from lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
from lfx.helpers.data import docs_to_data
from lfx.inputs.inputs import DictInput, FloatInput
from lfx.io import (
    BoolInput,
    DropdownInput,
    HandleInput,
    IntInput,
    MultilineInput,
    SecretStrInput,
    StrInput,
)
from lfx.schema.data import Data


class HCDVectorStoreComponent(LCVectorStoreComponent):
    display_name: str = "Hyper-Converged Database"
    description: str = "Implementation of Vector Store using Hyper-Converged Database (HCD) with search capabilities"
    name = "HCD"
    documentation: str = "https://docs.langflow.org/bundles-datastax"
    icon: str = "HCD"

    inputs = [
        StrInput(
            name="collection_name",
            display_name="Collection Name",
            info="The name of the collection within HCD where the vectors will be stored.",
            required=True,
        ),
        StrInput(
            name="username",
            display_name="HCD Username",
            info="Authentication username for accessing HCD.",
            value="hcd-superuser",
            required=True,
        ),
        SecretStrInput(
            name="password",
            display_name="HCD Password",
            info="Authentication password for accessing HCD.",
            value="HCD_PASSWORD",
            required=True,
        ),
        SecretStrInput(
            name="api_endpoint",
            display_name="HCD API Endpoint",
            info="API endpoint URL for the HCD service.",
            value="HCD_API_ENDPOINT",
            required=True,
        ),
        *LCVectorStoreComponent.inputs,
        StrInput(
            name="namespace",
            display_name="Namespace",
            info="Optional namespace within HCD to use for the collection.",
            value="default_namespace",
            advanced=True,
        ),
        MultilineInput(
            name="ca_certificate",
            display_name="CA Certificate",
            info="Optional CA certificate for TLS connections to HCD.",
            advanced=True,
        ),
        DropdownInput(
            name="metric",
            display_name="Metric",
            info="Optional distance metric for vector comparisons in the vector store.",
            options=["cosine", "dot_product", "euclidean"],
            advanced=True,
        ),
        IntInput(
            name="batch_size",
            display_name="Batch Size",
            info="Optional number of data to process in a single batch.",
            advanced=True,
        ),
        IntInput(
            name="bulk_insert_batch_concurrency",
            display_name="Bulk Insert Batch Concurrency",
            info="Optional concurrency level for bulk insert operations.",
            advanced=True,
        ),
        IntInput(
            name="bulk_insert_overwrite_concurrency",
            display_name="Bulk Insert Overwrite Concurrency",
            info="Optional concurrency level for bulk insert operations that overwrite existing data.",
            advanced=True,
        ),
        IntInput(
            name="bulk_delete_concurrency",
            display_name="Bulk Delete Concurrency",
            info="Optional concurrency level for bulk delete operations.",
            advanced=True,
        ),
        DropdownInput(
            name="setup_mode",
            display_name="Setup Mode",
            info="Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
            options=["Sync", "Async", "Off"],
            advanced=True,
            value="Sync",
        ),
        BoolInput(
            name="pre_delete_collection",
            display_name="Pre Delete Collection",
            info="Boolean flag to determine whether to delete the collection before creating a new one.",
            advanced=True,
        ),
        StrInput(
            name="metadata_indexing_include",
            display_name="Metadata Indexing Include",
            info="Optional list of metadata fields to include in the indexing.",
            advanced=True,
        ),
        HandleInput(
            name="embedding",
            display_name="Embedding or Astra Vectorize",
            input_types=["Embeddings", "dict"],
            # TODO: This should be optional, but need to refactor langchain-astradb first.
            info="Allows either an embedding model or an Astra Vectorize configuration.",
        ),
        StrInput(
            name="metadata_indexing_exclude",
            display_name="Metadata Indexing Exclude",
            info="Optional list of metadata fields to exclude from the indexing.",
            advanced=True,
        ),
        StrInput(
            name="collection_indexing_policy",
            display_name="Collection Indexing Policy",
            info="Optional dictionary defining the indexing policy for the collection.",
            advanced=True,
        ),
        IntInput(
            name="number_of_results",
            display_name="Number of Results",
            info="Number of results to return.",
            advanced=True,
            value=4,
        ),
        DropdownInput(
            name="search_type",
            display_name="Search Type",
            info="Search type to use",
            options=["Similarity", "Similarity with score threshold", "MMR (Max Marginal Relevance)"],
            value="Similarity",
            advanced=True,
        ),
        FloatInput(
            name="search_score_threshold",
            display_name="Search Score Threshold",
            info="Minimum similarity score threshold for search results. "
            "(when using 'Similarity with score threshold')",
            value=0,
            advanced=True,
        ),
        DictInput(
            name="search_filter",
            display_name="Search Metadata Filter",
            info="Optional dictionary of filters to apply to the search query.",
            advanced=True,
            is_list=True,
        ),
    ]

