A Guide to Implementing a GraphRAG Workflow Using FalkorDB, LangChain and LangGraph
Discover how to implement GraphRAG using FalkorDB’s hybrid query capabilities combined with LangChain and LangGraph to build AI systems that leverage both graph relationships and semantic search.
NoSQL Database: Modern Architecture for Scalable Apps
NoSQL database architectures overcome relational limitations with schema flexibility and distributed processing. Learn how graph, document, key-value, and columnar NoSQL models power AI and real-time analytics.
Graph Clustering Algorithms: Usage and Comparison
Learn about graph clustering algorithms: hierarchical, modularity-based, label propagation, spectral, and edge betweenness. Analyze their strengths, weaknesses, and optimal use cases.
Prepare for RedisGraph EOL: A Practical Guide to Transition to FalkorDB
Explore RedisGraph EOL in this dev guide with practical migration steps and technical validation tips for switching to FalkorDB.
Reducing High GraphRAG Indexing Costs: Strategies for Efficient Graph Database Management
Explore practical methods to reduce GraphRAG Indexing Costs, including query optimization, efficient indexing techniques, and scalable LLM integration for graph databases.
FalkorDB vs Neo4j: Choosing the Right Graph Database for AI
When building AI-driven systems, FalkorDB vs Neo4j graph databases offer different advantages. Find the best fit for your AI needs.
Vector Database vs. Graph Database for AI: When to Use Each and When to Combine Them
Vector databases retrieve semantically similar content from embeddings. Graph databases retrieve explicit entities and relationships through traversals. Use vectors when similarity is enough; use graphs when an AI application must reason across connected facts, multi-hop relationships, provenance, or permissions. Many production AI systems combine both approaches: vector search finds the relevant content, and graph traversal […]
Knowledge Graph Tools: What They Are and Their Benefits
Knowledge graphs have become a game-changer in building Retrieval-Augmented Generation (RAG) applications, often referred to as GraphRAG. These applications enhance the reasoning capabilities of large language models (LLMs) by providing structured context from a knowledge base. By organizing information into a graph format, knowledge graphs allow for more interconnected and structured data, enabling LLMs to […]
Edges in FalkorDB
Edges in FalkorDB enable efficient graph representation and traversal using GraphBLAS tensors. Learn how FalkorDB uses GraphBLAS to support advanced graph operations and scalable graph processing, making Edges in FalkorDB a useful tool for graph data management.
How to Build a Knowledge Graph: A Step-by-Step Guide
Driving meaningful insights from vast amounts of unstructured data has often been a daunting task. As data volume and variety continue to explode, businesses are increasingly seeking technologies that can effectively capture and interpret the information contained within these datasets to inform strategic decisions. Recent advancements in large language models (LLMs) have opened new avenues […]