This video provides a demonstration of how to use large language models (LLMs), retrieval-based models, and vector databases to ask questions to a PDF file. Here's a concise summary:
- The video begins by introducing the concept of asking questions to a PDF file using advanced technologies.
- It highlights the strengths and weaknesses of large language models (LLMs) and retrieval-based models in generating text and finding information.
- The video explains the concept of retrieval augmented generation (RAG), which combines the strengths of both LLMs and retrieval-based models.
- It mentions the use of vector databases, specifically Apache Cassandra, to store embeddings of the PDF documents.
- The video demonstrates the process of setting up the vector database using DataStax Astra DB, a managed Cassandra cloud service.
- It shows how to connect to Cassandra using Python, set up the necessary libraries, and initialize the OpenAI API.
- The video covers the indexing of PDF documents and the creation of embeddings, which are stored in the vector database.
- It displays a sample query result from the vector database, showcasing the rows, embeddings, and source information.
- The video concludes by emphasizing the scalability and availability of Cassandra for handling larger PDFs and questions.
Overall, the video provides a comprehensive overview of the process involved in asking questions to a PDF file using advanced technologies and database systems.
Here are the key facts extracted from the provided text:
1. You can ask questions to a PDF file using large language models and vector databases.
2. Large language models (LLMs) are good at generating new text, such as code, poems, emails, etc.
3. Retrieval-based models are good at finding information in large databases or on the Internet but cannot generate new text.
4. Retrieval Augmented Generation (RAG) models combine retrieval-based models and generative models to provide informative and accurate responses.
5. RAG systems convert user inputs into embedding vectors, search for relevant documents, and generate responses.
6. Vector databases, like Apache Cassandra, are used to store embeddings with respect to documents.
7. Apache Cassandra is a highly available and resilient distributed database used by companies like Apple.
8. DataStax Astra DB is a managed Cassandra cloud service.
9. The process involves connecting to Astra DB, generating an application token, and downloading a secure connect bundle.
10. Python is used to interact with Cassandra, and the Cassandra driver is installed.
11. The PDF document is split into pages using PyPDF2.
12. Pages are indexed and stored in the vector database.
13. The indexing process involves chunking the document, creating embeddings, and saving them in the database.
These facts provide an overview of the text's content without including opinions or additional details.