Play 26
Semantic Search Engine
Medium✅ Ready
Hybrid search with reranking, personalization, and answer generation.
A complete search experience powered by Azure AI Search and LLMs. Combines full-text search (BM25), vector search (embeddings), and hybrid fusion with semantic reranking. Query expansion uses GPT to generate alternative phrasings. Personalization layer adapts results based on user history. Answer generation synthesizes a direct answer from top results with citations.
Architecture Pattern
Hybrid search: BM25 + vector + reranking, query expansion, answer generation
Azure Services
Azure AI SearchAzure OpenAI (gpt-4o)Blob StorageContainer Apps
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Search Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (107 lines), evaluate (105 lines), tune (103 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with AI Search + OpenAI inputs + envFile
TuneKit (AI Config)
- config/search.json — hybrid weights (60/40), reranker model, top-k
- config/openai.json — query expansion + answer gen prompts
- config/guardrails.json — content filtering, PII in search results
- evaluation/eval.py — NDCG@10 >0.75, Answer accuracy >85%
Tuning Parameters
Hybrid weights (full-text vs vector)Reranker model selectionPersonalization featuresAnswer generation styleQuery expansion depthTop-k results (5→20)
Estimated Cost
Dev/Test
$80–200/mo
Production
$1K–4K/mo