Text summarization remains one of the most practical entry points for integrating large language models into production pipelines. Whether you are condensing support tickets, research papers, or meeting transcripts, the OpenAI SDK provides a standardized interface that keeps your code portable across providers. This article walks through a complete summarization implementation, then shows how to route the same workload to Oxlo.ai without changing your application logic.
Basic Summarization with the OpenAI SDK
The standard pattern for summarization uses the chat.completions endpoint with a low temperature and a concise system prompt. The SDK handles retries, streaming, and response parsing uniformly regardless of which provider hosts the model.
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
def summarize(text, model="gpt-4o"):
response = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": "You are a precise summarization engine. Extract key points and reduce the text to one concise paragraph."
},
{
"role": "user",
"content": f"Summarize the following text:\n\n{text}"
}
],
temperature=0.3,
max_tokens=256
)
return response.choices[0].message.contentLowering the temperature keeps the output factual and avoids halluc
