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.. _howto/operator:llamaindex_retrieval:

LlamaIndex `LlamaIndexRetrievalOperator`

Load a persisted LlamaIndex index and run similarity search. Designed to sit between :class:~airflow.providers.common.ai.operators.llamaindex_embedding.LlamaIndexEmbeddingOperator (which builds the index) and :class:~airflow.providers.common.ai.operators.llm.LLMOperator (which synthesises an answer from the retrieved chunks).

Passes the embedding model directly to load_index_from_storage(..., embed_model=...) -- no LlamaIndex Settings mutation. The embedding model must match the one used when the index was originally built.

Basic usage

.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_llamaindex_hook.py :language: python :start-after: [START howto_hook_llamaindex_retrieve] :end-before: [END howto_hook_llamaindex_retrieve]

query is templated, so DAG-run params, XCom, and Variables all flow through cleanly.

Cloud-persisted indexes

index_persist_dir accepts the same local-path-or-URI shape as LlamaIndexEmbeddingOperator.persist_dir. Pass persist_conn_id to point at the Airflow connection that holds cloud credentials. The operator raises FileNotFoundError with a clear "did you run LlamaIndexEmbeddingOperator first?" message when the path is missing.

Bring-your-own embedding model

Same shape as LlamaIndexEmbeddingOperator: embed_model accepts either a string model name (OpenAI via the hook) or a pre-built BaseEmbedding instance for non-OpenAI vendors. See the BYO example in :doc:llamaindex_embedding.

Parameters

.. list-table:: :header-rows: 1 :widths: 25 75

- Parameter
- Description
- query
- The query string. Templated.
- index_persist_dir
- Local path or storage URI pointing at the persisted index. Templated.
- persist_conn_id
- Cloud credentials connection ID for index_persist_dir URIs. Templated.
- embed_model
- String model name OR pre-built BaseEmbedding instance. Must match the model used when the index was built. Templated.
- llm_conn_id
- Airflow connection ID used when embed_model is a string. Falls back to LlamaIndexHook.default_conn_name (llamaindex_default) when None.
- embed_conn_id
- Optional separate connection ID for the embedding provider. Falls back to llm_conn_id when None.
- top_k
- Number of top similarity results to return (default 5).

Output

Returns a dict with::

{
    "query": str,
    "chunks": [
        {
            "text": str,
            "score": float,
            "metadata": dict,
            "node_id": str,
        },
        ...
    ],
}

LlamaIndex ``LlamaIndexRetrievalOperator``