pdf-harvester（PDF harvester 搜索）

PDF Harvester Skill

Extract and ingest PDF documents into RAG with proper text extraction, table handling, and metadata.

Overview

PDFs are common for research papers, reports, manuals, and ebooks. This skill covers:

• Text extraction with layout preservation
• Table extraction and conversion to markdown
• Academic paper patterns (abstract, sections, citations)
• OCR for scanned documents
• Multi-page chunking strategies

Prerequisites

# Core extraction
pip install pdfplumber pymupdf

# For OCR (scanned documents)
pip install pytesseract pdf2image
# Also need: brew install tesseract poppler (macOS)

# For academic papers
pip install arxiv  # If fetching from arXiv

Extraction Methods

Method 1: pdfplumber (Recommended)

Best for structured PDFs with tables.

#!/usr/bin/env python3
"""PDF extraction using pdfplumber."""

import pdfplumber
from pathlib import Path
from typing import Dict, List, Optional
import re

def extract_pdf_text(
    pdf_path: str,
    extract_tables: bool = True
) -> Dict:
    """
    Extract text and tables from PDF.

    Args:
        pdf_path: Path to PDF file
        extract_tables: Whether to extract tables separately

    Returns:
        Dict with pages, tables, and metadata
    """
    result = {
        "pages": [],
        "tables": [],
        "metadata": {},
        "total_pages": 0
    }

    with pdfplumber.open(pdf_path) as pdf:
        result["total_pages"] = len(pdf.pages)
        result["metadata"] = pdf.metadata or {}

        for page_num, page in enumerate(pdf.pages, 1):
            # Extract text
            text = page.extract_text() or ""

            result["pages"].append({
                "page_number": page_num,
                "text": text,
                "width": page.width,
                "height": page.height
            })

            # Extract tables
            if extract_tables:
                tables = page.extract_tables()
                for table_num, table in enumerate(tables, 1):
                    if table and len(table) > 0:
                        result["tables"].append({
                            "page_number": page_num,
                            "table_number": table_num,
                            "data": table,
                            "markdown": table_to_markdown(table)
                        })

    return result

def table_to_markdown(table: List[List]) -> str:
    """Convert table data to markdown format."""
    if not table or len(table) == 0:
        return ""

    # Clean cells
    def clean_cell(cell):
        if cell is None:
            return ""
        return str(cell).replace("
", " ").strip()

    # Header row
    headers = [clean_cell(c) for c in table[0]]
    md = "| " + " | ".join(headers) + " |
"
    md += "| " + " | ".join(["---"] * len(headers)) + " |
"

    # Data rows
    for row in table[1:]:
        cells = [clean_cell(c) for c in row]
        # Pad if necessary
        while len(cells) < len(headers):
            cells.append("")
        md += "| " + " | ".join(cells[:len(headers)]) + " |
"

    return md

Method 2: PyMuPDF (fitz)

Faster, better for large PDFs.

#!/usr/bin/env python3
"""PDF extraction using PyMuPDF."""

import fitz  # PyMuPDF
from typing import Dict, List

def extract_with_pymupdf(pdf_path: str) -> Dict:
    """
    Extract text using PyMuPDF.

    Faster than pdfplumber, good for large documents.
    """
    doc = fitz.open(pdf_path)

    result = {
        "pages": [],
        "metadata": doc.metadata,
        "total_pages": len(doc)
    }

    for page_num, page in enumerate(doc, 1):
        # Get text with layout preservation
        text = page.get_text("text")

        # Get text blocks for better structure
        blocks = page.get_text("dict")["blocks"]

        result["pages"].append({
            "page_number": page_num,
            "text": text,
            "blocks": len(blocks)
        })

    doc.close()
    return result

def extract_with_structure(pdf_path: str) -> Dict:
    """Extract with heading detection."""
    doc = fitz.open(pdf_path)

    pages = []
    for page_num, page in enumerate(doc, 1):
        blocks = page.get_text("dict")["blocks"]

        structured_content = []
        for block in blocks:
            if block["type"] == 0:  # Text block
                for line in block.get("lines", []):
                    for span in line.get("spans", []):
                        text = span["text"].strip()
                        font_size = span["size"]
                        is_bold = "bold" in span["font"].lower()

