Plan1014/paper_reader

Paper Reader

Overview

Read academic papers from PDF files, extract structured information, and save as JSON to the papers directory.

Input

| Parameter | Type | Description | |-----------|------|-------------| | pdf_path | string | Local file path to the PDF |

Output JSON Structure

{
  "metadata": {
    "title": "Paper title",
    "authors": ["Author 1", "Author 2"],
    "year": "2024",
    "venue": "Journal/Conference name",
    "pdf_path": "path/to/file.pdf"
  },
  "abstract": "2-3 sentence summary",
  "key_contributions": [
    "Contribution 1",
    "Contribution 2"
  ],
  "methodology": "Brief description of approach",
  "key_findings": [
    "Finding 1",
    "Finding 2"
  ],
  "limitations": [
    "Limitation 1"
  ],
  "saved_at": "2026-01-01"
}

Workflow

Step 1: Setup Directories

• Scripts directory: .\LabPilot\tmp
• Papers directory: .\LabPilot\docs\papers
• Create directories if not exist

Step 2: Copy PDF

• Copy original PDF to .\LabPilot\docs\papers
• Use original filename or generate based on paper title

Step 3: Extract Content

• Use pypdf to read PDF text
• Save extracted text to .\LabPilot\tmp for analysis

Step 4: Generate JSON

• Parse extracted text to extract structured information
• Generate JSON file with same name as PDF (e.g., paper_title.json)

Step 5: Save to Fact Library

• Call remember_fact() to save key insights to long-term fact library
• Use structured JSON content as the fact value
• Do NOT use core_block functions (save_core_block, append_core_block, etc.)

Step 6: Save JSON File

• Save JSON to .\LabPilot\docs\papers
• Clean up temp files in .\LabPilot\tmp

Directory Structure

.\LabPilot\
├── tmp\                    # Python scripts and temp files
│   ├── extract_pdf.py
│   └── page_*.txt
└── docs\
    └── papers\             # Original PDFs and JSON summaries
        ├── paper1.pdf
        ├── paper1.json
        └── paper2.json

Important Constraints

| # | Constraint | Description | |---|------------|-------------| | 1 | Python Interpreter | Use D:\PDHlocking\LabPilot\venv\Scripts\python.exe | | 2 | Working Directory | All operations within D:\PDHlocking\LabPilot | | 3 | Script Location | Scripts run from .\LabPilot\tmp | | 4 | Paper Location | Save to .\LabPilot\docs\papers | | 5 | Output Format | JSON only | | 6 | Fact Library | Use remember_fact() for long-term storage | | 7 | No Core Blocks | Do NOT use core_block functions (save_core_block, append_core_block, etc.) |

Technical Notes

PDF Extraction Script Template

# extract_pdf.py - save to .\LabPilot\tmp
from pypdf import PdfReader
import sys
import os

pdf_path = sys.argv[1] if len(sys.argv) > 1 else r"PATH_TO_PDF"
output_dir = r"D:\PDHlocking\LabPilot\tmp"

reader = PdfReader(pdf_path)
print(f"Total pages: {len(reader.pages)}")

for i, page in enumerate(reader.pages):
    text = page.extract_text()
    output_file = os.path.join(output_dir, f"page_{i+1}.txt")
    with open(output_file, "w", encoding="utf-8") as f:
        f.write(text)
    print(f"Extracted page {i+1}")

File Path Handling

• Use raw strings (r"...") for paths with spaces
• Always use absolute paths starting from D:\PDHlocking

Response Handling

| Scenario | Behavior | |----------|----------| | PDF not found | Return error with suggestion to check path | | Encrypted PDF | Notify user | | Very long paper (>50 pages) | Focus on abstract, intro, conclusion, key results |