LangChain Crash Course with Easy & Working Codes | Prompts + Memory + RAG + Agents

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AI Summary

Course Summary: Lank Chain Framework Tutorial

Introduction

• Lank Chain is an open-source framework for building AI applications with large language models (LLMs).
• The course covers four main components of Lank Chain with sample Python code.
• Google Colab is used for running the code examples, but they can also be run in Python.
• An open-source LLM model is used for free testing without an API key.

Part 1: Prompts and Prompt Templates

• Prompts: Text queries passed to LLMs for responses.
• Prompt Templates: Text with dynamic variables for creating prompts.
• Implementation:
  • Install Lank Chain, import necessary modules, and define the LLM.
  • Use the prompt_template class with dynamic variables.
  • Build and invoke the LLM chain to get responses.

Part 2: Memory System

• Purpose: To store chat history for generating relevant responses.
• Implementation:
  • Use the conversation_buffer_memory class to store previous responses.
  • Create a prompt template that includes the memory variable.
  • Define the LLM chain with the memory object and invoke it with new queries.

Part 3: RAG Framework (Retrieval Augmented Generation)

• Purpose: To connect an external data source to an LLM for specific queries.
• Process:
  • Tokenize and embed data, store in a vector database.
  • Retrieve relevant information for input queries.
  • Generate prompts with context information for the LLM.
• Implementation:
  • Install required packages, import modules, and load data (e.g., PDF).
  • Split data into chunks, embed, and store in a database.
  • Define the LLM and create a retrieval chain.
  • Invoke the chain with a query to get responses based on external data.

Part 4: Agents and Tools

• Agents: Perform tasks or actions based on queries or LLM responses.
• Tools: Used by agents to perform tasks (e.g., Google search).
• Implementation:
  • Install Lank Chain and tool packages.
  • Define the LLM and import agent modules.
  • Define and load tools, initialize the agent, and run it with a query.
  • Agents use tool outputs to provide context for LLM responses.

Conclusion

• The course provides a comprehensive introduction to building AI applications with Lank Chain.
• It includes practical examples and code for prompts, memory systems, RAG, and agents with tools.
• Custom tools can be created for specific tasks in Lank Chain.