content-core 1.1.2

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

Content Core

Content Core is a versatile Python library designed to extract and process content from various sources, providing a unified interface for handling text, web pages, and local files.

Overview

Note: As of v0.8, the default extraction engine is 'auto'. Content Core will automatically select the best extraction method based on your environment and available API keys, with a smart fallback order for both URLs and files. For files/documents, 'auto' now tries Docling first, then falls back to simple extraction. You can override the engine if needed, but 'auto' is recommended for most users.

The primary goal of Content Core is to simplify the process of ingesting content from diverse origins. Whether you have raw text, a URL pointing to an article, or a local file like a video or markdown document, Content Core aims to extract the meaningful content for further use.

Key Features

• Multi-Source Extraction: Handles content from:
  • Direct text strings.
  • Web URLs (using robust extraction methods).
  • Local files (including automatic transcription for video/audio files and parsing for text-based formats).
• Intelligent Processing: Applies appropriate extraction techniques based on the source type. See the Processors Documentation for detailed information on how different content types are handled.
• Smart Engine Selection: By default, Content Core uses the 'auto' engine, which:
  • For URLs: Uses Firecrawl if FIRECRAWL_API_KEY is set, else tries Jina. Jina might fail because of rate limits, which can be fixed by adding JINA_API_KEY. If Jina failes, BeautifulSoup is used as a fallback.
  • For files: Tries Docling extraction first (for robust document parsing), then falls back to simple extraction if needed.
  • You can override this by specifying an engine, but 'auto' is recommended for most users.
• Content Cleaning (Optional): Likely integrates with LLMs (via prompter.py and Jinja templates) to refine and clean the extracted content.
• MCP Server: Includes a Model Context Protocol (MCP) server for seamless integration with Claude Desktop and other MCP-compatible applications.
• macOS Services: Right-click context menu integration for Finder (extract and summarize files directly).
• Asynchronous: Built with asyncio for efficient I/O operations.

Getting Started

Installation

Install Content Core using pip:

# Install the package
pip install content-core

# Install with MCP server support
pip install content-core[mcp]

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 with MCP support
pip install content-core[mcp]

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

Add to your claude_desktop_config.json:

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

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

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.

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

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.