Using NVIDIA NIM for Agent-Enhanced AI Query Engines with LlamaIndex

NVIDIA NIM™ microservices are designed to enhance generative AI applications by supporting agents that leverage models trained for agentic behavior, integrating with frameworks like LlamaIndex and LangChain. These microservices facilitate the deployment of high-performance AI model inferencing across various platforms with industry-standard APIs, and are available for free testing from NVIDIA’s API catalog. Agents, empowered by large language models (LLMs), perform complex tasks through reasoning and decision-making, excelling in systems requiring subtasks delegation. In a practical example, retail chatbots can utilize agents to enhance customer interactions by using tools to provide more insightful responses, such as analyzing customer reviews for product inquiries. Additionally, agents can decompose complex queries into subqueries, as demonstrated in a use case involving San Francisco city budget data, where an enhanced query engine with LlamaIndex breaks down queries to provide accurate responses using various tools. This capability is particularly highlighted in a financial data scenario, where an agent processes a query about NVIDIA’s earnings by generating and answering subquestions, thereby utilizing tools to provide comprehensive responses, which can be adapted to other datasets using the provided Jupyter notebook and NVIDIA NIM microservices.