🧠 OpenAI Responses API Hub

A hands-on, methodical hub for learning and mastering the OpenAI Responses API using clean, reusable patterns, curated model helpers, and step-by-step Jupyter notebooks.

This repo intentionally evolves one notebook at a time. Each notebook builds on a consistent structure and introduces a new capability — from basic chat to streaming, structured outputs, tools, and eventually full RAG pipelines.

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🚀 Repo Purpose

This repo exists to give you (and anyone learning from your work) a repeatable, professional-grade template for:

• Understanding how to use the OpenAI Responses API properly
• Comparing model categories (fast / quality / reasoning / vision)
• Using a shared utilities package across notebooks
• Demonstrating real-world patterns you can use in consulting and training
• Building RAG, tools, and multimodal workflows one small step at a time

Every notebook follows the same setup pattern, so this repo doubles as both:

1. A personal learning platform, and
2. A teaching-ready curriculum you can use for AI training services.

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📁 Project Structure

openai-responses-api-hub/
│
├── notebooks/                # Jupyter notebooks for each lesson/module
│   ├── 01_basic_chatbot.ipynb
│   ├── 02_tools_and_reasoning.ipynb
│   └── (future notebooks follow same format)
│
├── utils/                    # Centralized helpers imported by all notebooks
│   ├── openai_client.py      # Canonical OpenAI client creation
│   ├── models.py             # Curated model catalog + selector
│   └── config.py             # Handles DEFAULT_MODEL via env + fallback
│
├── assets/                   # Images, sample docs, misc resources
├── .env                      # Local secrets (NOT committed)
├── .gitignore
├── LICENSE
├── README.md                 # You are here
└── requirements.txt          # Dependencies

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🔧 Utilities Overview (House Style)

The utils/ folder is the backbone of this repo. All notebooks import from here instead of writing ad-hoc code.

utils/openai_client.py

Centralizes creation of the OpenAI() client.

• Automatically loads .env
• Ensures one consistent client across notebooks
• Encourages best practices for API usage

Imported as:

from utils.openai_client import get_openai_client
client = get_openai_client()

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utils/models.py

Your curated model table + helper functions:

• list_recommended_models() returns a structured catalog (id, label, category, notes)

• choose_default_model(preference) lets you choose:

  • "fast"
  • "quality"
  • "reasoning"
  • "vision"

This avoids hard-coding and teaches students how to think about model selection.

Imported as:

from utils.models import list_recommended_models, choose_default_model, DEFAULT_MODEL

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utils/config.py

Defines how DEFAULT_MODEL is chosen:

• If OPENAI_DEFAULT_MODEL exists in the .env, use it
• Otherwise fallback to choose_default_model("fast")

This gives you predictable behavior across notebooks.

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📚 Notebook Series (Growing Curriculum)

✅ 01 — Basic Chatbot (Completed)

• Environment setup + sanity checks
• Project root path handling
• Curated model table preview
• First Responses API request
• Clean JSON parsing

This notebook establishes the house style for all future notebooks.

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✅ 02 — Web Search & Agentic Reasoning (Completed)

• Uses the hosted web_search tool to find live music events

• Lets the model:

  • gather real-world information,
  • pick a concert near a location, and
  • draft a ready-to-send email invitation

• Prompts the model to separate:

  • a "Reasoning" section (how it searched and chose), and
  • the "Final email"

• Includes an advanced, optional section:

  • custom basic_calculator function tool
  • tool call inspection
  • clean, non-technical summary of what the calculator did

This notebook showcases both built-in tools and the first taste of custom tools, in a way non-technical people can still follow.

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🔭 Roadmap: Next 3 Notebooks

📌 03 — Structured Output & JSON Mode

Goal: move from “nice prose” to machine-usable data.

Planned topics:

• Prompting the model to return strict JSON (e.g., task lists, meeting summaries)
• Validating / parsing the JSON in Python
• Displaying the result in a pandas DataFrame (tables, filters, simple analytics)
• Pattern: unstructured text → structured rows → human + machine readable

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📌 04 — Custom Tools & Multi-step Workflows

Goal: go beyond the simple calculator and build real Python-powered tools.

Planned topics:

• Defining multiple custom tools (e.g., simple datastore reader/writer, formatter)
• Letting the model decide which tool to call and in what order
• Executing tool calls in Python and feeding results back into Responses
• Pattern: model plans → calls tools → uses tool output to refine the answer

This is where the “agent” idea becomes concrete for business workflows.

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📌 05 — File Search + Mini-RAG

Goal: introduce Retrieval-Augmented Generation on a small, controlled dataset.

Planned topics:

• Adding a short PDF / text document to the project
• Using OpenAI’s file_search / vector store tools from the Responses API
• Asking questions and getting answers with citations back to the source
• Pattern: upload docs → index them → Q&A with references

This will directly connect to future real-world RAG demos (insurance, legal, JFK project, etc.).

(Beyond that, additional notebooks like a Model Explorer Dashboard and UI demos can be added later as the repo grows.)

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🛠️ Local Environment Setup

Create a .env file at the repo root:

OPENAI_API_KEY=your-key-here
OPENAI_DEFAULT_MODEL=gpt-4.1-mini  # Optional

Install dependencies:

pip install -r requirements.txt

Run notebooks:

jupyter notebook notebooks/01_basic_chatbot.ipynb
jupyter notebook notebooks/02_tools_and_reasoning.ipynb

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💡 Example Usage (From Utilities)

from utils.openai_client import get_openai_client
from utils.models import choose_default_model

client = get_openai_client()
model = choose_default_model("fast")

response = client.responses.create(
    model=model,
    input="Explain the OpenAI Responses API in 2 sentences."
)
print(response.output_text)

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🧭 Notes on the Curated Model List

The model table in utils/models.py is curated by hand.

• It reflects current best-practice suggestions
• It won’t automatically update when OpenAI adds or deprecates models
• A future “Model Explorer” notebook will show how to keep this list fresh

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🤝 License

MIT License

You are free to use, modify, and teach from this repo.

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🎯 Final Thoughts

The first two notebooks are now complete and validated. Each new notebook should follow the same clean structure, using the helpers in utils/ to keep everything consistent and professional.

From here, the next three notebooks (03–05) will add structured outputs, richer tool workflows, and file search / mini-RAG — the same building blocks you’ll reuse in real client projects.