【日本語字幕版】Mastering OpenAI APIs: Unleash the Power of GPT4

Learn to make your first completion API request using the OpenAI Python client in a Jupyter notebook, set and manage API keys, and test simple prompts.

Learn to hide API keys by moving them to a dot env file and loading them with python dot env, avoiding hardcoding and safely sharing code.

Explore how tokens power text-based OpenAI models, covering tokenization, input and output token counts, price differences by model, and using a tokenizer tool to predict response length.

Explore how input and output tokens affect pricing and learn to control the maximum generated tokens with practical examples from the OpenAI API.

Explore the stop sequence parameter in OpenAI completions, learn how max tokens and stop strings shape output, and apply it to chat bot prompts.

Explore the n and echo parameters to generate multiple completions and optionally echo the prompt, with per-completion max tokens and handling multi-part outputs.

Explore OpenAI model families for text completion, including GPT-3, 3.5, and 4, with DaVinci 003 as the most capable option. Compare capabilities and pricing across Curie, Babbage, and Ada.

Compare OpenAI models from Ada to DaVinci 003 on output quality and price, showing when cheaper models suffice for simple tasks and when more capable models excel.

Design prompts by listing clear instructions, input data, and desired output format for AI APIs. Emphasize precision, and use separators or explicit formatting (JSON, CSV, or text).

Learn how to design prompts with explicit output instructions, choosing formats like sql, json, comma-delimited lists, or json objects, to easily parse results into databases or code.

Explore practical uses of the completion model for sentiment analysis and summarization, with hands-on demonstrations of summarizing Wikipedia archaeology text and adapting output formats for large datasets.

Extract food items from restaurant reviews using the completion API and output them as a JSON array for database storage, scalable to thousands of reviews.

Practice sentiment analysis with the completion API to classify text as positive, neutral, or negative, using GPT for scalable batch processing and numeric formats.

Explore zero-shot prompting vs one-shot and few-shot prompting, and learn how adding examples to prompts can guide keyword extraction and sentiment classification without retraining the model.

Learn chain-of-thought prompting and zero-shot reasoning by using 'let's think step by step' prompts to guide GPT-4 in solving word problems more accurately.

Explore text transformation with the completion API, including translating text to multiple languages, changing perspective and tense, and outputting structured formats like JSON or emojis for movie titles.

Launch the project using the completion API to generate color palettes from prompts. Build a Flask server with a front end and JavaScript to render swatches and copy hex codes.

Develop a color palette generator in a notebook by crafting prompts that return a json array of hex colors, constrain counts to two to eight, and use few-shot examples.

Demonstrates building a notebook color palette renderer with IPython display, markdown, and HTML/CSS, rendering color blocks from a JSON palette using a get_and_render_colors function.

Build a Flask-based web app that turns a color palette generator into a live front-end with JavaScript, HTML, and CSS, including hex copying.

Build a Flask single-page app with a template and Ajax, posting to /palette to fetch colors via OpenAI's completion API, using python-dotenv for the API key.

Set up a Flask post route at /palette to extract form data, call a get_colors function that uses the GPT completion API, and return a list of colors as JSON.

Create a front end form that submits a query to /palette via fetch, preventing page refresh, using form-encoded data, and parse the JSON colors for rendering.

Extract colors from data.colors, create a div for each color, and render them inside a full-width black container using calc to evenly divide widths.

Implement a copy-paste color tool by clearing results on new searches, using flexbox to lay out color boxes, and showing hex codes that copy to the clipboard on click.

Explore practical color block styling with flexbox, center-aligned forms, full-width layouts, and interactive hex code copy with subtle active states and transitions.

Master form styling by using absolute positioning to center a floating form with top 50% left 50%, then style inputs and a green button with padding, borders, hover effects.

Refactor the frontend by extracting css and js into a static directory, linking app.css and app.js, and modularizing color palette rendering with get colors and create color blocks.

Understand how temperature, a 0–2 parameter with default 1, sets output randomness by shaping logits and the softmax distribution; learn examples and tips for deterministic results at lower values.

Explore how top p controls nucleus sampling to shape creativity by shrinking or expanding the window of candidate tokens, without changing probabilities, and compare it to temperature for OpenAI APIs.

