Chat with Multiple PDFs | LangChain App Tutorial in Python (Free LLMs and Embeddings)
TLDRThis video tutorial demonstrates how to create a chatbot application that allows users to interact with multiple PDF documents at once. By utilizing open-source models and services like OpenAI and Hugging Face, the application can embed PDFs into a database and answer questions related to the content. The tutorial covers setting up the environment, creating a graphical user interface with Streamlit, and processing user inputs to generate responses. The result is an application that can handle real-world problems by providing information from uploaded documents.
Takeaways
- 📌 The video tutorial demonstrates how to build a chatbot application that interacts with multiple PDFs from a user's computer.
- 🔍 The application uses open-source models and libraries such as Streamlit, PiPDF, LangChain, and Hugging Face for its functionality.
- 📚 The process involves uploading PDF documents, embedding their content into a database, and then using that information to answer questions related to the documents.
- 🔑 API keys from OpenAI and Hugging Face are used and stored securely in a .env file to access the language models and embedding services.
- 💻 The video provides a step-by-step guide on setting up the environment, installing dependencies, and coding the application.
- 🛠️ The user interface is created using Streamlit, which allows for easy text input and file uploads.
- 📊 The application divides the PDF content into text chunks, converts these chunks into embeddings, and stores them in a vector store for semantic searching.
- 🔍 The chatbot can answer questions by retrieving relevant text chunks from the vector store and using them as context for language model responses.
- ⚙️ The tutorial also shows how to handle multiple files, process them, and display the results to the user.
- 🎯 The application is designed to be cost-effective, using both free and paid models to balance functionality and expense.
- 📈 The video provides a comprehensive overview of the process, including code snippets and explanations, making it a valuable resource for those interested in building similar applications.
Q & A
What is the main purpose of the application discussed in the video?
-The main purpose of the application is to build a chatbot that allows users to chat with multiple PDFs from their computer simultaneously.
Which programming language is used to create the application?
-Python is used to create the application.
What are the key components of the application's graphical user interface?
-The key components of the application's graphical user interface include Streamlit for creating the interface, a text input for user questions, a sidebar for uploading PDF documents, and a button to process the uploaded files.
How does the application process the uploaded PDF documents?
-The application processes the uploaded PDF documents by reading their text, dividing the text into chunks, and converting these chunks into vector representations or embeddings which are then stored in a database.
What are embeddings in the context of this application?
-In the context of this application, embeddings are numerical representations of text chunks that contain information about the meaning of the text, allowing the application to find and retrieve semantically similar text chunks based on user queries.
How does the chatbot retrieve relevant information in response to user queries?
-The chatbot retrieves relevant information by using the vector representations of text chunks to perform a semantic search in the database, finding and ranking the chunks with similar meaning to the user's query, and then using these as context for a language model to generate an answer.
Which API keys are required for the application to access external services?
-The application requires API keys from OpenAI and Hugging Face to access their services for language models and embeddings.
How are the API keys stored to ensure security?
-The API keys are stored in a .env file, which is a standard way to manage environment variables and secrets that should not be tracked by version control systems like Git.
What is the role of the 'conversation chain' in the application?
-The 'conversation chain' is responsible for managing the flow of the conversation, remembering the context of previous questions and answers, and generating new messages in the conversation based on user input and the retrieved information.
How does the application display chat messages to the user?
-The application displays chat messages to the user using custom HTML templates that define the styling and structure of user and bot messages, which are then filled with content from the conversation chain.
Can the application use language models other than OpenAI's?
-Yes, the application can use language models from other sources, such as Hugging Face, by importing the appropriate module and initializing the language model within the application.
Outlines
📌 Introduction to Building a Chatbot with PDFs
This paragraph introduces the viewer to a new video tutorial focused on building a chatbot application. The chatbot is designed to interact with multiple PDF documents from the user's computer simultaneously. The creator demonstrates the application by uploading the Constitution and the Bill of Rights, and explains how the chatbot embeds and stores these documents in a database. The tutorial promises to cover the process using both OpenAI and Hugging Face free models, emphasizing affordability and complexity compared to previous projects.
🛠️ Setting Up the Development Environment
The second paragraph delves into the technical setup required for the project. The creator guides the viewer through establishing a virtual environment and installing necessary dependencies using Python 3.9. Essential tools such as Streamlit for GUI, PyPDF2 for PDF handling, and the OpenAI and Hugging Face models are mentioned. The creator also discusses the use of a .env file to store secrets and the initial structure of the app.py file, which houses the core functionality of the application.
🎨 Designing the User Interface with Streamlit
This section focuses on the design and implementation of the application's user interface using Streamlit. The creator explains how to set the page configuration, title, and icon, as well as adding a header. The paragraph details the creation of a text input for user queries and a sidebar for uploading PDF documents. The creator also demonstrates how to add a button to process the uploaded files and mentions the importance of testing the application to ensure proper execution.
🔐 Storing API Keys and Handling Secrets
The creator discusses the management of API keys for OpenAI and Hugging Face services, emphasizing the importance of keeping these keys secure. The paragraph explains how to store API keys in a .env file to prevent tracking on Git and how to use the 'load_env' function from the 'python-dotenv' package to access these keys within the application. The process of generating API keys from OpenAI and Hugging Face platforms is also briefly outlined.
