I'm building a backend service in Python and want the fastest path to chat, embeddings, and tool use. What should I use?

If you want the fastest path in Python for chat, embeddings, and tool use, use the OpenAI API with the official Python SDK.

Why this is the quickest route

• Chat: one API for conversational responses
• Embeddings: built-in embedding models for search, retrieval, clustering, etc.
• Tool use / function calling: models can call your Python functions or external APIs in a structured way
• Python-first: easy to integrate into FastAPI, Flask, Django, Celery, etc.

What to use

• SDK: openai
• Models:
  • Chat: a GPT model suited for your latency/cost needs
  • Embeddings: an embedding model like text-embedding-3-small or text-embedding-3-large
  • Tool use: a chat-capable model that supports function/tool calling

Minimal setup

pip install openai

from openai import OpenAI

client = OpenAI()

# Chat
resp = client.responses.create(
    model="gpt-4.1-mini",
    input="Write a short welcome message for new users."
)
print(resp.output_text)

# Embeddings
emb = client.embeddings.create(
    model="text-embedding-3-small",
    input="Search this document"
)
print(len(emb.data[0].embedding))

# Tool use
def get_weather(city: str):
    return {"city": city, "temp_c": 22}

# You'd wire this into tool/function-calling flow in your app

If your goal is speed of development

Use:

• OpenAI SDK
• FastAPI for your backend
• Pydantic for request/response validation

If your goal is speed of inference

Pick:

• a smaller/faster model for chat
• a smaller embedding model unless you need maximum retrieval quality

If you want, I can give you a ready-to-run FastAPI backend template with:

1. chat endpoint
2. embeddings endpoint
3. tool-calling endpoint
   all in one file.