This project is a Python Flask web application that leverages the power of Pinecone for vector storage and ChatGPT embeddings to create a highly interactive custom agent. The primary goal of the agent is to ingest and understand PDF documents, allowing users to ask questions about the content and receive accurate, context-aware responses.
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Flask Backend: The application is built on Flask, a lightweight web framework, providing an easy-to-use and scalable backend for handling user requests and serving responses.
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Pinecone for Vector Storage: Pinecone is used for efficiently storing and retrieving document embeddings. It allows the system to search through ingested PDF data and find the most relevant information based on user queries.
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ChatGPT Embeddings: The project uses OpenAI's ChatGPT to generate embeddings from the text extracted from PDFs. These embeddings are then stored in Pinecone, enabling semantic search for precise answers to user questions.
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PDF Ingestion: The application can process and ingest PDF documents, extracting the text and converting it into a format that can be embedded and stored. This ensures the chatbot can analyze and query the document's contents.
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Custom Agent: The custom-built chatbot is designed to be capable of understanding and answering questions about the ingested PDFs, making it useful for a wide range of applications, including document summarization, knowledge extraction, and FAQ generation.
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Upload PDFs: Users upload one or more PDF documents to the system. The application extracts the text from the PDFs and processes them into a machine-readable format.
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Embedding Generation: The extracted text is sent to ChatGPT to generate embeddings, which capture the semantic meaning of the content.
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Vector Storage with Pinecone: The embeddings are then stored in Pinecone, a fast and scalable vector database, for efficient searching and retrieval.
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Querying the Bot: Users can ask questions related to the content of the PDF. The system compares the user's question against the stored embeddings to find the most relevant sections and generates a context-aware response.
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Real-time Interaction: The custom agent provides responses in real time, allowing users to interact with the PDF content as if they were conversing with an expert on the document.
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Academic Research: Students and researchers can upload research papers and ask questions to better understand the content.
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Business Documents: Quickly analyze long reports, contracts, or other documents by querying specific sections or details.
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Customer Support: Build an FAQ bot by feeding customer support documents and allowing the agent to answer user inquiries.
- Flask: For building the backend web application.
- Pinecone: For vector storage and retrieval of document embeddings.
- ChatGPT API: For generating text embeddings and enabling natural language understanding.
git clone https://github.com/username/pdf-chatbot.git
cd pdf-chatbot
pip install -r requirements.txt
flask run