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ContextualAIParseTool

Description

This tool is designed to integrate Contextual AI's enterprise-grade document parsing capabilities with CrewAI, enabling you to leverage advanced AI-powered document understanding for complex layouts, tables, and figures. Use this tool to extract structured content from your documents using Contextual AI's powerful document parser.

Installation

To incorporate this tool into your project, follow the installation instructions below:

pip install 'crewai[tools]' contextual-client

Note: You'll need a Contextual AI API key. Sign up at app.contextual.ai to get your free API key.

Example

from crewai_tools import ContextualAIParseTool

tool = ContextualAIParseTool(api_key="your_api_key_here")

result = tool._run(
    file_path="/path/to/document.pdf",
    parse_mode="standard",
    page_range="0-5",
    output_types=["markdown-per-page"]
)
print(result)

The result will show the parsed contents of your document. For example:

{
  "file_name": "attention_is_all_you_need.pdf",
  "status": "completed",
  "pages": [
    {
      "index": 0,
      "markdown": "Provided proper attribution ...
    },
    {
      "index": 1,
      "markdown": "## 1 Introduction ...
    },
    ...
  ] 
}

Parameters

api_key: Your Contextual AI API key
file_path: Path to document to parse
parse_mode: Parsing mode (default: "standard")
figure_caption_mode: Figure caption handling (default: "concise")
enable_document_hierarchy: Enable hierarchy detection (default: True)
page_range: Pages to parse (e.g., "0-5", None for all)
output_types: Output formats (default: ["markdown-per-page"])

Key Features

Advanced Document Understanding: Handles complex PDF layouts, tables, and multi-column documents
Figure and Table Extraction: Intelligent extraction of figures, charts, and tabular data
Page Range Selection: Parse specific pages or entire documents

Use Cases

Extract structured content from complex PDFs and research papers
Parse financial reports, legal documents, and technical manuals
Convert documents to markdown for further processing in RAG pipelines

For more detailed information about Contextual AI's capabilities, visit the official documentation.

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