Knowledge Extraction AI Agent Using Pydantic Data Models

🤖 AI-Powered Document Extraction System

**Transform unstructured documents into structured business intelligence using LlamaExtract**

🎯 Overview

This code teaches how to automate the extraction of structured information from unstructured reports using **LlamaExtract**. As a sample data and use-case, this code uses example data of AI consultancy reports. The code can be easily modified for other documents.

**📖 The details of the development of this application are discussed in the article [Creating a Knowledge Extraction AI Agent](https://medium.com/data-science-collective/creating-a-knowledge-extraction-ai-agent-697e94f44afb)**

✨ Features

• 🧠 AI-Powered Extraction: Uses LlamaExtract for intelligent document processing
• 📊 Structured Output: Converts unstructured reports to structured Excel data
• 🎯 Schema-Based: Pydantic models ensure data consistency and validation
• 📈 Analytics Ready: Generates structured documents ready for analysis
• 🔄 Batch Processing: Handles multiple documents simultaneously
• 🛡️ Error Handling: Robust fallback mechanisms for data quality
• 🌍 Multi-Field Support: Extracts different data fields per document

🏗️ System Architecture


🚀 Installation

Prerequisites

• Python 3.8 or higher
• LlamaCloud API key Get the LlamaCloud API key from https://cloud.llamaindex.ai/. If you are interested in using LlamaCloud services in the EU, you can adjust your base URL to EU_BASE_URL. In that case, create the API key from https://cloud.eu.llamaindex.ai/.

llama_extract = LlamaExtract(api_key=LLAMA_CLOUD_API_KEY, base_url=EU_BASE_URL) 

Step 1: Clone the Repository

git clone https://github.com/your-username/ai-document-extraction.git
cd ai-document-extraction

Step 2: Create Virtual Environment

# Create virtual environment
python -m venv venv