content-core 1.3.1

Extract what matters from any media source. Available as Python Library, macOS Service, CLI and MCP Server

Content Core

Content Core is a powerful, AI-powered content extraction and processing platform that transforms any source into clean, structured content. Extract text from websites, transcribe videos, process documents, and generate AI summaries—all through a unified interface with multiple integration options.

🚀 What You Can Do

Extract content from anywhere:

• 📄 Documents - PDF, Word, PowerPoint, Excel, Markdown, HTML, EPUB
• 🎥 Media - Videos (MP4, AVI, MOV) with automatic transcription
• 🎵 Audio - MP3, WAV, M4A with speech-to-text conversion
• 🌐 Web - Any URL with intelligent content extraction
• 🖼️ Images - JPG, PNG, TIFF with OCR text recognition
• 📦 Archives - ZIP, TAR, GZ with content analysis

Process with AI:

• ✨ Clean & format extracted content automatically
• 📝 Generate summaries with customizable styles (bullet points, executive summary, etc.)
• 🎯 Context-aware processing - explain to a child, technical summary, action items
• 🔄 Smart engine selection - automatically chooses the best extraction method

🛠️ Multiple Ways to Use

🖥️ Command Line (Zero Install)

# Extract content from any source
uvx --from "content-core" ccore https://example.com
uvx --from "content-core" ccore document.pdf

# Generate AI summaries  
uvx --from "content-core" csum video.mp4 --context "bullet points"

🤖 Claude Desktop Integration

One-click setup with Model Context Protocol (MCP) - extract content directly in Claude conversations.

🔍 Raycast Extension

Smart auto-detection commands:

• Extract Content - Full interface with format options
• Summarize Content - 9 summary styles available
• Quick Extract - Instant clipboard extraction

🖱️ macOS Right-Click Integration

Right-click any file in Finder → Services → Extract or Summarize content instantly.

🐍 Python Library

import content_core as cc

# Extract from any source
result = await cc.extract("https://example.com/article")
summary = await cc.summarize_content(result, context="explain to a child")

⚡ Key Features

• 🎯 Intelligent Auto-Detection: Automatically selects the best extraction method based on content type and available services
• 🔧 Smart Engine Selection:
  • URLs: Firecrawl → Jina → BeautifulSoup fallback chain
  • Documents: Docling → Enhanced PyMuPDF → Simple extraction fallback
  • Media: OpenAI Whisper transcription
  • Images: OCR with multiple engine support
• 📊 Enhanced PDF Processing: Advanced PyMuPDF engine with quality flags, table detection, and optional OCR for mathematical formulas
• 🌍 Multiple Integrations: CLI, Python library, MCP server, Raycast extension, macOS Services
• ⚡ Zero-Install Options: Use uvx for instant access without installation
• 🧠 AI-Powered Processing: LLM integration for content cleaning and summarization
• 🔄 Asynchronous: Built with asyncio for efficient processing

Getting Started

Installation

Install Content Core using pip:

# Basic installation (PyMuPDF + BeautifulSoup/Jina extraction)
pip install content-core

# With enhanced document processing (adds Docling)
pip install content-core[docling]

# With MCP server support (now included by default)
pip install content-core

# Full installation (with enhanced document processing)
pip install content-core[docling]

Alternatively, if you’re developing locally:

# Clone the repository
git clone https://github.com/lfnovo/content-core
cd content-core

# Install with uv
uv sync

Command-Line Interface

Content Core provides three CLI commands for extracting, cleaning, and summarizing content: ccore, cclean, and csum. These commands support input from text, URLs, files, or piped data (e.g., via cat file | command).

