easonlai/chatbot_with_pdf_streamlit
This code example shows how to make a chatbot for semantic search over documents using Streamlit, LangChain, and various vector databases. The chatbot lets users ask questions and get answers from a document collection. The code is in Python and can be customized for different scenarios and data.
This project helps you build an interactive chatbot that can answer questions based on a collection of your own documents, such as PDFs. You provide the documents, and the chatbot allows users to ask natural language questions, retrieving and summarizing relevant information. This is ideal for anyone who needs to make large amounts of information easily searchable and consumable by an end-user, like customer support, internal knowledge management, or educational resource providers.
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Use this if you need to create a custom chatbot that can answer specific questions by searching through your own private documents or knowledge base.
Not ideal if you're looking for a pre-built, ready-to-deploy chatbot solution without any development work or if your primary need is general knowledge outside of your specific document collection.
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Sep 03, 2023
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