This project is a Streamlit application that allows users to interact with multiple PDF documents. It uses the Langchain library to provide a conversational interface for asking questions about the contents of the uploaded PDFs.

The application works in several steps:

1. PDF Upload: Users can upload one or more PDF documents via the sidebar.
2. PDF Processing: The text is extracted from each page of each uploaded PDF.
3. Text Splitting: The raw text from the PDFs is split into chunks using a character-based text splitter from the Langchain library.
4. Vector Store Creation: A vector store is created from the text chunks using OpenAI embeddings. This vector store is used for retrieving relevant information in response to user queries.
5. Conversation Chain Creation: A conversation chain is created, which uses a ChatOpenAI model, a FAISS retriever (created from the vector store), and a conversation buffer memory. This conversation chain is used to handle user queries.

To use the application:

1. Clone this repository.
2. Install the required Python packages (listed in requirements.txt).
3. Run the Streamlit application with streamlit run app.py.
4. Upload one or more PDFs via the sidebar.
5. Ask a question about the documents in the main text input field.

This project is an example of how to use the Langchain library for document retrieval. It should be adapted to your specific use case and requirements.