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# OpenRouter API Key (recommended)
OPENROUTER_API_KEY=your_openrouter_api_key_here
# Optional: Other model configurations
# OPENAI_API_KEY=your_openai_key
# ANTHROPIC_API_KEY=your_anthropic_key
# GOOGLE_API_KEY=your_google_key

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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
# Virtual environments
.venv
# Environment variables
.env
# Output files
*_insights.json

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3.12

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# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
`pdf-to-kcf` is a Python CLI tool that uses AI agents to parse PDF documents and extract structured insights. It uses `pydantic-ai` to create an intelligent agent that can autonomously decide how much of a document to analyze, requesting additional pages as needed.
## Commands
### Development Setup
```bash
# Install dependencies
uv sync
# Set up OpenRouter API key
cp .env.example .env
# Edit .env and add your OPENROUTER_API_KEY
# Run the CLI tool
uv run pdf-to-kcf <pdf-path>
# Run with options
uv run pdf-to-kcf <pdf-path> --output custom_output.json --start-page 2 --model anthropic/claude-3-opus
```
### Running the Tool
```bash
# Basic usage (uses Claude 3.5 Sonnet via OpenRouter by default)
uv run pdf-to-kcf document.pdf
# Specify custom output file
uv run pdf-to-kcf document.pdf -o insights.json
# Start from a specific page (0-indexed)
uv run pdf-to-kcf document.pdf -s 3
# Use a different AI model from OpenRouter
uv run pdf-to-kcf document.pdf -m meta-llama/llama-3.1-70b-instruct
uv run pdf-to-kcf document.pdf -m google/gemini-pro-1.5
uv run pdf-to-kcf document.pdf -m openai/gpt-4o
```
## Architecture
### Core Components
**models.py** - Data structures following the agentic document parsing format specification:
- `ContentInsightType`: Enum for insight classification (FACT, OPINION, COMMENT)
- `ContentInsightAttribute`: Key-value metadata for insights
- `ContentInsight`: A single extracted insight with type, content, and attributes
- `PageContentAnalysis`: Agent output containing all insights
- `PageContent`: Context passed to the agent (page number, content, total pages)
**pdf_reader.py** - PDF document handling:
- `PDFDocument`: Wrapper class for reading PDF files using pypdf
- Provides `get_page_text()` for single page extraction
- Provides `get_all_pages()` for full document extraction
**agent.py** - AI agent implementation:
- `DocumentAnalyzer`: Main analyzer using pydantic-ai Agent
- Configures the AI model and system prompt
- Implements `read_page` tool that allows the agent to request additional pages autonomously
- The agent decides when to fetch more pages based on context needs
- Agent is instructed to classify insights as facts, opinions, or comments with relevant attributes
**cli.py** - Command-line interface:
- Built with Click framework
- Handles PDF loading, analysis orchestration, and JSON output
- Provides user feedback during processing
### Agentic Behavior
The AI agent is autonomous and can:
1. Start analyzing from an initial page
2. Determine if more context is needed from other pages
3. Use the `read_page` tool to fetch additional pages
4. Extract structured insights with proper classification
5. Return all insights in the specified JSON format
### Output Format
The tool outputs JSON files with the following structure:
```json
{
"insights": [
{
"type": "fact",
"insight": "Summary of the insight",
"content": "Original text that was analyzed",
"attributes": [
{"attribute": "source", "value": "Page 1"},
{"attribute": "confidence", "value": "high"}
]
}
]
}
```
## Requirements
- Python 3.12+
- OpenRouter API key set as `OPENROUTER_API_KEY` environment variable
- Provides access to all major AI models (Claude, GPT-4, Gemini, Llama, etc.)
- Get API key at https://openrouter.ai/
- Alternatively supports direct provider keys (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, etc.)
- Dependencies managed via uv
## Model Configuration
The tool is configured to use OpenRouter by default, which provides:
- Access to multiple AI providers through a single API
- Automatic fallback and load balancing
- Competitive pricing
- Support for the latest models
When `OPENROUTER_API_KEY` is set, the agent automatically configures the OpenAI-compatible interface with OpenRouter's base URL. Models should be specified in the format: `<provider>/<model-name>` (e.g., `anthropic/claude-3.5-sonnet`, `openai/gpt-4o`)
## Format Specification
The project follows the format defined in `../docs/AGENTIC_DOCUMENT_PARSING_FORMAT.md`, which specifies:
- How agents interact with documents
- The structure of insights and their attributes
- The `read_page` tool interface for autonomous page navigation
- Classification system for different insight types

