"""
Opensearch reader over REST api.

This only uses the basic search api, so it will work Opensearch.

"""

from typing import List, Optional

from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document


class OpensearchReader(BaseReader):
    """
    Read documents from an Opensearch index.

    These documents can then be used in a downstream Llama Index data structure.

    Args:
        endpoint (str): URL (http/https) of cluster without port
        index (str): Name of the index (required)
        basic_auth (set): basic authentication username password

    """

    def __init__(
        self, host: str, port: int, index: str, basic_auth: Optional[set] = None
    ):
        """Initialize with parameters."""
        from opensearchpy import OpenSearch

        self._opster_client = OpenSearch(
            hosts=[{"host": host, "port": port}],
            http_compress=True,  # enables gzip compression for request bodies
            http_auth=basic_auth,
            use_ssl=True,
            verify_certs=False,
            ssl_assert_hostname=False,
            ssl_show_warn=False,
        )
        self._index = index

    def load_data(
        self,
        field: str,
        query: Optional[dict] = None,
        embedding_field: Optional[str] = None,
    ) -> List[Document]:
        """
        Read data from the Opensearch index.

        Args:
            field (str): Field in the document to retrieve text from
            query (Optional[dict]): Opensearch JSON query DSL object.
                For example:
                { "query" : {"match": {"message": {"query": "this is a test"}}}}
            embedding_field (Optional[str]): If there are embeddings stored in
                this index, this field can be used
                to set the embedding field on the returned Document list.


        Returns:
            List[Document]: A list of documents.

        """
        res = self._opster_client.search(body=query, index=self._index)
        documents = []
        for hit in res["hits"]["hits"]:
            value = hit["_source"][field]
            _ = hit["_source"].pop(field)
            embedding = hit["_source"].get(embedding_field or "", None)
            documents.append(
                Document(text=value, extra_info=hit["_source"], embedding=embedding)
            )
        return documents

"""Opensearch reader over REST api.

This only uses the basic search api, so it will work Opensearch.

"""

from typing import List, Optional

from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document


class OpensearchReader(BaseReader):
    """
    Read documents from an Opensearch index.

    These documents can then be used in a downstream Llama Index data structure.

    Args:
        endpoint (str): URL (http/https) of cluster without port
        index (str): Name of the index (required)
        basic_auth (set): basic authentication username password
    """

    def __init__(
        self, host: str, port: int, index: str, basic_auth: Optional[set] = None
    ):
        """Initialize with parameters."""
        from opensearchpy import OpenSearch

        self._opster_client = OpenSearch(
            hosts=[{"host": host, "port": port}],
            http_compress=True,  # enables gzip compression for request bodies
            http_auth=basic_auth,
            use_ssl=True,
            verify_certs=False,
            ssl_assert_hostname=False,
            ssl_show_warn=False,
        )
        self._index = index

    def load_data(
        self,
        field: str,
        query: Optional[dict] = None,
        embedding_field: Optional[str] = None,
    ) -> List[Document]:
        """Read data from the Opensearch index.

        Args:
            field (str): Field in the document to retrieve text from
            query (Optional[dict]): Opensearch JSON query DSL object.
                For example:
                { "query" : {"match": {"message": {"query": "this is a test"}}}}
            embedding_field (Optional[str]): If there are embeddings stored in
                this index, this field can be used
                to set the embedding field on the returned Document list.


        Returns:
            List[Document]: A list of documents.

        """
        res = self._opster_client.search(body=query, index=self._index)
        documents = []
        for hit in res["hits"]["hits"]:
            value = hit["_source"][field]
            _ = hit["_source"].pop(field)
            embedding = hit["_source"].get(embedding_field or "", None)
            documents.append(
                Document(text=value, extra_info=hit["_source"], embedding=embedding)
            )
        return documents
