Ementorhub

What you'll learn

Gain practical experience building your own real-world gen AI application that you can talk about in interviews.

Get hands-on using LangChain to load documents and apply text splitting techniques with RAG and LangChain to enhance model responsiveness.

Create and configure a vector database to store document embeddings and develop a retriever to fetch document segments based on queries.

Set up a simple Gradio interface for model interaction and construct a QA bot using LangChain and an LLM to answer questions from loaded documents.

Skills you'll gain

Data Processing

User Interface (UI)

Natural Language Processing

Generative AI

Large Language Modeling

Databases

Data Storage Technologies

Unstructured Data

Prompt Engineering

Document Management

Data Storage

There are 3 modules in this course

During this course, you will fill the final gaps in your knowledge to extend your understanding of document loaders from LangChain. You will then apply your new skills to uploading your own documents from various sources. Next, you will look at text-splitting strategies and use them to enhance model responsiveness. Then, you will use watsonx to embed documents, a vector database to store document embeddings, and LangChain to develop a retriever to fetch documents. As you work through your project, you will also implement RAG to improve retrieval, create a QA bot, and set up a simple Gradio interface to interact with your models. By the end of the course, you will have a hands-on project that provides engaging evidence of your generative AI engineering skills that you can talk about in interviews. If you’re ready to add some real-world experience to your portfolio, enroll today and fuel your AI engineering career.

RAG Using LangChain

Create a QA Bot to Read Your Document