Chat with Multiple PDFs | LangChain App Tutorial in Python (Free LLMs and Embeddings)

This tutorial video guides viewers through the process of creating a chatbot application that enables users to interact with multiple PDF documents simultaneously. The application uploads and processes PDFs, allowing users to ask questions related to the content. It utilizes open-source models from OpenAI and Hugging Face to keep costs low. The video promises a more complex but rewarding project, encouraging viewers to follow along for the full experience.

The presenter begins by setting up a virtual environment for the project, using Python 3.9 and managing dependencies via a .env file. The file structure is explained, including the use of '.gitignore' to keep secrets and local configurations private. The 'app.py' file is highlighted as the core component. Dependencies such as Streamlit for GUI, PyPDF2 for PDF reading, Langchain for language model interaction, and others are installed using pip. The video then demonstrates the initial setup for the graphical user interface using Streamlit.

The video continues with the design of the chat interface, featuring a header, text input for questions, and a sidebar for PDF document uploads. The sidebar includes a subheader and a file uploader component from Streamlit. A 'Process' button is added to initiate the document processing. The presenter runs the Streamlit application to showcase the GUI, which allows users to ask questions and upload files, although the functionality behind these actions is yet to be implemented.

To utilize OpenAI and Hugging Face services, API keys are necessary. The video explains how to securely store these keys in a .env file and access them within the application using the 'load_env' function from the 'python-dotenv' package. It's emphasized that this approach keeps sensitive information out of the public domain, especially when code is pushed to platforms like GitHub.

The presenter explains the concept of embeddings as a numerical representation of text that captures semantic meaning, allowing for semantic search within a vector store or knowledge base. The process involves converting text from PDFs into chunks, generating embeddings for these chunks, and storing them in a vector store. The video provides a quick refresher on how this application logic works, especially for those who have seen previous videos on the subject.

The video details the function to extract raw text from PDFs. Using the PyPDF2 library, the function initializes a PDF reader object for each document, iterates through its pages, and extracts text. This process concatenates all text from the PDFs into a single string stored in a variable called 'raw_text'. The presenter demonstrates this functionality within the Streamlit application, showing the raw text after processing the uploaded documents.

After extracting text from PDFs, the video moves on to splitting the text into manageable chunks using the 'CharacterTextSplitter' class from Langchain. The chunk size is set to a thousand characters with an overlap of 200 characters to ensure continuity and context. The resulting chunks are intended for creating embeddings and populating the vector store.

The presenter discusses two methods for creating embeddings: using OpenAI's paid service and Hugging Face's free 'Instructor' model. While OpenAI's service is fast, it incurs costs, whereas Instructor, though slower without GPU acceleration, is more performant and free. The video shows how to implement both methods, emphasizing the importance of considering costs and performance in one's business model.

The video demonstrates the creation of a vector store using embeddings with OpenAI's service. It shows the process of defining a function that initializes the vector store locally using the 'Files' method from Langchain, which serves as a database for storing the embeddings. The presenter highlights the speed and efficiency of using OpenAI's servers for this task.

The presenter explains how to integrate conversational memory into the chatbot using Langchain's 'ConversationalBufferMemory'. This allows the chatbot to remember the context of previous interactions. The video shows how to initialize this memory and incorporate it into the 'ConversationalRetrievalChain', which is responsible for managing the conversation flow and generating responses.

To display chat messages, the video introduces a custom HTML approach using templates for user and bot messages. It demonstrates creating an 'HTML_templates.py' file containing CSS styles and HTML structures for chat messages. The presenter then shows how to import and apply these templates in the Streamlit application to create a professional-looking chat interface.

The video concludes with handling user input from the chat interface and generating responses using the conversation chain. It details capturing user questions, utilizing the conversation object to produce answers, and updating the chat history. The presenter also discusses using session state in Streamlit to maintain the state of variables throughout the application lifecycle.

In the final part of the video, the presenter tests the chatbot's functionality by asking questions related to the First and Second Amendments after processing relevant documents. The chatbot demonstrates its ability to recall previous context and provide relevant answers. The video also shows how to switch between using OpenAI and Hugging Face models for language processing within the chatbot application.

The video wraps up by highlighting the successful creation of the chatbot application and its capabilities. The presenter expresses hope that viewers found the tutorial educational and will apply the knowledge to build productive applications. They also mention plans to continue publishing content for beginners and invite feedback on the type of projects viewers would like to see in the future.