Abstract

DocumentIQ is an AI-powered file query assistant designed to help users extract precise answers from uploaded PDF documents. It leverages state-of-the-art technologies, such as OpenAI’s GPT for natural language generation, OpenAIEmbeddings for text vectorization, LangChain’s RecursiveCharacterTextSplitter for document segmentation, and Chroma as a vector database, to provide a seamless, interactive question-answering experience. This paper outlines the architecture, methodology, and experimental evaluation of DocumentIQ, demonstrating its effectiveness in contextual document retrieval and real-time response generation.

Introduction

The increasing volume of digital documents necessitates efficient information retrieval and comprehension systems. Traditional keyword searches often fail to capture the contextual nuances present in complex documents. DocumentIQ addresses this challenge by integrating advanced natural language processing (NLP) techniques with vector-based retrieval methods. By combining transformer-based models with efficient document chunking and real-time streaming capabilities, DocumentIQ enables users to query PDFs and receive accurate, contextually grounded answers. This project is motivated by the need for tools that empower users to navigate large-scale document repositories effortlessly.

Methodology

DocumentIQ’s workflow is designed to efficiently process and analyze PDF documents to facilitate precise, context-aware querying.

1. PDF Upload and Processing:

• Users upload one or more PDF files via a Streamlit-based interface.
• The system reads the uploaded documents.
• Utilizes LangChain’s RecursiveCharacterTextSplitter to split PDFs into manageable, contextually coherent chunks.

2. Vectorization:

• Each text chunk is transformed into high-dimensional vectors using the OpenAIEmbeddings model.
• These vectors capture the semantic nuances and contextual information of the text.

3. Vector Storage:

• The generated embeddings are stored in a Chroma vector database.
• This setup is optimized for fast similarity searches and scalability as the dataset grows.

4. Interactive Querying:

• User queries are converted into vector representations.
• Relevant document chunks are retrieved by comparing query vectors with stored embeddings using cosine similarity.

5. Response Generation:

• OpenAI’s GPT model synthesizes the retrieved chunks into a coherent, natural language answer.
• The system displays the top three document sources used for generating the response to enhance transparency.

6. Real-Time Streaming:

• A StreamHandler updates the user interface in real time with incremental responses.
• This ensures a seamless and responsive user experience from query submission to final answer delivery.

Experiments

A series of experiments were conducted to evaluate DocumentIQ’s performance. Test PDFs covering diverse topics were uploaded, and a range of queries was executed to assess:

1. Retrieval Accuracy: The system’s ability to return relevant chunks was measured using cosine similarity between query and chunk embeddings.

2. Response Quality: Generated answers were evaluated for coherence, accuracy, and relevance by comparing them with manual summaries.

3. System Responsiveness: Streaming response times were monitored to ensure the application provided real-time feedback. Initial trials indicated that DocumentIQ effectively retrieves pertinent information and constructs accurate, context-aware responses, even when dealing with unstructured data.

Results

The experiments demonstrated that DocumentIQ delivers high-quality responses with a strong alignment to user queries. The integration of transformer-based language models and vector retrieval methods resulted in accurate answer generation and effective source tracking. Feedback from test users highlighted the tool’s ability to simplify complex documents into accessible insights, with fast response times and reliable performance across a variety of document types.

Conclusion

DocumentIQ successfully demonstrates the feasibility of combining advanced NLP models with vector-based retrieval systems to facilitate precise document querying. The system not only enhances the user experience by providing clear, actionable insights but also serves as a scalable solution for managing large document repositories. Future work will focus on refining the chunking algorithm, expanding support for additional document formats, and further optimizing model inference to reduce latency. This project underscores the potential of AI-driven tools in revolutionizing how we interact with and derive value from digital documents.