In this hands-on course, you will use the knowledge gained in the Neo4j & LLM Fundamentals course to create a Movie Recommendation Chatbot backed by a Neo4j database.
You will take a simple chat interface that repeats the user’s input, and modify it to answer questions about movies via the Neo4j Recommendations Dataset using GPT 3.5 Turbo, complete with conversational history.
The chatbot will be able to answer questions like:
How many movies has Tom Hanks acted in?
What is the most popular movie in the database?
Can you recommend a movie for fans of The Matrix and Casino?
At the end of the course, you will have a working chatbot built with Streamlit.
This course relies heavily on the information contained in the Neo4j & LLM Fundamentals course. If you have not already done so, we recommend completing this course first.
We also assume a basic understanding of Python. It is not necessary, but you may also benefit from taking the Building Neo4j Applications with Python course to understand how the Neo4j Python Driver works.
What you will learn
How to build a Neo4j-backed Chatbot with Langchain and 3.5-Turbo
Answering user queries with an LLM
Retrieval Augmented Generation (RAG)
Langchain Agents and QA Chains
If you find yourself stuck at any stage then our friendly community will be happy to help. You can reach out for help on the Neo4j Community Site, or head over to the Neo4j Discord server for real-time discussions.
If you have any comments or feedback on this course you can email us on firstname.lastname@example.org.