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LlamaIndex Framework
Component Guides
Querying
Retriever

Retriever Modes

Here we show the mapping from retriever_mode configuration to the selected retriever class.

Note that retriever_mode can mean different thing for different index classes.

Specifying retriever_mode has no effect (silently ignored). vector_index.as_retriever(...) always returns a VectorIndexRetriever.

  • default: SummaryIndexRetriever
  • embedding: SummaryIndexEmbeddingRetriever
  • llm: SummaryIndexLLMRetriever
  • select_leaf: TreeSelectLeafRetriever
  • select_leaf_embedding: TreeSelectLeafEmbeddingRetriever
  • all_leaf: TreeAllLeafRetriever
  • root: TreeRootRetriever
  • default: KeywordTableGPTRetriever
  • simple: KeywordTableSimpleRetriever
  • rake: KeywordTableRAKERetriever
  • keyword: KGTableRetriever
  • embedding: KGTableRetriever
  • hybrid: KGTableRetriever
  • llm: DocumentSummaryIndexLLMRetriever
  • embedding: DocumentSummaryIndexEmbeddingRetrievers
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/