class LangChainWiseAgentGraphDB(WiseAgentGraphDB):
    """
    An abstract class that makes use of a LangChain graph database.
    """

    def __new__(cls, *args, **kwargs):
        """Create a new instance of the class, setting default values for the instance variables."""
        obj = super().__new__(cls)
        enforce_no_abstract_class_instances(cls, LangChainWiseAgentGraphDB)
        obj._embedding_model_name = DEFAULT_EMBEDDING_MODEL_NAME
        model_kwargs = dict()
        model_kwargs['tokenizer_kwargs']={"clean_up_tokenization_spaces": True}
        obj._embedding_function = HuggingFaceEmbeddings(model_name=DEFAULT_EMBEDDING_MODEL_NAME,  model_kwargs=model_kwargs)
        return obj

    def __init__(self, embedding_model_name: Optional[str] = DEFAULT_EMBEDDING_MODEL_NAME):
        """
        Initialize a new instance of LangChainWiseAgentGraphDB.


        Args:
            embedding_model_name (Optional[str]): the optional name of the embedding model to use 
        """
        self._embedding_model_name = embedding_model_name
        model_kwargs = dict()
        model_kwargs['tokenizer_kwargs']={"clean_up_tokenization_spaces": True}
        self._embedding_function = HuggingFaceEmbeddings(model_name=self.embedding_model_name, model_kwargs=model_kwargs )

    @property
    def embedding_model_name(self):
        """Get the name of the embedding model."""
        return self._embedding_model_name

    def convert_to_lang_chain_node(self, entity: Entity) -> Node:
        return Node(id=entity.id, type=entity.label, properties=entity.metadata)

    def convert_to_lang_chain_relationship(self, relationship: Relationship) -> LangChainRelationship:
        return LangChainRelationship(source=self.convert_to_lang_chain_node(relationship.source),
                                     target=self.convert_to_lang_chain_node(relationship.target),
                                     type=relationship.label,
                                     properties=relationship.metadata)

    def convert_to_lang_chain_graph_document(self, graph_document: GraphDocument) -> LangChainGraphDocument:
        return LangChainGraphDocument(nodes=[self.convert_to_lang_chain_node(entity)
                                             for entity in graph_document.entities],
                                      relationships=[self.convert_to_lang_chain_relationship(relationship)
                                                     for relationship in graph_document.relationships],
                                      source=self.convert_to_lang_chain_document(graph_document.source))

    def convert_to_lang_chain_document(self, source: Source) -> LangChainDocument:
        return LangChainDocument(id=source.id, page_content=source.content, metadata=source.metadata)

    @abstractmethod
    def get_schema(self) -> str:
        ...

    @abstractmethod
    def refresh_schema(self):
        ...

    @abstractmethod
    def query(self, query: str, params: Optional[dict] = None):
        ...

    @abstractmethod
    def insert_entity(self, entity: Entity, source: Source):
        ...

    @abstractmethod
    def insert_relationship(self, relationship: Relationship, source: Source):
        ...

    @abstractmethod
    def insert_graph_documents(self, graph_documents: List[GraphDocument]):
        ...

    @abstractmethod
    def create_vector_db_from_graph_db(self, retrieval_query: str = ""):
        ...

class Neo4jLangChainWiseAgentGraphDB(LangChainWiseAgentGraphDB):
    """
    A LangChainWiseAgentGraphDB implementation that makes use of a LangChain Neo4j graph database
    and a corresponding Neo4j vector database.
    """
    yaml_tag = u'!wiseagents.graphdb.Neo4jLangChainWiseAgentGraphDB'

    def __new__(cls, *args, **kwargs):
        """Create a new instance of the class, setting default values for the instance variables."""
        obj = super().__new__(cls)
        obj._url = None
        obj._refresh_graph_schema = True
        obj._entity_label = "entity"
        obj._neo4j_graph_db = None
        obj._neo4j_vector_db = None
        return obj

    def __init__(self, properties: List[str], collection_name: str, url: Optional[str] = None,
                 refresh_graph_schema: Optional[bool] = True,
                 embedding_model_name: Optional[str] = DEFAULT_EMBEDDING_MODEL_NAME,
                 entity_label: Optional[str] = "entity"):
        """
        Initialize a new instance of Neo4jLangChainWiseAgent


