falkordb/graphrag-sdk is a Python SDK for developing retrieval-augmented generation (RAG, a method that answers questions using context found through search) applications by building a knowledge graph (a structure that binds the relationships between concepts and entities with edges) on the graph database FalkorDB. The latest release is v1.4.0, and the available information contains no changelog for this version. The source of the information is the falkordb/graphrag-sdk repository on GitHub.
The README describes this SDK as a modular GraphRAG framework based on FalkorDB. The repository aims to build fast and accurate GenAI applications at scale. In the documentation, it builds a knowledge graph and generates answers based on retrieved context. The ontology (a framework that defines which concepts and relations the graph covers) supports ontology configuration, graph construction, and question answering that leverages the graph. It also provides relationship traversal and source tracing back to the original chunks. When there is insufficient evidence, it can be configured to withhold the answer. Schema definition, PDF collection, incremental updates, multi-tenant configuration, and connecting custom LLMs and embedders are also supported. The license is Apache-2.0, the primary language is Python, and the repository has 1023 GitHub stars. The repository was created on January 25, 2024.
However, no performance figures backing this goal are presented. The evaluation score is out of 10, with only the momentum item at 1.0 and the novelty, technology, and quality items empty. No actual deployment cases could be confirmed either. How accurate the answers are on large-scale documents cannot be known from this information alone. At the current stage, it is more appropriate to view this as a developer tool for trying out graph-based RAG setups directly, and there is still not enough evidence to apply it straight to a production environment.