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Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
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License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
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GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
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to choose that version for the Program.
Later license versions may give you additional or different
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later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
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IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
{one line to give the program's name and a brief idea of what it does.}
Copyright (C) {year} {name of author}
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
Copyright (C) 2025 qrk.ng
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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# Quick Start Guide
## 1. Get Your OpenRouter API Key
1. Visit [OpenRouter](https://openrouter.ai/)
2. Sign up for a free account
3. Go to [Keys](https://openrouter.ai/keys) section
4. Create a new API key
5. Copy your API key
## 2. Configure the Tool
```bash
# Copy the environment template
cp .env.example .env
# Edit .env and add your API key
echo "OPENROUTER_API_KEY=your_key_here" >> .env
```
Or set it directly in your shell:
```bash
export OPENROUTER_API_KEY=your_key_here
```
## 3. Run Your First Analysis
```bash
# Basic usage
uv run pdf-to-kcf your_document.pdf
# This will create a file: your_document_insights.json
```
## Example Output
The tool will generate a JSON file with structured insights like:
```json
{
"insights": [
{
"type": "fact",
"insight": "The company revenue increased by 25% in Q4",
"content": "According to the financial report, Q4 revenue reached $2.5M, up 25% from Q3...",
"attributes": [
{"attribute": "source", "value": "Financial Report"},
{"attribute": "quarter", "value": "Q4"},
{"attribute": "confidence", "value": "high"}
]
},
{
"type": "opinion",
"insight": "The author recommends investing in AI infrastructure",
"content": "We strongly believe that investing in AI infrastructure is critical...",
"attributes": [
{"attribute": "sentiment", "value": "positive"},
{"attribute": "section", "value": "recommendations"}
]
}
]
}
```
## Advanced Usage
### Try Different Models
```bash
# Use GPT-4
uv run pdf-to-kcf document.pdf -m openai/gpt-4o
# Use Gemini
uv run pdf-to-kcf document.pdf -m google/gemini-pro-1.5
# Use Llama 3.1
uv run pdf-to-kcf document.pdf -m meta-llama/llama-3.1-70b-instruct
```
### Custom Output Location
```bash
uv run pdf-to-kcf document.pdf -o /path/to/output.json
```
### Start from Specific Page
```bash
# Start analysis from page 5 (0-indexed, so this is the 6th page)
uv run pdf-to-kcf document.pdf -s 5
```
## How It Works
1. The tool loads your PDF and extracts text
2. An AI agent analyzes the content starting from page 0 (or your specified page)
3. The agent autonomously decides if it needs to read more pages
4. It extracts structured insights classified as facts, opinions, or comments
5. Each insight includes the original content and relevant metadata
6. Results are saved to a JSON file
## Pricing
OpenRouter charges based on the model you use:
- Claude 3.5 Sonnet (default): ~$3 per million input tokens
- GPT-4o: ~$2.50 per million input tokens
- Llama 3.1 70B: ~$0.35 per million input tokens
Most PDFs will cost just a few cents to analyze.
## Troubleshooting
### "No API key found"
Make sure you've set `OPENROUTER_API_KEY` in your `.env` file or environment.
### "Model not found"
Check the model name format: `<provider>/<model-name>` (e.g., `anthropic/claude-3.5-sonnet`)
See available models at https://openrouter.ai/models
### "PDF not found"
Use the full path to your PDF file, or navigate to the directory containing it first.
## Next Steps
- Read the full [README.md](README.md) for more details
- Check [CLAUDE.md](CLAUDE.md) for architecture details
- See [OpenRouter models](https://openrouter.ai/models) for all available models

