Vector Search in ArangoDB: Practical Insights and Hands-On Examples

Estimated reading time: 6 minutes

Vector search is gaining traction as a go-to tool for handling large, unstructured datasets like text, images, and audio. It works by comparing vector embeddings, numerical representations generated by machine learning models, to find items with similar properties. With the integration of Facebook’s FAISS library, ArangoDB brings scalable, high-performance vector search directly into its core, accessible via AQL (ArangoDB Query Language). Vector Search is now just another, fully-integrated data type/model in ArangoDB’s multi-model approach.

This guide will walk you through setting up vector search, combining it with graph traversal for advanced use cases, and using tools like LangChain to power natural language queries that integrate Vector Search and GraphRAG.

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Some Perspectives on HybridRAG in an ArangoDB World

Estimated reading time: 7 minutes

Introduction

Graph databases continue to gain momentum, thanks to their knack for handling intricate relationships and context. Developers and tech leaders are seeing the potential of pairing them with the creative strength of large language models (LLMs). This combination is opening the door to more precise, context-aware answers to natural language prompts. That’s where RAG comes in—it pulls in useful information, whether from raw text (VectorRAG) or a structured knowledge graph (GraphRAG), and feeds it into the LLM. The result? Smarter, more relevant responses that are grounded in actual data.

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ArangoDB vs. Neo4J

Estimated reading time: 7 minutes

Advantages & Implications for Architects and Developers

ArangoDB and Neo4j are both powerful graph databases, but they have different strengths and features. Here’s a comparison showing how ArangoDB might be superior to Neo4j in certain scenarios, along with code snippets to illustrate:

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ArangoDB 3.12 – Performance for all Your Data Models

Estimated reading time: 6 minutes

We are proud to announce the GA release of ArangoDB 3.12!

Congrats to the team and community for the latest ArangoDB release 3.12! ArangoDB 3.12 is focused on greatly improving performance and observability both for the core database and our search offering. In this blog post, we will go through some of the most important changes to ArangoDB and give you an idea of how this can be utilized in your products.

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Advanced Fraud Detection in Financial Services with ArangoDB and AQL

Estimated reading time: 3 minutes

Advanced Fraud Detection: ArangoDB’s AQL vs. Traditional RDBMS

In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).

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Update: Evolving ArangoDB’s Licensing Model for a Sustainable Future

Estimated reading time: 7 minutes

Update: https://arangodb.com/2023/10/evolving-arangodbs-licensing-model-for-a-sustainable- future/

Last October the first iteration of this blog post explained an update to ArangoDB’s 10-year-old license model. Thank you for providing feedback and suggestions. As mentioned, we will always remain committed to our community and hence today, we are happy to announce yet another update that integrates your feedback.

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The world is a graph: How Fix reimagines cloud security using a graph in ArangoDB

‘Guest Blog’

Estimated reading time: 5 minutes

In 2015, John Lambers, a Corporate Vice President and Security Fellow at Microsoft wrote “Defenders think in lists. Attackers think in graphs. As long as this is true, attackers win.ˮ

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Reintroducing the ArangoDB-RDF Adapter

Estimated reading time: 1 minute

ArangoRDF allows you to export Graphs from ArangoDB into RDFLib, the standard library for working with Resource Description Framework (RDF) in Python, and vice-versa.

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Introducing ArangoDB’s Data Loader : Revolutionizing Your Data Migration Experience

Estimated reading time: 7 minutes

At ArangoDB, our commitment to empowering companies, developers, and data enthusiasts with cutting edge tools and resources remains unwavering. Today, we’re thrilled to unveil our latest innovation, the Data Loader, a game-changing feature designed to simplify and streamline the migration of relational databases to ArangoGraph. Let’s dive into what makes Data Loader a must-have tool for your data migration needs.

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How ArangGraphML Leverages Intel’s PyG Optimizations

Estimated reading time: 3 minutes

ArangoGraphML + Intel: Next-level Machine Learning Accelerated

ArangoDB and Intel have announced a groundbreaking partnership to enhance Graph Machine Learning (GraphML) using Intel’s high-performance processors. This collaboration, part of the Intel Disruptor Program, will seek to integrate ArangoDB’s graph database solutions with Intel’s Xeon CPU. This synergy promises to revolutionize data analytics and pattern recognition in complex graph structures, marking a new era in database technology and GraphML advancements.

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