LangGraph- Develop LLM powered agents with LangGraph

In this video, we discuss the prerequisites for the course, which focuses on advanced technology and sophisticated GenAI agents using LangGraph. It's crucial to have a strong foundation in certain areas to keep up with the course content, as we won't be covering basic topics.

• Python Proficiency:

  • Familiarity with concepts like:

    • Managing and storing environment variables using a .env file.

    • Package management tools such as Poetry, Pipenv, or Virtualenv.

    • Configuring the IDE with the interpreter.

    • Debugging and running files in the IDE.

    • Object-oriented programming.

    • Git version control.

  • Assumption of knowledge in these areas to maintain focus on LangGraph and advanced topics.

• LangGraph and LangChain:

  • Overview:

    • LangGraph is an extension of the LangChain framework, tailored for building complex agent flows.

    • Use of LangChain is necessary to work with LangGraph.

  • Course Content:

    • Utilizes LangChain objects like prompt templates, chains, and possibly LangChain Expression Language.

    • Familiarity with these topics is beneficial.

    • Recommendation to check out a prior course covering these foundational topics if unfamiliar.

• Ideal Students:

  • Proficiency in Python and LangChain is essential.

  • The course is challenging for those not comfortable with the mentioned topics.

  • Recommendation to reconsider taking the course if lacking proficiency in these areas.

To conclude, this course is designed for those with a solid understanding of Python and LangChain. If you meet these prerequisites, you'll be well-prepared to dive into the advanced concepts of LangGraph and build sophisticated agentic applications.

In this video, we introduce the topic of LangGraph, explaining its purpose and how it differs from LangChain. We highlight the advancements and flexibility of LangChain in building generative applications.

• LangChain Overview:

  • Features:

    • Suitable for building DAG applications and agents.

    • Improved security, flexibility, readability, and usability.

    • Uses LangChain Expression Language for composability and convenient interaction with components.

  • Limitations:

    • Challenges in building complex agentic systems.

    • Autonomous agents have freedom but are not yet production-ready or highly usable.

    • Regular LLM calls are limited in complexity and control.

    • Router chains or agents can decide steps using LLMs but cannot create cycles.

• LangGraph:

  • Introduction:

    • LangGraph addresses limitations by enabling the implementation of cycles in agents.

    • Provides an additional dimension of freedom and complexity.

  • Capabilities:

    • Allows defining flows with nodes and edges, including cycles.

    • Important for building complex agents with more freedom.

    • Integrates with flow engineering to define and control program flows.

    • LLMs can assist in deciding the flow direction (e.g., flow A, flow B, finishing, or restarting).

  • Implementation:

    • Elegant and easy to implement advanced solutions using LangGraph.

    • Entire logic and flow can be expressed as a graph with cycles, enhancing convenience and capability.

Conclusion: We emphasize the convenience and advanced capabilities of LangGraph in developing sophisticated agentic applications. We encourage you to explore the course to see practical implementations of LangGraph.

1. Graphs:

   • Definition:

     • A graph is a mathematical object that represents relationships.

     • Consists of nodes (vertices) and edges that connect the nodes.

   • Applications:

     • Used in various fields, such as social networks, transportation maps, and cloud security.

     • Helps solve real-world problems through algorithms and property extraction.

   • Formal Definition:

     • A graph G is comprised of V (vertices) and E (edges), where an edge is a pair (x, y) belonging to the vertices set.

2. State Machines:

   • Definition:

     • A model of computation consisting of states and transitions between states.

     • Defines different states and rules for transitions to manage complex conditions and sequences in software systems.

   • Graph Representation:

     • State machines can be visualized as graphs, with states as nodes and transitions as edges.

     • This helps in understanding and managing the complexity of state machines.

3. LangGraph:

   • Overview:

     • A powerful library built on top of LangChain.

     • Allows describing flows using nodes and edges.

   • Capabilities:

     • Enables building sophisticated agentic applications.

     • Facilitates writing and running advanced agents in LangGraph.

1. Flow Engineering Overview:

   • Systematic and strategic approach for developing AI-driven software.

   • Manages and optimizes AI systems with LLMs by defining clear flows or sequences of operations.

   • Involves complex decision-making nodes where AI may generate multiple outputs, often refined iteratively.

2. Goals of Flow Engineering:

   • Guides AI through well-defined steps to improve output quality.

   • Incorporates systematic planning and testing phases mimicking human development processes.

   • Enhances reliability and functionality of AI-generated solutions.

3. Challenges with Autonomous Agents:

   • Projects like auto-GPT and baby AGI struggle with long-term planning.

   • AI creating and executing tasks autonomously can lead to problems.

   • Developers need to define tasks and ensure AI stays within the task context.

