name: llamaindex-framework description: This skill should be used when the user wants to "build a RAG pipeline with LlamaIndex (OSS)", work with "VectorStoreIndex / query engine", follow the "load-index-store-query" model, consult "LlamaIndex component/module guides", or do "self-managed retrieval in code". Owns the open-source LlamaIndex Python/TS framework mental model and component selection; defers managed/hosted RAG to llamacloud-index, agent orchestration to llamaindex-workflows, deployment to llamaindex-deploy, and cloud parsing to llamacloud-parse. version: 0.1.0

LlamaIndex Framework (OSS) — Build RAG Yourself

Guide building retrieval-augmented generation in code with the open-source LlamaIndex framework: own the data flow, pick components, run retrieval and synthesis in your own process. Use this skill for self-managed RAG. For a fully managed pipeline (hosted indexing, parsing, retrieval API) hand off to llamacloud-index.

The mental model: load → index → store → query

Every LlamaIndex RAG application is the same four-stage pipeline. Hold this map; it determines which component category does each job.

1. Load — get data in and chunk it. Readers/connectors ingest from files, URLs,

databases, APIs. A node parser splits documents into Nodes (chunks + metadata).

1. Index — build a queryable structure. Most commonly embed nodes into a

VectorStoreIndex. Embeddings turn text into vectors for semantic similarity.

1. Store — persist the index so re-indexing is not repeated on every run. Either

persist the default in-memory stores to disk, or back the index with an external vector database.

1. Query — answer questions. A query engine wires a retriever (fetch top-k nodes) →

optional node postprocessors (filter/rerank) → response synthesizer (LLM composes the answer). Add memory with a chat engine; add reasoning/tools with an agent.

A fifth concern, evaluate, measures retrieval and answer quality once the pipeline runs. Treat it as the feedback loop, not a build step. When an answer is wrong, the mental model tells you where to look: evaluate retrieval and generation separately, then fix the stage that broke (chunking, embedding, top_k, or synthesizer) rather than guessing at the prompt.

This skill owns the framework-vs-managed decision too. Build with the OSS framework when the team wants control over chunking, the vector store, retrieval logic, and hosting, and is willing to operate them. Choose the managed Cloud Index (llamacloud-index) when the team would rather not run indexing infrastructure at all — hand off the whole pipeline. The two are not mutually exclusive at the component level, but pick one as the spine of the application.

The framework offers a high-level path (VectorStoreIndex.from_documents(...) then .as_query_engine() — sensible defaults, few lines) and a low-level path (assemble retriever, postprocessors, and synthesizer by hand). Start high-level: get an answer end to end, measure it, and only then drop to the low-level API for the one stage that needs control. Reaching for the low-level API, custom retrievers, routers, or sub-question decomposition before the defaults have been measured is the most common way these pipelines accrue complexity that never pays for itself. See references/rag-pipeline.md for the sequence and the key decision at each stage.

Component map (which category for which job)

Selection judgment — which index, which retrieval shape, which synthesizer mode, when to move from local to an external vector store — lives in references/component-map.md. Consult it before defaulting; the defaults are good but not universal.

Key choices, fast

• Index type: VectorStoreIndex for semantic search (the default and the right

answer for most RAG). Summary index for "read everything and synthesize." Property graph index for relationship/multi-hop questions. Keyword table for exact-term lookup.

• Store: prototype with the in-memory default and persist/load to disk. Move to

an external vector database when data outgrows memory, multiple processes share the index, or production durability matters.

• Synthesizer mode: compact (default) for normal Q&A; tree_summarize for

summarizing many chunks; refine for maximum detail at higher cost. Mode trades LLM calls (latency/cost) against answer completeness — table in component-map.md.

• Query interface: query engine for one-shot Q&A; chat engine when conversation

memory matters; agent when the query needs tool use or multi-step reasoning. For full event-driven agent orchestration, defer to llamaindex-workflows.

Packaging note (durable)

LlamaIndex is modular: a small core plus many independently versioned integration packages (each reader, vector store, LLM, and embedding provider ships separately). Install the core plus only the integrations a pipeline uses. The exact distribution names have shifted over time — confirm them live (see below) rather than hardcoding from memory. Illustrative Python uses the llama-index family; TypeScript uses LlamaIndex.TS.

# Python — high-level, illustrative (confirm package/signature via docs)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(docs)
answer = index.as_query_engine().query("What changed in Q3?")

// TypeScript (LlamaIndex.TS) — illustrative
import { VectorStoreIndex, Document } from "llamaindex";
const index = await VectorStoreIndex.fromDocuments([new Document({ text })]);
const res = await index.asQueryEngine().query({ query: "What changed in Q3?" });

Set the embedding model and LLM once via the global Settings object (or pass them per-call) instead of threading them through every component.

Fetching facts (signatures, package names, parameters)

Do not trust memory for exact class/method signatures, parameter names, or current package names — they drift. Look them up live:

• mcp__plugin_llamacloud_docs__search_docs — concept / BM25 search.
• mcp__plugin_llamacloud_docs__read_doc — read a known doc page.
• mcp__plugin_llamacloud_docs__grep_docs — exact symbol / regex search.
• Fallback (verified working for OSS framework docs): append index.md to any

https://developers.llamaindex.ai/... page URL and WebFetch it. Prefer this fallback for OSS framework/ pages.

Anchor doc paths for this pillar:

• https://developers.llamaindex.ai/python/framework/understanding/rag/ — the staged model.
• https://developers.llamaindex.ai/python/framework/module_guides/ — component index.
• .../module_guides/indexing/index_guide/ — index type comparison.
• .../module_guides/storing/ — persistence and stores.
• .../module_guides/querying/response_synthesizers/ — synthesizer modes.

Boundaries (defer, do not teach here)

• Managed / hosted RAG (LlamaCloud indexing, retrieval API, no infra) →

llamacloud-index. Choose managed over this framework when the team does not want to run/maintain the indexing and vector store themselves. (Cloud Index v2 is beta; teach stable Cloud Index there.)

• Event-driven agent orchestration (multi-step Workflow, steps/events) →

llamaindex-workflows.

• Deploying agents/services → llamaindex-deploy.
• Cloud document parsing (complex PDFs, tables, OCR) → llamacloud-parse. This skill

uses basic local readers; reach for managed parsing when documents are hard.