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trungka982004
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Agentic RAG System

Route queries across local knowledge bases, web search, and expert consultants.

Updated Apr 8, 2026
Search Tags:#information retrieval#web search#knowledge base#RAG#interactive reasoning
One-click install
npx skills add https://github.com/trungka982004/Agentic_rag_final_project --skill agentic-rag-system
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Agentic RAG System
Source: https://github.com/trungka982004/Agentic_rag_final_project/tree/main
Command: npx skills add https://github.com/trungka982004/Agentic_rag_final_project --skill agentic-rag-system

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, sqlalchemy, psycopg2, ollama, chromadb, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The Agentic RAG System addresses the challenge of providing highly accurate, context-aware answers by intelligently routing queries across multiple knowledge tiers, including local knowledge bases, web search, and expert consultants.

Core Features & Use Cases

  • Multi-Tiered Knowledge Base: Integrates local knowledge bases, web search, and expert consultants for comprehensive information retrieval.
  • Hybrid Search: Combines BM25 keyword retrieval and vector search for optimal recall and precision.
  • Real-time Interaction: Utilizes WebSocket for real-time updates and interactive feedback during the reasoning process.
  • Automated Report Generation: Automatically compiles report text, generates tables, and uploads to Google Workspace.
  • Use Case: Ideal for researchers or professionals needing to quickly gather and summarize information across various sources.

Quick Start

Start the Agentic RAG System and ask a question or request a specific piece of information.

Frequently Asked Questions about Agentic RAG System

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

FAQPage Schema
How does retrieval-augmented generation combine local knowledge bases and web search?▼

Yes, retrieval-augmented generation can automate report generation by compiling text and tables. This system automatically generates reports and uploads them directly to Google Workspace for streamlined research workflows.

Do I need PostgreSQL and Ollama to run an agentic RAG system?▼

You can get real-time interactive feedback during reasoning by utilizing WebSocket connections. This enables real-time updates and interactive feedback throughout the query resolution process.

What is the best way to handle complex queries across multiple knowledge tiers?▼

Yes, retrieval-augmented generation can automate report generation by compiling text and tables. This system automatically generates reports and uploads them directly to Google Workspace for streamlined research workflows.

How do I set up hybrid search combining BM25 and vector search for information retrieval?▼

You can get real-time interactive feedback during reasoning by utilizing WebSocket connections. This enables real-time updates and interactive feedback throughout the query resolution process.

Can I automate report generation and upload to Google Workspace from a RAG system?▼

Yes, you need PostgreSQL and Ollama to run this agentic RAG system, along with FastAPI, SQLAlchemy, and ChromaDB. These dependencies are required to manage databases, local models, and vector storage.

Does FastAPI support WebSocket connections for real-time interactive reasoning?▼

The best way to handle complex queries across multiple knowledge tiers is using an agentic RAG system. It routes requests across local knowledge bases, web search, and expert consultants to ensure comprehensive information retrieval.

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