    @check_cached_vector_store
    def build_vector_store(self):
        try:
            from langchain_astradb import AstraDBVectorStore
            from langchain_astradb.utils.astradb import SetupMode
        except ImportError as e:
            msg = (
                "Could not import langchain Astra DB integration package. "
                "Please install it with `pip install langchain-astradb`."
            )
            raise ImportError(msg) from e

        try:
            from astrapy.authentication import UsernamePasswordTokenProvider
            from astrapy.constants import Environment
        except ImportError as e:
            msg = "Could not import astrapy integration package. Please install it with `pip install astrapy`."
            raise ImportError(msg) from e

        try:
            if not self.setup_mode:
                self.setup_mode = self._inputs["setup_mode"].options[0]

            setup_mode_value = SetupMode[self.setup_mode.upper()]
        except KeyError as e:
            msg = f"Invalid setup mode: {self.setup_mode}"
            raise ValueError(msg) from e

        if not isinstance(self.embedding, dict):
            embedding_dict = {"embedding": self.embedding}
        else:
            from astrapy.info import VectorServiceOptions

            dict_options = self.embedding.get("collection_vector_service_options", {})
            dict_options["authentication"] = {
                k: v for k, v in dict_options.get("authentication", {}).items() if k and v
            }
            dict_options["parameters"] = {k: v for k, v in dict_options.get("parameters", {}).items() if k and v}
            embedding_dict = {"collection_vector_service_options": VectorServiceOptions.from_dict(dict_options)}
            collection_embedding_api_key = self.embedding.get("collection_embedding_api_key")
            if collection_embedding_api_key:
                embedding_dict["collection_embedding_api_key"] = collection_embedding_api_key

        token_provider = UsernamePasswordTokenProvider(self.username, self.password)
        vector_store_kwargs = {
            **embedding_dict,
            "collection_name": self.collection_name,
            "token": token_provider,
            "api_endpoint": self.api_endpoint,
            "namespace": self.namespace,
            "metric": self.metric or None,
            "batch_size": self.batch_size or None,
            "bulk_insert_batch_concurrency": self.bulk_insert_batch_concurrency or None,
            "bulk_insert_overwrite_concurrency": self.bulk_insert_overwrite_concurrency or None,
            "bulk_delete_concurrency": self.bulk_delete_concurrency or None,
            "setup_mode": setup_mode_value,
            "pre_delete_collection": self.pre_delete_collection or False,
            "environment": Environment.HCD,
        }

        if self.metadata_indexing_include:
            vector_store_kwargs["metadata_indexing_include"] = self.metadata_indexing_include
        elif self.metadata_indexing_exclude:
            vector_store_kwargs["metadata_indexing_exclude"] = self.metadata_indexing_exclude
        elif self.collection_indexing_policy:
            vector_store_kwargs["collection_indexing_policy"] = self.collection_indexing_policy

        try:
            vector_store = AstraDBVectorStore(**vector_store_kwargs)
        except Exception as e:
            msg = f"Error initializing AstraDBVectorStore: {e}"
            raise ValueError(msg) from e

        self._add_documents_to_vector_store(vector_store)
        return vector_store

    def _add_documents_to_vector_store(self, vector_store) -> None:
        # Convert DataFrame to Data if needed using parent's method
        self.ingest_data = self._prepare_ingest_data()

        documents = []
        for _input in self.ingest_data or []:
            if isinstance(_input, Data):
                documents.append(_input.to_lc_document())
            else:
                msg = "Vector Store Inputs must be Data objects."
                raise TypeError(msg)

        if documents:
            self.log(f"Adding {len(documents)} documents to the Vector Store.")
            try:
                vector_store.add_documents(documents)
            except Exception as e:
                msg = f"Error adding documents to AstraDBVectorStore: {e}"
                raise ValueError(msg) from e
        else:
            self.log("No documents to add to the Vector Store.")

    def _map_search_type(self) -> str:
        if self.search_type == "Similarity with score threshold":
            return "similarity_score_threshold"
        if self.search_type == "MMR (Max Marginal Relevance)":
            return "mmr"
        return "similarity"

    def _build_search_args(self):
        args = {
            "k": self.number_of_results,
            "score_threshold": self.search_score_threshold,
        }

        if self.search_filter:
            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}
            if len(clean_filter) > 0:
                args["filter"] = clean_filter
        return args

    def search_documents(self) -> list[Data]:
        vector_store = self.build_vector_store()

        self.log(f"Search query: {self.search_query}")
        self.log(f"Search type: {self.search_type}")
        self.log(f"Number of results: {self.number_of_results}")

        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
            try:
                search_type = self._map_search_type()
                search_args = self._build_search_args()

                docs = vector_store.search(query=self.search_query, search_type=search_type, **search_args)
            except Exception as e:
                msg = f"Error performing search in AstraDBVectorStore: {e}"
                raise ValueError(msg) from e

            self.log(f"Retrieved documents: {len(docs)}")

            data = docs_to_data(docs)
            self.log(f"Converted documents to data: {len(data)}")
            self.status = data
            return data
        self.log("No search input provided. Skipping search.")
        return []

    def get_retriever_kwargs(self):
        search_args = self._build_search_args()
        return {
            "search_type": self._map_search_type(),
            "search_kwargs": search_args,
        }