                        # Detect headings by font size
                        if font_size > 14 or is_bold:
                            structured_content.append({
                                "type": "heading",
                                "text": text,
                                "size": font_size
                            })
                        else:
                            structured_content.append({
                                "type": "paragraph",
                                "text": text
                            })

        pages.append({
            "page_number": page_num,
            "content": structured_content
        })

    doc.close()
    return {"pages": pages, "total_pages": len(pages)}

Method 3: OCR for Scanned PDFs

#!/usr/bin/env python3
"""OCR extraction for scanned PDFs."""

import pytesseract
from pdf2image import convert_from_path
from typing import Dict, List

def extract_with_ocr(
    pdf_path: str,
    language: str = "eng",
    dpi: int = 300
) -> Dict:
    """
    Extract text from scanned PDF using OCR.

    Args:
        pdf_path: Path to PDF
        language: Tesseract language code
        dpi: Resolution for conversion
    """
    # Convert PDF pages to images
    images = convert_from_path(pdf_path, dpi=dpi)

    pages = []
    for page_num, image in enumerate(images, 1):
        # Run OCR
        text = pytesseract.image_to_string(image, lang=language)

        pages.append({
            "page_number": page_num,
            "text": text,
            "ocr": True
        })

    return {
        "pages": pages,
        "total_pages": len(pages),
        "ocr_used": True
    }

def is_scanned_pdf(pdf_path: str) -> bool:
    """Detect if PDF is scanned (image-based)."""
    import fitz

    doc = fitz.open(pdf_path)

    # Check first few pages
    for page in doc[:min(3, len(doc))]:
        text = page.get_text().strip()
        if len(text) > 100:  # Has extractable text
            doc.close()
            return False

    doc.close()
    return True

Chunking Strategies

Strategy 1: Page-Based

Simple chunking by page boundaries.

def chunk_by_pages(
    extracted: Dict,
    pages_per_chunk: int = 1
) -> List[Dict]:
    """Chunk PDF by page boundaries."""
    chunks = []
    pages = extracted["pages"]

    for i in range(0, len(pages), pages_per_chunk):
        page_group = pages[i:i + pages_per_chunk]

        text = "

".join(p["text"] for p in page_group)

        chunks.append({
            "content": text,
            "page_start": page_group[0]["page_number"],
            "page_end": page_group[-1]["page_number"],
            "chunk_index": len(chunks)
        })

    return chunks

Strategy 2: Section-Based

Chunk by document sections/headings.

def chunk_by_sections(
    extracted: Dict,
    heading_patterns: List[str] = None
) -> List[Dict]:
    """Chunk PDF by section headings."""
    if heading_patterns is None:
        heading_patterns = [
            r'^#+\s',                    # Markdown headings
            r'^\d+\.\s+[A-Z]',           # Numbered sections
            r'^[A-Z][A-Z\s]+$',          # ALL CAPS headings
            r'^(Abstract|Introduction|Conclusion|References)',
        ]

    full_text = "

".join(p["text"] for p in extracted["pages"])

    # Find section boundaries
    sections = []
    current_section = {"title": "Introduction", "content": "", "start_pos": 0}

    lines = full_text.split("
")

    for line in lines:
        is_heading = any(
            re.match(pattern, line.strip())
            for pattern in heading_patterns
        )

        if is_heading and current_section["content"].strip():
            sections.append(current_section)
            current_section = {
                "title": line.strip(),
                "content": "",
                "start_pos": len(sections)
            }
        else:
            current_section["content"] += line + "
"

    # Don't forget last section
    if current_section["content"].strip():
        sections.append(current_section)

    return [
        {
            "content": s["content"].strip(),
            "section": s["title"],
            "chunk_index": i
        }
        for i, s in enumerate(sections)
    ]

Strategy 3: Semantic Paragraphs

Chunk by paragraph with size limits.

