In this video, Mayor from Chartered Data demonstrates how to create a powerful chatbot capable of interacting with lengthy PDF documents. The chatbot leverages the combined capabilities of LangChain and GPT-4 to deliver an enhanced user experience.
Splitting PDF into Chunks and Converting to Text
The chatbot employs LangChain and GPT-4 to split the PDF document into manageable chunks and convert them into text format. This enables easier processing and analysis of the document's content.
Converting Text Chunks into Embeddings and Storage
The text chunks are further transformed into embeddings, which are numerical representations, and stored in a vector base. This vector base serves as a repository for efficient retrieval and comparison of relevant document information.
Generating Relevant Responses using GPT-4
When a user poses a question, the chatbot combines the question with the chat history and sends it to GPT-4 to create a standalone question. The question is then converted into embeddings and compared with the embeddings in the vector base to identify relevant documents.
Utilizing Document Context for Answer Generation
The chatbot uses the identified relevant documents as context to provide an accurate and contextually appropriate answer to the user's question. By leveraging the specific sections of the document, the chatbot can offer references to the PDF and guide users to further explore the document.
Ingestion and Chat Phases
The chatbot's code consists of two main phases: the ingestion phase and the chat phase. The ingestion phase involves converting the PDF into vector numbers, which are then stored in a vector store. In the chat phase, the user's question is combined with the chat history, converted into embeddings, and matched with the relevant document chunks to generate a tailored response.
This chatbot architecture can be customized to meet the specific needs and requirements of individual users, making it a versatile solution for interacting with lengthy PDF documents.
Q&A:
Q: What technologies are used to create the chatbot?
A: The chatbot utilizes LangChain and GPT-4 to facilitate interaction with long PDF documents.
Q: How is the PDF document processed?
A: The PDF is split into chunks, converted to text, and further divided into embeddings for efficient analysis.
Q: How does the chatbot generate relevant responses?
A: The chatbot compares the embeddings of the user's question with the document chunks to retrieve the most relevant information for generating responses.
Q: Can the chatbot provide references to the PDF and specific sections of the document?
A: Yes, the chatbot can offer references to the PDF and guide users to specific sections within the document.
Q: What are the main phases of the chatbot's code?
A: The code consists of an ingestion phase, where the PDF is converted into vector numbers for storage, and a chat phase, where the user's questions are processed and relevant responses are generated.
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