Building Your Personal Knowledge Base with LLaMA

I. Introduction

A personal knowledge base is a repository of personal learning, experiences, and observations that can assist individuals in decision-making, problem-solving, and creativity. Imagine it as a personal library that stores all important books (or articles, notes, or ideas).
A personal knowledge base can include: key concepts, summaries of research papers or articles, notes from books, lectures, or expert dialogues, ideas and insights generated through reflection, brainstorming, or problem-solving, personal experiences, stories, or cases demonstrating important lessons or principles, and links to online resources, videos, podcasts, or other digital content related to your interests.
LLaMA as a tool that can help build and manage personal knowledge bases

II. What is LLaMA?

LLaMA is an AI-based text generation tool that can understand natural language and generate relevant text content based on input.

The capabilities of LLaMA include:

Natural language understanding: LLaMA can understand human language, recognizing semantics, keywords, and sentence structures.
Text generation: LLaMA can generate relevant text content based on input, such as articles, reports, emails, or dialogues.
Question answering: LLaMA can answer corresponding questions based on input questions, providing relevant information and explanations.
Text analysis: LLaMA can analyze input text to identify keywords, themes, and relationships.
Language translation: LLaMA can translate input text into other languages, such as English to Chinese or French.
Text summarization: LLaMA can summarize long texts into shorter versions while maintaining key information and points.

In summary, LLaMA is a powerful AI tool that can help people generate, organize, and share information, making communication more efficient, intelligent, and convenient.

III. Benefits of Using LLaMA to Build a Personal Knowledge Base

In summary, using LLaMA to build a personal knowledge base can help you better organize, manage, and share knowledge, thus improving learning, productivity, and decision-making abilities.

Improved learning and memory: LLaMA can help you organize new information into useful notes, enhancing learning and memory.
Increased productivity and efficiency: With content generated by LLaMA, you can quickly access and retrieve relevant information, reducing time and energy loss.
Enhanced decision-making and problem-solving skills: LLaMA can provide relevant information and analytical results, helping you make wiser decisions and solve problems.
Boosted creativity and innovation: Through content generated by LLaMA, you can gain new ideas and inspiration, enhancing creativity and innovation.
Realization of knowledge management and sharing: LLaMA can help you organize knowledge and experiences into useful notes and share them with others, achieving knowledge management and sharing.
Improved work efficiency and career development: With content generated by LLaMA, you can quickly access and retrieve relevant information, improving work efficiency and career development.

Demonstration after successful setup, with key points being <AI used local data for responses>:

The BUF_RING here is code I wrote a long time ago. If the same question is asked to ChatGPT, the response would be broad and not include the code I previously wrote.

Additionally, it can not only recognize code but also learn Excel. For example, I imported a document.

Then I input a question:

To start building, you first need a high-performance computer, preferably with more than 27GB of RAM and more than 5GB of VRAM.

You need to download two software: one is the execution software for LLaMA, and the other is the desktop/browser software to interact with the model.

OllamaSetup.exe

AnythingLLMDesktop.exe

The software can be installed with default settings.

Next, download specific models, choosing different models based on your computer’s performance. You can search and download models at https://huggingface.co/models, filtering for Chinese and LLaMA.

Download the .guff format model file, which is more convenient to use.

Create a txt file and write the following content:

FROM "D:\llama\ggml-model-q2_k.gguf"
TEMPLATE """{{- if .System }}&lt;|im_start|&gt;system {{ .System }}&lt;|im_end|&gt;{{- end }}&lt;|im_start|&gt;user{{ .Prompt }}&lt;|im_end|&gt;&lt;|im_start|&gt;assistant"""
SYSTEM """"""
PARAMETER stop &lt;|im_start|&gt;PARAMETER stop &lt;|im_end|&gt;

<span>"D:\llama\ggml-model-q2_k.gguf" is the path of the model I downloaded, and needs to be replaced.</span><span>.</span>

Use the following command to import the model into the LLaMA software