
from awesome-omni-skill43
Framework for building RAG and document-centric LLM applications: ingestion connectors, vector indices, query engines and agent integrations for document Q&A an
LlamaIndex provides a comprehensive toolkit for connecting LLMs to your data: document ingestion (300+ connectors), vector indices, retrievers, query engines and patterns for RAG-based apps. It includes code examples for building indexes, query engines, agents and multi-modal retrieval workflows.
Use this skill when building document Q&A systems, knowledge-base chatbots, retrieval-augmented agents, or pipelines that need structured document ingestion, metadata filtering and persistence to vector stores. Prefer LlamaIndex for data-centric LLM tasks; consider LangChain for more agent-centric workflows.
Works well with developer-facing assistant agents that can run code snippets and help build RAG pipelines (Copilot-like assistants, Claude/Codex-style code agents).
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