Build Azure AI Search Knowledge Agents for citation-backed multi-hop retrieval.
npx skills add https://github.com/aiappsgbb/awesome-gbb --skill foundry-iq-aiappsgbb
Please help me install this Agent Skill. Skill: foundry-iq Source: https://github.com/aiappsgbb/awesome-gbb/tree/main/skills/foundry-iq Command: npx skills add https://github.com/aiappsgbb/awesome-gbb --skill foundry-iq-aiappsgbb
Foundry IQ eliminates slow, non-grounded answers by building an enterprise RAG knowledge layer that can retrieve policy-backed evidence with citations, including multi-hop reasoning for complex questions.
Use the foundry-iq skill to set up an Azure AI Search index and Knowledge Agent as the default retrieval pattern for a threadlight process, then answer user questions grounded in your knowledge base with citations.
High-intent search queries and answers about installing and using this skill.
Build cited agentic RAG by configuring Azure AI Search Knowledge Agents to ingest policy documents via chunking, apply vector indexing, and return citation-backed answers. This grounds responses in your knowledge base with end-to-end citation tracking.
Multi-hop agentic retrieval decomposes complex user questions into sub-queries, retrieves relevant policy sections across single or multiple indexes, and synthesizes a single answer annotated with supporting citations.
Configure retrieval reasoning effort during Knowledge Agent setup to control the depth of multi-hop agentic retrieval. You can adjust the configurable retrievalReasoningEffort and output modality to match your policy QA requirements.
Yes, you can create and manage search indexes with vector search to ground document retrieval for knowledge base assistants. Smart chunking and semantic search enable citation-backed retrieval for multi-turn agentic QA.
Yes, Foundry IQ supports multi-turn agentic retrieval across single or multiple indexes. It tracks citations end-to-end while answering user questions grounded in retrieved evidence from your document corpus.
MarcoDavaEnable semantic vector search with HNSW indexing and MCP integration.
aurora-atomsBuild governed hybrid retrieval projections with lexical and semantic search stages.
AnveioBuild autonomous coding agents with the Claude Agent SDK for TypeScript.
samelhousseiniCoordinate Azure AI Search and embeddings for citation-backed policy document answers.
