This course will teach you to design, build, and optimize AI chatbots using transformers and RAG through hands-on projects and Streamlit UI.

Building Chatbots with Open Source LLMs_ Langchain and Streamlit.png

Dockerfile-NLP.tar.gz

GROQ_API_KEY

Docker-NLP

Docker-NLP-Debug

text_classification

table_question_answering

translation

summarization

sentence_similarity

text_generation_inference

retrieval strategies

vector-stores

langchain_chains_QA

sentiment-analysis

Streamlit

This course provides a comprehensive guide to designing, building, and optimizing chatbots using modern AI technologies. It begins with an introduction to chatbots, foundational concepts in AI, and the mechanics of transformer models, equipping you with a solid understanding of how natural language processing underpins chatbot systems.

You’ll learn to implement large language models (LLMs) for tasks such as summarization, translation, and inference alongside data preparation techniques and retrieval-augmented generation (RAG) workflows using tools like LangChain. You’ll also cover advanced concepts like prompt engineering, retrieval chains, and developing user interfaces with Streamlit.

The course concludes with an integration and evaluation phase, where you’ll deploy your chatbot systems and assess performance. A hands-on capstone project solidifies these skills, making this course ideal for developing robust, user-friendly chatbots using cutting-edge AI methodologies.

Build AI Chatbots with Open-Source LLMs, LangChain, and Streamlit

Creating interactive web apps from Python scripts

What is Streamlit primarily used for?

Which command is used to install Streamlit?

Which command is used to install Streamlit?

The script partially reruns based on user input.

The script does not rerun; it only runs once.

The entire script reruns from top to bottom.

How does Streamlit handle script execution upon user interaction?

How does Streamlit handle script execution upon user interaction?

Which feature allows Streamlit to maintain data or computation across reruns?

Which feature allows Streamlit to maintain data or computation across reruns?

It saves the user's session to a database.

It manages state variables that persist across reruns of the app.

It tracks the number of users currently active on the app.

It stores session data temporarily on the client's browser.

What is the purpose of `st.session_state` in Streamlit?

What is the purpose of <code>st.session_state</code> in Streamlit?

There is no specific function; scripts are placed in the pages directory.

Which Streamlit function is used to create multi-page applications by including additional Python scripts in a specific directory?

Which Streamlit function is used to create multi-page applications by including additional Python scripts in a specific directory?

Introduction to Building Chatbots

Understanding Transformers

Understanding Large Language Models (LLMs)

Data Collection and Preparation

Optimizing RAG Workflows with LangChain

Prompt Engineering and Retrieval Chains

Chatbot User Interface Development with Streamlit

Chatbot Integration and Evaluation

Capstone Project

Conclusion and Future Developments

Quiz: Streamlit Framework