def chunk_by_paragraphs(
    extracted: Dict,
    max_chunk_size: int = 500,  # words
    overlap: int = 50
) -> List[Dict]:
    """Chunk by paragraphs with overlap."""
    full_text = "

".join(p["text"] for p in extracted["pages"])

    # Split into paragraphs
    paragraphs = [p.strip() for p in full_text.split("

") if p.strip()]

    chunks = []
    current_chunk = []
    current_size = 0

    for para in paragraphs:
        para_size = len(para.split())

        if current_size + para_size > max_chunk_size and current_chunk:
            # Save current chunk
            chunks.append({
                "content": "

".join(current_chunk),
                "chunk_index": len(chunks),
                "word_count": current_size
            })

            # Start new chunk with overlap
            overlap_text = current_chunk[-1] if current_chunk else ""
            current_chunk = [overlap_text] if overlap_text else []
            current_size = len(overlap_text.split()) if overlap_text else 0

        current_chunk.append(para)
        current_size += para_size

    # Last chunk
    if current_chunk:
        chunks.append({
            "content": "

".join(current_chunk),
            "chunk_index": len(chunks),
            "word_count": current_size
        })

    return chunks

Academic Paper Pattern

Special handling for research papers.

def extract_academic_paper(pdf_path: str) -> Dict:
    """
    Extract academic paper with structure detection.

    Identifies: title, authors, abstract, sections, references
    """
    extracted = extract_pdf_text(pdf_path)
    full_text = "
".join(p["text"] for p in extracted["pages"])

    paper = {
        "title": "",
        "authors": [],
        "abstract": "",
        "sections": [],
        "references": [],
        "tables": extracted["tables"]
    }

    # Title is usually first large text
    lines = full_text.split("
")
    for line in lines[:10]:
        if len(line) > 20 and len(line) < 200:
            paper["title"] = line.strip()
            break

    # Abstract
    abstract_match = re.search(
        r'Abstract[:\s]*
?(.*?)(?=
(?:1\.?\s+)?Introduction|

[A-Z])',
        full_text,
        re.DOTALL | re.IGNORECASE
    )
    if abstract_match:
        paper["abstract"] = abstract_match.group(1).strip()

    # Sections
    section_pattern = r'
(\d+\.?\s+[A-Z][^
]+)
'
    section_matches = re.finditer(section_pattern, full_text)

    section_positions = [(m.group(1), m.start()) for m in section_matches]

    for i, (title, start) in enumerate(section_positions):
        end = section_positions[i+1][1] if i+1 < len(section_positions) else len(full_text)
        content = full_text[start:end]

        paper["sections"].append({
            "title": title.strip(),
            "content": content.strip()
        })

    # References section
    ref_match = re.search(
        r'(?:References|Bibliography)\s*
(.*?)$',
        full_text,
        re.DOTALL | re.IGNORECASE
    )
    if ref_match:
        paper["references_text"] = ref_match.group(1).strip()

    return paper

Full Harvesting Pipeline

#!/usr/bin/env python3
"""Complete PDF harvesting pipeline."""

from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional
import hashlib

async def harvest_pdf(
    pdf_path: str,
    collection: str,
    chunk_strategy: str = "paragraphs",  # pages, sections, paragraphs
    is_academic: bool = False,
    use_ocr: bool = False
) -> Dict:
    """
    Harvest a PDF document into RAG.

    Args:
        pdf_path: Path to PDF file
        collection: Target RAG collection
        chunk_strategy: How to chunk the document
        is_academic: Use academic paper extraction
        use_ocr: Force OCR extraction
    """
    path = Path(pdf_path)

    # Check if OCR needed
    if use_ocr or is_scanned_pdf(pdf_path):
        extracted = extract_with_ocr(pdf_path)
    else:
        extracted = extract_pdf_text(pdf_path)

    # Get document metadata
    doc_metadata = {
        "source_type": "pdf",
        "source_path": str(path.absolute()),
        "filename": path.name,
        "total_pages": extracted["total_pages"],
        "harvested_at": datetime.now().isoformat(),
        "pdf_metadata": extracted.get("metadata", {})
    }

