Grounding Our Chat Towards Data Science Results

In this tutorial, we learn how to ground our Retriever- Augmenter-Generator (RAG) results using LlamaIndex and citations. We start by setting up the necessary libraries and environment variables for our chatbot. Next, we define the parameters of our RAG chatbot, including the embedding model, vector database, and data abstractions. Finally, we implement citations via LlamaIndex's CitationQueryEngine module to ensure grounded results. This tutorial uses Zilliz Cloud as a fully managed and optimized version of Milvus for persisting data across multiple projects.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.