building ai summarization feature api

To build an AI summarization feature as an API, think of it as 5 parts:

1. Input

   • Text, document URL, transcript, or uploaded file
   • Optional parameters: summary length, tone, format, language

2. Processing

   • Clean/extract text
   • Chunk long content
   • Send to LLM with a summarization prompt
   • Merge partial summaries if needed

3. API design

   • POST /summarize
   • GET /jobs/{id} if asynchronous
   • POST /feedback for quality tracking

4. Output

   • Short summary
   • Key points
   • Action items
   • Optional bullets / executive summary / TL;DR

5. Ops

   • Rate limits, auth, logging
   • Cost controls
   • Caching
   • Monitoring and evaluation

Example API request

POST /summarize
{
  "text": "Long article text here...",
  "style": "bullet",
  "length": "short",
  "language": "en"
}

Example response

{
  "summary": "This article explains...",
  "key_points": [
    "Point one",
    "Point two",
    "Point three"
  ],
  "model": "gpt-4.1-mini",
  "usage": {
    "input_tokens": 1200,
    "output_tokens": 150
  }
}

Recommended backend flow

• Validate request
• Detect text size
• If short: summarize in one call
• If long: chunk + map-reduce summarization
• Return structured JSON
• Store logs for evaluation

Good summarization prompt

Summarize the following text in 5 bullet points.
Keep it factual, concise, and do not add information not present in the text.
If the text contains action items, list them separately.

Suggested stack

• Backend: FastAPI / Node.js / Express
• LLM: OpenAI API or similar
• Storage: Postgres / Redis for jobs and caching
• Queue: Celery / BullMQ / SQS for long docs

Best practices

• Support async jobs for large files
• Enforce max input length
• Redact sensitive data if needed
• Add human evaluation on sample outputs
• Track latency and token costs

If you want, I can give you:

1. a FastAPI implementation
2. a Node.js/Express implementation
3. a database schema
4. a production-ready architecture diagram