FalkorDB’s GraphRAG to the rescue!
Gil Dibner

We are super excited to have backed Guy Korland, Roi Lipman, Avi Avni and the entire team at FalkorDB to build the future of real-world enterprise AI applications. A few days ago, the investment broke stealth mode and was covered by the press in both English by (Calcalist) and Hebrew (by Geektime).

Everyone knows that AI is important and that LLMs are very powerful. So every enterprise has already run off and tried to build enterprise-grade applications based on LLMs, mostly on-prem open source. As Bill Gurley recently said in the BG2Pod, “LLMs may not be the best tool for every use case, but we are definitely going to find out” because every founder and CIO on the planet is busy throwing LLMs at every imaginable problem. That is actually long-term great for innovation, but it's short-term confusing.

In many ways, however, the results of this experiment are already becoming clear to those on the cutting edge of these endeavors: we are all gradually coming to the same conclusions: LLMs are great at some things, but they simply can not serve as the backbone of real-world enterprise (or consumer) applications. To do that, they must be married to a traditional (human readable) database - ideally a Graph Database.

LLMs excel at two things. First, they enable powerful bi-directional human interfaces: natural language in and easy-to-consume natural language out. Second, they are outstanding at unifying disparate data sources: LLMs are great at enabling vector-based similarity searches across vast stores of unstructured information, and they are also powerful tools for fusing data together, including structured and unstructured data.

But LLMs are not great for reasoning, and they are very difficult for humans to understand, debug, control, and limit. The use of LLMs (as any other technology) brings with it a host of issues that need to get solved: error checking, debugging, permissions, security, audit, etc. Hallucination - the tendency of LLM-powered applications to emit nonsensical answers that “sound” truthy but are straight-up wrong - are the most famous bug of LLMs, and provide a great illustration of why traditional (i.e. human readable) databases need to be coupled with LLMs to produce reliable results which enable useful real-world applications. The same architecture can be used to implement other types of controls that will unleash the true potential ofLLMs. They will not be the solution, but they will be part of it.

Guy Korland and the team at FalkorDB understood this from day one. Having built RedisGraph (the most performant in-memory Graph Database in the world), there is no team better suited to solve this problem - which is arguably the biggest problem in enterprise data infrastructure today: how do we truly democratize the creation of reliable, debuggable, auditable, useful, scalable, secure, enterprise-grade AI applications that can leverage the powerful novel capabilities of LLMs without sacrificing the tremendous advantages of traditional databases?

The answer to the limitations of LLMs is FalkorDB. And the key insight is that no enterprise user ever wanted a vector database or an "LLM." What they wanted was a knowledge base, which is the core benefit that FalkorDB’s approach enables. FalkorDB achieves this by creating an architecture that fuses knowledge graphs (KGs) and LLMs together into one seamless technology stack, something they call GraphRAG (graph-retrieval-augmented generation).

According to a recent blog post by FalkorDB, “Knowledge Graphs are a powerful tool for representing and querying structured data. They can capture the relationships and attributes of entities, such as people, places, events, products, etc. Knowledge Graphs can also enrich the data with external sources, such as Wikipedia, DBpedia, or other domain-specific ontologies….Knowledge Graphs and LLMs can work together to create a powerful synergy. By combining Knowledge Graphs and LLMs, we can leverage the strengths of both approaches and overcome their weaknesses. We can use Knowledge Graphs to provide structured and factual information to LLMs, and use LLMs to provide natural language generation and understanding capabilities to Knowledge Graphs.” GraphRAG can become a powerful tool across a range of use cases, but it uses Knowledge Graphs not only as an input or a source of information for LLMs, but also as an output or a target of information for LLMs. “In other words, GraphRAG uses LLMs not only to generate natural language texts from Knowledge Graphs, but also to generate Knowledge Graphs from natural language texts.”

It’s rare indeed to come across an experienced team that has worked together in the past to build and deliver best-of-breed fundamental technology. It’s equally rare to come across a team that has the unique perspective necessary to re-imagine the way things ought to be done in one of the most important areas of human endeavor. Amazingly, in FalkorDB, we found both in one company. We are incredibly excited to be joined in our investment in FalkorDB by Keenan Rice of Tokyo Black, Jerry Dischler (President, Cloud Applications at Google), Aryeh Mergi (co-founder of M-systems and XtreamIO), and Eldad Farkash and Saar Bitner (CEO and COO of Firebolt). We can’t wait to see what the FalkorDB team will deliver to the world in the near future, and - even more importantly - what this fundamentally novel approach to AI tooling will enable people to build as it unlocks the next level of value creation in the evolution of enterprise AI.

To get started, check out FalkorDB on Github.

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