AI’nt That Easy #3: Chat with Multiple PDFs Using Langchain and Google Gemini

Github Link

Welcome to an exciting exploration of a Generative AI project that enables seamless interactions with multiple PDFs. In this blog, we’ll delve into the code behind a Streamlit app powered by Langchain and Google Gemini, showcasing the potential to unlock knowledge hidden within PDF documents.

The Scenario: Conversing with PDFs

Imagine having a pile of research papers filled with valuable information, but the challenge lies in extracting insights locked away in text format. What if you could have a conversation with these PDFs, ask questions, and receive meaningful answers? This blog introduces a project that makes this vision a reality, combining Langchain, Google Gemini, Streamlit, and Python.

• Langchain: Provides the framework for building conversational AI applications, including text retrieval, vector databases, and conversation chains.
• Google Gemini: An open-source, large language model from Google AI, excels at understanding and generating text, powering the response generation in this project.
• Streamlit: Builds the user-friendly interface, allowing you to upload PDFs, ask questions, and view the conversation history.