4. Developer's Role:

   • Developers define the scope and plan for LLMs.

   • LLMs can make decisions about task readiness and subsequent steps within the defined flow.

   • Developers provide a blueprint for LLMs to follow, similar to a state machine where developers define the states and steps.

5. LangGraph and Flow Engineering:

   • LangGraph as an intermediate solution between fully autonomous agents and fully deterministic chains.

   • Allows building complex solutions by defining state machines and incorporating LLMs for specific tasks or decision-making.

6. Graph Components in LangGraph:

   • Nodes and edges, with the ability to include cycles.

   • Advanced logic can be built for complex AI systems.

   • Example: Creating a tweet, refining it iteratively using LLMs until achieving a high-quality post.

7. Future of AI Software Development:

   • Development time distribution:

     • 60% on flow engineering and architecture of state machines.

     • 35% on fine-tuning models for specific tasks.

     • 5% on prompt engineering.

1. LangGraph Core Components:

   • Nodes:

     • Python functions that can contain any code, including LLM calls or agents.

   • Edges:

     • Connect nodes within the graph's execution.

   • Conditional Edges:

     • Help in making dynamic decisions within the graph's execution.

2. Special Nodes:

   • Start Node:

     • Entry point for the graph's execution.

   • End Node:

     • Exit point for the graph's execution.

   • Both nodes act as no-operations (no-op).

3. State or Agent State:

   • A dictionary storing important information for the graph.

   • Can store node execution results, temporary results, or chat history.

   • Available for all nodes within the graph.

   • Can be made persistent for robust and fault-tolerant software.

4. Node Functions:

   • Always receive the current state as input.

   • Return an updated state, ensuring the state evolves over time.

5. Advanced Concepts:

   • Cyclic Graphs:

     • Enable loops within the graph.

   • Human-in-the-Loop:

     • Allows for dynamic decision-making with human feedback.

   • Persistence:

     • Allows storing and retrieving graph states, enhancing robustness and user experience.

Corrective Retrieval-Augmented Generation, or CRAG is a strategy for Retrieval-Augmented Generation (RAG) that incorporates self-reflection and self-grading on retrieved documents. This innovative approach aims to enhance the relevance and accuracy of generated responses.

Flow of CRAG:

1. Retrieve Documents: The process starts by retrieving relevant documents from a dataset.

2. Evaluate Relevance: These documents are then evaluated for their relevance to the user question.

3. Fallback Mechanism: If any documents are found irrelevant, a web search is used as a fallback to find more pertinent information.

4. Dynamic Control Flow: Using LangGraph, we create a dynamic and adaptive workflow where each node in the graph modifies the state, and edges dictate the next steps based on relevance checks.

Inspiration and Refactoring:
This video series is inspired by the LangChain Mistral AI Cookbook Notebook. I took their example and made several refinements and refactoring to make it more suitable for production use. The refactoring focuses on improving readability, maintainability, and adding tests to ensure robust performance.

Reference:

• LangChain Mistral AI Cookbook Notebook

• https://github.com/mistralai/cookbook/blob/main/third_party/langchain/corrective_rag_mistral.ipynb

Adaptive-RAG
Is designed to efficiently manage computational resources while providing accurate and quick answers to user queries. This system dynamically chooses the best method for retrieval-augmented generation based on the complexity of the input question.

How Adaptive-RAG Works

Adaptive-RAG can switch between iterative, single-step, and no-retrieval methods based on how complex the question is. Unlike static methods that don't consider question complexity and may either use too much or too little computational power, Adaptive-RAG uses a smaller LLM classifier to predict the difficulty of the query and select the most efficient retrieval strategy accordingly.

Efficiency and Effectiveness

Adaptive-RAG is highly efficient and effective at balancing complex and simple queries. Its adaptability ensures each query is processed in the most suitable way, saving computational resources and improving user experience. This dynamic strategy selection allows for more accurate and up-to-date responses, which is crucial in a rapidly changing information landscape.

Implementing Parallel Execution

This video explains how to implement parallel execution in LangGraph, a Python library for graph-based workflows. Topics covered:

1. Parallel node fan-out and fan-in

2. Multi-step parallel processes

3. Conditional branching in parallel workflows

4. Stable sorting for consistent parallel execution results

LangGraph Studio: Installation and Usage Guide

Summary

This guide introduces LangGraph Studio (also known as LangGraph IDE), a new beta tool from the LangChain team for debugging and visualizing LangGraph applications. It offers real-time node execution monitoring, state inspection, and supports rapid development iterations.

