Course (Part 718)

Course: Mastering OpenAI Python APIs: Unleash ChatGPT

In this introductory chapter, we explore the foundational concepts and practical applications of the OpenAI Python APIs, with particular emphasis on harnessing ChatGPT. We begin by outlining the required environment: installing the OpenAI client via pip and securely configuring the API key as an environment variable. Simple examples demonstrate how to call the ChatCompletion endpoint to send prompts and receive natural language responses, and we show how to wrap such calls in reusable functions. The chapter highlights how ChatGPT can augment data processing tasks, for example by summarizing DataFrame samples or generating sentiment summaries for customer reviews. Practical code patterns are provided for building chatbots with context management, for retrying on rate limits, and for logging interactions into SQLite. We explain core API parameters—model, temperature, max_tokens, top_p, and frequency_penalty—and show how tuning them affects output. The material also covers embeddings for semantic search and retrieval, and demonstrates how to compute cosine similarity to find the most relevant documents. Advanced project examples, such as a color-palette generator, a Spotify playlist creator, and a GPT-4 automatic code reviewer, illustrate multi-API integrations. We include guidance on using DALL·E and Whisper for image generation and speech-to-text transcription, respectively. Cost-awareness is stressed: using token-estimation tools and batching to manage expenses. Throughout, emphasis is placed on prompt engineering, iterative refinement, error handling, and logging as best practices. By the end of the chapter, readers will have a practical toolkit of functions, patterns, and projects to begin integrating OpenAI’s Python APIs into real-world workflows.

Lesson

Topic: Mastering OpenAI Python APIs — Getting Started with ChatGPT

Learning Outcomes

• Install and configure the OpenAI Python client and API key.
• Make basic ChatCompletion requests and wrap calls in functions.
• Tune core API parameters to control behavior and output.
• Apply ChatGPT to data processing tasks (summaries, sentiment, SQL assistance).
• Understand embeddings for semantic search and context-aware responses.
• Integrate OpenAI services with other APIs (Spotify, DALL·E, Whisper).
• Implement error handling, retry strategies, and cost estimation practices.

Conceptual Explanation

OpenAI provides Python bindings for interacting with language, image, and audio models. The typical flow is:

1. Install the client: pip install openai.
2. Set OPENAI_API_KEY in your environment and access it in code via os.getenv.
3. Call endpoints such as openai.ChatCompletion.create, openai.Embedding.create, or openai.Image.create.

Core parameters:

• model — choose capacity (e.g., gpt-3.5-turbo, gpt-4).
• temperature — controls randomness (0.0 deterministic → 1.0 creative).
• max_tokens — caps generated token length.
• top_p, frequency_penalty, and presence_penalty — further refine sampling.

Common patterns: wrap API calls in helper functions, add retry/backoff for rate limits, log prompts/responses for audit and prompt engineering, and use embeddings + cosine similarity for semantic retrieval.

Example helper (conceptual):

def get_chatgpt_response(prompt):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[

user","content":prompt}],
        temperature=0.5,
        max_tokens=200
    )
    return response['choices'][0]['message']['content']

Bilingual (EN → বাংলা)

Key Terms and Translations

English	বাংলা
API Key	এপিআই কী
ChatCompletion	চ্যাট রেসপন্স (চ্যাট-কমপ্লিশন)
Embedding	এম্বেডিং / ভেক্টর উপস্থাপনা
Temperature	র্যান্ডমনেস নিয়ামক (টেম্পারেচার)
Rate Limit	রেট সীমা
Prompt Engineering	প্রম্পট ডিজাইন

Important Notes

• Never hard-code API keys in source code; use environment variables or secret managers.
• Monitor token usage and cost — batch requests and use concise prompts where possible.
• Handle errors gracefully: implement retries with exponential backoff for rate limits.
• When sending data (e.g., DataFrame snippets), sanitize or redact PII before transmission.
• Test prompts iteratively and store good examples for few-shot templates.

