About FalkorDB Podcast: Innovating at the Intersection of Graphs, AI, and Databases
Welcome to the FalkorDB Podcast, where we dive deep into the world of graph databases, AI-driven applications, and cutting-edge data technology. Hosted by industry veterans, this podcast brings you conversations with leading developers, innovators, and thought leaders who are shaping the future of knowledge graphs and retrieval-augmented generation (RAG).
Explore real-world applications of graph technology, discover how FalkorDB is revolutionizing enterprise data infrastructure, and learn how companies are leveraging AI and machine learning to build smarter, faster systems.
Architecting Intelligent Knowledge Graphs with FalkorDB GraphRAG-SDK
FalkorDB GraphRAG SDK 1.0 is the #1 ranked open-source framework on GraphRAG-Bench, outperforming Microsoft and LightRAG by over 14 points.
https://github.com/FalkorDB/graphRAG-SDK
3 Feb 2026TRANSCRIPT
Advances in Graph-Enhanced Retrieval-Augmented Generation Frameworks
This episode explores how modern GraphRAG frameworks leverage graph structures to enhance retrieval accuracy and LLM reasoning through structured memory.
11 Mar 2025TRANSCRIPT
NoSQL Databases: Modern Architecture and FalkorDB Implementation
This episode explores NoSQL databases, focusing on their modern architecture and FalkorDB's implementation, which provides scalability and flexibility for managing large, diverse data in high-velocity environments.
30 Jan 2025
GraphRAG-SDK: Simplifying Knowledge Graph Integration with LLM
FalkorDB introduces GraphRAG-SDK 0.5, which simplifies knowledge graph integration with LLMs by automating ontology loading and streamlining data import for Retrieval Augmented Generation applications.
8 Jan 2025
Ontologies and Knowledge Graphs: A Comprehensive Guide
This episode explores ontologies and knowledge graphs, explaining how ontologies act as blueprints for structuring data and enabling efficient querying within knowledge graphs.
2 Dec 2024TRANSCRIPT
AI Agents: Memory, Graphs, and Autonomous Decisions
This episode explores AI agents, detailing their evolution, examining memory systems and knowledge graphs, and discussing applications across industries, alongside challenges like data privacy.
1 Dec 2024TRANSCRIPT
LlamaIndex RAG: Build Efficient GraphRAG Systems
This episode explores how LlamaIndex and FalkorDB create efficient GraphRAG systems, which enhances LLM responses with real-time information, and provides a step-by-step guide with code examples.
15 Nov 2024TRANSCRIPT
Vector Database vs Graph Database: Key Technical Differences
This episode explores the technical differences between vector databases and graph databases, focusing on data storage, management, strengths, and limitations for complex datasets.
2 Nov 2024TRANSCRIPT
Poll results: What is the biggest challenge using RAG in production?
This episode reviews a poll identifying high accuracy in domain-specific tasks as the biggest challenge when using Retrieval-Augmented Generation in production for machine learning applications.
22 Oct 2024
Announcement: New autonomous agents scale your team like never before - (Microsoft Blog)
Microsoft announces new AI-powered autonomous agents designed to automate business processes and scale teams, offering increased revenue and improved conversion rates.
17 Oct 2024
Building Advanced RAG Applications Using FalkorDB, LangChain, Diffbot API, and OpenAI
This episode details how knowledge graph databases like FalkorDB enhance Retrieval-Augmented Generation applications by offering comprehensive context to large language models, improving response accuracy; this is an informational episode.
17 Oct 2024TRANSCRIPT
Knowledge Graph Tools: What They Are and Their Benefits
This episode explores knowledge graph tools, distinguishing them from graph databases and explaining how they enhance reasoning in large language models by providing structured context.
16 Oct 2024TRANSCRIPT
Beyond Rows and Columns: Exploring the Missing Third Dimension
Base on a blog post by Guy Korland, CEO and
Co-Founder of FalkorDB, discussing the various ways in which data is stored in databases. He highlights three main database storage models: row-based, column-based, and network-based (graph). The author then explains that FalkorDB is a graph database that utilizes a novel approach, using GraphBLAS to store edges in adjacency matrices, thus optimizing data retrieval
and traversal for graph-based applications. The blog post concludes by promoting FalkorDB's capabilities and encouraging readers to explore its features.
To learn more see: https://www.falkordb.com/blog/beyond-rows-and-columns-exploring-the-missing-third-dimension/ (https://www.falkordb.com/blog/beyond-rows-and-columns-exploring-the-missing-third-dimension/)
6 Oct 2024
Knowledge graph vs vector database: Which one to choose?
