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FalkorDB

An open-source toolkit for building RAG (Retrieval-Augmented Generation) applications using graph databases. We built this because we saw many developers struggling to effectively leverage graph structures in their LLM-powered apps.

Location Tel Aviv, Israel Joined Joined on  Twitter logo GitHub logo External link icon
Support email

info@falkordb.com

Employees

12

FalkorDB V4.8: Neo4j requires 7x the memory to hold the same dataset

FalkorDB V4.8: Neo4j requires 7x the memory to hold the same dataset

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Comments 1
1 min read
HN: cypher queries tips (Graph dbms)

HN: cypher queries tips (Graph dbms)

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Comments 1
1 min read
NoLiMA: GPT-4o achieve 99.3% accuracy in short contexts (<1K tokens), performance degrades to 69.7% at 32K tokens.

NoLiMA: GPT-4o achieve 99.3% accuracy in short contexts (<1K tokens), performance degrades to 69.7% at 32K tokens.

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Comments 1
1 min read
Streamline Document Processing Pipelines with FalkorDB’s String Loader

Streamline Document Processing Pipelines with FalkorDB’s String Loader

10
Comments
1 min read
Why Graph-Based RAG Beats Guesswork in Sensitive AI

Why Graph-Based RAG Beats Guesswork in Sensitive AI

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Comments 2
1 min read
Stop wasting time on ontology setup

Stop wasting time on ontology setup

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Comments
1 min read
Reducing GraphRAG Indexing Costs

Reducing GraphRAG Indexing Costs

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Comments
1 min read
Why top AI architects are DITCHING relationalDBs for knowledge graphs

Why top AI architects are DITCHING relationalDBs for knowledge graphs

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Comments
2 min read
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