings, enabling efficient similarity search for unstructured data like text and images.Intro to Vector Databases
Unstructured Data Objects
Data without a predefined structure, making them difficult to manage conventionally...Intro to Vector Databases
Vector
A quantity having both magnitude and direction represented by an array of numerical values...Intro to Vector Databases
Vector Embedding
Numerical representations of objects, such as words or images, in a vector space...Hybrid Search
A search method that combines vector search with traditional keyword search to improve retrieval accuracy and relevance.Hybrid Search
Sparse Vectors
Sparse embeddings are generated from algorithms like BM25 and SPLADE...Hybrid Search
Dense Vectors
In contrast to sparse vectors, dense vectors contain mostly non-zero values and are generated from machine learning models like GloVe and Transformers...Hybrid Search
BM25/BM25F
BM25 is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is part of the family of probabilistic information retrieval models...Hierarchical Navigable Small World
An indexing algorithm used in vector databases to enable fast and efficient similarity search.Hierarchical Navigable Small World
Hierarchical Graph Structure
HNSW utilizes a multi-layered graph structure where each layer is a graph of the data points. The top layers contain fewer points with long-range connections...Hierarchical Navigable Small World
ANN - Approximate Nearest Neighbor
ANN algorithms calculate the approximate nearest neighbors to a query, as opposed to kNN algorithms, which calculate the true nearest neighbors...Hierarchical Navigable Small World
Layered Navigation
During a search, HNSW starts at the topmost layer and makes 'big jumps' across the graph to quickly move closer to the target region...Multimodal RAG
A technique that combines retrieval of relevant multimodal data, such as images, text, audio, or video, with generative large language models to generate natural language responses or content to a query.Multimodal RAG
Cross Modal Reasoning
Cross-modal reasoning refers to the ability to make connections by integrating information from different modalities or sources including text, images, audio and video...Multimodal RAG
Multimodal Embeddings Models
Multimodal Embeddings Models produce a joint embedding space for multimodal data that understands text, images, audio and more...Multimodal RAG
Multimodal Contrastive Finetuning
Multimodal Embeddings Models produce a joint embedding space for multimodal data that understands text, images, audio and more...Databases
Systems for storing and storing, organizing, and retrieving structured or unstructured data efficiently.Databases
Graph Database
A graph database stores data in nodes and edges, representing entities and their relationships. It's optimized for...Databases
Inverted Indexes
An inverted index is a database indexing structure that maps keywords to their locations in documents...Databases
Sharding
Sharding is splitting a database into smaller, faster, more easily managed parts called shards...Large Language Models
Deep learning models trained on massive datasets to understand and generate human-like text, used in applications like chatbots and content generation.Large Language Models
Large Language Model (LLM)
A Large Language Model (LLM) is a machine learning model that is trained on vast text data, learning language...Large Language Models
Finetuning
Fine-tuning is a process where a pre-trained machine learning model is further trained on a specific dataset...Large Language Models
