agentsop-llamaindex

LlamaIndex · SOP

Third-person analytical view of how LlamaIndex thinks about turning private documents into a grounded answering system. The skill is for an LLM agent that writes / reviews / debugs RAG code — not for an end user reading docs.

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何时激活 (Activation Rules)

Activate this skill when any of the following holds:

1. The user's request involves building, modifying, or debugging a RAG pipeline (retrieval over private/unstructured data + LLM synthesis).
2. The user mentions LlamaIndex (from llama_index...), LlamaParse, LlamaCloud, or a LlamaIndex-style primitive (VectorStoreIndex, SummaryIndex, IngestionPipeline, QueryEngine, SubQuestionQueryEngine, RouterQueryEngine, Settings, Workflows).
3. The user is comparing RAG frameworks (LlamaIndex vs LangChain vs Haystack vs raw vector store).
4. The user is choosing between stuffing context, RAG, or an agent for a knowledge task.
5. The user is debugging retrieval quality (hallucinations, wrong chunks, stale data, embedding drift) — even if the codebase predates LlamaIndex, the failure-mode taxonomy applies.
6. The user is evaluating a RAG system (faithfulness, relevancy, MRR, hit-rate).

Do not activate when:

• The task is pure agent orchestration with no retrieval (use LangGraph/CrewAI skill instead).
• The corpus is tiny (<100k tokens, static) and prompt-stuffing is the correct answer.
• The data is pure SQL/tabular with no unstructured component.

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核心心智模型 (Core Mental Model)

LlamaIndex's design rests on three principles that distinguish it from "vector DB SDK + custom glue":

Principle 1 — The Index is a noun, not a verb

In LangChain, "indexing" is something you do to a vector store. In LlamaIndex, an Index is a first-class typed object with its own retrieval semantics. Picking the right Index is half the architecture decision.

The 5-layer pipeline:

Documents → Nodes → Index → Retriever → Query Engine → Response
   ↓         ↓        ↓         ↓             ↓
parsing   chunking  storage   filters    synthesis
metadata  graph     primitive  rerank    (refine/tree_sum/compact)