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