This talk centers on the tangible applications of GPT4 and Plan and Execute agent types. We explore real-world examples where GPT-4 acts as a bridge, allowing natural language interactions with varied data sources. This deep integration transforms data accessibility, enabling organizations to communicate with their data in an intuitive, conversational manner. Learn how this approach enhances data consumption, making diverse data sources comprehensible via natural language interfaces.

In this talk we explore a pattern to expose existing disparate data sources to a LLM using LangChain's agents. With the right kind of prompting, it can create a plan to answer your query using a catalogue of data sources. This means you could easily expose your data to a natural language interface without much effort.

Join me to follow along with this experiment and learn how you can bring the benefit of LLM to your organisation starting today.

https://github.com/dasiths/llm-plan-and-execute-knowledge-provider-mesh

This is an experiment to show that you can use GPT4 with Plan and Execute agent type to work with "existing" APIs and data sources. This means you can start integrating the LLM with existing information systems. The LLM is powerful enough to understand where to go for the data and connect different disparate sources of data to formulate an answer than spans many domains within the same org or different orgs.

While "slow", this demonstrates the true power of the LLM. The responses and steps are not deterministic but the final answer is mostly are accurate for it to be useful in many scenarios.