GraphRAG Community Projects
Check out community projects with FalkorDB
Wondering what people actually build with a graph database once GraphRAG moves past the demo stage? This page collects real community work — podcasts, tutorials, notebooks and write-ups — that uses FalkorDB to build knowledge graphs, agent memory and retrieval-augmented generation pipelines, so you can see working patterns and copy the ones that fit your stack.
The One Tool You Absolutely Need to Efficiently Scale Retrieval-Augmented Generation
To further enhance the efficiency and scalability of RAG workflows, integrating a high-performance database like FalkorDB is essential. FalkorDB provides a reliable and scalable storage solution for the dynamic knowledge bases that RAG systems depend on, ensuring rapid data retrieval and seamless integration with messaging systems like KubeMQ.
Knowledge Graphs and the Future of Hybrid RAG | Our Agentic Future
TrustGraph Podcast: The CTO and Cofounder of FalkorDB, Roi Lipman, describes how to model knowledge graphs with linear algebra and how it will impact RAG and explainability with LLMs. Is matrix multiplication the most effective way to query knowledge? Is Cypher or GQL the future of query languages?
GraphRAG — Knowledge Graph & LLM
Scalability and Efficiency: GraphRAG can handle large-scale data efficiently due to its reliance on Graph databases (e.g., FalkorDB), which are inherently scalable and optimized for querying complex datasets.
Building a Mental Health QA Chatbot Using FalkorDB Knowledge Graph and LlamaIndex
A Knowledge Graph converts data into machine-understandable knowledge. But what is the difference between ‘data’ and ‘knowledge’?
Top Open-Source AI GitHub Repositories — Visualised As Code Graphs Using FalkorDB Code Graph
Code graphs provide a way to visualize the structure and lineage of complex codebases, presenting elements such as files, classes, and functions in a graph format. This visualization can help in several ways:
- Understanding specific components of a large codebase.
- Identifying requirements and dependencies.
- Simplifying debugging and refactoring.
- Effectively managing and visualizing large codebases.
Building Advanced RAG Applications Using FalkorDB, LangChain, Diffbot API, and OpenAI
The introduction of the Knowledge Graph Database in the realm of evolving Large Language Models has changed the way RAG applications are getting built. Since RAG mitigates knowledge limitations like hallucinations and knowledge cut-offs, we use RAG to build QA chatbots. Knowledge Graphs store and query the original data and capture different entities and relations embedded in one’s data.
How to Build a GraphRAG-Powered AI Assistant For The BFSI Sector
Technical guide on implementing GraphRAG architecture with FalkorDB for BFSI applications. Covers knowledge graph creation, Cypher query generation, and LLM integration for enterprise-grade AI assistants
Trip planning with a FalkorDB GraphRAG agent using a Swarm
In this notebook, we're building a trip planning swarm which has an objective to create an itinerary together with a customer. The end result will be an itinerary that has route times and distances calculated between activities.
The following diagram outlines the key components of the Swarm, with highlights being:
- FalkorDB agent using a GraphRAG database of restaurants and attractions