Zero-install usage with uvx:

# Extract content
uvx --from "content-core" ccore https://example.com

# Clean content  
uvx --from "content-core" cclean "messy content"

# Summarize content
uvx --from "content-core" csum "long text" --context "bullet points"

ccore - Extract Content

Extracts content from text, URLs, or files, with optional formatting. Usage:

ccore [-f|--format xml|json|text] [-d|--debug] [content]

Options:

• -f, --format: Output format (xml, json, or text). Default: text.
• -d, --debug: Enable debug logging.
• content: Input content (text, URL, or file path). If omitted, reads from stdin.

Examples:

# Extract from a URL as text
ccore https://example.com

# Extract from a file as JSON
ccore -f json document.pdf

# Extract from piped text as XML
echo "Sample text" | ccore --format xml

cclean - Clean Content

Cleans content by removing unnecessary formatting, spaces, or artifacts. Accepts text, JSON, XML input, URLs, or file paths. Usage:

cclean [-d|--debug] [content]

Options:

• -d, --debug: Enable debug logging.
• content: Input content to clean (text, URL, file path, JSON, or XML). If omitted, reads from stdin.

Examples:

# Clean a text string
cclean "  messy   text   "

# Clean piped JSON
echo '{"content": "  messy   text   "}' | cclean

# Clean content from a URL
cclean https://example.com

# Clean a file’s content
cclean document.txt

csum - Summarize Content

Summarizes content with an optional context to guide the summary style. Accepts text, JSON, XML input, URLs, or file paths.

Usage:

csum [--context "context text"] [-d|--debug] [content]

Options:

• --context: Context for summarization (e.g., "explain to a child"). Default: none.
• -d, --debug: Enable debug logging.
• content: Input content to summarize (text, URL, file path, JSON, or XML). If omitted, reads from stdin.

Examples:

# Summarize text
csum "AI is transforming industries."

# Summarize with context
csum --context "in bullet points" "AI is transforming industries."

# Summarize piped content
cat article.txt | csum --context "one sentence"

# Summarize content from URL
csum https://example.com

# Summarize a file's content
csum document.txt

Quick Start

You can quickly integrate content-core into your Python projects to extract, clean, and summarize content from various sources.

import content_core as cc

# Extract content from a URL, file, or text
result = await cc.extract("https://example.com/article")

# Clean messy content
cleaned_text = await cc.clean("...messy text with [brackets] and extra spaces...")

# Summarize content with optional context
summary = await cc.summarize_content("long article text", context="explain to a child")

Documentation

For more information on how to use the Content Core library, including details on AI model configuration and customization, refer to our Usage Documentation.

MCP Server Integration

Content Core includes a Model Context Protocol (MCP) server that enables seamless integration with Claude Desktop and other MCP-compatible applications. The MCP server exposes Content Core's powerful extraction capabilities through a standardized protocol.

Quick Setup with Claude Desktop

# Install Content Core (MCP server included)
pip install content-core

# Or use directly with uvx (no installation required)
uvx --from "content-core" content-core-mcp

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "content-core": {
      "command": "uvx",
      "args": [
        "--from",
        "content-core",
        "content-core-mcp"
      ]
    }
  }
}

For detailed setup instructions, configuration options, and usage examples, see our MCP Documentation.

Enhanced PDF Processing

Content Core features an optimized PyMuPDF extraction engine with significant improvements for scientific documents and complex PDFs.

Key Improvements

• 🔬 Mathematical Formula Extraction: Enhanced quality flags eliminate <!-- formula-not-decoded --> placeholders
• 📊 Automatic Table Detection: Tables converted to markdown format for LLM consumption
• 🔧 Quality Text Rendering: Better ligature, whitespace, and image-text integration
• ⚡ Optional OCR Enhancement: Selective OCR for formula-heavy pages (requires Tesseract)

Configuration for Scientific Documents

For documents with heavy mathematical content, enable OCR enhancement:

# In cc_config.yaml
extraction:
  pymupdf:
    enable_formula_ocr: true      # Enable OCR for formula-heavy pages
    formula_threshold: 3          # Min formulas per page to trigger OCR
    ocr_fallback: true           # Graceful fallback if OCR fails

