Summary
In this chapter, we explored the concept of agentic RAG and its implementation using LangChain and Google Cloud’s Vertex AI. We demonstrated how to create a knowledge base, build a vector store, and define a cognitive architecture using LangGraph. This architecture enables the language model to interact with the knowledge base, retrieve relevant information, and generate contextually appropriate responses.
Furthermore, we also delved into the application of agents for analyzing structured data sources. You learned how to construct an agent capable of querying a database and delivering answers based on the retrieved results. The provided examples highlighted the agent’s ability to not only fetch data but also perform calculations, such as currency conversions, based on the user’s query.
