LangChain Crash Course, Learn LangChain, its components, and how it can be used with RAG to set up a QA chain for summarizing documents.
Course Description
Welcome to the LangChain course. LangChain is a framework designed to build applications powered by large language models (LLMs). It provides tools and abstractions to make it easier to integrate LLMs into applications, enabling tasks like question answering, text generation, retrieval-augmented generation (RAG), chatbots, and more.
LangChain – Use Cases
Here are some of the use cases of LangChain:
- Question Answering: Build systems that answer questions by retrieving relevant information and generating answers using LLMs.
- Chatbots: Create conversational agents that can maintain context across interactions.
- Retrieval-Augmented Generation (RAG): Combine retrieval of relevant documents with text generation for more accurate and context-aware responses.
- Text Summarization: Generate summaries of long documents or articles.
- Code Generation: Build tools that generate code based on natural language descriptions.
- Personal Assistants: Create virtual assistants that can perform tasks like scheduling, email drafting, or information retrieval.
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
