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    Building RAG

    Unit 1

    Langraph Introduction

    Overview of Langraph and Retrieval-Augmented Generation
    Key Concepts and Architecture of Langgraph
    Installing and Configuring Langgraph Environment
    Basic Operations and First Example in Langgraph

    Unit 2

    Example Building Tutorial

    Designing a Complete RAG Pipeline with Langgraph
    Data Ingestion, Chunking and Vector Store Integration
    Building Retrieval and Generation Nodes in Langgraph
    Testing, Debugging and Evaluating the RAG System
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    Unit 1 • Chapter 3

    Installing and Configuring Langgraph Environment

    Summary

    To set up Langgraph, install Python 3.10+ and create a virtual environment with `python -m venv .venv`, then activate it. Upgrade pip and install Langgraph via `pip install langgraph`. For full functionality, add extras like `langchain`, `openai`, and `chromadb` using `pip install "langgraph[all]"`. Create a project folder and place a `.env` file containing `OPENAI_API_KEY` and any other service credentials. Configure the graph by defining nodes and edges in a Python script or `graph.yaml`, accessing variables with `os.getenv`. Verify the setup by running `langgraph run` or executing the script; ensure no import or key errors. For persistence, configure a vector store (e.g., Chroma) and point the graph to its URI. Launch a local API with `langgraph serve`, optionally adjusting CORS settings. Debug typical issues such as incorrect Python version, missing API keys, or outdated dependencies. Finally, test the deployment with a sample query to confirm the graph processes inputs and returns expected outputs.

    Concept Check

    Which command creates a virtual environment for Langgraph?

    How do you install Langgraph with all optional dependencies?

    Where should the OpenAI API key be stored for Langgraph configuration?

    Which command starts the local API server for a Langgraph project?

    What is a common cause of Langgraph startup failures?

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