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Papers Survey
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Survey: GraphRAG and Knowledge Graphs for Large Language Models

The seminal paper “Unifying Large Language Models and Knowledge Graphs: A Roadmap” published on June 14, 2023, presents a comprehensive framework for integrating the emergent capabilities of Large Language Models (LLMs) with the structured knowledge representation of Knowledge Graphs (KGs)

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state machine 1 FalkorDB
Blog

Efficient State Machine Modeling Using FalkorDB

The latest release of FalkorDB V4.0.5 includes a new ability to easily clone graphs. In this blog post we’ll be developing a state machine framework where a machine is represented by a graph. Whenever a FSM (finite state machine) is executed

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movies
Blog

How to Build a Movie Recommendation System Powered by Knowledge Graph FalkorDB

Source: https://medium.com/@akriti.upadhyay/building-advanced-rag-applications-using-falkordb-langchain-diffbot-api-and-openai-083fa1b6a96c When you surf through Amazon Prime, you are met with a screen that lists the top movies for you. Netflix and Hulu do the same. These platforms use powerful recommendation systems to keep you interested — by tracking

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GraphRAG
Blog

Knowledge Graph & LLM or what is GraphRAG?

By using GraphRAG, we can achieve a bidirectional communication between Knowledge Graphs and LLMs, where both sides can benefit from each other’s strengths and compensate for each other’s weaknesses. We can also enable a more natural and intuitive interaction between

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RAG Application Overview
Blog

Building Advanced RAG Applications Using FalkorDB, LangChain, Diffbot API, and OpenAI

Source: https://medium.com/@akriti.upadhyay/building-advanced-rag-applications-using-falkordb-langchain-diffbot-api-and-openai-083fa1b6a96c   Introduction 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

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Blog 1 1 FalkorDB
Blog

Code Graph

In most RAG (Retrieval Augmented Generation) solutions we’ve seen thus far the common setup is a combination of LLM and a Vector database. Early feedback indicates that this setup is simply not good enough, applications suffer from hallucination and users

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Blog 4 FalkorDB
Blog

RAG battle: vector database vs knowledge graph

LLMs today The potential of using LLMs for knowledge extraction is nothing less than amazing, in this last couple of months we’ve seen a rush towards integrating large language models to perform a variety of tasks, e.g. data summarization, Q&A

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Latency & Throughput Graph
Blog

Scale Out

The ability to scale out a database is crucial, in this short post I would like to walk through how FalkorDB scales out. As a quick recap I should mention that FalkorDB is a native graph database, developed as a

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Blog 8 FalkorDB
Blog

What is a RAG (Retrieval Augmented Generation)?

In this blog post, I will explain what RAG is, why it is useful, and how to build it using Vector Database and Knowledge Graph as a leading option for RAG. I will also give some examples of use cases that need RAG and

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Blog 10 FalkorDB
Blog

Building a Q&A System

If you are looking for a simple way to build a Q&A system based on your knowledge graph, you should check out LangChain. Langchain allows you to easily query your Knowledge Graph using natural language.  In this blog post, I will show

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Graph Database vs Vector Database
Blog

Knowledge Graph vs Vector Database

If you are working on a large language model (LLM) project, you might have encountered the problem of hallucination. Hallucination is when the LLM generates text that is fluent and coherent, but not factual or accurate. For example, the LLM might generate a sentence

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Blog 9 FalkorDB
Blog

Building and Querying a Knowledge Graph

If you are looking for a simple way to create and query a knowledge graph based on your internal documents, you should check out LlamaIndex. LlamaIndex is a tool that allows you to easily build and search a knowledge graph using natural language

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Blog 5 FalkorDB
Blog

Building & Querying a Knowledge Graph from Unstructured Data

Diffbot API, FalkorDB, and LangChain are a great combination for building intelligent applications that can understand and answer questions from unstructured data. Diffbot API has a powerful API that can extract structured data from unstructured documents, such as web pages, PDFs, or emails. With Diffbot

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