Exercises → True / False / Fill in the Blanks / MCQ / Matching / Descriptive / Creative

True / False

#	Statement	Answer
1	Setting openai.api_key in code is always safer than using environment variables.	Answer False — environment variables or secret stores are safer than embedding keys in code.
2	Lower temperature values produce more deterministic responses.	Answer True — lower temperature reduces randomness and increases determinism.

Fill in the Blanks

#	Prompt	Answer
1	To generate semantic vectors with OpenAI, use the ______ endpoint.	Answer Embedding: use openai.Embedding.create.
2	To retry after a rate limit error implement ______ backoff.	Answer Exponential: implement exponential backoff (e.g., time.sleep(2**attempt)).

MCQ (Multiple Choice)

1. Which parameter controls the randomness of model outputs?
   • A. max_tokens
   • B. temperature
   • C. model
   • D. top_p
   Answer

   B — temperature controls randomness (though top_p also affects sampling).
2. Which method is typically used to store conversation context for Chat API calls?
   • A. environment variable
   • B. messages array
   • C. embeddings only
   • D. file upload
   Answer

   B — use the messages array to pass conversation history (roles: system/user/assistant).

Matching

Match the left column (concept) with the right column (purpose).

Concept	Purpose
A. Embeddings	1. Generate images from text
B. DALL·E	2. Semantic search & similarity
C. Whisper	3. Speech-to-text transcription

Answer

A → 2 (Embeddings for semantic search), B → 1 (DALL·E for images), C → 3 (Whisper for transcription).

Descriptive

1. Explain why prompt engineering is critical when using ChatGPT.
   Answer

   Prompt engineering guides the model to produce focused, relevant, and predictable outputs. Precise instructions, contextual examples, and explicit format requests reduce ambiguity and token waste. Iterative refinement improves accuracy and cost-effectiveness.
2. Describe a safe pattern for logging prompts and responses.
   Answer

   Log metadata (timestamps, token counts, non-sensitive prompt IDs) and store full prompts/responses only after redacting PII. Use encryption for logs and limit access via role-based controls.

Creative

Design a single prompt (max 2 sentences) that instructs ChatGPT to produce a 5-item checklist for preparing a dataset for NLP modeling.

Answer

Prompt example: "You are a data engineer. Provide a concise 5-item checklist (each item one sentence) for preparing a textual dataset for NLP modeling, including data cleaning, labeling, and tokenization considerations."

Activities, Vocabulary, Homework, Practice Task

Activities

1. Install openai, set your API key in an environment variable, and run a basic ChatCompletion example.
2. Build a small function that summarizes a DataFrame head using ChatGPT and logs the result to SQLite.
3. Create an embeddings-based retrieval script: store document embeddings and query with cosine similarity.

Answer

Follow the examples in the lesson: use pip install openai pandas, set OPENAI_API_KEY, and implement the helper functions with error handling and logging.

Vocabulary

• API Key
• ChatCompletion
• Embedding
• Temperature
• Rate Limit

Answer

Review the bilingual table above for translations and short definitions: e.g., Embedding = numeric vector that represents semantics of text.

Homework

1. Implement a get_response_with_retry function that handles rate limits with exponential backoff.
2. Build a small notebook that computes embeddings for five short documents and ranks them by similarity to a sample query.

Answer

For (1) catch openai.error.RateLimitError, sleep with time.sleep(2**attempt), and reattempt up to N times. For (2) use openai.Embedding.create, convert to numpy arrays, and compute cosine similarity.

Practice Task

Create a minimal chatbot loop that stores the last 6 messages, sends them as the messages payload, and persists the conversation to SQLite.

Answer

Implement a loop that appends dictionaries like user","content":...} and assistant","content":...} to a list; slice the last 6 entries for each API call. Use sqlite3 to insert rows of user_input and bot_response into a table.

End of lecture sheet — use the details blocks for quick answers and the exercises for hands-on practice.