RAG Project: Build an AI Onboarding Chatbot with Streamlit, LangChain, and ChromaDB
Key Takeaways
This video demonstrates building an AI onboarding chatbot using Streamlit, LangChain, and ChromaDB, covering environment variables, PDF parsing, RAG pipelines, and vector search integration.
Original Description
In this comprehensive video, you'll learn how to build a fully functional AI-powered onboarding chatbot tailored for new employees at a fictional company (Umbrella Corporation). The tutorial walks through each stage of development, from managing environment variables and parsing company policy PDFs, to implementing retrieval-augmented generation (RAG) pipelines using LangChain, integrating vector search with Chroma, building a rich frontend with Streamlit, and instrumenting your app for traceability with LangSmith. Ideal for AI engineers, data scientists, and Python developers, the video provides hands-on implementation details, best practices, and debugging insights, making it a perfect portfolio project or a foundation for enterprise onboarding solutions.
## Topics
- Working with OpenAI and Groq API keys
- Understanding and using LangSmith for tracing/debugging
- Using Faker for generating sample employee data
- Parsing and extracting text from PDFs using PyPDF
- Handling structured and unstructured PDF data
- Loading, vectorizing, and splitting documents using LangChain and text splitters
- Creating and managing Chroma vector stores for document retrieval
- Building RAG (Retrieval Augmented Generation) applications
## Links and code
*Important*: This video is a module from the AI Engineering Bootcamp. Sign up here:
- 🚀 Complete AI Engineer Bootcamp: https://aibootcamp.dev
👉 Code (starting point): https://github.com/alejandro-ao/client-onboarding-rag-demo
👉 Code (solution): https://github.com/alejandro-ao/client-onboarding-rag-demo/tree/solution
- ❤️ Buy me a coffee... or a beer (thanks): https://link.alejandro-ao.com/l83gNq
- 💬 Join the Discord Help Server: https://link.alejandro-ao.com/HrFKZn
- ✉️ Get the news from the channel and AI Engineering: https://link.alejandro-ao.com/AIIguB
## Timestamps
0:00:00 - Intro
0:02:39 - Project setup
0:07:31 - Data Service
0:18:10 - Assistant Class
0:34:56 - GUI With Streamlit
0:44:35 - Run the Chatbot
0:51:22 - Trac
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Chapters (7)
Intro
2:39
Project setup
7:31
Data Service
18:10
Assistant Class
34:56
GUI With Streamlit
44:35
Run the Chatbot
51:22
Trac
🎓
Tutor Explanation
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