Summary
In this chapter, we explored the creation of a scalable knowledge-graph-based RAG system using the Wikipedia API and LlamaIndex. The techniques and tools developed are applicable across various domains, including data management, marketing, and any field requiring organized and accessible data retrieval.
Our journey began with data collection in Pipeline 1. This pipeline focused on automating the retrieval of Wikipedia content. Using the Wikipedia API, we built a program to collect metadata and URLs from Wikipedia pages based on a chosen topic, such as marketing. In Pipeline 2, we created and populated the Deep Lake vector store. The retrieved data from Pipeline 1 was embedded and upserted into the Deep Lake vector store. This pipeline highlighted the ease of integrating vast amounts of data into a structured vector store, ready for further processing and querying. Finally, in Pipeline 3, we introduced knowledge graph index-based RAG. Using LlamaIndex, we automatically...
