Llamaindex

LlamaIndex Skill

Data framework for connecting custom data sources to large language models.

Ecosystem Graph Preview

graph LR
  llamaindex["llamaindex"]:::core
  classDef core fill:#f9f,stroke:#333,stroke-width:4px;
  langchain -- "alternative to" --> llamaindex
  llamaindex -- "alternative to" --> langchain
  llamaindex -- "integrates with" --> openai
  llamaindex -- "integrates with" --> pinecone

Recommended Next Skills

• langchain (Score: 0.93) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Can deploy to any, Similar network profile
• pinecone (Score: 0.73) Why: Direct relationship, Both are AI, Similar network profile
• openai (Score: 0.72) Why: Direct relationship, Both are AI, Similar network profile

Quick Start

While LangChain focuses on Agents and Chains, LlamaIndex focuses heavily on Data. It is the premier framework for building advanced Retrieval-Augmented Generation (RAG) applications over unstructured data.

pip install llama-index

Production Patterns

Advanced Retrieval Strategies

Do not rely on naive Top-K semantic search. Production RAG requires advanced strategies like Sentence Window Retrieval (fetching the surrounding context of a hit), Auto-Merging Retrieval, or Re-ranking (using Cohere) to improve hallucination resistance.

Architecture & Scaling

Document Ingestion Pipeline

LlamaIndex handles the entire ingestion pipeline: Data Connectors (PDFs, Notion, SQL) -> Data Indexes (VectorStore, TreeIndex) -> Query Engines.

Error Recovery

If the LLM complains about missing context, it means your Chunk Size is too small or your retrieval strategy is pulling irrelevant nodes. Inspect the source_nodes array attached to the LlamaIndex response to debug exactly what text was fed to the LLM.

Security Notes

When ingesting documents, respect ACLs (Access Control Lists). Ensure that when User A queries the index, the retriever is strictly filtered to only pull vector embeddings derived from documents User A has permission to read.

References

• LlamaIndex Docs

Why use this skill

Use this when your agent works with llamaindex — structured patterns beat pasted docs and prevent common hallucinations.

AI pitfalls

• Using deprecated model IDs or wrong API endpoints
• Confusing chat vs completions vs embeddings APIs
• Omitting rate-limit and token budget handling

Production checklist

• Secrets in environment variables, not source code
• Error handling and logging in place
• Rate limits and timeouts configured

Related skills

• langchain — alternative to
• openai — integrates with
• pinecone — integrates with

Last Verified: 2026-07-02