Key Features

1. Real-time node execution monitoring

2. State inspection before and after node execution

3. Breakpoint setting

4. Live updates reflecting code changes

Prerequisites

• Mac computer with Apple Silicon (currently)

• LangSmith account (free tier available)

• Docker installed and running

Installation Steps

1. Download the DMG file from the provided repository

2. Drag the application to the Applications folder

3. Run the application and log in with LangSmith credentials

Configuration

1. Create a `langraph.json` file with the following structure:

   ```json

   {

     "agent": {

       "path": "/graph/graph.py:app",

       "env": ".env",

       "dependencies": ["."]

     }

   }

   ```

2. Update `pyproject.toml` to include the graph package

Starting the Application

1. Open the project in LangGraph Studio

2. Wait for the Docker containers to load (including LangServe debugger and Postgres)

Interface Overview

- Left side: Visualization of the graph

- Top left: Display name of the graph (e.g., "agent")

- Input box: For entering the initial state (e.g., question)

Running and Debugging

1. Enter a question in the input field (e.g., "What is agent memory?")

2. Submit to run the graph

3. Observe real-time node execution

4. Inspect state at each node

5. Use the "fork" feature to modify execution (e.g., skipping web search)

How It Works

- Uses Docker containers for the debugger, Postgres database, and the LangGraph application

- Persists state after each node execution in the Postgres database

Benefits

- Shortens development lifecycle

- Enables quick iterations in LangGraph agent development

- Provides better visibility into LangGraph logic and application flow

Limitations

- Currently in beta

- Only supports Mac computers with Apple Silicon (as of the recording)

Future Prospects

- Expected support for other operating systems

- Integration with LangGraph Cloud for similar functionality in the cloud

LangGraph Local Setup and Deployment Guide

## Summary

This guide walks through the process of setting up and running a LangGraph application locally using the LangGraph CLI. It covers the necessary steps from configuring the environment to running the application in a Docker container.

## Outline

1. Accessing LangGraph Cloud Console

   - Navigate through LangSmith

   - Click on the rocket icon to access LangGraph cloud console

2. LangGraph JSON file

     - Contains environment variables

     - Specifies graph path

     - Lists dependencies

3. Local Setup Process

   - Install LangGraph CLI

   - Run `landgraph up` command

4. LangGraph CLI Commands

   - `langgraph help`: View available commands

   - `langgraph dockerfile`: Generate Dockerfile for LangGraph API server

   - `langgraph build`: Build Docker image

   - `langgraph up`: Create and run Docker container

5. Dockerfile Generation

   - Base image: Pre-built LangGraph API image

   - Includes necessary environment variables

7. Running the Application

   - Execute `landgraph up`

   - API accessible at localhost:8123

   - Documentation available at localhost:8123/docs

8. API Documentation

    - Automatically generated by LangChain

    - Accessible through provided URL

## Key Points

- LangGraph simplifies the process of setting up and running LLM-powered applications locally

- The LangGraph CLI provides easy-to-use commands for generating Dockerfiles, building images, and running containers

- The local setup includes both an API server and a Postgres database for state management

- Documentation is automatically generated, making it easier to understand and interact with the API

## Next Steps

- Explore the automatically generated API documentation

- Test the locally running LangGraph application

LangGraph Cloud API Video Summary and Outline

Summary

This video discusses the LangGraph Cloud API, created by LangChain to simplify the process of building and deploying LLM-powered applications.
The API provides endpoints for managing assistants, threads, runs, and cron jobs, automatically generated from a compiled graph.

We will explain the key components of the API, demonstrates how to use various endpoints, and highlights the benefits of using this system for developing and deploying AI applications.

Introduction to LangGraph Cloud API

•    Created by LangChain and not open sourced

•    Built with OpenAPI specification

•    Automatically generated from compiled graph

Key Components of the API

•    Assistants

•    Threads

•    Runs

•    Cron jobs

Assistants

• Definition: Abstraction of compiled graph instance

• Creating an assistant

• Required parameters: assistant ID, graph ID

• Optional: configuration, metadata

•   Retrieving assistant information

  
  

Threads

• Definition: Container for accumulated state of multiple invocations

• Sharing threads across assistants

Runs

• Definition: Invocation of a graph with provided input

• Creating a run

• Required parameters: assistant ID, thread ID

• Optional: checkpoint ID, configuration, metadata

• Monitoring run status

Data Storage and Management

• Local storage: PostgreSQL container

• Production environment: LangGraph cloud offering

API Usage Demonstration

• Creating an assistant

• Retrieving assistant information

• Creating a thread

• Creating and monitoring a run

• Retrieving thread information and results

Benefits of LangGraph Cloud API

• Simplifies backend-frontend integration

• Handles user management

• Provides useful endpoints for LLM applications

• Manages data storage and scalability

  
  

Advanced Features

• Persistence and checkpoints

• State management across executions

• Filtering and tagging

Deployment Options

•     Local development setup

•     Cloud deployment (mentioned for next video)