https://github.com/dooml/langchain-langgraph-tutorial

Comprehensive tutorials for LangChain, LangGraph, and LangSmith using Groq LLM. Learn to build advanced AI systems, from basics to production-ready applications. Covers key concepts, real-world examples, and best practices. Ideal for beginners and experts alike. Elevate your AI development skills!
https://github.com/dooml/langchain-langgraph-tutorial

groq langchain langchain-python langgraph langgraph-python ollama rag

Last synced: about 1 month ago
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Comprehensive tutorials for LangChain, LangGraph, and LangSmith using Groq LLM. Learn to build advanced AI systems, from basics to production-ready applications. Covers key concepts, real-world examples, and best practices. Ideal for beginners and experts alike. Elevate your AI development skills!

• Host: GitHub
• URL: https://github.com/dooml/langchain-langgraph-tutorial
• Owner: doomL
• Created: 2024-10-17T16:33:41.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-15T17:23:40.000Z (6 months ago)
• Last Synced: 2025-03-26T13:11:26.414Z (about 2 months ago)
• Topics: groq, langchain, langchain-python, langgraph, langgraph-python, ollama, rag
• Language: Jupyter Notebook
• Homepage:
• Size: 1.55 MB
• Stars: 14
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# LangChain, LangGraph, and LangSmith Tutorials with Groq

## What's Inside

- In-depth tutorials covering fundamental to advanced concepts

- Practical examples demonstrating real-world applications

- Integration of LangChain, LangGraph, and LangSmith for building sophisticated AI systems

- Leveraging Groq's high-performance LLM for fast and efficient language processing

## Key Topics

- LangChain basics and advanced features

- Building complex workflows with LangGraph

- Optimizing and monitoring your LLMs with LangSmith

- Best practices for prompt engineering and chain development

- Integrating external tools and APIs

- Deploying production-ready AI applications

Whether you're new to these technologies or looking to deepen your expertise, these tutorials offer valuable insights into building state-of-the-art language AI systems using the latest tools and techniques.

## Tutorial 1: Introduction to LangChain

- What is LangChain?

- Installation and setup

- Basic concepts: Chains, Agents, and Memory

- Your first LangChain application

## Tutorial 2: Working with Language Models in LangChain

- Connecting to different language models

- Creating a simple prompt chain

- Handling model responses

- Best practices for prompt engineering

## Tutorial 3: Document Processing with LangChain

- Loading and parsing different document types

- Text splitting and chunking

- Building a simple question-answering system

- Implementing semantic search

## Tutorial 4: Agents in LangChain

- Understanding the agent architecture

- Types of agents:

  - Zero-shot React Agent

  - Conversational Agent

  - Self-ask Agent

  - Plan-and-Execute Agent

  - ReAct Agent

- Creating custom tools for agents

- Implementing a multi-tool agent

## Tutorial 5: Advanced Agent Techniques

- Debugging and optimizing agent performance

- Using the JSON Toolkit with agents

- Integrating Pydantic for structured inputs and outputs

- Building complex workflows with agents

## Tutorial 6: Memory Systems in LangChain

- Types of memory in LangChain

- Implementing conversation memory

- Creating a chatbot with long-term memory

- Advanced memory techniques

## Tutorial 7: Introduction to LangGraph

- What is LangGraph and how does it differ from LangChain?

- Basic concepts: Nodes, Edges, and Graphs

- Setting up LangGraph

- Creating your first LangGraph flow

## Tutorial 8: Building Complex Flows with LangGraph

- Designing multi-step workflows

- Handling state and transitions

- Implementing conditional logic in flows

- Error handling and fallback strategies

## Tutorial 9: Combining LangChain and LangGraph

- Integrating LangChain components into LangGraph flows

- Building a conversational AI system with both libraries

- Optimizing performance in complex applications

- Case study: A task planning and execution system

## Tutorial 10: Real-world Applications

- Building a content moderation system

- Implementing a language translation service

- Creating an automated customer support chatbot

- Developing a text-based game with AI-driven narrative

## Tutorial 11: Working with Structured Data

- Introduction to Pydantic for data modeling

- Creating structured inputs and outputs with Pydantic

- Using the JSON Toolkit for complex data manipulation

- Integrating structured data with LangChain and LangGraph

## Tutorial 12: Advanced LangChain Techniques

- Custom chain development

- Prompt templating and management

- Implementing retrieval-augmented generation (RAG)

- Fine-tuning language models for specific tasks

## Tutorial 13: Best Practices and Advanced Topics

- Performance optimization techniques

- Handling rate limits and API costs

- Security considerations

- Deploying LangChain and LangGraph applications

- Monitoring and logging in production

### Useful Repositories

- #### LangChain

  - https://github.com/langchain-ai/langchain

  - https://github.com/kyrolabs/awesome-langchain

- #### LangGraph 

  - https://github.com/NirDiamant/GenAI_Agents

  - https://github.com/langchain-ai/langgraph

  - https://github.com/langchain-ai/langgraph-example/tree/main