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# pdf-to-kcf
A Python CLI tool that uses AI agents to parse PDF documents and extract structured insights. Built with `pydantic-ai`, this tool creates an intelligent agent that autonomously analyzes documents, requesting additional pages as needed to form complete insights.
## Features
- **Autonomous Document Analysis**: AI agent decides how much of the document to read
- **Structured Insight Extraction**: Classifies content as facts, opinions, or comments
- **Rich Metadata**: Adds attributes like source, confidence, dates, and more
- **Multiple AI Models**: Supports OpenAI and other compatible models
- **JSON Output**: Exports insights in a structured, machine-readable format
## Installation
This project uses [uv](https://github.com/astral-sh/uv) for dependency management:
```bash
# Install dependencies
uv sync
```
## Setup
1. Copy the environment template:
```bash
cp .env.example .env
```
2. Add your OpenRouter API key to `.env`:
```bash
OPENROUTER_API_KEY=your_openrouter_api_key_here
```
3. Get your API key from [OpenRouter](https://openrouter.ai/) (free tier available)
## Usage
```bash
# Basic usage (uses OpenRouter with Claude 3.5 Sonnet by default)
uv run pdf-to-kcf document.pdf
# Specify custom output file
uv run pdf-to-kcf document.pdf -o insights.json
# Start from a specific page (0-indexed)
uv run pdf-to-kcf document.pdf -s 3
# Use a different AI model from OpenRouter
uv run pdf-to-kcf document.pdf -m meta-llama/llama-3.1-70b-instruct
uv run pdf-to-kcf document.pdf -m google/gemini-pro-1.5
```
### Options
- `--output, -o`: Output JSON file path (default: `<pdf_name>_insights.json`)
- `--start-page, -s`: Starting page number, 0-indexed (default: 0)
- `--model, -m`: AI model to use via OpenRouter (default: `anthropic/claude-3.5-sonnet`)
### Available Models
When using OpenRouter, you can specify any model using the format `<provider>/<model-name>`:
- `anthropic/claude-3.5-sonnet` (default, recommended)
- `anthropic/claude-3-opus`
- `openai/gpt-4o`
- `meta-llama/llama-3.1-70b-instruct`
- `google/gemini-pro-1.5`
- See [OpenRouter models](https://openrouter.ai/models) for full list
## Output Format
The tool generates JSON files with structured insights:
```json
{
"insights": [
{
"type": "fact",
"insight": "Global temperatures have risen 1.1<EFBFBD>C since pre-industrial times",
"content": "According to the IPCC, global temperatures have risen approximately 1.1<EFBFBD>C...",
"attributes": [
{"attribute": "source", "value": "IPCC Report"},
{"attribute": "confidence", "value": "high"},
{"attribute": "year", "value": "2023"}
]
},
{
"type": "opinion",
"insight": "The author believes immediate action is required",
"content": "We must act now to prevent catastrophic consequences...",
"attributes": [
{"attribute": "sentiment", "value": "urgent"},
{"attribute": "section", "value": "conclusion"}
]
}
]
}
```
## How It Works
1. **PDF Loading**: Extracts text content from PDF using pypdf
2. **Agent Initialization**: Creates a pydantic-ai agent with the specified model
3. **Autonomous Analysis**: Agent analyzes content and can request additional pages
4. **Insight Extraction**: Classifies and structures insights with metadata
5. **JSON Export**: Saves all insights to a JSON file
## Requirements
- Python 3.12 or higher
- OpenRouter API key (set as `OPENROUTER_API_KEY` environment variable)
- Get your free API key at [OpenRouter](https://openrouter.ai/)
- Supports all major AI models (Claude, GPT-4, Gemini, Llama, etc.)
- Alternatively, use `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or other provider keys
## Architecture
The tool follows the agentic document parsing format with these core components:
- **models.py**: Data structures (ContentInsight, PageContentAnalysis, etc.)
- **pdf_reader.py**: PDF text extraction (PDFDocument class)
- **agent.py**: AI agent with autonomous page reading capability
- **cli.py**: Command-line interface
See `CLAUDE.md` for detailed architecture documentation.
## License
MIT

