Open-source GraphRAG Application with your own data
Hello. This is an easy-to-use application for exploring your own data with retrieval augmented generation (RAG) backed by txtai. txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. txtai has a feature to automatically create knowledge graphs using semantic similarity. This enables running Graph RAG queries with path traversals. This RAG application generates a visual network to illustrate the path traversals and help understand the context from which answers are generated from. Embeddings databases are used as the knowledge store.…
In plain words
This open-source application lets users explore their own data using retrieval augmented generation backed by txtai, an embeddings database for semantic search and language model workflows. It automatically creates knowledge graphs from data and runs Graph RAG queries with path traversals, displaying results as visual networks to show how answers were derived. Users can start with a blank database or existing sources like Wikipedia, then add custom information using the textractor pipeline. The application extracts content from documents and stores it in an embeddings database for retrieval.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello. This is an easy-to-use application for exploring your own data with retrieval augmented generation (RAG) backed by txtai. txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. txtai has a feature to automatically create knowledge graphs using semantic similarity. This enables running Graph RAG queries with path traversals. This RAG application generates a visual network to illustrate the path traversals and help understand the context from which answers are generated from. Embeddings databases are used as the knowledge store. The application can start with a blank database or an existing one such as Wikipedia. In both cases, new data can be added. This enables augmenting a large data source with new/custom information. Adding new data is done with the textractor pipeline. This pipeline can extract content from documents (PDF, Word, etc) along with websites. The website extraction logic detects the likely sections with main content removing noisy sections such as headers and sidebars. This helps improve the overall RAG accuracy. This RAG application is open source with the code available here: https://github.com/neuml/rag
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