llamaindex
LlamaIndex data framework for LLMs. Use for RAG applications.
Install
npx skillscat add g1joshi/agent-skills/llamaindex Install via the SkillsCat registry.
We need to produce a 2-3 sentence plain-text summary, objective, factual, no marketing language, no superlatives, no calls to action, natural prose, no bullet points, no headings, no markdown. At most 60 words. Must be only the summary text, no extra. We need to explain what the skill does, what problem it solves, when to use it. So something like: "The LlamaIndex skill provides a framework for connecting large language models to custom data sources, enabling retrieval‑augmented generation, natural‑language querying of documents, databases, and other structured data.
LlamaIndex
LlamaIndex (formerly GPT Index) connects LLMs to your data. 2025 introduces Workflows, an event-driven way to build complex RAG pipelines.
When to Use
- RAG (Retrieval Augmented Generation): Indexing PDFs, Docs, SQL to chat with them.
- Structured Data: Querying SQL/Pandas with natural language (
NLSQL). - Agents: Building research agents that browse the web and summarize.
Core Concepts
Workflows
Event-driven architecture for agents. Replace DAGs with event listeners (@step).
Query Engine
High-level API (index.as_query_engine()) to ask questions.
Data Loaders (LlamaHub)
Connectors for Notion, Slack, Discord, PDF, etc.
Best Practices (2025)
Do:
- Use Workflows: They are harder to learn but easier to debug than monolithic engines.
- Use Hybrid Search: BM25 (Keyword) + Vector Search for best retrieval accuracy.
- Use Rerankers: Always rerank retrieved nodes (Cohere/BGE) before sending to LLM.
Don't:
- Don't dump raw text: Use "Node Parsers" to chunk data intelligently (Markdown, Semantic).
References
Categories
Install
npx skillscat add g1joshi/agent-skills/llamaindex Install via the SkillsCat registry.