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[project]
name = "pdf-to-kcf"
version = "0.1.0"
description = "CLI tool to parse PDFs and convert them into structured insights using AI"
readme = "README.md"
authors = [
{ name = "neutrino2211", email = "neutrino2211@gmail.com" }
]
requires-python = ">=3.12"
dependencies = [
"pydantic-ai>=0.0.14",
"pypdf>=4.0.0",
"click>=8.1.0",
"pydantic>=2.0.0",
]
[project.scripts]
pdf-to-kcf = "pdf_to_kcf.cli:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

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"""PDF to KCF - CLI tool for parsing PDFs and extracting structured insights."""
from .models import (
ContentInsight,
ContentInsightAttribute,
ContentInsightType,
PageContent,
PageContentAnalysis,
)
from .pdf_reader import PDFDocument
from .agent import DocumentAnalyzer
__all__ = [
"ContentInsight",
"ContentInsightAttribute",
"ContentInsightType",
"PageContent",
"PageContentAnalysis",
"PDFDocument",
"DocumentAnalyzer",
]

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"""AI agent for analyzing PDF content and extracting insights."""
import os
from typing import Optional
from pydantic_ai import Agent, RunContext
from .models import PageContent, PageContentAnalysis
from .pdf_reader import PDFDocument
class DocumentAnalyzer:
"""Analyzes PDF documents using an AI agent to extract structured insights."""
def __init__(
self,
pdf_document: PDFDocument,
model: str = "anthropic/claude-3.5-sonnet",
):
"""Initialize the document analyzer.
Args:
pdf_document: The PDF document to analyze
model: The AI model to use (default: anthropic/claude-3.5-sonnet via OpenRouter)
"""
self.pdf_document = pdf_document
self.current_page = 0
# Configure OpenRouter if API key is present
if os.getenv("OPENROUTER_API_KEY"):
# Set environment variables for OpenAI SDK to use OpenRouter
os.environ["OPENAI_API_KEY"] = os.getenv("OPENROUTER_API_KEY") or ""
os.environ["OPENAI_BASE_URL"] = "https://openrouter.ai/api/v1"
# Use openai: prefix for pydantic-ai to recognize it as OpenAI-compatible
model_instance = f"openai:{model}"
else:
# Use default model configuration
model_instance = model
# Define the agent with tools for reading pages
self.agent = Agent(
model_instance,
output_type=PageContentAnalysis,
system_prompt="""You are a document analysis expert. Your task is to analyze document content and extract structured insights.
For each piece of content you analyze, classify insights into three types:
- FACT: Factual statements, data, statistics, or verifiable information
- OPINION: Viewpoints, beliefs, recommendations, or subjective statements
- COMMENT: Observations, notes, or descriptive information
For each insight, provide:
1. The type (fact, opinion, or comment)
2. A clear summary of the insight
3. The original content that was analyzed
4. Relevant attributes (e.g., source, author, date, confidence, sentiment, section)
You have access to a read_page tool that allows you to request additional pages if the current page contains incomplete information or if you need more context. Use this tool when:
- Information spans multiple pages
- You need context from surrounding pages
- The current page references content on other pages
Be thorough but concise in your analysis. Focus on extracting meaningful insights rather than restating obvious information.""",
deps_type=PageContent,
)
# Register the read_page tool
@self.agent.tool
async def read_page(
ctx: RunContext[PageContent], page_number: Optional[int] = None
) -> str:
"""Read a specific page from the document.
Args:
ctx: The run context containing current page information
page_number: The page number to read (0-indexed). If not provided, reads the next page.
Returns:
The text content of the requested page
"""
if page_number is None:
page_number = ctx.deps.page_number + 1
if page_number < 0 or page_number >= self.pdf_document.total_pages:
return f"Error: Invalid page number. Document has {self.pdf_document.total_pages} pages (0-indexed)."
return self.pdf_document.get_page_text(page_number)
async def analyze_page(self, page: int = 0) -> PageContentAnalysis:
"""Analyze the document starting from a specific page.
Args:
page: The page to analyse
Returns:
PageContentAnalysis containing all extracted insights
"""
initial_content = self.pdf_document.get_page_text(page)
page_content = PageContent(
page_number=page,
content=initial_content,
total_pages=self.pdf_document.total_pages,
)
result = await self.agent.run(
f"Analyze this document page and extract structured insights. "
f"This is page {page + 1} of {self.pdf_document.total_pages}. "
f"If you need to see more pages to get complete context, use the read_page tool.",
deps=page_content,
)
# In pydantic-ai 1.x, extract the output from AgentRunResult
return result.output
async def analyze_pages(
self, start_page: int = 0, end_page: int = 0
) -> PageContentAnalysis:
"""Analyze the document starting from a specific page.
Args:
start_page: The starting page to analyze
end_page: The ending page to analyze
Returns:
PageContentAnalysis containing all extracted insights
"""
analysis = PageContentAnalysis()
analysis.insights = []
for page in range(start_page, end_page + 1):
initial_content = self.pdf_document.get_page_text(page)
page_content = PageContent(
page_number=page,
content=initial_content,
total_pages=self.pdf_document.total_pages,
)
result = None
retries = 0
while result is None and retries < 3:
try:
result = await self.agent.run(
f"Analyze this document page and extract structured insights. "
f"This is page {page + 1} of {self.pdf_document.total_pages}. "
f"If you need to see more pages to get complete context, use the read_page tool.",
deps=page_content,
)
# In pydantic-ai 1.x, extract the output from AgentRunResult
analysis.insights.extend(result.output.insights)
except Exception as e:
print(f"Error analyzing page {page}: {e}, Retrying...")
retries += 1
return analysis

