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GPT-4o-mini

As explained in Hugging Face in Action

gpt-4o-mini is a compact OpenAI multimodal language model designed for capable, cost-efficient generation and chat tasks.

GPT-4o-mini is an OpenAI language model used for chat and more complex tasks than GPT-3.5 models. It runs through OpenAI’s cloud service, requires an API key, and incurs usage-based charges. section 7.2.8 section 7.3.9

from langchain_openai import ChatOpenAI
import os

os.environ["OPENAI_API_KEY"] = "OpenAI_API_Key"
openai_llm = ChatOpenAI(temperature = 0.7,
                        model_name = "gpt-4o-mini")

query_engine = index.as_query_engine(llm = openai_llm)

Create a ChatOpenAI instance with the model name, temperature, and OPENAI_API_KEY, then connect it to a LlamaIndex query engine. section 7.3.9

A typical application workflow:

  • Install the integration package, obtain an OpenAI API key, and store it in OPENAI_API_KEY. section 7.2.8
  • Pass gpt-4o-mini to ChatOpenAI, combine it with a PromptTemplate and output parser, and invoke the resulting chain. section 7.2.8
  • Use it as a LlamaIndex query engine to ask questions about indexed documents or other local data. section 7.3.9

Querying a Pandas DataFrame

A prompt can show the model a sample DataFrame row and require a single Python expression in response. GPT-4o-mini can translate questions such as “Who were born in Down Under?” or “Who were born after 1980?” into Pandas filtering expressions, which the application then extracts and executes. section 12.2.4

How Hugging Face in Action covers it

How deep it goes
Demonstrates package setup, API-key configuration, model parameters, prompt chains, query engines, and generated-code execution.
Examples
Uses Steve Jobs questions, indexed purchase queries, and natural-language filters for birthplace and dates.
What it assumes
Assumes basic Python and framework usage while demonstrating API installation, environment configuration, prompting, and invocation.
What you can do after
Apply it via LangChain or LlamaIndex to build chat chains and question-answering interfaces over indexed or tabular data.
How it connects
Connects OpenAI API usage with LangChain, LlamaIndex, prompt templates, output parsing, and Pandas DataFrames.

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This explainer was generated by Manning's knowledge graph from the text of Hugging Face in Action. The section links point to the parts of the book it draws on.