Leveraging Documents and Data to Create a Custom LLM Chatbot
The Real Python Podcast
Technical Discussion on Custom Chatbot Development with Database Storage
Exploring the technical intricacies of building a personalized chatbot using Django and database storage. From model settings to API calls and interactions, learn how to enhance chatbot performance and customization for tailored user experiences.
How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.
Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares a recent project for a family-owned seed company that wanted to build a tool for customers to access years of farm research. These documents were stored as brochure-style PDFs and spanned 50 years.
We discuss several of the tools used to augment a LLM. Calvin covers working with LangChain and vectorizing data with ChromaDB. We talk about the obstacles and limitations of capturing documentation.
Calvin also shares a smaller project that you can try out yourself. It takes the information from a conference website and creates a chatbot using Django and Python prompt-toolkit.
This episode is sponsored by Mailtrap.
Course Spotlight: Command Line Interfaces in Python
Command line arguments are the key to converting your programs into useful and enticing tools that are ready to be used in the terminal of your operating system. In this course, you’ll learn their origins, standards, and basics, and how to implement them in your program.
Topics:
- 00:00:00 – Introduction
- 00:02:21 – Background on the project
- 00:03:51 – Complexity of adding documents
- 00:09:01 – Retrieval-augmented generation and providing links
- 00:13:46 – Updating information and larger conversation context
- 00:18:08 – Sponsor: Mailtrap
- 00:18:43 – Working with context
- 00:21:02 – Temperature adjustment
- 00:22:07 – Rally Conference Chatbot Project
- 00:26:20 – Vectorization using ChromaDB
- 00:32:49 – Employing Python prompt-toolkit
- 00:35:07 – Learning libraries on the fly
- 00:37:38 – Video Course Spotlight
- 00:39:00 – Problems with tables in documents
- 00:42:30 – Everything looks like a chat box
- 00:44:26 – Finding the right fit for a client and customer
- 00:49:05 – What are questions you ask a new client now?
- 00:51:54 – Canada Air anecdote
- 00:56:20 – How do you stay up to date on these topics?
- 01:01:03 – What are you excited about in the world of Python?
- 01:03:22 – What do you want to learn next?
- 01:04:58 – How can people follow your work online?
- 01:05:31 – IndyPy
- 01:07:13 – Thanks and goodbye
Show Links:
- Transforming Agricultural Data with AI — Six Feet Up
- Build ChatGPT-like Apps with AI — Six Feet Up
- Innovate with AI: Build ChatGPT-like Apps - YouTube
- What is retrieval-augmented generation? - IBM Research Blog
- rally-llm-presentation - sixfeetup - GitHub
- Python Prompt Toolkit 3.0 — Documentation
- Chroma - the AI-native open-source embedding database
- Embeddings and Vector Databases With ChromaDB – Real Python
- LangChain
- Build an LLM RAG Chatbot With LangChain – Real Python
- Air Canada must pay after chatbot lies to grieving passenger - The Register
- I’d Buy That for a Dollar: Chevy Dealership’s AI Chatbot Goes Rogue
- Omnivore
- TLDR AI - Get smarter about AI in 5 minutes
- Tech Brew
- Simon Willison’s Weblog
- llm: Access large language models from the command-line - simonw - GitHub
- PyCon US 2024
- Syntorial: The Ultimate Synthesizer Tutorial
- Blog — Six Feet Up
- Calvin Hendryx-Parker - LinkedIn
- Eclipse Insights: How AI is Transforming Solar Astronomy - YouTube
Level up your Python skills with our expert-led courses:
Remember Everything You Learn from Podcasts