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"""CLI interface for pdf-to-kcf tool."""
import asyncio
import json
from pathlib import Path
import click
from dotenv import load_dotenv
from .agent import DocumentAnalyzer
from .pdf_reader import PDFDocument
# Load environment variables from .env file
load_dotenv()
@click.command()
@click.argument("pdf_path", type=click.Path(exists=True))
@click.option(
"--output",
"-o",
type=click.Path(),
help="Output JSON file path (default: <pdf_name>_insights.json)",
)
@click.option(
"--start-page",
"-s",
type=int,
default=0,
help="Starting page number (0-indexed)",
)
@click.option(
"--end-page",
"-e",
type=int,
default=0,
help="Ending page number (0-indexed)",
)
@click.option(
"--page",
"-p",
type=int,
default=0,
help="Page number (0-indexed)",
)
@click.option(
"--model",
"-m",
type=str,
default="anthropic/claude-3.5-sonnet",
help="AI model to use via OpenRouter (default: anthropic/claude-3.5-sonnet)",
)
def main(
pdf_path: str, output: str, start_page: int, end_page: int, page: int, model: str
):
"""Parse PDF documents and extract structured insights using AI.
PDF_PATH: Path to the PDF file to analyze
"""
try:
# Load PDF
click.echo(f"Loading PDF: {pdf_path}")
pdf_doc = PDFDocument(pdf_path)
click.echo(f"Document loaded: {pdf_doc.total_pages} pages")
# Create analyzer
click.echo(f"Initializing analyzer with model: {model}")
analyzer = DocumentAnalyzer(pdf_doc, model=model)
if page > 0:
click.echo(f"Starting analysis from page {page + 1}...")
analysis = asyncio.run(analyzer.analyze_page(page))
elif end_page > 0 and end_page - start_page > 0:
click.echo(
f"Starting analysis from page {start_page + 1} to {end_page + 1}..."
)
analysis = asyncio.run(analyzer.analyze_pages(start_page, end_page))
else:
click.echo(f"Starting analysis from page {start_page + 1}...")
analysis = asyncio.run(analyzer.analyze_page(start_page))
# Determine output path
if not output:
pdf_name = Path(pdf_path).stem
output = f"{pdf_name}_insights.json"
# Write results
output_path = Path(output)
with output_path.open("w", encoding="utf-8") as f:
json.dump(
analysis.model_dump(mode="json"),
f,
indent=2,
ensure_ascii=False,
)
click.echo(f"\nAnalysis complete!")
click.echo(f"Found {len(analysis.insights)} insights")
click.echo(f"Results written to: {output_path.absolute()}")
# Print summary
if analysis.insights:
click.echo("\nInsight summary:")
for idx, insight in enumerate(analysis.insights, 1):
click.echo(
f" {idx}. [{insight.type.value.upper()}] {insight.insight[:80]}..."
)
except FileNotFoundError as e:
click.echo(f"Error: {e}", err=True)
raise click.Abort()
except Exception as e:
click.echo(f"Error during analysis: {e}", err=True)
raise click.Abort()
if __name__ == "__main__":
main()

