llamaindex
Category: llm-applications · License: MIT · Version: 1.0.0
Tags: Agents, LlamaIndex, RAG, Document Ingestion, Vector Indices, Query Engines, Knowledge Retrieval, Data Framework, Multimodal, Private Data, Connectors
When to use it
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.Example requests
Ask your agent in plain language, for example:- “How do I build a quick RAG over a folder of PDFs?”
- “I have about 400 PDFs of oncology papers in ./papers and a Postgres table with trial metadata (nct_id, phase, status). I want to ask questions over the papers and filter retrieval by phase and status. Using llama-index 0.14 with Chroma as the store. How should I set this up, and how do I stop it from re-embedding everything every time I restart?”
- “My lab has years of internal protocols, meeting notes and Notion pages. I’d like a chatbot that lets students ask questions about them and remembers earlier turns in the conversation, answering from our own documents. What’s a sensible way to build it?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole llm-applications category with /plugin install llm-applications@research-agent-skills.