rag-expert

Name: rag-expert
Availability: InStock
Author: phuhao

Manage document ingestion, vector indexing, and semantic retrieval for RAG knowledge bases.

12|3|Updated Jun 17, 2026

Search Tags:#rag #semantic-search #knowledge-base #vector-database #document-management #llamaindex

Ready to run

npx skills add https://github.com/phuhao00/bony-agent --skill rag-expert-phuhao00

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-expert
Source: https://github.com/phuhao00/bony-agent/tree/main/.agent/skills/rag-expert
Command: npx skills add https://github.com/phuhao00/bony-agent --skill rag-expert-phuhao00

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llama-index, zhipuai, fastapi.

What problem does it solve?

This skill solves the challenge of managing and querying private knowledge bases by providing a structured, automated pipeline for document ingestion, vectorization, and semantic retrieval.

Core Features & Use Cases

Full-Lifecycle Management: Handles document uploading, indexing, status monitoring, and deletion with automatic index reconstruction.
Semantic Search: Leverages LlamaIndex and ZhiPu AI to perform high-accuracy semantic queries across diverse document formats like PDF, DOCX, and MD.
Use Case: A team can upload technical documentation or project reports to the system, allowing the AI agent to provide precise, context-aware answers based on that specific private data.

Quick Start

Use the rag-expert skill to search the knowledge base for the latest project configuration guidelines.

Frequently Asked Questions about rag-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a private knowledge base for semantic search?▼

To build a private knowledge base for semantic search, you need a pipeline for document ingestion, vectorization, and retrieval. This approach uses LlamaIndex to index diverse formats like PDF and DOCX into persistent vector stores for accurate context-aware responses.

Can I use LlamaIndex with ZhiPu AI for document management?▼

Yes, you can use LlamaIndex with ZhiPu AI for document management. This integration enables automated vectorization and semantic retrieval, allowing an AI agent to perform high-accuracy queries across your uploaded technical documentation and project reports.

What is the best way to manage document CRUD operations in a RAG system?▼

Managing document CRUD operations in a RAG system requires automatic index reconstruction upon deletion or updates. This ensures the persistent vector store remains synchronized with the current private data, maintaining accurate semantic search results.

Does RAG knowledge base management support PDF and DOCX formats?▼

RAG knowledge base management supports PDF, DOCX, and MD formats. The system handles document ingestion and vector-based indexing for these diverse file types, enabling high-accuracy semantic queries across your structured and unstructured private data.

How do I monitor system status for vector database indexing?▼

You can monitor system status for vector database indexing by checking the administrative tasks within the RAG-enabled AI agent. This tracks the health of document ingestion, LlamaIndex vectorization, and semantic retrieval operations to ensure persistent stores function correctly.