GraphRAG on SoloSoft

Code-Graph: Open-Source Tool for Analyzing Source Code as Queryable Knowledge Graphs

Fri, 01 May 2026 00:00:00 +0000

Understanding unfamiliar codebases is one of the hardest challenges in software development. Code-Graph by FalkorDB tackles this problem in a novel way: by transforming source code repositories into fully queryable knowledge graphs that you can interrogate in natural language.

Instead of reading through files linearly or relying on code search tools that treat code as flat text, Code-Graph analyzes your codebase at the Abstract Syntax Tree (AST) level, extracting every significant entity – classes, functions, methods, modules, arguments, variables – and mapping their relationships into a property graph stored in FalkorDB. The result is a structured, navigable representation of your entire codebase that supports natural language queries like “Show me all classes that depend on the DatabaseConnection class” or “Find unused utility functions in the auth module.”

FalkorDB: Ultra-Fast Open-Source Graph Database for Knowledge Graphs and GraphRAG

Fri, 01 May 2026 00:00:00 +0000

FalkorDB is an ultra-fast, open-source multi-tenant property graph database built specifically for LLM Knowledge Graphs and GraphRAG (Graph-based Retrieval-Augmented Generation). As the direct successor to RedisGraph – which was discontinued by Redis Inc. in 2023 – FalkorDB has been adopted by a growing community of AI practitioners who need graph database performance optimized for the age of large language models.

Under the hood, FalkorDB uses sparse matrix operations via the GraphBLAS standard to represent and query graph adjacency matrices. This approach is fundamentally different from the index-based traversal used by most graph databases, and it is the key to FalkorDB’s millisecond-latency query performance even on graphs with millions of nodes.

LLM Graph Builder: Neo4j's RAG-to-Graph Pipeline

Fri, 01 May 2026 00:00:00 +0000

The limitations of traditional Retrieval-Augmented Generation (RAG) have become increasingly clear as organizations deploy AI systems in production. Vector search – the backbone of conventional RAG – does a reasonable job of finding semantically similar document chunks, but it fundamentally lacks structural understanding. It cannot express that “Apple acquired Beats in 2014” involves a relationship between two entities with a specific type and date. It cannot follow a chain of relationships across multiple documents. It treats the knowledge base as a flat bag of vectors rather than an interconnected web of facts.

Neo4j’s LLM Graph Builder addresses this limitation by bridging the gap between large language models and graph databases. It is an open-source tool that uses LLMs to automatically extract entities and relationships from unstructured documents, then populates a Neo4j knowledge graph with the resulting structured data. The output is a GraphRAG pipeline that combines the semantic understanding of LLMs with the structural precision of graph databases.