This project aims to develop a bot capable of answering questions from multiple uploaded PDF documents. Users can upload PDFs, ask questions related to the content of these documents, and receive relevant answers from the bot.
The primary objective is to create an interactive web application that facilitates easy querying of information from PDFs, enhancing document accessibility and searchability.
- Streamlit: Used for building the interactive web application.
- PyPDF2: Utilized for extracting text from PDF documents.
- LangChain: Integrated for text processing, embeddings, vector stores, and conversational models.
- OpenAI GPT-3 Model: Employed for generating responses to user queries.
- FAISS: Implemented for efficient similarity search over document embeddings.
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PDF Text Extraction: PDF documents are uploaded and processed using PyPDF2 to extract text content.
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Text Chunking: Text from PDFs is segmented into manageable chunks using LangChain's CharacterTextSplitter.
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Vectorization: The segmented text is converted into embeddings using OpenAI's embeddings or Hugging Face models via LangChain.
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Conversation Chain: LangChain's ConversationalRetrievalChain is set up to handle user queries based on the vector store, providing relevant responses.
LangChain is used in the project for several critical tasks:
- Text Processing: It handles text extraction from uploaded PDFs using PyPDF2 and segments this text into manageable chunks.
- Vector Embeddings: LangChain converts segmented text into embeddings, which are numerical representations of text that capture semantic meanings. These embeddings are crucial for understanding and comparing the content of PDFs.
- Conversational Models: LangChain's capabilities are leveraged to set up a conversational retrieval chain. This chain uses embeddings to match user queries with relevant sections of PDF documents, facilitating accurate question answering.
FAISS is employed for efficient similarity search over document embeddings:
- Vector Storage: It stores and indexes document embeddings generated by LangChain, enabling fast retrieval of documents that are most similar to a given query.
- Search Optimization: FAISS optimizes the process of comparing embeddings, making it feasible to quickly find and retrieve relevant PDF sections based on user queries.
Vector embeddings are numerical representations of text derived from LangChain:
- Purpose: They encode the semantic content of PDF documents, allowing for effective comparison and retrieval.
- Implementation: LangChain employs methods to embed text segments into vectors, which are then stored and indexed using FAISS for efficient search operations.
- Interface: The Streamlit interface allows users to upload PDFs, input questions, and view bot responses.
- Session Management: Utilizes
st.session_statein Streamlit to manage conversation history (chat_history) and maintain the current state of the conversation (conversation).
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PDF Handling: Managing multiple PDF uploads and extracting meaningful text content posed initial challenges, resolved through robust handling in PyPDF2.
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Model Integration: Integrating and fine-tuning conversational models like OpenAI's GPT-3 for accurate and contextually relevant responses required careful setup and configuration within LangChain.
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Performance: Addressing response time and system performance considerations, especially with larger PDFs and complex queries, involved optimizations in text processing and model inference.
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Performance Improvements: Further optimize text extraction, vectorization processes, and model inference for enhanced speed and efficiency.
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User Experience: Enhance the interface for improved usability and engagement, potentially integrating features like document summarization or highlighting relevant passages.
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Model Selection: Explore and integrate different conversational models or embeddings to improve accuracy and response quality based on user feedback and ongoing research.