This project enables you to chat with multiple PDF documents using a conversational AI model. The system extracts text from uploaded PDFs, creates a vector store of the text chunks, and utilizes a conversational chain model to provide interactive chat capabilities. It is built with Streamlit, a Python framework for building web applications.

1. Clone the repository: git clone

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2. Install the required dependencies: pip install -r requirements.txt

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3. Set up environment variables:

• Create a .env file in the root directory.
• Add the following variables to the .env file:

  VARIABLE_NAME=VALUE
  

  Replace VARIABLE_NAME with the actual variable name and VALUE with the corresponding value.

1. Run the script: streamlit run main.py

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2. Open the provided URL in your web browser.

3. Upload PDF Documents:

• In the sidebar, use the file uploader to upload your PDFs.
• You can upload multiple PDFs by selecting them together.

4. Ask Questions:

• In the main chat window, enter your question about the documents in the text input field.
• Press Enter or click outside the input field to submit the question.

5. View Responses:

• The system will process your question and generate a response.
• The conversation history will be displayed in the chat window.
• User messages are displayed on the left, and bot responses are displayed on the right.

6. Explore More Questions:

• You can ask additional questions by entering them in the text input field.
• The system will maintain the conversation history and provide relevant responses.

• main.py: This is the main script that handles the Streamlit UI and user interactions.
• langchain: This directory contains the language chain implementation and related modules.
• htmlTemplates: This directory contains HTML templates used for displaying chat messages.

• The ConversationChain class from the langchain.chains module is used to manage the conversational flow and retrieve relevant responses.
• The PyPDF2 library is utilized to extract text from the uploaded PDF documents.
• The CharacterTextSplitter class from the langchain.text_splitter module is used to split the extracted text into smaller chunks.
• The OpenAIEmbeddings class from the langchain.embeddings module is used to generate embeddings for the text chunks.
• The FAISS class from the langchain.vectorstores module is used to create a vector store from the text chunks and their embeddings.
• The ChatOpenAI class from the langchain.chat_models module is an alternative model for conversational responses.
• The ConversationBufferMemory class from the langchain.memory module is used to store and retrieve the conversation history.
• The HuggingFaceHub class from the langchain.llms module is used to load the conversational model from the Hugging Face model hub.