Build your private GPT

The "PrivateGPT step-by-step guide" presents a comprehensive methodology for building chatbots using open-source large language models (LLMs) like GPT4All and Llama. The guide begins with a problem statement focused on the construction industry, highlighting the potential of transforming unstructured data from various documents into structured data that can be incorporated into databases. It emphasizes the ease of querying and chatting with text documents using data loaders, supporting multiple formats such as text files, Word documents, PDFs, and PowerPoint slides. The guide outlines a process to utilize these data loaders to upload PDF documents for querying using Llama 13B and GPT4All models.

The building process of the chatbot involves several key components: Queries (prompts from the user), Input Documents (of various formats), Embedding Model (for converting text into numeric values), Vector Storage (storing numeric values of text chunks), Database (for storing vectors), and the Large Language Model (for processing input and providing text output). The guide elaborately describes each component's role in the chatbot building process. Additionally, it discusses two main options for building chatbots: using open-source LLMs, which offer data confidentiality but require significant initial investment in infrastructure, and using licensed LLMs with APIs, which are more cost-effective but have less control over data storage. The guide also provides a detailed breakdown of costs associated with both approaches.

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You can contact the author of this document via email on mohamed_ashour@apcmasterypath.co.uk & mo_ashour1@outlook.com.