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

# PyMuPDF Loader

> Load PDF documents using PyMuPDF for high performance

## Overview

PyMuPDF loader provides high-performance PDF processing with advanced features like structured text extraction, image handling, and annotation extraction. Ideal for large-scale document processing.

**Loader Class:** `PyMuPDFLoader`

**Config Class:** `PyMuPDFLoaderConfig`

## Install

<Note>
  Install the PyMuPDF loader optional dependency group:

  ```bash theme={null}
  uv pip install "upsonic[pymupdf-loader]"
  ```
</Note>

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.loaders.pymupdf import PyMuPDFLoader
from upsonic.loaders.config import PyMuPDFLoaderConfig
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.text_splitter.recursive import RecursiveChunker, RecursiveChunkingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Configure loader
loader_config = PyMuPDFLoaderConfig(
    extraction_mode="hybrid",
    text_extraction_method="dict",
    preserve_layout=True
)
loader = PyMuPDFLoader(loader_config)

# Setup KnowledgeBase
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
chunker = RecursiveChunker(RecursiveChunkingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="pymupdf_docs",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.pdf"],
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader],
    splitters=[chunker]
)

# Query with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task("Summarize the document", context=[kb])
result = agent.do(task)
print(result)
```

## Parameters

| Parameter | Type | Description | Default | Source |
| - | - | - | - | - |
| `encoding` | `str \| None` | File encoding (auto-detected if None) | None | Base |
| `error_handling` | `"ignore" \| "warn" \| "raise"` | How to handle loading errors | "warn" | Base |
| `include_metadata` | `bool` | Whether to include file metadata | True | Base |
| `custom_metadata` | `dict` | Additional metadata to include | {} | Base |
| `max_file_size` | `int \| None` | Maximum file size in bytes | None | Base |
| `skip_empty_content` | `bool` | Skip documents with empty content | True | Base |
| `extraction_mode` | `"hybrid" \| "text_only" \| "ocr_only"` | Content extraction strategy | "hybrid" | Specific |
| `start_page` | `int \| None` | First page to process (1-indexed) | None | Specific |
| `end_page` | `int \| None` | Last page to process (inclusive) | None | Specific |
| `clean_page_numbers` | `bool` | Remove page numbers from headers/footers | True | Specific |
| `page_num_start_format` | `str \| None` | Format string for page start markers | None | Specific |
| `page_num_end_format` | `str \| None` | Format string for page end markers | None | Specific |
| `extra_whitespace_removal` | `bool` | Normalize whitespace | True | Specific |
| `pdf_password` | `str \| None` | Password for encrypted PDFs | None | Specific |
| `text_extraction_method` | `"text" \| "dict" \| "html" \| "xml"` | Text extraction method | "text" | Specific |
| `include_images` | `bool` | Extract and include image information | False | Specific |
| `image_dpi` | `int` | DPI for image rendering (72-600) | 150 | Specific |
| `preserve_layout` | `bool` | Preserve text layout and positioning | True | Specific |
| `extract_annotations` | `bool` | Extract annotations and comments | False | Specific |
| `annotation_format` | `"text" \| "json"` | Format for extracted annotations | "text" | Specific |


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.