AI Knowledge Base

What is a RAG Knowledge Base?

RAG（Retrieval-Augmented Generation） 是一种结合了检索与生成的AI技术。系统会先从Knowledge Base中检索相关文档片段，再将这些内容作为上下文传递给大模型，生成精准的答案。

High Accuracy:Generates answers from the company's own documents, avoiding large-model hallucinations

Real-Time Updates:Newly uploaded documents take effect instantly, with no need to retrain the model

Traceable:Answers come with source citations you can verify

Upload document → vectorize → retrieve → generate answer

How It Works

Document Upload

Supports formats such as PDF, Word, and TXT, automatically parsing and extracting the text content

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Intelligent Chunking

Split long documents into roughly 800-word segments while preserving contextual links.

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Vectorized Storage

Use an embedding model to convert document chunks into vectors and build an index

User Question

Vector Search

Vectorize the query and retrieve the Top-K most similar document fragments from the knowledge base

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Generate Answer

Use the retrieved documents as context and call Qwen2.5 to generate the answer.

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Return Result

Returns answers with cited sources, supporting traceable verification

System Architecture

Document processing layer → vector storage layer → retrieval service layer → Q&A API layer

Document Processing Layer

Supports parsing of PDF/Word/TXT with automatic chunking and vectorization, and real-time progress tracking.

Vector Storage Layer

Nine tables including document chunks, vector indexes and query history, deployed and upgraded automatically

Retrieval Service Layer

Cosine-similarity vector retrieval, supporting Top-K configuration and threshold filtering.

Q&A Interface Layer

RESTful API supporting streaming output and batch Q&A

Core Features

Document Management

Supports formats such as PDF, Word, and TXT
Automatic parsing and chunking
Real-Time Progress Display
Document categorization and tag management

Intelligent Search

Vectorized semantic search
Supports Top-K and similarity threshold
Millisecond-level response
Traceable search results

RAG Q&A

Based on the Qwen2.5 large model
Retrieval-Augmented Generation
Supports Streaming Output
Query History Records

Configuration Management

Visual configuration interface
Supports multiple Embedding models
Chunk size and overlap configuration
Dynamic Tuning of Retrieval Parameters

Model Training

Supports Model Fine-Tuning
Training Job Management
Real-Time Training Progress Monitoring
Automatic checkpoint saving

Security & Auditing

Complete query history
Document Access Permission Control
Encrypted Data Storage
Operation Log Auditing

Use Cases

Technical support, internal training and user self-service

Intelligent Customer Service

Built on knowledge bases such as product docs and FAQs, it automatically answers common user questions, cutting support costs and speeding up responses.

Internal Knowledge Management

Unified management of internal documents, technical manuals, and operating procedures, allowing employees to query them anytime to improve work efficiency.

Education & Training

Turn teaching materials and course handouts into a knowledge base where students can ask questions anytime and receive personalized learning guidance.

Technical Support

Intelligent Q&A over O&M documents and troubleshooting manuals quickly locates solutions, reducing fault recovery time.

Compliance & Auditing

Smart querying of regulations and policy documents ensures operations meet compliance requirements and remain auditable.

Featured Products

Generate personalized recommendations from product manuals and user reviews to improve user experience and conversion.