GraphRAG with Wikipedia
txtai is a versatile tool combining vector indexes, graph networks, and databases for semantic search and language workflows. It showcases using semantic graphs to enhance LLM generation, enabling comprehensive knowledge collection and history book creation.
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txtai is a versatile embeddings database used for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable various functionalities like vector search with SQL, topic modeling, and retrieval augmented generation (RAG). While a standard RAG process involves a single vector search query, more complex scenarios demand an advanced approach. This article showcases how semantic graphs can enhance LLM generation by providing a richer context. By leveraging txtai-wikipedia database and graph path traversal, a comprehensive set of articles related to English history from the fall of the Roman Empire to the Norman conquest is collected. The process involves building a graph query to extract relevant articles and visualizing the interconnected data. Subsequently, a short history book is generated using a language model, incorporating insights from the collected articles. This approach highlights the power of graph path traversal in gathering diverse knowledge compared to traditional vector search methods, paving the way for further advancements in AI technologies.
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