1. Abstract

Multi-Document Question Answering is an AI-powered system that retrieves relevant information from multiple documents and generates accurate, context-aware answers to user questions. The system processes documents in formats such as PDF, DOCX, and TXT, extracts their text, and organizes the information for efficient retrieval. By using Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG), it identifies relevant content across different documents and generates answers based on the available information. This project reduces the time required for manual document searching and helps users obtain meaningful insights from large collections of documents.

2. Objectives

1. To develop an intelligent question-answering system that works with multiple documents.
2. To extract and process text from different document formats.
3. To understand user questions using Natural Language Processing.
4. To retrieve relevant information from multiple sources.
5. To generate context-aware answers using Large Language Models.
6. To reduce manual searching and improve information accessibility.
7. To provide source references so users can verify generated answers.

3. Existing System

In the existing system, users generally open individual documents and manually search for the required information. Traditional keyword-search tools locate matching words but may not understand the meaning or context of a question. When information is distributed across several documents, users must compare the content manually to find a suitable answer.

Limitations of the Existing System

1. Requires considerable time and manual effort.
2. Makes cross-document information retrieval difficult.
3. Traditional keyword searches may miss semantically relevant content.
4. Comparing information across multiple files is challenging.
5. Users may overlook important details.
6. Does not consistently provide natural-language answers with supporting sources.

4. Proposed System

The proposed system provides an AI-based interface where users can upload multiple documents and ask questions in natural language. The application extracts the document content, divides it into smaller text chunks, converts the chunks into vector embeddings, and stores them in a searchable vector database. When a user submits a question, the system retrieves relevant passages from the document collection and supplies them as context to an LLM to generate an answer.

Key Features

1. Supports multiple document uploads.
2. Extracts text from supported file formats.
3. Uses semantic search to identify relevant information.
4. Generates natural-language answers using an LLM.
5. Retrieves information across different documents.
6. Displays source document names and relevant passages where available.
7. Provides a user-friendly interface for asking follow-up questions.
8. Includes error handling for unsupported, empty, or unreadable documents.

5. Implementation Procedure

1. Document Upload: Allow users to upload supported files such as PDF, DOCX, and TXT.
2. Text Extraction: Extract readable text using appropriate document-processing libraries.
3. Text Preprocessing: Clean the extracted content and divide it into manageable chunks.
4. Embedding Generation: Convert text chunks into vector representations using an embedding model.
5. Vector Storage: Store the embeddings and associated document metadata in a vector database.
6. Question Processing: Accept a natural-language question and convert it into an embedding.
7. Relevant Information Retrieval: Search the vector database for the most relevant passages across the uploaded documents.
8. Answer Generation: Provide the retrieved passages to an LLM to generate a grounded response.
9. Source Display: Show the answer along with available source document names or excerpts.
10. Testing and Evaluation: Test the system with different questions and documents to assess relevance, answer quality, and response time.

6. Software Requirements

1. Programming Language: Python
2. Frontend: Streamlit or React.js
3. Backend: Flask or FastAPI
4. AI Framework: LangChain or LlamaIndex
5. Language Model: An available LLM API or a locally hosted model
6. Embedding Model: Sentence Transformers or an API-based embedding model
7. Vector Database: FAISS, ChromaDB, or another compatible vector store
8. Document Processing: PyMuPDF, python-docx, and standard text-processing libraries
9. Development Environment: VS Code, PyCharm, or Jupyter Notebook
10. Operating System: Windows, Linux, or macOS

7. Hardware Requirements

1. Processor: Intel Core i3 or equivalent and above
2. RAM: Minimum 8 GB; 16 GB recommended for larger document collections
3. Storage: At least 5 GB of free space, depending on document size and model requirements
4. Internet Connection: Required when using cloud-based LLM or embedding APIs
5. GPU: Optional for API-based models; a suitable GPU may help with locally hosted models

8. Advantages of the Project

1. Retrieves information from multiple documents in one workflow.
2. Reduces the time spent manually searching files.
3. Understands questions beyond simple keyword matching.
4. Generates readable, context-aware answers.
5. Supports follow-up questions about uploaded content.
6. Provides source information to help users verify answers.
7. Can be adapted for education, research, business documentation, and knowledge management.
8. Can scale to larger document collections with suitable indexing and infrastructure.

9. Applications

1. Academic research and study material analysis.
2. Company policies and internal knowledge bases.
3. Legal and compliance document review.
4. Financial reports and business documentation.
5. Technical manuals and product documentation.
6. Healthcare research documents, with appropriate privacy and professional oversight.

10. Future Enhancements

1. Add support for scanned documents using OCR.
2. Support multilingual document retrieval and question answering.
3. Introduce role-based access controls for private document collections.
4. Improve retrieval using hybrid search and reranking.
5. Add document comparison and automatic summarization.
6. Display page numbers and precise source citations.
7. Evaluate answer accuracy and identify unsupported responses.