Graph powered RAG (Retrieval Augmented Generation) Framework for building modular, open source GenAI applications for productions.
RAG frameworks like Langchain/LlamaIndex provide easy to use abstractions that can be used for quick experimentation and prototyping on jupyter notebooks. On top of them, for Graph RAG to move to production more smoothly, there are constraints like the components should be modular, easily scalable and extendable. This is where graph-rag.io comes in action.
graph-rag.io uses Langchain/Llamaindex under the hood, adopts Neo4j as knowledge and vector storage, and provides an organisation to your codebase, where each of the RAG component is modular, API driven and easily extendible. graph-rag-io can be used easily in a local setup, at the same time, offers you a production ready environment along with no-code UI support. Cognita also supports incremental indexing by default.
Github project folder: link.
