Overview of the application

1. Import the necessary libraries. This includes the LangChain library, the Streamlit library, and the FAISS library.

2. Load the PDF file and extract the text. You use the LangChain library to load the PDF file and extract the text.

3. Split the text into chunks. You use the RecursiveCharacterTextSplitter class from LangChain to split the text into chunks. This is done to improve the performance of the vector store and the large language model.

4. Create a vector store for the text. You use the FAISS library to create a vector store for the text. This helps to efficiently index the text and generate more accurate responses to user queries.

5. Accept user questions/query. You use the text_input() function from Streamlit to accept user questions or queries.

6. Find the most relevant chunks to the user's question. You use the similarity_search() method on the vector store to find the most relevant chunks to the user's question.

7. Generate a response to the user's question. You use the load_qa_chain() function from LangChain to load a question-answering chain. Then, you use the chain.run() method to generate a response to the user's question based on the most relevant chunks.

8. Display the response to the user. You use the write() function from Streamlit to display the response to the user.

Here is a more detailed explanation of each step:

Loading the PDF file and extracting the text

The LangChain library provides a PdfReader class that can be used to load PDF files and extract the text. The PdfReader class has a pages() method that returns a list of Page objects. Each Page object has an extract_text() method that can be used to extract the text from the page.

Splitting the text into chunks

The RecursiveCharacterTextSplitter class from LangChain can be used to split the text into chunks. This is done by recursively splitting the text into smaller and smaller chunks until the chunks are of a certain size. The size of the chunks can be controlled using the chunk_size and chunk_overlap parameters.

Creating a vector store for the text

The FAISS library provides a variety of data structures for storing and querying vectors. In this case, you use the FAISS class to create a vector store for the text. The FAISS class has a from_texts() method that can be used to create a vector store from a list of text strings.

Accepting user questions/query

The text_input() function from Streamlit can be used to accept user questions or queries. The text_input() function returns the text that the user has entered into the text field.

Finding the most relevant chunks to the user's question

The similarity_search() method on the vector store can be used to find the most relevant chunks to the user's question. The similarity_search() method takes a query string as input and returns a list of the most similar chunks, ranked by similarity.

Generating a response to the user's question

The load_qa_chain() function from LangChain can be used to load a question-answering chain. A question-answering chain is a sequence of models that are used to generate answers to questions. The chain.run() method can be used to generate a response to the user's question based on the most relevant chunks.

Displaying the response to the user

The write() function from Streamlit can be used to display the response to the user. The write() function can be used to display text, HTML, images, and other types of content.

Why is this approach successful?

This approach is successful because it uses LangChain and Streamlit to make it easy to build a PDF reader. LangChain provides a variety of tools for processing text, including text extraction, text splitting, and vectorization. Streamlit provides a variety of tools for building web applications, including user interfaces and data visualization.

This approach is also successful because it is scalable. The vector store can be used to efficiently index a large number of PDF files. The question-answering chain can be used to generate