13 Repos
Operations for creating, updating, and deleting nodes, relationships, and properties within a graph database.
Distinct from Graph Data Models: Distinct from Graph Data Models: focuses on the transactional modification of graph data rather than the structural definition of the schema.
Explore 13 awesome GitHub repositories matching data & databases · Graph Data Modifiers. Refine with filters or upvote what's useful.
Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries
Create, update, or delete nodes, relationships, and their properties, including conditional merging and bulk removal of labels or properties.
Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin
Updates knowledge graphs by removing nodes and relationships in-place or by generating modified copies.
This project is a collection of programming language references and syntax cheat sheets designed for rapid developer onboarding. It serves as a library of code-based documentation that uses valid source code files to provide whirlwind tours of various language specifications. The project focuses on programming language learning by providing concise, commented code examples that explain core features and syntax in place. This approach enables developers to quickly grasp language-specific patterns, data types, and execution flow through a consistent reference format. The content covers a broad
Provides syntax references for removing nodes, relationships, and properties from graph databases.
Titan ist eine verteilte Graphdatenbank und Computing-Engine, die für das Speichern und Abfragen massiver Datensätze aus miteinander verbundenen Knoten und Kanten über Multi-Maschinen-Cluster hinweg entwickelt wurde. Sie fungiert als skalierbare Graph-Speicherschicht und transaktionaler Speicher und bietet ein Framework für die Ausführung großskaliger Graph-Verarbeitungsjobs und tiefer Traversierungen. Das System zeichnet sich durch sein austauschbares Speicher-Backend aus, das die Graph-Engine von der physischen Persistenzschicht entkoppelt. Es nutzt Vertex-Cut-Datenpartitionierung, um Verarbeitungslasten auszugleichen, sowie ein Set-Kardinalitäts-Eigenschaftsmodell, das es ermöglicht, dass einzelne Eigenschaften mehrere Werte speichern können. Die Plattform deckt ein breites Spektrum an Funktionen ab, einschließlich Multi-Modell-Graph-Indizierung für geografische und Volltextsuchen, globales Schema-Management für die Neuindizierung von Datensätzen und transaktionale Operationen, die durch Write-Ahead-Logging sichergestellt werden. Zudem integriert es Element-Ablauf mittels Time-to-Live-Einstellungen und System-Performance-Monitoring zur Verfolgung von Abfrageaktivitäten und Transaktionslatenz.
Provides a system for defining vertex labels and property types independently of physical indexing to support global re-indexing.
Apache AGE is a graph database extension for PostgreSQL that adds openCypher graph query capabilities directly within the relational database environment. It functions as a loadable extension that translates Cypher graph traversal queries into SQL expressions, enabling users to run pattern matching and path analysis alongside standard SQL operations within a single database instance. The extension stores labeled, directed property graphs as isolated schemas with internal relational tables for vertices, edges, and labels, preventing cross-graph interference. It supports hybrid query execution
Removes a graph and optionally cascades to delete all associated labels and data.
Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr
The product modifies labels and properties on nodes and relationships including nested map values.
Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di
Supports standard query syntax for creating, merging, updating, and deleting nodes and relationships to maintain graph state.
Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist
Executes Cypher queries to create or update data structures in Neo4j.
Helix DB is a distributed graph database and knowledge graph platform that persists nodes and edges on object storage for durable and unlimited scaling. It operates as an ACID-compliant system, ensuring data consistency through serializable snapshot isolation during concurrent operations. The project distinguishes itself by combining a vector search engine and a property graph, utilizing hybrid vector and full-text search to locate entry points for graph traversals. It enables dynamic graph querying through a domain-specific language, allowing complex logic and recursive queries to be execute
Creates, updates, and deletes nodes and edges or modifies their properties through batch operations.
Jet is a schema-driven code generation tool and type-safe SQL builder for Go. It introspects database schemas to automatically generate builders and data models, enabling compile-time type checking for table and column references to prevent runtime errors. The project distinguishes itself through a fluent interface that mirrors native SQL syntax, allowing for the orchestration of complex queries including common table expressions, recursive queries, and nested JSON structures. It further optimizes data retrieval by binding query outputs directly into generated Go structures or raw byte slices
Returns modified records immediately after an update operation using a returning clause.
FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It ut
Defines the structural layout of graphs through manual specifications or automated extraction.
BloodHound is an identity risk management platform and graph-based attack path analyzer used to map identity relationships and permissions in Active Directory. It functions as a security tool for auditing directory services, uncovering unintended privilege relationships, and visualizing sequences of permissions that can lead to domain compromise. The project differentiates itself as a comprehensive adversary emulation framework that coordinates remote agents and executes post-exploitation commands. It includes a reverse proxy for bypassing multi-factor authentication via real-time session hij
Creates extension definitions to map custom nodes and edges into a structured graph.
Simple Graph ist eine leichtgewichtige Graphdatenbank-Engine, die SQLite zur Persistierung von Knoten und Kanten verwendet. Sie fungiert als relationale Graph-Engine, indem sie Graphstrukturen auf Standard-Datenbanktabellen abbildet, was die Speicherung sowohl strukturierter Daten als auch flexibler, schemaloser Informationen durch JSON-Dokument-Einbettung ermöglicht. Das System bietet ein Dienstprogramm zur Durchführung komplexer Graph-Traversierungen und Pfadfindungen durch die Nutzung rekursiver Common Table Expressions (CTEs). Dieser Ansatz ermöglicht die Erkundung tiefer Verbindungen und Sequenzen verbundener Knoten innerhalb des gespeicherten Datennetzwerks. Das Projekt unterstützt Standard-Datenverwaltungsoperationen, einschließlich des Erstellens, Aktualisierens und Löschens von Graph-Datensätzen. Alle Interaktionen werden durch die Ausführung vorbereiteter Statements (Prepared Statements) abgewickelt, um eine konsistente und sichere Datenmanipulation innerhalb der zugrunde liegenden relationalen Speicherung zu gewährleisten.
Provides operations for creating, updating, and deleting nodes and edges within the graph.