llamaindex

LlamaIndex data framework for LLMs. Use for RAG applications.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

llamaindex is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

LlamaIndex data framework for LLMs. Use for RAG applications.

Teams using llamaindex should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/llamaindex/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/llamaindex/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/llamaindex/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How llamaindex Compares

Feature / Agent	llamaindex	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

LlamaIndex data framework for LLMs. Use for RAG applications.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

- [LlamaIndex Documentation](https://docs.llamaindex.ai/)