Learn how to create a chatbot that can effectively respond to queries based on PDF documents. This video tutorial provides step-by-step instructions on building a chatbot using the Retrieval Augmented Generation (RAG) architecture, ensuring accurate and context-based responses. Follow the key points below:
LLMS Limitations and Document Utilization
LLMS (Language Models like GPT-3) have limitations as they are trained with data at a specific point in time. By providing a document, such as a PDF, the chatbot can utilize it to enhance its understanding and response capabilities.
The RAG Architecture
The chatbot is built using the Retrieval Augmented Generation (RAG) architecture. This approach involves breaking down the PDF document into smaller data chunks, which are then stored in a vector database. This allows the chatbot to efficiently retrieve relevant information when responding to user queries.
Utilizing Context and Chat History
The chatbot utilizes the context from the PDF document and the chat history to generate accurate and contextually relevant responses. By considering previous interactions and the information contained in the PDF, the chatbot can provide more accurate and informed answers.
Implementing the Chatbot with Code
The chatbot is implemented using the chat library, along with OpenAI for the LLMS (Language Model). This combination ensures a smooth integration of the chatbot functionality into the project.
Preparing the PDF Document
To utilize the PDF document, it needs to be prepared for integration. This involves loading the document using the PDF loader library and splitting it into manageable data chunks using the character text splitter library. These steps ensure that the chatbot can effectively process and respond to user queries.
Bullet Summary:
- The video tutorial demonstrates building a chatbot that answers queries from a PDF document.
- The chatbot is created using the RAG architecture, which stores PDF data in a vector database.
- The chatbot utilizes context from the PDF and chat history to generate accurate responses.
- The chatbot can be trained using any PDF document, making it versatile for various applications.
Q&A:
Q: What are the limitations of LLMS?
A: LLMS (Language Models like GPT-3) have limitations as they are trained with data at a specific point in time.
Q: How does the chatbot utilize the PDF document?
A: The chatbot utilizes the PDF document by breaking it down into data chunks, which are stored in a vector database for efficient retrieval and response generation.
Q: How does the chatbot generate responses?
A: The chatbot generates responses by considering the context from the PDF document and the chat history, ensuring accurate and contextually relevant answers.
Q: What can the chatbot be trained on?
A: The chatbot can be trained using any PDF document, making it suitable for various applications such as policy documents, legal documents, and more.
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