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

By finding meaningful patterns from the collected data

How does machine learning primarily learn and improve?

How does machine learning primarily learn and improve?

By predicting the purchasing power of consumers

By recommending products based on purchase history

In the retail sector, how is AI used to enhance the customer shopping experience?

In the retail sector, how is AI used to enhance the customer shopping experience?

How is AI transforming health care laboratories?

How is AI transforming health care laboratories?

How is AI used in the manufacturing industry?

How is AI used in the manufacturing industry?

To increase the computational power of large language models 

To enhance the capabilities of large language models by augmenting the database with task-specific information

To reduce the size of the large language models, thus enabling them to run on a CPU

To steer the style of the output or conversation in a certain manner

What is the purpose of retrieval-augmented generation (RAG)?

What is the purpose of retrieval-augmented generation (RAG)?

Test your understanding of artificial intelligence.

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: Introduction to Building Chatbots