1. https://github.com/sunnysavita10/Agentic-2.0
  2. https://python.langchain.com/docs/integrations/tools/
  3. https://langchain-ai.github.io/langgraph/concepts/why-langgraph/
  4. https://www.youtube.com/watch?v=qAF1NjEVHhY&t=2s
  5. https://www.youtube.com/watch?v=1bUy-1hGZpI
  6. Concepts covered
    • any model
    • any embedding
    • prompting
    • chaining
    • vector DB
    • RAG
    • Multi Model RAG
    • parser
  7. Agent
    • Use langraph for agentic flow.
    • Agent is an advanced AI assistant that can perform complex tasks by chaining together multiple models and tools.
    • Agent is an autonomous system containing
      • Tools - action
      • LLM
  8. Agentic Workflow (orchestrator creates the workflow)
graph LR A(User Input) --> B(LLM) B --> C(Think) C --> D(Action - Tool) D --> E(Observation - validate) E --> C E --> F(Output)
  1. Agentic AI - orchestration of multiple agents.
  2. Tools using langchain
    1. standard
    2. custom
  3. Langraph is a framework for building agentic flows in AI applications. It works as an orchestrator. Orchestrator is used to create agentic workflow.
    1. Node
    2. Edges
    3. State
    4. Conditional edges

Langraph

  1. graph()
  2. state()
  3. edges - connectivity between the functions.
  4. node - function
  5. invoke
  6. stategraph()
  7. conditional edges.
  8. Supervisor node -
    • It is a special node that can be used to supervise the execution of the graph.
    • It can be used to monitor the execution of the graph and take action if something goes wrong.
    • It can also be used to log the execution of the graph.
graph LR A([start]) --> B(supervisor) B --> C(LLM) B --> E(RAG) C --> D(end) E --> D