Explore frequency penalty, a -2 to 2 parameter that reduces token repetition by penalizing previously generated tokens; contrast with presence penalty and note practical ranges and formatting effects.

Explore presence penalty, a -2 to 2 one-off token penalty that promotes new topics, and contrast it with frequency penalty that reduces repetition by usage; tune to 0.1 to 1.

Learn how the stream parameter in OpenAI's completion API enables streaming output by sending partial progress tokens as data. See how a Python generator handles streamed data.

This lecture introduces the chatgpt api, a chat-based interface to gpt models for translation, text generation, jokes, color palettes, and sentiment analysis, with gpt-3.5 turbo and gpt-4 via chat endpoints.

Learn to use the chat completion API with the Python client, sending a history of messages to access GPT-4 or GPT-3.5 Turbo, and generate responses such as trivia questions.

Compare the chat and completion endpoints to cut costs; GPT 3.5 turbo is ten times cheaper than text DaVinci 003 and works on chat endpoint, while GPT four remains expensive.

Explore the chat API message format for prompts, learn to structure with system, user, and assistant roles, and compare chat completion to text completion for translation and sentiment tasks.

Compare GPT-3.5 Turbo models and avoid outdated 0301 snapshots; use the updated 3.5 Turbo for chat API, due to lower cost than text DaVinci 003.

Rewrite a complex prompt as a chat API conversation with a system message to generate color palettes. Compare chat completion to the completion endpoint and note cost savings.

The chat API mirrors completion API parameters, uses messages instead of prompts, and supports max tokens, temperature, top_p, stream, and penalties; Echo is not available, as demonstrated with GPT-3.5 Turbo.

Build a simple chat bot using GPT-4 and the chat API, with a customizable personality, to practice creating a customer service bot and a command-line GPT client.

Build a basic chat bot in a command-line utility using openai chat completion endpoints, loading the api key from a .env file, and experimenting with gpt four or 3.5 turbo.

Persist chat history by maintaining a messages list, appending user and assistant messages with proper roles, and passing the full conversation to the API for a coherent chat experience.

Learn to add a personality to a chat bot via a Python argparse argument, with a default and system message, including options like customer service, essay writing, or travel agent.

Colorize chatbot output to distinguish user input from the assistant by applying bold and color (blue for you, red for the assistant) using simple wrap functions.

Explore how GPT-3.5 Turbo and GPT-4 handle code related tasks—explain, translate, complete, fix, debug, and write code—using practical chat messaging setups; codex is deprecated.

Explore calculating time complexity with GPT-4 using real code examples for bubble sort and quicksort, including o(n^2) and o(n log n) analyses, with notes on accuracy.

See how GPT-4 translates JavaScript to Python and test translated functions in a notebook, including a min finder, an email validator, and a title-capitalization utility; learn limitations and testing tips.

See how GPT-4 helps find, fix, and explain bugs in Python code, with examples showing underscore insertion, identifying syntax and logic errors, and improving a quiz generator.

GPT-4 writes functions from scratch, like a Python RGB to HSL converter with CSS-friendly output. Rely on few-shot prompting to guide outputs and shape formatting, noting limits for complex code.

Explore how Tik Token counts tokens across encodings for GPT-3.5 Turbo and GPT-4 to estimate pricing and manage max token limits for long texts.

Learn how to count chat API tokens for GPT-4 and 3.5 turbo by calculating per-message tokens, accounting for roles and content, and using OpenAI's token-counting function.

Explore a simple command-line code reviewer using GPT-4 to critique Python code, including a binary search tree and color gradient functions, with docstrings and snake_case naming improvements.

Build a python code reviewer CLI that uses an env file for the OpenAI API key and GPT-4 to generate code review with system prompt, code contents, and line-numbered suggestions.

Wrap the code review flow in make code review request function, read file content, and pass a model. Use argparse to supply file and model inputs via the command line.

Present an interactive GPT-4 code review that generates diffs, lets you accept or reject changes, auto updates files, and demonstrates type annotations and f-strings in a tic tac toe example.

Learn how to build an automated code reviewer with GPT-4 that uses a structured find-and-replace diff format, system prompts, and accept/ignore lists to propose precise code edits and user-facing messages.

Explore the interactive code review workflow that loads a file, prompts GPT-4 for changes, shows a color diff, and uses accept or ignore lists to apply edits in memory.