Project Overview
In this project, I developed an AI-powered document analysis system that automatically extracts key information from technical documents. The system uses advanced natural language processing techniques to understand document structure and content, significantly reducing the time required for manual document review.
Technical Challenge
The main challenge was processing diverse document formats and extracting structured data with high accuracy. Technical documents often contain complex terminology, tables, and specialized formats that traditional OCR and text extraction tools struggle with.
Solution
I built a solution using LangChain and OpenAI’s language models that:
- Processes multiple document formats (PDF, DOCX, HTML)
- Uses custom prompt engineering to guide the AI in understanding technical content
- Implements a document chunking strategy to handle large documents
- Creates structured JSON output from unstructured text
- Provides confidence scores for extracted information
Technologies Used
- LangChain: For building the document processing pipeline and connecting various components
- OpenAI API: Leveraging GPT models for natural language understanding
- NodeJS: Backend server implementation
- TypeScript: For type-safe code and better developer experience
- MongoDB: Storing processed documents and extraction results
- Docker: Containerization for easy deployment
Results
The system achieved remarkable results:
- 75% reduction in document processing time
- 92% accuracy in information extraction
- Ability to process 200+ pages per minute
- Successful integration with existing document management systems
This project demonstrates my ability to work with cutting-edge AI technologies and apply them to solve real business problems. The solution is now being used by multiple teams, saving hundreds of hours of manual document review each month.