This project demonstrates a chatbot system that interacts with PDF files in a conversational manner. The chatbot extracts the text from a PDF document and allows users to query the content through a conversational interface. It uses LangChain and OpenAI models to provide responses based on the information in the PDF.
- Extracts text from PDF files.
- Splits text into smaller, manageable chunks for better processing.
- Creates a vector store using FAISS to index the text.
- Uses OpenAI's language models for conversational retrieval.
- Stores chat history for context in a conversation.
This chatbot has many practical applications across different fields. Here are a few examples:
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Educational Materials: Imagine being able to interact with textbooks or research papers. Students could ask their textbooks questions directly and get answers immediately, making studying more efficient.
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Legal Documents: Lawyers and paralegals can use this system to interact with contracts, legal briefs, and statutes. It would save time and ensure that important clauses are never overlooked.
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Manuals and Guides: Whether it's a product manual, user guide, or technical documentation, this chatbot can help users quickly find solutions without reading through pages of text.
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Business Reports: Businesses could use it to analyze reports and extract insights, making meetings and decision-making processes faster and more informed.
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This paper presents a Conversational PDF Chatbot system capable of interacting with users by answering queries based on PDF content. The proposed solution extracts text from PDFs, processes it using advanced language models, and enables a conversational interface. Such a system can enhance productivity in various domains by simplifying the retrieval of specific information from large, unstructured documents.
PDF Chatbot, Natural Language Processing (NLP), LangChain, Conversational AI, Document Retrieval, AI-powered Assistant
With the increasing volume of digital documentation in various sectors such as education, healthcare, law, and corporate environments, retrieving relevant information quickly has become a major challenge. Traditional search mechanisms require manual input and effort to locate specific data within lengthy documents. This project aims to solve this problem by developing a chatbot that allows users to interact with PDFs conversationally, providing precise answers to their queries.
This chatbot leverages LangChain, OpenAI embeddings, and FAISS for efficient document retrieval and conversational capabilities.
The objective of this project is to create a system that:
- Extracts content from PDFs.
- Processes the extracted text for optimal searchability.
- Provides a conversational interface to answer user queries about the document.
- Improves productivity by minimizing manual search time.
Several methods have been developed for document retrieval, including traditional keyword-based search engines and modern AI-driven solutions. Recent advancements in NLP and machine learning have enabled more intuitive interactions with text-based content. LangChain and FAISS provide a powerful framework for building AI chatbots capable of understanding and retrieving contextual information from documents. However, many existing solutions lack the ability to maintain context over multiple queries, which this project addresses through conversational memory.
The chatbot begins by reading the input PDF file and extracting its content using the PyPDF2 library.
Each page of the document is processed, and its textual content is extracted for further analysis.
Given the limitations of language models in processing large volumes of text at once, the extracted content is split into smaller chunks using CharacterTextSplitter from LangChain.
- Chunk size: 900 characters
- Overlap: 300 characters
This ensures that the chunks retain sufficient context for accurate retrieval.
Each chunk is transformed into a vector representation using OpenAI embeddings. This allows the system to compare user queries with document chunks based on semantic similarity. The FAISS library is used to store and retrieve these vectors efficiently.
A conversational retrieval chain is established using LangChain’s ConversationalRetrievalChain class, which incorporates:
- LLM (ChatOpenAI): Handles language understanding and response generation.
- FAISS Retriever: Fetches relevant document chunks based on the user’s query.
- Memory: Maintains conversation history, enabling context-aware responses.
The user interacts with the system via a simple command-line interface. The chatbot responds to queries until the user types 'quit' to exit.
The chatbot was tested using a sample PDF document, and it successfully answered queries related to specific sections. The use of conversational memory enhanced the user experience by providing contextually relevant responses across multiple queries.
- Accuracy: Measured by the chatbot’s ability to provide correct answers to user queries.
- Response Time: Averaged 1-2 seconds per query.
- Memory Retention: The system successfully maintained conversational context across multiple turns.
This Conversational PDF Chatbot can be applied in various domains, including:
- Education: Students can interact with textbooks to retrieve answers quickly.
- Corporate: Professionals can query lengthy reports, guidelines, and contracts.
- Healthcare: Medical professionals can refer to specific sections in clinical documents or research papers.
- Legal: Lawyers can retrieve information from legal documents or case files.
The Conversational PDF Chatbot developed in this project demonstrates the potential of AI-driven solutions to enhance document interaction. By enabling users to ask questions and receive instant answers from PDFs, the system improves efficiency and reduces the cognitive load associated with manual searching. Future work can focus on developing a more user-friendly interface and integrating support for multiple document formats.
- Web-based Interface: Develop a web application for easier access and usability.
- Multi-Document Support: Enable querying across multiple PDFs simultaneously.
- Enhanced NLP Models: Experiment with different language models to improve accuracy.
- Context Summarization: Provide summaries of large sections when direct answers are unavailable.
- LangChain Documentation – https://docs.langchain.com
- FAISS Documentation – https://faiss.io
- OpenAI Embeddings API – https://platform.openai.com
- PyPDF2 Library – https://pypdf2.readthedocs.io