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# Source code for langchain_community.retrievers.llama_index

    from typing import Any, Dict, List, cast

    from langchain_core.callbacks import CallbackManagerForRetrieverRun
    from langchain_core.documents import Document
    from langchain_core.pydantic_v1 import Field
    from langchain_core.retrievers import BaseRetriever

    class LlamaIndexRetriever(BaseRetriever):
        """`LlamaIndex` retriever.

        It is used for the question-answering with sources over
        an LlamaIndex data structure."""

        index: Any
        """LlamaIndex index to query."""
        query_kwargs: Dict = Field(default_factory=dict)
        """Keyword arguments to pass to the query method."""

        def _get_relevant_documents(
            self, query: str, *, run_manager: CallbackManagerForRetrieverRun
        ) -> List[Document]:
            """Get documents relevant for a query."""
            try:
                from llama_index.core.base.response.schema import Response
                from llama_index.core.indices.base import BaseGPTIndex
            except ImportError:
                raise ImportError(
                    "You need to install `pip install llama-index` to use this retriever."
                )
            index = cast(BaseGPTIndex, self.index)

            response = index.query(query, **self.query_kwargs)
            response = cast(Response, response)
            # parse source nodes
            docs = []
            for source_node in response.source_nodes:
                metadata = source_node.metadata or {}
                docs.append(
                    Document(page_content=source_node.get_content(), metadata=metadata)
                )
            return docs

    class LlamaIndexGraphRetriever(BaseRetriever):
        """`LlamaIndex` graph data structure retriever.

        It is used for question-answering with sources over an LlamaIndex
        graph data structure."""

        graph: Any
        """LlamaIndex graph to query."""
        query_configs: List[Dict] = Field(default_factory=list)
        """List of query configs to pass to the query method."""

        def _get_relevant_documents(
            self, query: str, *, run_manager: CallbackManagerForRetrieverRun
        ) -> List[Document]:
            """Get documents relevant for a query."""
            try:
                from llama_index.core.base.response.schema import Response
                from llama_index.core.composability.base import (
                    QUERY_CONFIG_TYPE,
                    ComposableGraph,
                )
            except ImportError:
                raise ImportError(
                    "You need to install `pip install llama-index` to use this retriever."
                )
            graph = cast(ComposableGraph, self.graph)

            # for now, inject response_mode="no_text" into query configs
            for query_config in self.query_configs:
                query_config["response_mode"] = "no_text"
            query_configs = cast(List[QUERY_CONFIG_TYPE], self.query_configs)
            response = graph.query(query, query_configs=query_configs)
            response = cast(Response, response)

            # parse source nodes
            docs = []
            for source_node in response.source_nodes:
                metadata = source_node.metadata or {}
                docs.append(
                    Document(page_content=source_node.get_content(), metadata=metadata)
                )
            return docs