# 🔹 1. How the models differ

OpenAI offers multiple models because each has different **strengths, costs, and speed**.

### ⚡ GPT-5 Family

* **GPT-5 (full)** – Most capable for general reasoning, coding, long documents, customer support. Balanced between cost and performance.
* **GPT-5 mini** – Cheaper, faster. Great for chatbots, FAQs, simple Q&A, real-time apps.
* **GPT-5 nano** – Ultra cheap, lightweight. Ideal for summarization, autocomplete, keyword extraction, or high-volume requests.

### 🧠 Reasoning Models (o-series)

* **o3, o3-mini, o1-pro** – Specially tuned for **complex reasoning, math, multi-step logic, strategy**.

* Example: advanced engineering questions, legal reasoning, debugging.
* Downside: much more expensive.

### 📚 GPT-4.1 Series

* **Full, mini, nano** versions – older than GPT-5, still very capable.
* Better for stable, lower-cost fine-tuning and enterprise use.
* Being gradually replaced by GPT-5.

👉 Think of it like cars:

* **GPT-5** = latest reliable SUV (good balance of everything).
* **GPT-5 mini/nano** = smaller cars (cheap and efficient).
* **o-series** = Formula 1 cars (super powerful but fuel-hungry).

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# 🔹 2. Can you use them for a custom service (like teaching product specs from PDF)?

✅ Yes! This is a **classic use case**: building a **Retrieval-Augmented Generation (RAG)** system.

Here’s how it works:

1. **Ingest the PDF**

* Use a library like PyPDF2, pdfplumber, or langchain to extract text from your product spec PDFs.

2. **Split into chunks**

* Long PDFs exceed token limits, so break into small sections (say 500–1000 characters each).

3. **Create embeddings**

* Use OpenAI’s **embedding model** (text-embedding-3-small or text-embedding-3-large) to convert each chunk into a vector (a mathematical representation of meaning).

4. **Store in a vector database**

* Examples: Pinecone, Weaviate, FAISS, ChromaDB.
* This lets you **search semantically** (find the right chunk when a user asks a question).

5. **User asks a question**

* Search the vector database → retrieve the most relevant PDF sections.

6. **Send to GPT-5 (or mini)**

* Pass both the **user’s question** + **retrieved PDF text** into the model.
* The model answers based only on that context.

## 🔹 Example (Python, simplified)
`python
from openai import OpenAI
import PyPDF2

client = OpenAI(api_key="your_api_key")

# 1. Extract text from PDF
def extract_text(pdf_file):
text = ""
reader = PyPDF2.PdfReader(pdf_file)
for page in reader.pages:
text += page.extract_text() + "n"
return text

pdf_text = extract_text("product_specs.pdf")

# (In production: split text, make embeddings, store in vector DB)

# 2. Ask a question with context
user_question = "What is the maximum operating temperature?"

response = client.chat.completions.create(
model="gpt-5-mini", # cheaper + fast for Q&A
messages=[
{"role": "system", "content": "You are a helpful assistant that answers using product specs."},
{"role": "user", "content": f"PDF Content:n{pdf_text}nnQuestion: {user_question}"}
],
max_tokens=300
)

print(response.choices[0].message.content)

# 🔹 Which model to choose?

* **GPT-5 mini** → Best balance for your PDF Q&A bot.
* **GPT-5 nano** → If you need very low cost (fast FAQ, summaries).
* **Full GPT-5** → If your documents are very technical and require precise reasoning.
* **o3 / o1-pro** → Only if you need **deep reasoning / multi-step logic** (overkill for product specs).