How to Connect LLMs to External Data with Function Calling

Learn how to build your agents decision making skills → https://mdb.link/EXuwQGXOTOg-decision Learn how to empower Large Language Models (LLMs) by giving them access to external tools through function calling. This tutorial demonstrates how to enable an AI agent to interact with MongoDB collections by generating structured outputs to fetch specific information. Discover the mechanics of function calling, how to use LangChain partials for reusable prompt templates, and the process of binding tools to a model like GPT-4o. The lesson also covers testing your agent's decision-making process to ensure it selects the correct tool based on various user queries. See what Atlas is capable of for free: https://mdb.link/YT-Atlas-Register 00:00:00 Introduction to AI Tools & Function Calling 00:01:22 Configuring the Model & Temperature Settings 00:01:50 Creating Dynamic Prompts with LangChain Partials 00:02:50 Binding Tools and Building the Execution Chain 00:03:19 Testing Agent Decision Making & Tool Analysis Resources: MongoDB main YouTube channel: https://www.youtube.com/@MongoDB Website: https://mdb.link/MongoDBYT LinkedIn: https://www.linkedin.com/company/mongodbinc MongoDB Developer Blog: https://mdb.link/developerblogYT