"""
Arize-Phoenix LlamaPack.
"""

from typing import TYPE_CHECKING, Any, Dict, List

from llama_index.core import set_global_handler
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.schema import TextNode

if TYPE_CHECKING:
    from phoenix import Session as PhoenixSession


class ArizePhoenixQueryEnginePack(BaseLlamaPack):
    """
    The Arize-Phoenix LlamaPack show how to instrument your LlamaIndex query
    engine with tracing. It launches Phoenix in the background, builds an index
    over an input list of nodes, and instantiates and instruments a query engine
    over that index so that trace data from each query is sent to Phoenix.

    Note: Using this LlamaPack requires that your OpenAI API key is set via the
    OPENAI_API_KEY environment variable.
    """

    def __init__(
        self,
        nodes: List[TextNode],
        **kwargs: Any,
    ) -> None:
        """
        Initializes a new instance of ArizePhoenixQueryEnginePack.

        Args:
            nodes (List[TextNode]): An input list of nodes over which the index
            will be built.

        """
        try:
            import phoenix as px
        except ImportError:
            raise ImportError(
                "The arize-phoenix package could not be found. "
                "Please install with `pip install arize-phoenix`."
            )
        self._session: "PhoenixSession" = px.launch_app()
        set_global_handler("arize_phoenix")
        self._index = VectorStoreIndex(nodes, **kwargs)
        self._query_engine = self._index.as_query_engine()

    def get_modules(self) -> Dict[str, Any]:
        """
        Returns a dictionary containing the internals of the LlamaPack.

        Returns:
            Dict[str, Any]: A dictionary containing the internals of the
            LlamaPack.

        """
        return {
            "session": self._session,
            "session_url": self._session.url,
            "index": self._index,
            "query_engine": self._query_engine,
        }

    def run(self, *args: Any, **kwargs: Any) -> Any:
        """
        Runs queries against the index.

        Returns:
            Any: A response from the query engine.

        """
        return self._query_engine.query(*args, **kwargs)

"""
Arize-Phoenix LlamaPack.
"""

from typing import TYPE_CHECKING, Any, Dict, List

from llama_index.core import set_global_handler
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.schema import TextNode

if TYPE_CHECKING:
    from phoenix import Session as PhoenixSession


class ArizePhoenixQueryEnginePack(BaseLlamaPack):
    """
    The Arize-Phoenix LlamaPack show how to instrument your LlamaIndex query
    engine with tracing. It launches Phoenix in the background, builds an index
    over an input list of nodes, and instantiates and instruments a query engine
    over that index so that trace data from each query is sent to Phoenix.

    Note: Using this LlamaPack requires that your OpenAI API key is set via the
    OPENAI_API_KEY environment variable.
    """

    def __init__(
        self,
        nodes: List[TextNode],
        **kwargs: Any,
    ) -> None:
        """
        Initializes a new instance of ArizePhoenixQueryEnginePack.

        Args:
            nodes (List[TextNode]): An input list of nodes over which the index
            will be built.
        """
        try:
            import phoenix as px
        except ImportError:
            raise ImportError(
                "The arize-phoenix package could not be found. "
                "Please install with `pip install arize-phoenix`."
            )
        self._session: "PhoenixSession" = px.launch_app()
        set_global_handler("arize_phoenix")
        self._index = VectorStoreIndex(nodes, **kwargs)
        self._query_engine = self._index.as_query_engine()

    def get_modules(self) -> Dict[str, Any]:
        """
        Returns a dictionary containing the internals of the LlamaPack.

        Returns:
            Dict[str, Any]: A dictionary containing the internals of the
            LlamaPack.
        """
        return {
            "session": self._session,
            "session_url": self._session.url,
            "index": self._index,
            "query_engine": self._query_engine,
        }

    def run(self, *args: Any, **kwargs: Any) -> Any:
        """
        Runs queries against the index.

        Returns:
            Any: A response from the query engine.
        """
        return self._query_engine.query(*args, **kwargs)
