LangChain Crash Course

Welcome to the LangChain course. LangChain is a framework that is used to develop applications powered by large language models (LLMs). LangChain offers tools and abstractions to simplify integrating LLMs into applications, allowing tasks such as question answering, text generation, retrieval-augmented generation (RAG), chatbots, and many more.

LangChain – Use Cases
The following are some of the use cases of LangChain:
Instruction:
Question Answering: Develop systems that respond to questions by looking up relevant information and producing responses through LLMs.
Chatbots: Design conversational assistants that are capable of holding context across conversations.
Retrieval-Augmented Generation (RAG): Integrate document retrieval and text generation with better accuracy and contextual awareness to produce responses.
Text Summarization: Provide summaries of long texts or news articles.
Code Generation: Design systems that automatically generate code according to natural language descriptions.
Personal Assistants: Develop virtual personal assistants that are capable of undertaking activities such as scheduling, writing emails, or fetching information.

Course Lessons
✔️LangChain – Introduction
1. LangChain - Introduction, Features, and Use Cases
2. What is Chaining in LangChain
✔️LangChain – Components
3.Components/ Modules of LangChain
4. Preprocessing Component of LangChain
5. Models Component of LangChain
6. Prompts Component of LangChain
7. Memory Component of LangChain
8. Chains Component of LangChain
9. Indexes Component of LangChain
10. Agents Component of LangChain
✔️LangChain with RAG
11. LangChain with RAG - Workflow
12. LangChain with RAG - Process
13. LangChain with RAG – Final Coding Example