In this session, we will build a practical application using Streamlit, leveraging open-source LLMs, embedding models, and vector stores—all implemented in Python. Attendees will learn how to integrate these components to create a user-friendly interface that enables seamless interaction with LLM agents.
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Join DevFest Scotland Online for a brilliant session on LLM Agents!
In this session, we will build a practical application using Streamlit, leveraging open-source LLMs, embedding models, and vector stores—all implemented in Python. Attendees will learn how to integrate these components to create a user-friendly interface that enables seamless interaction with LLM agents. The session will cover the iterative development process, highlight the role of human evaluators in assessing output quality, and demonstrate the creation of efficient query engines for summarizing and semantically searching meeting notes. Additionally, we will explore other tools accessible to the agents to enhance their functionality and effectiveness.
AXA Mansard
Technology Business Analyst
Charles River Laboratories
Tech Director | AI/ML GDE
Charles River Laboratories
Organizer
JP Morgan Chase & Co
Organizer
JPMorgan Chase & Co.
Organizer
Glasgow Caledonian University
Organiser
Barclays
Software Developer - Organizer
Jordanhill School
GDG Glasgow Youth Team Leader
University of Strathclyde
Team member
University of Glasgow
Team member
Charles River Laboratories
Team member
JP Morgan
Technical BA
PGT Student
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