In this project, step through the process of using information from multiple documents to create a vector database. A vector database is specially designed to manage data as vectors, which in the area of machine learning and natural language processing, can represent everything from words and sentences to entire documents. With this knowledge and large language models (LLM) such as Llama2, you are able to retrieve information in the context of AI-assisted conversations. Essentially, you have an AI assistant that's tailored to the knowledge that you have.

This approach is beneficial for companies with vast repositories of information. By integrating AI, they can efficiently and quickly access and communicate with their knowledge databases. This information could range from text-based customer conversations to sales data described in CSV formats.

The information processing pipeline of the documents processing and feeding to LLM is shown in the following image. Information processing pipeline

A look at the project ahead

1. Vector database construction: Establish a database that manages data as vectors, optimizing it for advanced machine learning and natural language processing applications. This database will serve as the foundation for the AI assistant to retrieve and interpret large volumes of document-based information efficiently.
2. AI-assisted information retrieval: Develop an AI assistant that can parse through extensive repositories of PDF documents, enabling quick and contextually accurate retrieval of information. This system will enhance the capability of companies to interact with and utilize their vast data stores, such as technical manuals, sales records, and customer correspondence.
3. Contextual understanding and response: Implement a language learning model that uses the vector database to understand queries in natural language and provide precise answers. This involves processing the text data to identify and understand the context, enabling the AI to deliver responses that are tailored to the specific knowledge encapsulated in the documents.
4. User interaction interface: Create a user-friendly interface, such as a Gradio chatbot, that allows users to interact with the AI assistant. This interface will enable users to ask questions and receive answers derived from the content within the PDF documents, providing an effective tool for knowledge discovery and customer service.

What You'll Need