LlamaIndex
Definition
LlamaIndex konzentriert sich auf die Verbindung von LLMs to your data: ingestion, indexing, and querying. It provides flexible RAG pipelines, multiple index types, and evaluation tools.
Es ergänzt LangChain: LlamaIndex emphasizes the data layer (documents, embeddings, vector stores, indexing strategies). Verwenden Sie es, wenn your priority is robust RAG over your own docs, APIs, or databases, with control over chunking, Abruf, and synthesis. Also supports agents and query engines.
Funktionsweise
Laden von Daten aus Dokumenten, APIs oder Datenbanken in ein einheitliches Dokumentformat. Indizes erstellen: Vektorindex (Embeddings + vector store), keyword index, or hybrid; you choose node parsers (chunking), embedding model, and index type. Query engines run Abruf (optionally with reranking) and then synthesis (the LLM answers from retrieved nodes). You can customize retrievers, node parsers, and response synthesis (z. B. tree summarization, simple concatenation). Evaluation tools (z. B. faithfulness, relevance) help tune chunking and Abruf for production RAG. Agents can use LlamaIndex query engines as tools inside LangChain or native agent loops.
Anwendungsfälle
LlamaIndex passt, wenn you need flexible RAG indexing, query engines, and evaluation over your own data and APIs.
- RAG and document Q&A with flexible indexing and query engines
- Connecting LLMs to internal data (docs, APIs, databases)
- Evaluating and tuning Abruf and synthesis for production RAG