# Runtime configuration
from content_core.config import set_pymupdf_ocr_enabled
set_pymupdf_ocr_enabled(True)

Requirements for OCR Enhancement

# Install Tesseract OCR (optional, for formula enhancement)
# macOS
brew install tesseract

# Ubuntu/Debian
sudo apt-get install tesseract-ocr

Note: OCR is optional - you get improved PDF extraction automatically without any additional setup.

macOS Services Integration

Content Core provides powerful right-click integration with macOS Finder, allowing you to extract and summarize content from any file without installation. Choose between clipboard or TextEdit output for maximum flexibility.

Available Services

Create 4 convenient services for different workflows:

• Extract Content → Clipboard - Quick copy for immediate pasting
• Extract Content → TextEdit - Review before using
• Summarize Content → Clipboard - Quick summary copying
• Summarize Content → TextEdit - Formatted summary with headers

Quick Setup

1. Install uv (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Create services manually using Automator (5 minutes setup)

Usage

Right-click any supported file in Finder → Services → Choose your option:

• PDFs, Word docs - Instant text extraction
• Videos, audio files - Automatic transcription
• Images - OCR text recognition
• Web content - Clean text extraction
• Multiple files - Batch processing support

Features

• Zero-install processing: Uses uvx for isolated execution
• Multiple output options: Clipboard or TextEdit display
• System notifications: Visual feedback on completion
• Wide format support: 20+ file types supported
• Batch processing: Handle multiple files at once
• Keyboard shortcuts: Assignable hotkeys for power users

For complete setup instructions with copy-paste scripts, see macOS Services Documentation.

Raycast Extension

Content Core provides a powerful Raycast extension with smart auto-detection that handles both URLs and file paths seamlessly. Extract and summarize content directly from your Raycast interface without switching applications.

Quick Setup

From Raycast Store (coming soon):

1. Open Raycast and search for "Content Core"
2. Install the extension by luis_novo
3. Configure API keys in preferences

Manual Installation:

1. Download the extension from the repository
2. Open Raycast → "Import Extension"
3. Select the raycast-content-core folder

Commands

🔍 Extract Content - Smart URL/file detection with full interface

• Auto-detects URLs vs file paths in real-time
• Multiple output formats (Text, JSON, XML)
• Drag & drop support for files
• Rich results view with metadata

📝 Summarize Content - AI-powered summaries with customizable styles

• 9 different summary styles (bullet points, executive summary, etc.)
• Auto-detects source type with visual feedback
• One-click snippet creation and quicklinks

⚡ Quick Extract - Instant extraction to clipboard

• Type → Tab → Paste source → Enter
• No UI, works directly from command bar
• Perfect for quick workflows

Features

• Smart Auto-Detection: Instantly recognizes URLs vs file paths
• Zero Installation: Uses uvx for Content Core execution
• Rich Integration: Keyboard shortcuts, clipboard actions, Raycast snippets
• All File Types: Documents, videos, audio, images, archives
• Visual Feedback: Real-time type detection with icons

For detailed setup, configuration, and usage examples, see Raycast Extension Documentation.

Using with Langchain

For users integrating with the Langchain framework, content-core exposes a set of compatible tools. These tools, located in the src/content_core/tools directory, allow you to leverage content-core extraction, cleaning, and summarization capabilities directly within your Langchain agents and chains.

You can import and use these tools like any other Langchain tool. For example:

from content_core.tools import extract_content_tool, cleanup_content_tool, summarize_content_tool
from langchain.agents import initialize_agent, AgentType

tools = [extract_content_tool, cleanup_content_tool, summarize_content_tool]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Extract the content from https://example.com and then summarize it.") 

Refer to the source code in src/content_core/tools for specific tool implementations and usage details.

