Open Extract AI Agent

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open-extract

Features • What is a Schema? • Use Cases • Getting Started • Documentation

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open-extract simplifies the ingestion and processing of unstructured data for those building AI Agents/Agentic Workflows using frameworks such as LangGraph, AG2, and CrewAI.

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🚀 Features

📄 Extract Relevant Information Seamlessly: Give your applications the ability to identify and extract relevant data from one or many large documents and websites with just a single API call. Get the content back in JSON or Markdown formats, making it easy to integrate into your workflows.

🔍 Multi-Schema/Multi-Document Support: Extract data based one or many predefined schemas from a variety of document types, without needing a vector database or specifying page numbers.

🔄 Built-in Caching: With built-in caching, previously extracted schemas can be instantly retrieved, enabling rapid repeat extractions without having to reprocess the original documents.

🚫 No Vendor Lock-In: Enjoy complete flexibility with your choice of model provider. Whether using open-source or closed-source models, you're never tied to a specific vendor, ensuring full control.

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🧰 What is a Schema?

A schema is a set of key-value pairs describing what needs to be extracted from a particular document.

<details> <summary>📋 Example Schema</summary>

{
    "Firm": "The name of the firm",
    "Number of Funds": "The number of funds managed by the firm",
    "Commitment": "The commitment amount in millions of dollars",
    "% of Total Comm": "The percentage of total commitment",
    "Exposure (FMV + Unfunded)": "The exposure including fair market value and unfunded commitments in millions of dollars",
    "% of Total Exposure": "The percentage of total exposure",
    "TVPI": "Total Value to Paid-In multiple",
    "Net IRR": "Net Internal Rate of Return as a percentage"
}

</details> </details>

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🎯 Use Cases

<table> <tr> <td align="center"><b>💼 Financial Report Analysis</b></td> <td align="center"><b>📊 Customer Feedback Processing</b></td> <td align="center"><b>🔬 Research Assistant</b></td> <td align="center"><b>🧠 Legal Contract Parsing</b></td> </tr> <tr> <td>Extract key financial metrics from quarterly PDF reports</td> <td>Categorize feedback from various document types</td> <td>Process research papers, extracting methodologies and findings</td> <td>Extract key legal terms and conditions from contracts</td> </tr> </table>

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🛠️ Getting Started

Build the Platform

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To build the platform from source, run the following command:

./start-oe.sh

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Run an example script or notebook

Once the platform is running you can test it out by trying one of our examples

1. Navigate to the examples folder:

   cd examples
   

2. Navigate to the scripts or notebooks folder:

   cd scripts
   

   or

   cd notebooks/autogen_example
   

3. Run one of our example scripts:

   python azure_example.py
   

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