arangodb/arangodb

Features

• Graph Databases - Stores and traverses complex relationships between data points using native graph structures and algorithms.
• Multi-Model Databases - Stores data as documents, graphs, and key-value pairs while supporting unified queries across all models.
• Knowledge Graphs - Provides a data management environment for building and querying structured knowledge bases for AI context.
• AI Grounding Services - Supplies large language models with verified enterprise context through graph retrieval and vector search.
• Graph Analytics - Executes structural algorithms like PageRank and centrality analysis to identify influential nodes and patterns within complex information networks.
• Vector Databases - Manages vector embeddings alongside structured data to support similarity search and retrieval-augmented generation.
• Declarative Query Languages - Provides a single interface for executing document lookups, graph traversals, and vector searches across diverse data models.
• Document Storage - Manages data as flexible JSON-like objects to allow schema-less persistence while maintaining high performance for complex retrieval operations.
• Graph Traversal Strategies - Navigates complex networks by calculating paths and relationships between nodes to answer connectivity questions within large-scale information structures.
• Knowledge Graph Construction Tools - Builds structured maps of entities and relationships from raw data to provide reliable context for intelligent systems.
• Multi-Modal Data Management - Manages information across document, graph, and vector formats within a single system.
• Complex Data Modeling - Persists information as documents, graphs, and key-value pairs within a single flexible database system.
• Vector Search - Combines structured graph data with high-dimensional embeddings to ground generative models in verified enterprise context.
• AI Knowledge Management - Transforms raw text into structured knowledge graphs to supply large language models with verified context for more accurate responses.
• Grounded Answer Generation - Combines structured knowledge graphs with vector embeddings to supply large language models with trusted context for accurate content generation.
• Graph-Based Retrieval Engines - Grounds generative responses in trusted enterprise information by combining knowledge graphs with vector embeddings.
• Unified Data Querying - Provides a unified interface for querying document, graph, and vector data models simultaneously.
• Knowledge Graph Retrieval - Supplies large language models with trusted context by retrieving relevant entities and relationships from a knowledge graph.
• Query Languages - Supports unified query language operations across document, graph, and vector data models.
• Knowledge Graph Extraction - Identifies key concepts and connections within raw text to build structured knowledge bases that reduce model hallucinations.
• Knowledge Graph Construction - Extracts information from internal data sources to create structured maps that provide domain-specific intelligence for automated assistants.
• Predictive Machine Learning Analytics - Applies advanced modeling to stored relationship data to classify elements and forecast connections.
• Database Systems - Multi-model database supporting document, graph, and key-value data structures.
• Graph Databases - Graph database and document store.
• Graph Querying - Executes traversals and pathfinding algorithms to navigate connected data structures.
• Graph Algorithms - Processes entire datasets to identify structural patterns such as node centrality and label propagation for deeper insight into network topology.
• Strongly Connected Component Algorithms - Calculates patterns and influence across large datasets by running algorithms like PageRank to reveal hidden connections.
• Knowledge Discovery Pipelines - Identifies internal data sources and maps their relationships into structured knowledge graphs to provide relevant context for automated intelligence systems.
• Graph Learning - Predicts connections and classifies elements by leveraging structural data to improve the accuracy of predictive models.
• Interactive Graph Visualizers - Provides an interactive web interface for the visual exploration and navigation of graph structures.

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