{
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  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-1.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Agentic RAG Stack (LlamaIndex, ChromaDB, LangChain, FastAPI, React)",
    "description": "A retrieval-augmented generation architecture where an AI agent\ndynamically decides what to retrieve, how to chunk and re-rank results,\nand when to perform multi-hop retrieval across heterogeneous data\nsources. LlamaIndex provides the data connectors, indexing pipelines,\nand query engines. ChromaDB (or Qdrant) serves as the vector store\nfor embedding-based similarity search.\n\nLangChain handles prompt orchestration, tool integration, and output\nparsing. FastAPI exposes the RAG pipeline as an async API with streaming\nsupport. React powers the chat frontend with real-time token streaming.\nThis stack is best for enterprise knowledge bases, legal document QA,\nand customer support copilots where static retrieval falls short and\nthe agent must reason over retrieval strategy. The tradeoff is latency —\nagentic retrieval adds LLM calls per query compared to single-shot RAG.\n",
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      "tool:chromadb",
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