> ## Documentation Index
> Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Text Summarization API

> Summarize articles, meeting notes, and research papers using DeepSeek V3 and Gemini Flash via a single API.

export const RuncrateStyles = () => {
  if (typeof document !== 'undefined' && !document.getElementById('runcrate-overrides')) {
    const s = document.createElement('style');
    s.id = 'runcrate-overrides';
    s.textContent = `
      /* Match Runcrate's rounding scale (--radius: 0.75rem) */
      .rounded-sm { border-radius: 0.5rem !important; }   /* 8px */
      .rounded-md { border-radius: 0.625rem !important; } /* 10px */
      .rounded-lg { border-radius: 0.75rem !important; }  /* 12px */
      .rounded-l-sm { border-top-left-radius: 0.5rem !important; border-bottom-left-radius: 0.5rem !important; }
      .rounded-r-sm { border-top-right-radius: 0.5rem !important; border-bottom-right-radius: 0.5rem !important; }
      .rounded-l-md { border-top-left-radius: 0.625rem !important; border-bottom-left-radius: 0.625rem !important; }
      .rounded-r-md { border-top-right-radius: 0.625rem !important; border-bottom-right-radius: 0.625rem !important; }
      .rounded-l-lg { border-top-left-radius: 0.75rem !important; border-bottom-left-radius: 0.75rem !important; }
      .rounded-r-lg { border-top-right-radius: 0.75rem !important; border-bottom-right-radius: 0.75rem !important; }

      /* Cards: never pure white in light mode */
      .card { background-color: #fcfcfc !important; border-radius: 0.75rem !important; }
      html.dark .card { background-color: #141414 !important; }

      /* Docs hero box */
      .rc-hero { background-color: #fcfcfc; border: 1px solid #e0e0e0; }
      html.dark .rc-hero { background-color: #141414; border-color: #242424; }
      html.dark .rc-hero h1 { color: #f5f5f5; }

      /* Runcrate scrollbar — thin, transparent track, hide-until-hover thumb */
      ::-webkit-scrollbar { width: 6px; height: 6px; background-color: transparent; }
      ::-webkit-scrollbar-track { background-color: transparent; }
      ::-webkit-scrollbar-thumb { background-color: rgba(155, 155, 155, 0.5); border-radius: 10px; transition: opacity 0.3s ease; opacity: 0; }
      ::-webkit-scrollbar-thumb:hover { background-color: rgba(155, 155, 155, 0.7); }
      *:hover::-webkit-scrollbar-thumb,
      *:focus::-webkit-scrollbar-thumb,
      *:active::-webkit-scrollbar-thumb { opacity: 1; }
      * { scrollbar-width: thin; scrollbar-color: rgba(155, 155, 155, 0.5) transparent; }
    `;
    document.head.appendChild(s);
  }
  return null;
};

<RuncrateStyles />

Summarize long-form content — articles, meeting transcripts, research papers, support tickets — using open-source and frontier models through a single API endpoint.

## Recommended models

| Model                | Context     | Best for                                  |
| -------------------- | ----------- | ----------------------------------------- |
| **DeepSeek-V3**      | 128K tokens | General-purpose summaries, cost-effective |
| **Gemini 2.5 Flash** | 1M tokens   | Very long documents, research papers      |

***

## Single-document summary

<CodeGroup>
  ```python Python theme={"theme":"github-dark"}
  from runcrate import Runcrate

  client = Runcrate(api_key="rc_live_YOUR_API_KEY")

  article = open("article.txt").read()

  response = client.models.chat_completion(
      model="deepseek-ai/DeepSeek-V3",
      messages=[
          {"role": "system", "content": "Return a 3–5 sentence summary capturing the key points."},
          {"role": "user", "content": f"Summarize this article:\n\n{article}"},
      ],
      max_tokens=512,
      temperature=0.3,
  )

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

  ```typescript Vercel AI SDK theme={"theme":"github-dark"}
  import { runcrate } from '@runcrate/ai';
  import { generateText } from 'ai';

  const { text } = await generateText({
    model: runcrate('deepseek-ai/DeepSeek-V3'),
    system: 'Return a 3–5 sentence summary capturing the key points.',
    prompt: `Summarize this article:\n\n${articleText}`,
  });
  ```
</CodeGroup>

***

## Structured summary with key points

```typescript theme={"theme":"github-dark"}
import { runcrate } from '@runcrate/ai';
import { generateText, Output } from 'ai';
import { z } from 'zod';

const SummarySchema = z.object({
  title: z.string().describe('A short title for the content'),
  summary: z.string().describe('A 2–3 sentence overview'),
  keyPoints: z.array(z.string()).describe('3–5 bullet points'),
  sentiment: z.enum(['positive', 'negative', 'neutral', 'mixed']),
});

const { output } = await generateText({
  model: runcrate('deepseek-ai/DeepSeek-V3'),
  output: Output.object({ schema: SummarySchema }),
  prompt: `Analyze and summarize this text:\n\n${articleText}`,
});

console.log(output.title);
console.log(output.keyPoints);
```

***

## Long documents with Gemini Flash

For documents exceeding 128K tokens — full research papers, legal contracts — use Gemini 2.5 Flash with its 1M-token context window:

```python theme={"theme":"github-dark"}
from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

long_document = open("research-paper.txt").read()

response = client.models.chat_completion(
    model="google/gemini-2.5-flash",
    messages=[
        {"role": "system", "content": "Summarize this research paper. Include: objective, methodology, key findings, and conclusions."},
        {"role": "user", "content": long_document},
    ],
    max_tokens=1024,
)

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

***

## Batch summarization

```python theme={"theme":"github-dark"}
from runcrate import Runcrate
from concurrent.futures import ThreadPoolExecutor

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

documents = [
    {"id": "mtg-01", "text": "Meeting notes from Monday standup..."},
    {"id": "mtg-02", "text": "Product review meeting discussion..."},
    {"id": "mtg-03", "text": "Quarterly planning session notes..."},
]

def summarize(doc):
    response = client.models.chat_completion(
        model="deepseek-ai/DeepSeek-V3",
        messages=[
            {"role": "system", "content": "Summarize in 3 bullet points. Include action items."},
            {"role": "user", "content": doc["text"]},
        ],
        max_tokens=256,
        temperature=0.2,
    )
    return {"id": doc["id"], "summary": response.choices[0].message.content}

with ThreadPoolExecutor(max_workers=5) as pool:
    results = list(pool.map(summarize, documents))

for r in results:
    print(f"\n--- {r['id']} ---\n{r['summary']}")
```

***

## Tips

* **Lower temperature** (0.2–0.3) produces more faithful summaries.
* **DeepSeek-V3** is the best cost-to-quality ratio for most summarization tasks.
* **Gemini 2.5 Flash** is the choice when the source document exceeds 128K tokens.

## Next steps

* [Structured output with Vercel AI SDK](/sdks/vercel-ai#structured-output)
* [Chat completions reference](/models/chat-completions)
* [AI Sentiment Analysis](/examples/ai-sentiment-analysis) — pair summarization with sentiment scoring