Getting Started with Poetry and FastAPI

Poetry Configuration and Installation

Install Poetry

• Use the official installer for Poetry:

  curl -sSL https://install.python-poetry.org | python3 -

• On Windows (using PowerShell):

  (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | py -

Install Project Dependencies

• To install all dependencies for an already created project:

  poetry install --no-root

Create a New Project

• To initialize a new Poetry project:

  poetry init

Manage Dependencies

• To add a new dependency:

  poetry add <package-name>

• To remove a dependency:

  poetry remove <package-name>

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Installing FastAPI and Uvicorn

• Add FastAPI with standard dependencies:

  poetry add "fastapi[standard]"

• Add Uvicorn (ASGI server):

  poetry add uvicorn

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Running Files

Run with Python

• Use poetry run to execute Python scripts:

  poetry run python <relative-path-to-file>

  Example:

  poetry run python 07-llm-and-prompt-engineering/01_gemeni_llm.py

Run with FastAPI/Uvicorn

• Use poetry run with Uvicorn to serve FastAPI applications:

  poetry run uvicorn <relative-path-to-module>:app --reload

  • Replace / with . in the path, and change .py to :app.
  • Example:

    poetry run uvicorn 09-langgraph.websocket-agent.ws_agent_server_gemini:app --reload

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Course Outline

Course Outline: Cloud-Native Applied AI Agentic Developer

1. Python Foundations

   • Overview of Python for AI development
   • Essential libraries and best practices

2. AI Theory & Terminologies

   • Key AI concepts and definitions
   • Understanding machine learning, deep learning, and reinforcement learning
   • Ethics and bias in AI

3. FastAPI

   • Introduction to FastAPI for building APIs
   • Designing, implementing, and testing AI-powered APIs

4. Databases

   • SQL Databases: Basics and advanced queries
   • NoSQL Databases: Understanding document, key-value, and graph databases

5. Third-Party Libraries

   • NumPy: Numerical computing
   • Pandas: Data manipulation and analysis
   • OpenCV: Computer vision basics and applications

6. Model Development Lifecycle

   • Model building and training using Keras
   • Data preprocessing, validation, and evaluation
   • Deploying AI models

7. Large Language Models (LLMs)

   • Overview of LLMs: Gemini, OpenAI, and Allama
   • Selecting the right LLM for applications

8. LLM Framework: LangChain

   • Building applications with LangChain
   • Advanced techniques for chaining and managing LLMs

9. Agentic Framework: LangGraph

   • Overview and integration with LangChain
   • Developing agentic systems for AI workflows

10. Agentic Framework: CrewAI

• Overview and integration with Mulit Ai Agents
• Developing agentic systems for AI workflows

11. Cloud Computing & DevOps

    • Docker: Containerizing AI applications
    • Kubernetes: Orchestrating containers at scale
    • Managing deployments in cloud-native environments

12. Frontend Development with Next.js

    • Designing chatbot UIs
    • Building interactive agent frontends for seamless user experience

This structured course ensures a comprehensive journey from fundamental concepts to advanced cloud-native AI development, emphasizing both backend and frontend technologies.