# How to Summarize a PDF Locally with Ollama

> Extract PDF text with pypdf, chunk it, summarize each chunk through the local Ollama API, and merge — about 30 lines, nothing uploaded.

- Canonical: https://guides-ai.pages.dev/guides/summarize-pdf-locally-with-ollama/
- Plate 13.03 · Topic: Everyday tasks (https://guides-ai.pages.dev/topics/tasks/)
- Published: 06 Sept 2026 · 3 min read
- Source site: guides-ai — https://guides-ai.pages.dev/

A 90-page contract will not fit in a small local model's context, and you probably do not want to upload it anyway. The fix is the standard map-reduce shape: summarize each chunk, then summarize the summaries. It costs nothing, runs offline, and the intermediate summaries are useful on their own — they are a section-by-section outline of the document.

## 1. Install

```bash
pip install pypdf requests
ollama pull llama3.2
```

`pypdf` reads the PDF, `requests` talks to Ollama. Check the server is up before running anything: `curl http://localhost:11434/api/tags` should return JSON.

## 2. The whole script

```python
import requests
from pypdf import PdfReader

MODEL, URL = "llama3.2", "http://localhost:11434/api/generate"
CHUNK = 6000  # characters, roughly 1,500 tokens

def ask(prompt: str) -> str:
    r = requests.post(URL, json={"model": MODEL, "prompt": prompt, "stream": False})
    r.raise_for_status()
    return r.json()["response"].strip()

pages = PdfReader("report.pdf").pages
text = "\n".join((p.extract_text() or "") for p in pages)
print(f"{len(pages)} pages, {len(text)} characters")

chunks = [text[i:i + CHUNK] for i in range(0, len(text), CHUNK)]
parts = []
for n, chunk in enumerate(chunks, 1):
    print(f"summarizing {n}/{len(chunks)}")
    parts.append(ask(
        "Summarize this section of a document in 3 bullet points. "
        "Use only what is written below; do not add background knowledge.\n\n" + chunk
    ))

summary = ask(
    "These are section summaries of one document, in order. Merge them into a single "
    "200-word summary with no repetition and no preamble:\n\n" + "\n\n".join(parts)
)
print("\n" + summary)
```

Reads every page, splits the text into 6,000-character blocks, sends each to your local model, then sends the collected bullet points back for one final pass. Save it as `summarize.py` and run `python summarize.py`. Progress prints as it goes, because a long PDF on a small machine takes minutes.

## 3. When it misbehaves

**Empty output, no error.** `extract_text()` returned nothing because the PDF is scanned images. The page count will print fine while the character count is near zero. Run it through OCR first (`ocrmypdf in.pdf out.pdf`) and try again.

**Summaries that ignore the end of a chunk.** The chunk is longer than the context window the model was loaded with. Lower `CHUNK`, or raise the window explicitly:

```python
json={"model": MODEL, "prompt": prompt, "stream": False,
      "options": {"num_ctx": 8192, "temperature": 0.2}}
```

Gives the model a larger context and steadier, less inventive output.

**Too slow.** Use a smaller model for the per-chunk pass — the merge step is the one that needs judgment. `parts` is just a list of strings, so you can print it, cache it, or feed it to a bigger model at the end.

---

Next: [call the Ollama API properly](/guides/call-ollama-api/) or [do the same job in ChatGPT](/guides/chatgpt-summarize-pdf/).