Play 26
Semantic Search Engine
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
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
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.