AI Development Tutorial Collection

This repository contains a comprehensive collection of Python-based tutorials for getting started with AI development. While primarily focused on Azure OpenAI, it also includes tutorials for other AI platforms like Ollama.

Each tutorial builds upon the previous ones, demonstrating progressively advanced concepts and techniques.

Prerequisites

1. Python 3: Make sure Python 3 is installed on your system
2. Virtual Environment: Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Dependencies: Install required packages:

   pip install -r requirements.txt

4. Environment Configuration: Copy .env.example to .env and fill in your Azure OpenAI credentials:

   cp .env.example .env

Tutorial Files

#	Tutorial Files	Category	Purpose	Description
1	1_ask_question_get_ans_rest_call.py 1_ask_question_get_ans_rest_call.ipynb	Basic API Interaction	Ask a question and get an answer via REST API	Demonstrates how to interact with Azure OpenAI API using direct REST API calls. Shows the foundational approach to making HTTP requests to Azure OpenAI endpoints.
2	2_ask_question_get_ans_azure_api.py 2_ask_question_get_ans_rest_azure_api.ipynb	Basic API Interaction	Ask a question and get an answer via Azure OpenAI library	Uses the Azure OpenAI Python library instead of REST API to achieve the same functionality with a more convenient and Pythonic approach.
3	3_conversational_chat.py 3_coversational_chat.ipynb	Conversational AI	Conversational Chat with Azure OpenAI	Builds upon single question-answer interactions to create a conversational chatbot. The AI maintains context throughout the conversation.
4	4_few_shot_prompting.py 4_few_shot_prompting.ipynb	Advanced Prompting	Few-Shot Prompting	Demonstrates few-shot prompting technique where you show the model what you want through example conversations rather than just telling it.
5	5_conversational_chat_with_token_limit_handling.py 5_coversational_chat_with_token_limit_handling.ipynb	Conversational AI	Conversational Chat with Token Limit Handling	Addresses the challenge of growing conversation history consuming more tokens. Implements a token limit handling mechanism.
6	6_reproducable_outputs.py 6_reproducable_outputs.ipynb	Advanced Prompting	Reproducible Outputs	Explores how to obtain consistent, reproducible outputs using the seed parameter as a better solution than setting temperature to 0.
7	7_chatbot_for_document.py 7_chatbot_for_document.ipynb	Document-Based AI	Chatbot for a Document	Creates a chatbot that can answer questions based on a specific document rather than relying solely on the model's training data.
8	8_function_calling.py 8_function_calling.ipynb	Function Calling	Function Calling	Introduces function calling to overcome context size limitations. Shows how to define functions that fetch information from external sources.
9	9_function_calling_chain_functions.py 9_function_calling_chain_functions.ipynb	Function Calling	Function calling with function chaining	Extends function calling by demonstrating how to chain multiple function calls together to achieve complex results.
10	10_structured_outputs.py 10_structured_outputs.ipynb	Structured Output	Structured Outputs with JSON Schema	Demonstrates how to force models to generate responses in specific JSON formats using Pydantic schemas, including confidence scoring and validation techniques.
11	11_code_interpreter_openai_reasoning_model.py 11_code_interpreter_openai_reasoning_model.ipynb	Code Interpretation	Code Interpreter	Demonstrates using Azure OpenAI's Code Interpreter feature for mathematical calculations and data analysis using Python code execution.
12	12_responses_api/*	Modern API	Responses API Migration	Demonstrates the required for Responses API migration
13	13_ollama/*	Local AI	Ollama Integration	Examples of using Ollama for running AI models locally
14	14_mcp/*	Model Context Protocol	MCP Server Implementation	Demonstrates how to create MCP servers using FastMCP library

Supporting Files

• requirements.txt: Lists all Python dependencies needed for the tutorials
• .env.example: Template for environment variables configuration
• .gitignore: Prevents sensitive files and local development artifacts from being committed
• test_document.txt: Sample document used in document-based chatbot tutorials
• dummy_build_data.json: Sample data file used in code interpreter examples
• 12_responses_api/: Folder containing tutorials for OpenAI's new Responses API with detailed migration guides
• 13_ollama/: Folder containing tutorials for using Ollama for running AI models locally
• 14_mcp/: Folder containing tutorials for explaining Model Context Protocol (MCP) servers and their implementation

Learning Path

For the best learning experience, follow this structured learning path:

Step	Focus Area	Tutorial Files	Key Concepts	Prerequisites
1	Basic API Interaction	Tutorial 1-2	REST API calls, Azure OpenAI Python library, basic chat completions	Python basics, Azure OpenAI account
2	Single-Turn Conversations	Tutorial 1-2	Request/response patterns, API authentication, basic prompting	Step 1 completed
3	Multi-Turn Conversations	Tutorial 3	Conversation history, context management, chatbot fundamentals	Step 2 completed
4	Advanced Prompting	Tutorial 4, 6	Few-shot prompting, reproducible outputs, seed parameter	Step 3 completed
5	Conversation Management	Tutorial 5	Token limits, conversation pruning, memory management	Step 3-4 completed
6	Document-Based AI	Tutorial 7	Context injection, document-aware responses, domain-specific knowledge	Step 4 completed
7	Function Calling Basics	Tutorial 8	External data access, function definitions, dynamic information retrieval	Step 6 completed
8	Advanced Function Calling	Tutorial 9	Function chaining, multi-step reasoning, orchestrated API calls	Step 7 completed
9	Structured Outputs	Tutorial 10	JSON schema response format, Pydantic models, confidence scoring	Step 8 completed
10	Code Interpretation	Tutorial 11	Mathematical reasoning, data analysis, Python code execution	Step 9 completed
11	Responses API Migration	Tutorial 12	Changes required for Responses API migration	Step 2 completed
12	Local AI with Ollama	Tutorial 13	Running AI models locally, Ollama setup and usage	Step 2 completed
13	Model Context Protocol	Tutorial 14	Creating MCP servers, exposing functions to AI systems, FastMCP usage	Step 2 and 7 and 8 completed

Quick Start Path

If you're already familiar with APIs and want to focus on Azure OpenAI specifics:

• Express Path: Tutorials 2 → 3 → 8 → 9 → 10 → 11 (Core concepts only)
• Full Path: Follow steps 1-11 for comprehensive understanding

Migration Path

• Tutorial 12 provides direct comparison and migration guide from Chat Completions to Responses API

MCP Development Path

• Tutorial 14 demonstrates how to create MCP servers that expose your functions to AI systems

Getting Help

Each tutorial file contains detailed comments explaining the concepts and implementation. The Jupyter notebook versions provide an interactive learning experience with explanations and outputs.

For Azure OpenAI documentation, visit: Microsoft Azure OpenAI Documentation