    # Academic paper special handling
    if is_academic:
        paper = extract_academic_paper(pdf_path)
        doc_metadata["title"] = paper["title"]
        doc_metadata["abstract"] = paper["abstract"]
        doc_metadata["is_academic"] = True

    # Chunk based on strategy
    if chunk_strategy == "pages":
        chunks = chunk_by_pages(extracted)
    elif chunk_strategy == "sections":
        chunks = chunk_by_sections(extracted)
    else:
        chunks = chunk_by_paragraphs(extracted)

    # Generate document ID from content hash
    content_hash = hashlib.md5(
        "".join(p["text"] for p in extracted["pages"]).encode()
    ).hexdigest()[:12]
    doc_id = f"pdf_{content_hash}"

    # Ingest chunks
    ingested = 0
    for chunk in chunks:
        chunk_metadata = {
            **doc_metadata,
            "chunk_index": chunk["chunk_index"],
            "total_chunks": len(chunks),
        }

        # Add page info if available
        if "page_start" in chunk:
            chunk_metadata["page_start"] = chunk["page_start"]
            chunk_metadata["page_end"] = chunk["page_end"]

        # Add section info if available
        if "section" in chunk:
            chunk_metadata["section"] = chunk["section"]

        await ingest(
            content=chunk["content"],
            collection=collection,
            metadata=chunk_metadata,
            doc_id=f"{doc_id}_chunk_{chunk['chunk_index']}"
        )
        ingested += 1

    # Ingest tables separately
    for table in extracted.get("tables", []):
        table_metadata = {
            **doc_metadata,
            "content_type": "table",
            "page_number": table["page_number"],
            "table_number": table["table_number"]
        }

        await ingest(
            content=table["markdown"],
            collection=collection,
            metadata=table_metadata,
            doc_id=f"{doc_id}_table_{table['page_number']}_{table['table_number']}"
        )

    return {
        "status": "success",
        "filename": path.name,
        "pages": extracted["total_pages"],
        "chunks": ingested,
        "tables": len(extracted.get("tables", [])),
        "collection": collection,
        "doc_id": doc_id
    }

async def harvest_pdf_url(
    url: str,
    collection: str,
    **kwargs
) -> Dict:
    """Download and harvest a PDF from URL."""
    import httpx
    import tempfile

    # Download PDF
    async with httpx.AsyncClient() as client:
        response = await client.get(url, follow_redirects=True)
        response.raise_for_status()

    # Save to temp file
    with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as f:
        f.write(response.content)
        temp_path = f.name

    try:
        result = await harvest_pdf(temp_path, collection, **kwargs)
        result["source_url"] = url
        return result
    finally:
        Path(temp_path).unlink()  # Clean up

Metadata Schema

# PDF chunk metadata
source_type: pdf
source_path: /path/to/document.pdf
source_url: https://... (if downloaded)
filename: document.pdf
total_pages: 45
page_start: 5
page_end: 7
section: "3. Methodology"
chunk_index: 12
total_chunks: 28
harvested_at: "2024-01-01T12:00:00Z"
is_academic: true
title: "Paper Title"
abstract: "Paper abstract..."
content_type: text|table

Usage Examples

# Local PDF
result = await harvest_pdf(
    pdf_path="/path/to/document.pdf",
    collection="research_papers",
    chunk_strategy="sections",
    is_academic=True
)

# PDF from URL
result = await harvest_pdf_url(
    url="https://arxiv.org/pdf/2301.00001.pdf",
    collection="ml_papers",
    is_academic=True
)

# Scanned document
result = await harvest_pdf(
    pdf_path="/path/to/scanned.pdf",
    collection="legacy_docs",
    use_ocr=True
)

Refinement Notes

Track improvements as you use this skill.

• Text extraction tested
• Table extraction working
• OCR fallback tested
• Academic paper pattern validated
• Chunking strategies compared
• Large PDF handling optimized