Basic Usage

The core functionality revolves around the extract_content function.

import asyncio
from content_core.extraction import extract_content

async def main():
    # Extract from raw text
    text_data = await extract_content({"content": "This is my sample text content."})
    print(text_data)

    # Extract from a URL (uses 'auto' engine by default)
    url_data = await extract_content({"url": "https://www.example.com"})
    print(url_data)

    # Extract from a local video file (gets transcript, engine='auto' by default)
    video_data = await extract_content({"file_path": "path/to/your/video.mp4"})
    print(video_data)

    # Extract from a local markdown file (engine='auto' by default)
    md_data = await extract_content({"file_path": "path/to/your/document.md"})
    print(md_data)

    # Per-execution override with Docling for documents
    doc_data = await extract_content({
        "file_path": "path/to/your/document.pdf",
        "document_engine": "docling",
        "output_format": "html"
    })
    
    # Per-execution override with Firecrawl for URLs
    url_data = await extract_content({
        "url": "https://www.example.com",
        "url_engine": "firecrawl"
    })
    print(doc_data)

if __name__ == "__main__":
    asyncio.run(main())

(See src/content_core/notebooks/run.ipynb for more detailed examples.)

Docling Integration

Content Core supports an optional Docling-based extraction engine for rich document formats (PDF, DOCX, PPTX, XLSX, Markdown, AsciiDoc, HTML, CSV, Images).

Enabling Docling

Docling is not the default engine when parsing documents. If you don't want to use it, you need to set engine to "simple".

Via configuration file

In your cc_config.yaml or custom config, set:

extraction:
  document_engine: docling  # 'auto' (default), 'simple', or 'docling'
  url_engine: auto          # 'auto' (default), 'simple', 'firecrawl', or 'jina'
  docling:
    output_format: markdown  # markdown | html | json

Programmatically in Python

from content_core.config import set_document_engine, set_url_engine, set_docling_output_format

# switch document engine to Docling
set_document_engine("docling")

# switch URL engine to Firecrawl
set_url_engine("firecrawl")

# choose output format: 'markdown', 'html', or 'json'
set_docling_output_format("html")

# now use ccore.extract or ccore.ccore
result = await cc.extract("document.pdf")

Configuration

Configuration settings (like API keys for external services, logging levels) can be managed through environment variables or .env files, loaded automatically via python-dotenv.

Example .env:

OPENAI_API_KEY=your-key-here
GOOGLE_API_KEY=your-key-here

# Engine Selection (optional)
CCORE_DOCUMENT_ENGINE=auto  # auto, simple, docling
CCORE_URL_ENGINE=auto       # auto, simple, firecrawl, jina

Engine Selection via Environment Variables

For deployment scenarios like MCP servers or Raycast extensions, you can override the extraction engines using environment variables:

• CCORE_DOCUMENT_ENGINE: Force document engine (auto, simple, docling)
• CCORE_URL_ENGINE: Force URL engine (auto, simple, firecrawl, jina)

These variables take precedence over config file settings and provide explicit control for different deployment scenarios.

Custom Prompt Templates

Content Core allows you to define custom prompt templates for content processing. By default, the library uses built-in prompts located in the prompts directory. However, you can create your own prompt templates and store them in a dedicated directory. To specify the location of your custom prompts, set the PROMPT_PATH environment variable in your .env file or system environment.

Example .env with custom prompt path:

OPENAI_API_KEY=your-key-here
GOOGLE_API_KEY=your-key-here
PROMPT_PATH=/path/to/your/custom/prompts

When a prompt template is requested, Content Core will first look in the custom directory specified by PROMPT_PATH (if set and exists). If the template is not found there, it will fall back to the default built-in prompts. This allows you to override specific prompts while still using the default ones for others.

Development

To set up a development environment:

# Clone the repository
git clone <repository-url>
cd content-core

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate
uv sync --group dev

# Run tests
make test

# Lint code
make lint

# See all commands
make help

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please see our Contributing Guide for more details on how to get started.