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Maheen-Zubair
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RAG Retrieval Service

Implement Qdrant semantic search with filtering strategies via FastAPI endpoints.

Updated Dec 15, 2025
Search Tags:#semantic search#RAG#FastAPI#content filtering#education search
One-click install
npx skills add https://github.com/Maheen-Zubair/Physical-AI-Humanoid-Robotics-Textbook--Hackathon-01- --skill rag-retrieval-service-maheen-zubair
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: RAG Retrieval Service
Source: https://github.com/Maheen-Zubair/Physical-AI-Humanoid-Robotics-Textbook--Hackathon-01-/tree/main/hackathon-I/.claude/skills/rag-retrieval-service
Command: npx skills add https://github.com/Maheen-Zubair/Physical-AI-Humanoid-Robotics-Textbook--Hackathon-01- --skill rag-retrieval-service-maheen-zubair

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires qdrant-client, openai, pydantic, pydantic-settings, fastapi, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The RAG Retrieval Service Skill addresses the challenge of providing efficient and relevant search capabilities for educational content, especially within technical and technical content-based learning environments.

Core Features & Use Cases

  • Comprehensive Semantic Search: Offers a high-level of semantic similarity search for educational documents.
  • Personalization and Context Expansion: Allows filtering and context expansion for a more tailored user experience.
  • Production-Grade Qdrant Filter Strategies: Implements advanced Qdrant filter architectures for tenant isolation, user personalization, and content location.
  • FastAPI Search Endpoints: Provides fast and secure search through FastAPI endpoints.

Quick Start

Implement a RAG Retrieval search in your system with the following command: "activate RAG Retrieval Service for content within the 'technical_documents' collection."

Frequently Asked Questions about RAG Retrieval Service

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

FAQPage Schema
How do I implement semantic search for educational content with filtering?▼

To implement semantic search for educational content, use Qdrant for similarity matching and apply filter strategies for tenant isolation, hardware tiers, and proficiency levels. FastAPI endpoints deliver fast, accurate search results suitable for production systems.

What is the best way to isolate tenant data in a Qdrant semantic search system?▼

The best way to isolate tenant data in a Qdrant semantic search system is to apply production-grade Qdrant filter architectures. These filters secure user personalization and restrict content retrieval based on specific tenant boundaries.

Can I use FastAPI to serve Qdrant retrieval endpoints for technical documents?▼

Yes, you can use FastAPI to serve Qdrant retrieval endpoints for technical documents. The service provides fast and secure search endpoints through FastAPI, utilizing Python to deliver precise, context-aware search results across educational collections.

Does Qdrant support content filtering by proficiency level and hardware tier?▼

Yes, Qdrant supports content filtering by proficiency level and hardware tier. The service implements advanced Qdrant filter architectures that handle these specific parameters, alongside tenant isolation and content location filters.

Why use Qdrant for semantic search over other vector databases in Python applications?▼

Use Qdrant for semantic search in Python applications because it provides advanced filter architectures for tenant isolation and user personalization. When paired with FastAPI, it delivers production-grade, context-aware retrieval for educational content.

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