Generative AI with LangChain and LangGraph

Participants will explore the various components and functionalities of LangChain and LangGraph, including:

• Model Interaction: Learn how LangChain interacts with Cloud and local language model
• Indices, Data Connections, and Retrievals: Understand how data can be transformed, stored, and retrieved through queries within LangChain
• Chains: Gain insights into the fundamental principle of chains in LangChain, which involves linking multiple LLMs and components to provide context-aware responses
• Agents: Discover how agents in LangChain orchestrate complex commands to solve problems and decide the best actions to take in response to specific requests
• Memory Modules: Learn about integrating short-term and long-term memory in language models, enhancing their interaction context
• Prompt Engineering: Acquire skills in designing and executing Prompts to increase the likelihood of receiving well-written and accurate responses from language models
• Application Development: Understand the process of defining, building, customizing, and fine-tuning applications powered by language models within LangChain
• LangGraph Functionalities
  – Explore LangGraph’s ability to create stateful, multi-agent workflows
  – Learn about cycles and branching, persistence, and human-in-the-loop
    workflows
  – Understand how to build complex state machines and manage state 
     across multiple nodes and sessions