Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
How to install llamaindex?
firecrawl/ai-research-skills/llamaindex
$npx -y skills add firecrawl/ai-research-skills --skill llamaindexInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/firecrawl/ai-research-skills" --skill "firecrawl/ai-research-skills/llamaindex"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/firecrawl/ai-research-skills" that are relevant to the current task. Run `npx skills add "https://github.com/firecrawl/ai-research-skills"` and select the relevant skills, then follow their instructions.
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