The article compares and contrasts knowledge graphs and vector databases, both of which are used to store and retrieve data but differ in their methods and strengths. Knowledge graphs are structured representations of information, organized into nodes and edges, and excel at handling complex queries involving relationships
between entities. They are beneficial for applications requiring precise and reliable answers, but require data modeling.
Vector databases, on the other hand, store data as numerical vectors, which allows for efficient similarity searches but may struggle with complex relationships and result in less accurate responses. The article argues that combining these two approaches, as FalkorDB does, can offer a more comprehensive understanding of data and enhance the capabilities of AI and NLP applications.
To read more see: https://www.falkordb.com/blog/knowledge-graph-vs-vector-database/ ( https://www.falkordb.com/blog/knowledge-graph-vs-vector-database/)
5 Oct 2024TRANSCRIPT
Code Visualization: Benefits, Best Practices & Popular Tools
The article "Code Visualization: Benefits, Best Practices & Popular Tools" explores the concept of code visualization, which is the process of transforming code into visual representations of its structure and relationships.
The article discusses the various benefits of code
visualization, such as improving code comprehension, identifying bugs, and facilitating onboarding new developers. It also reviews several popular code visualization tools, such as CodeSee, Gource, and Understand, highlighting their key features and comparing their strengths.
Finally, the article introduces FalkorDB's Code Graph, a new tool that leverages knowledge graphs and large language models (LLMs) to provide advanced code visualization and querying capabilities.
To read more see: https://www.falkordb.com/blog/code-visualization/ (https://www.falkordb.com/blog/code-visualization/)
4 Oct 2024TRANSCRIPT
Advanced RAG Techniques: What They Are & How to Use Them
This
article provides an overview of advanced Retrieval-Augmented Generation (RAG) techniques used to improve the performance and accuracy of large language models (LLMs). It describes how to use techniques such as
pre-retrieval, retrieval, post-retrieval, and generation to address the challenges of Naive RAG, including inaccurate results and slow response times. The article specifically focuses on how knowledge graphs and GraphRAG can be used to optimize RAG applications. It concludes by highlighting the benefits of FalkorDB, a specialized database designed for advanced RAG techniques, particularly with its knowledge graph support and seamless integration with LLMs.
To read more see: https://www.falkordb.com/blog/advanced-rag/ (https://www.falkordb.com/blog/advanced-rag/)
2 Oct 2024TRANSCRIPT
How to Build a Knowledge Graph: A Step-by-Step Guide
This article from the FalkorDB blog focuses on the use of tensors and sparse matrices for efficiently representing and manipulating graph data within the FalkorDB database. The article highlights the power of GraphBLAS,
an API that enables operations on sparse vectors and matrices, as a
crucial element within FalkorDB's architecture. It explains how
FalkorDB leverages tensors to represent multiple edges between the same
two entities in a graph and how these tensors can be treated as regular
sparse matrices, providing flexibility and performance in graph
operations. The article also discusses the use of semirings for efficient computation of graph expressions, combining various types of matrices to achieve faster processing times.
To read more see: https://www.falkordb.com/blog/how-to-build-a-knowledge-graph/ (https://www.falkordb.com/blog/how-to-build-a-knowledge-graph/)
2 Oct 2024
Edges in FalkorDB
This article from the FalkorDB blog focuses on the use of tensors and sparse matrices for efficiently representing and manipulating graph data within the FalkorDB database. The article highlights the power of GraphBLAS, an API that enables operations on sparse vectors and matrices, as a crucial element within FalkorDB's architecture. It explains how
FalkorDB leverages tensors to represent multiple edges between the same
two entities in a graph and how these tensors can be treated as regular
sparse matrices, providing flexibility and performance in graph operations.
The article also discusses the use of semirings for efficient computation of graph expressions, combining various types of matrices to achieve faster processing times.
To read more see: https://www.falkordb.com/blog/edges-in-falkordb/ (https://www.falkordb.com/blog/edges-in-falkordb/)
Inside a recent episode
Advances in Graph-Enhanced Retrieval-Augmented Generation Frameworks
Published 3 Feb 2026 · Transcript excerpt
[…] In a nutshell, yes. Think of standard rag as a high -speed total plus F on a massive document. You ask a question. And the system hunts for keywords. Srooge. London. Transformation. It finds the paragraphs where those words appear, pastes them into the prompt, and tells the AI, here, answer the question using this text. Which works fine if the answer is sitting nicely in one paragraph. Sure. But if the answer relies on understanding that a comment in chapter one caused a subtle emotion in chapter three, which resulted in a decision in chapter five, Schroldplus F is useless. It completely misses the bigger picture. […]
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