DocuQuery is an intelligent, scalable Question & Answer (Q&A) API designed to extract relevant information from PDFs, URLs, and CSV files. The API leverages advanced Natural Language Processing (NLP) and Machine Learning (ML) models to analyze and answer questions based on the content of these documents, providing a powerful tool for automated data retrieval and decision support.
This project demonstrates the ability to build a robust API that can interface with diverse data sources, process unstructured content, and deliver precise answers in response to user queries. DocuQuery can be used in various applications, including data extraction, document automation, and interactive knowledge systems.
- Multi-format Support: Supports extraction from PDFs, URLs, and CSV files.
- Intelligent Q&A: Utilizes NLP and ML models to provide context-aware answers.
- Flexible Integration: Easy to integrate into existing systems via a RESTful API.
- Text Extraction: Efficient extraction of text and tables from PDFs and CSVs, and web scraping from URLs.
- Real-time Queries: Offers real-time responses to user questions based on document content.
- Programming Language: Python
- API Framework: Flask (for RESTful API development)
- Natural Language Processing: Hugging Face Transformers (for pre-trained language models)
- File Parsing:
- PyPDF2 for PDF text extraction
- BeautifulSoup for web scraping from URLs
- Pandas for parsing and reading CSV data
- Machine Learning: Pre-trained models (e.g., GPT-3, BERT) for contextual question answering