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"""Data models for document parsing following the agentic document parsing format."""
from enum import Enum
from typing import List
from pydantic import BaseModel, Field
class ContentInsightType(str, Enum):
"""Classification of the type of insight extracted."""
COMMENT = "comment"
OPINION = "opinion"
FACT = "fact"
class ContentInsightAttribute(BaseModel):
"""Metadata or attributes associated with an insight."""
name: str = Field(
description="The name of the attribute (e.g., 'author', 'date', 'source')"
)
value: str = Field(description="The value of the attribute")
class ContentInsight(BaseModel):
"""A single insight extracted from the document content."""
type: ContentInsightType = Field(description="Classification of the insight")
insight: str = Field(description="The insight description/summary")
content: str = Field(description="The original content that was analyzed")
attributes: List[ContentInsightAttribute] = Field(
default_factory=list, description="Metadata about the insight"
)
class PageContentAnalysis(BaseModel):
"""The complete analysis output from the agent."""
insights: List[ContentInsight] = Field(
default_factory=list, description="Array of insights extracted from the content"
)
class PageContent(BaseModel):
"""Context passed to the agent for each analysis run."""
page_number: int = Field(description="Current page being analyzed (0-indexed)")
content: str = Field(description="The text content of the page")
total_pages: int = Field(description="Total number of pages in the document")

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"""PDF reading and text extraction utilities."""
from pathlib import Path
from typing import List
from pypdf import PdfReader
class PDFDocument:
"""Wrapper for PDF document reading."""
def __init__(self, pdf_path: str):
"""Initialize PDF reader with the given path.
Args:
pdf_path: Path to the PDF file
"""
self.pdf_path = Path(pdf_path)
if not self.pdf_path.exists():
raise FileNotFoundError(f"PDF file not found: {pdf_path}")
self.reader = PdfReader(str(self.pdf_path))
self.total_pages = len(self.reader.pages)
def get_page_text(self, page_number: int) -> str:
"""Extract text from a specific page.
Args:
page_number: 0-indexed page number
Returns:
Extracted text content from the page
"""
if page_number < 0 or page_number >= self.total_pages:
raise ValueError(
f"Invalid page number {page_number}. Document has {self.total_pages} pages."
)
page = self.reader.pages[page_number]
return page.extract_text()
def get_all_pages(self) -> List[str]:
"""Extract text from all pages.
Returns:
List of text content for each page
"""
return [self.get_page_text(i) for i in range(self.total_pages)]

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