Leveraging Documents and Data to Create a Custom LLM Chatbot
The Real Python Podcast
Introduction
Calvin Hendrix Parker shares insights on customizing a chatbot for document and data analysis, utilizing tools like Langchain and ChromaDB. Projects discussed include accessing farm research data and creating a chatbot from conference website information.
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.
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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
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