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llama-index-wiki

Searches an offline LlamaIndex documentation corpus to answer Python framework questions.

733|99|Updated Dec 19, 2025
Search Tags:#Python#documentation search#vector store#LlamaIndex#llm framework#rAG#offline knowledge base
One-click install
npx skills add https://github.com/chujianyun/skills --skill llama-index-wiki
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-index-wiki
Source: https://github.com/chujianyun/skills/tree/main/skills/knowledge/llama-index-wiki
Command: npx skills add https://github.com/chujianyun/skills --skill llama-index-wiki

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Developers working with LlamaIndex often need accurate, documentation-backed answers about RAG pipelines, indexes, retrievers, agents, and integrations without browsing hundreds of online pages. This Skill bundles 455 official LlamaIndex Python framework documents with a local search tool so answers are grounded in real documentation with cited sources.

Core Features & Use Cases

  • Offline Full-Text Search: A zero-dependency Python script searches the bundled Markdown corpus by keywords, class names, or package names, with directory-prefix filtering.
  • Documentation-Grounded Answers: Responses cite the exact offline document paths used, and version-sensitive claims are flagged against the snapshot boundary.
  • Source Code Fallback: When the offline snapshot is insufficient, the workflow cross-checks the official GitHub repository and clearly labels source-derived conclusions.
  • Use Case: Ask how to persist a VectorStoreIndex to disk, and receive a working code example with the correct package imports plus references to the exact documentation pages consulted.

Quick Start

Use the llama-index-wiki skill to explain how to build a local RAG pipeline with Ollama and HuggingFace embeddings, citing the relevant documentation.

Frequently Asked Questions about llama-index-wiki

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search LlamaIndex documentation offline?▼

Run the bundled search_docs.py script with English keywords or an exact class name, optionally narrowing results with a --prefix directory filter. It returns ranked document paths with matching line snippets, no third-party packages required.

What topics does the LlamaIndex documentation corpus cover?▼

The snapshot includes 455 pages covering getting started, module guides, LLM and embedding integrations, retrievers, use cases, optimizing, understanding, community FAQ, LlamaCloud, and the changelog. It focuses on the Python framework, not the TypeScript SDK.

Does this offline documentation reflect the latest LlamaIndex version?▼

No, it is a snapshot imported on 2026-08-13 and may lag behind the latest release. For version-sensitive questions, the workflow cross-checks the official GitHub repository and labels those findings as source supplements.

Can I use this for LlamaIndex TypeScript SDK questions?▼

The corpus targets the Python framework and does not reliably cover TypeScript SDK APIs. For TypeScript-specific questions, consult the official TypeScript documentation directly instead of relying on this snapshot.

Why does the documentation search return no results?▼

No matches usually means the query terms do not appear verbatim in the corpus. Retry with synonyms, exact class or package names, or a broader --prefix; persistent failure means the topic is not covered by the snapshot.

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