📖 Processing PDFs and Creating Embeddings
This paragraph explains the process of extracting text from PDFs and converting them into smaller, manageable chunks. The creator describes how these chunks are then turned into embeddings, which are numerical representations of text that capture semantic meaning. The paragraph also touches on the concept of a vector store or knowledge base, where these embeddings are stored for future reference and semantic searches. The process is demonstrated using both OpenAI and Hugging Face models, with a focus on the latter for its superior performance.
🔄 Splitting Text into Chunks and Preparing for Embeddings
The creator continues the discussion on processing PDF text by explaining how to split the content into chunks using the 'character_text_splitter' from LangChain. The paragraph details the parameters used for chunk size and overlap to ensure that the meaning of the text is preserved. The creator also demonstrates how to display these chunks in the application interface, setting the stage for the next step of creating embeddings from these text chunks.
🤖 Utilizing OpenAI and Hugging Face Embeddings
This section compares the use of OpenAI and Hugging Face embedding models. The creator notes the cost implications of using OpenAI's paid models and contrasts this with the free, yet slower, Hugging Face 'instructor' model. The paragraph provides insights into the performance of these models, highlighting that while OpenAI leads in language models, Hugging Face's 'instructor' model ranks higher for embeddings. The creator guides the viewer through the process of creating a vector store using both models, with a focus on the efficiency and speed of OpenAI's API.
📈 Speed Comparison of Embedding Models
The creator conducts a speed comparison between OpenAI and Hugging Face embedding models. The paragraph details the process of embedding a small document using both models and the time taken for each. The creator notes that while OpenAI's model is faster, Hugging Face's model requires additional dependencies and is better suited for users with their own hardware. The paragraph also touches on the potential for using an external persistent database for storing embeddings.
🗣️ Implementing Conversational Memory in the Chatbot
This paragraph introduces the concept of adding conversational memory to the chatbot, allowing it to understand and retain context across multiple interactions. The creator explains how to initialize a 'conversational_buffer_memory' and integrate it with a 'conversational_retrieval_chain' from LangChain. The paragraph details the process of creating a conversation object that utilizes a language model, vector store, and memory to generate responses. The creator also discusses the use of session state in Streamlit to maintain the conversation object throughout the application's lifecycle.
🖋️ Displaying Chat Messages with Custom HTML
The creator shifts focus to the presentation layer, discussing how to display chat messages using custom HTML templates. The paragraph details the creation of HTML templates for user and bot messages, including CSS styling and the use of images. The creator demonstrates how to import and apply these templates in the application, replacing variables with user and bot messages to create a dynamic chat interface. The paragraph also touches on the use of 'unsafe HTML' in Streamlit to render the custom HTML content.
🔄 Handling User Input and Generating Responses
The creator explains how to handle user input and generate responses using the previously created conversation object. The paragraph details the process of capturing user queries from the text input, utilizing the conversation object to generate responses, and updating the chat history. The creator also discusses the use of session state to maintain chat history across user interactions and demonstrates how to display this history using the custom HTML templates created earlier.
🌐 Testing with Hugging Face Models
The creator concludes the tutorial by showing how to test the chatbot application using Hugging Face models. The paragraph details the process of importing Hugging Face's 'hub' and using a different language model, in this case, Google's 'Flax T5'. The creator notes the use of Hugging Face's inference API for testing purposes and demonstrates how to adjust parameters for optimal performance. The paragraph wraps up with a brief overview of the entire process and an invitation for viewers to engage with further content.
Mindmap
Keywords
💡Chatbot
💡Embeddings
💡Database
💡Streamlit
💡API Keys
💡Hugging Face
💡OpenAI
💡Vector Store
💡Language Models
💡Semantic Search
Highlights
This tutorial demonstrates how to build a chatbot application that can interact with multiple PDF documents.
The application uses open-source libraries and free models from Hugging Face to avoid high costs.
The process involves uploading PDFs, embedding their content, and storing them in a database for retrieval.
The chatbot can answer questions related to the uploaded PDF documents by using the embedded data.
The application uses Streamlit for the graphical user interface, allowing users to easily interact with it.
The tutorial covers the installation of necessary dependencies like streamlit, pypdf2, line-chain, and others.
The use of .env files ensures that API keys and sensitive information are not tracked on git.
The tutorial explains how to create a vector store using open AI and Hugging Face models for semantic search.
The process of dividing PDF content into chunks for embedding is detailed, highlighting the importance of context preservation.
The tutorial shows how to use the conversational retrieval chain from LangChain for memory-enabled chat interactions.
The application can handle multiple PDF uploads and process them efficiently, making it scalable for larger datasets.
The use of session state in Streamlit is discussed to maintain the state of variables throughout the application lifecycle.
The tutorial provides a practical example of embedding the entire Bill of Rights and Constitution for chat interactions.
The process of creating a chat history and displaying messages using HTML templates is demonstrated.
The tutorial concludes by showing how to use the conversation chain with Hugging Face models instead of open AI.
The chatbot application can be used to solve real-world problems by providing interactive access to document databases.