Why use DeepSeek to build a personal knowledge base

This is an article about how to use DeepSeek to build a personal knowledge base? The main contents of the mind map include: description, 3. Typical application scenarios, 2. Key techniques and precautions, 1. Selection of core methods and tools.

Edited at 2025-02-11 14:12:45

Gavinnblake

Recent works View more works>>

Why use DeepSeek to build a personal knowledge base

Gavinnblake

Recent works View more works>>

Recommended to you
Outline

Use DeepSeek to build a personal knowledge base
- 132
- 1
Gavinnblake
Guide to building a personal knowledge base with DeepSeek
- 1.9k
- 40
Gavinnblake

Why use DeepSeek to build a personal knowledge base?

1. Core methods and tool selection

DeepSeek builds a personal knowledge base mainly with the following three mainstream solutions, which can be selected according to your needs:

1. Local deployment solution (high privacy, computing power support)

Core tools: Ollama DeepSeek-R1 model vectorization tools (such as BGE-M3) visual interface (CherryStudio/AnythingLLM)

step

Install Ollama: Download and install the tool for running DeepSeek models locally

Download the model: Install the DeepSeek-R1 model through Ollama (parameters can be optional 1.5B or 7B), and download the embedded model BGE-M3:

Deploy visualization tool: Install CherryStudio or AnythingLLM for uploading documents and interactive Q&A

Upload knowledge base files: drag local documents (such as PDF, TXT) into the tool, automatically generate vector indexes and support Q&A

Advantages: The data is fully localized and suitable for processing sensitive information (such as medical records, corporate reports)

Disadvantages: requires higher configuration GPU support, and the deployment complexity is high

2. Cloud-based lightweight solution (quickly get started, low threshold).

Core tool: Seta AI Q&A pair generation (Doubao/Kimi)

step

Generate Q&A pairs: Use bean buns or Kimi to analyze documents (such as the instruction manual) to generate structured Q&A pairs (to improve model understanding effect)

Upload to Secret AI: Save the Q&A pair as a TXT file, drag it into Secret AI to automatically generate a vector database

Interactive Q&A: Select the "deep thinking" or "concise answer" mode in Secret Tower AI, and ask questions based on the knowledge base.

Advantages: No local computing power is required, suitable for personal quick construction of knowledge bases (such as study notes, family documents)

Disadvantages: Relying on third-party platforms may pose data privacy risks.

3. Enterprise-level solution (open source framework DeepSeek V3)

Core Tools: MaxKB (Open Source Knowledge Base System) DeepSeek V3 API

step

Deploy MaxKB: Install MaxKB on the server, support Ubuntu/CentOS system

Connect to DeepSeek: Bind DeepSeek API Key in MaxKB settings (register on the official website for free to get 5 million tokens)

Building a knowledge base: upload documents and automatically chunked vectorization, supporting web page crawling and multi-format file import.

Advantages: Supports privatized deployment and API calls, suitable for enterprise-level knowledge management (such as customer service system, internal training)

2. Key techniques and precautions

Document preprocessing

Using question-and-answer formats (rather than plain text) can significantly improve model understanding accuracy

Complex documents are recommended to split into 1000-2000 characters fragments to avoid information loss

Model selection

DeepSeek-R1 is preferred for Chinese scenarios (Chinese optimization), and DeepSeek-V3 is recommended in English or multi-language (open source and supports 128K context)

Cost control

In the initial local deployment, 1.5B small model is recommended, and at least 16GB of video memory is required for 7B and above.

Cloud API call cost as low as 1 yuan/million tokens, suitable for high-frequency query

3. Typical application scenarios

Scene Recommended plan Tool combination Personal privacy document management Local deployment Ollama DeepSeek-R1 AnythingLLM Corporate Knowledge Base MaxKB DeepSeek V3 Cloud API private server Quickly build temporary libraries Secret Tower AI Q&A pair generation Beanbao/Kimi Seita AI

Through the above solutions, users can flexibly choose construction methods based on data sensitivity, computing power conditions and application scenarios.

illustrate

1. Map making tool: Mindmaster, making: empty tea cup