🧠 Knowledge Base Overview

The Pop Knowledge Base is a collection of structured knowledge, document information, and content retrievable by AI.
It is one of the core capabilities of Pop for information management and AI-powered question answering. By segmenting documents, generating embeddings, and building indexes, Pop enables AI to understand, retrieve, and use your content to provide accurate answers.

The Pop Knowledge Base works well for personal users and also scales to team environments. It is an essential infrastructure for AI-driven content creation.

🔍 1. What is the purpose of the Knowledge Base?

The goals of the Pop Knowledge Base are to:

Enable AI to understand your documents (PDF, Word, Markdown, web pages, etc.)
Answer questions directly based on your materials
Support content generation such as reports, proposals, summaries, and Q&A
Serve as the foundation for AI customer service, product documentation, and team knowledge hubs
Allow AI to cite original sources when answering

In short:

Knowledge Base = Your private library that AI can truly understand

📘 2. What capabilities are included in the Pop Knowledge Base?

The Pop Knowledge Base system is composed of several technical capabilities:

2.1 Document Parsing

Automatically parses multiple formats: PDF / DOCX / PPTX / MD
Extracts titles, paragraphs, lists, and page structures
Identifies page content, body text, and code blocks
Cleans noise (headers, footers, repeated content)

2.2 Document Chunking

To help embedding models better understand documents, Pop applies intelligent chunking strategies:

Splits content based on heading hierarchy
Automatically segments by semantic paragraphs
Balances chunk length to avoid segments that are too short or too long

Each chunk is used for AI retrieval to ensure accuracy.

2.3 Embedding (Vectorization)

Pop supports multiple embedding models:

bge-m3 (default)
OpenAI Embedding
Jina / Cohere
Local models (LLM Studio, Ollama, etc.)

Every document chunk is converted into a vector for semantic search.

2.4 Retrieval Methods

Pop provides three retrieval mechanisms:

Retrieval Method	Feature	Best Use Cases
BM25	Keyword-based search	FAQ, exact terminology matching
KNN Vector Search	Strong semantic search	Document-based or scenario-based QA
Hybrid	Combined strengths	Default recommendation, long content, customer service

2.5 RAG (Retrieval-Augmented Generation)

The complete process includes:

Retrieve relevant chunks
Re-rank chunks for best results
LLM generates answers using retrieved content
AI cites sources and explains reasoning

Pop's RAG is more than just “search + answer”—it includes multi-step optimization for high-quality answers.

📚 3. Structure of the Pop Knowledge Base

A complete Pop Knowledge Base includes:

Knowledge Base
 ├── Documents (PDF / Word / Markdown / URL)
 ├── Chunks
 ├── Embeddings & Indexes (Embedding / BM25)
 ├── Retrieval Strategies
 ├── QA Configuration (RAG)
 ├── Analytics Dashboard
 └── Index Rebuild & Task History

You can create different knowledge bases for:

Product manuals
Technical documentation
Customer support FAQs
Research papers and notes
Internal team SOPs

⭐ 4. Use Cases

Pop Knowledge Base is ideal for the following scenarios:

4.1 Intelligent Q&A (AI QA)

Ask questions such as:

“What is our refund policy?”
“What are the key terms of this agreement?”
“Summarize the risks in this PDF I uploaded.”

4.2 Customer Support AI

Automatically answer:

User guides
Frequently asked questions
How-to steps

4.3 Content Generation

AI can use Knowledge Base content to generate:

Proposals
Reports
Emails
Product documentation

4.4 Team Knowledge Management

Used for:

SOPs
Training manuals
Project documentation

⚙️ 5. Advantages of the Pop Knowledge Base

Capability	Advantage
Multi-format support	Works with PDF / Word / Markdown / URLs
Fast vectorization	Auto-segmentation + embedding in seconds
Powerful retrieval	BM25 / KNN / Hybrid coverage
RAG answers	Source citation, reasoned responses
Visual management	Index status, storage size, performance metrics
Scalable	Future support for team permissions, API access

📌 Summary

The Pop Knowledge Base is one of the core modules of the Pop system, providing reliable and verifiable information for AI.

Through:

Document parsing
Chunking
Embedding
Multi-model retrieval
RAG intelligent answering

Pop can continuously offer higher-quality, more precise AI services based on your knowledge.