        Args:
            properties (List[str]): the properties to be used as text node properties for the graph database
            collection_name (str): the collection name to use for the vector database
            url (Optional[str]): the URL of the Neo4j database (the username, password, and database name to be used
            will be obtained from the NEO4J_USERNAME, NEO4J_PASSWORD, and NEO4J_DATABASE environment variables)
            refresh_graph_schema (Optional[bool]): whether to refresh the graph schema
            embedding_model_name (Optional[str]): the optional name of the embedding model to use
            entity_label (Optional[str]): the label to use for entities in the graph database
        """
        super().__init__(embedding_model_name)
        self._properties = properties
        self._collection_name = collection_name
        self._url = url
        self._refresh_graph_schema = refresh_graph_schema
        self._entity_label = entity_label
        self._neo4j_graph_db = None
        self._neo4j_vector_db = None

    def __repr__(self):
        """Return a string representation of the graph DB."""
        return (f"{self.__class__.__name__}(properties={self.properties}, url={self.url}, refresh_schema={self.refresh_graph_schema},"
                f"embedding_model_name={self.embedding_model_name}, collection_name={self.collection_name},"
                f"entity_label={self._entity_label}")

    def __getstate__(self) -> object:
        """Return the state of the graph DB. Removing the instance variable neo4j_graph_db to avoid it being serialized/deserialized by pyyaml."""
        state = super().__getstate__()
        del state['neo4j_graph_db']
        del state['neo4j_vector_db']
        del state['embedding_function']
        return state

    @property
    def properties(self):
        """Get the properties to be used as text node properties for the graph database."""
        return self._properties

    @property
    def collection_name(self):
        """Get the collection name to use for the vector database."""
        return self._collection_name

    @property
    def entity_label(self):
        """Get the label to use for entities in the graph database."""
        return self._entity_label

    @property
    def url(self):
        """Get the URL of the Neo4j database."""
        return self._url

    @property
    def refresh_graph_schema(self):
        """Get whether to refresh the graph schema."""
        return self._refresh_graph_schema

    def connect(self):
        if self._neo4j_graph_db is None:
            self._neo4j_graph_db = Neo4jGraph(url=self.url, refresh_schema=self.refresh_graph_schema)

    def get_schema(self) -> str:
        self.connect()
        return self._neo4j_graph_db.get_schema

    def refresh_schema(self):
        self.connect()
        self._neo4j_graph_db.refresh_schema()

    def query(self, query: str, params: Optional[dict] = None):
        self.connect()
        return self._neo4j_graph_db.query(query=query, params=params)

    def insert_entity(self, entity: Entity, source: Source):
        self.connect()
        self.insert_graph_documents([GraphDocument(entities=[entity],
                                                   relationships=[],
                                                   source=source)])

    def insert_relationship(self, relationship: Relationship, source: Source):
        self.connect()
        self.insert_graph_documents([GraphDocument(entities=[],
                                                   relationships=[relationship],
                                                   source=source)])

    def insert_graph_documents(self, graph_documents: List[GraphDocument]):
        self.connect()
        self._neo4j_graph_db.add_graph_documents([self.convert_to_lang_chain_graph_document(graph_document)
                                                  for graph_document in graph_documents])

    def create_vector_db_from_graph_db(self, retrieval_query: str = ""):
        """
        Create a vector database from the graph database.


        Args:
            retrieval_query (str): the retrieval query to use for the vector database
        """
        self.connect()
        self._neo4j_vector_db = Neo4jVector.from_existing_graph(embedding=self._embedding_function,
                                                                node_label=self.entity_label,
                                                                embedding_node_property="embedding",
                                                                text_node_properties=self.properties,
                                                                url=self.url,
                                                                index_name=self.collection_name,
                                                                retrieval_query=retrieval_query)

    def query_with_embeddings(self, query: str, k: int, retrieval_query: str = "",
                              params: Optional[Dict[str, Any]] = None,
                              metadata_filter: Optional[Dict[str, Any]] = None) -> List[Document]:
        """
        Query the vector database that corresponds to this graph database using the given query and
        retrieve the top k documents.


        Args:
            query (str): the query to execute
            k (int): the number of documents to retrieve
            retrieval_query (str): the retrieval query to use for the vector database
            params (Optional[Dict[str, Any]]): the optional parameters for the query
            metadata_filter (Optional[Dict[str, Any]]): the optional metadata filter to use with similarity search

        Returns:
            List[Document]: the list of documents retrieved from the vector database
        """
        if self._neo4j_vector_db is None:
            self.create_vector_db_from_graph_db(retrieval_query=retrieval_query)
        return [Document(content=doc.page_content, metadata=doc.metadata)
                for doc in self._neo4j_vector_db.similarity_search(query=query, k=k, params=params if params else {},
                                                                   filter=metadata_filter if metadata_filter else {})]

    def delete_vector_db(self):
        """
        Delete the vector database that corresponds to this graph database.
        """
        if self._neo4j_vector_db is not None:
            self._neo4j_vector_db.delete_index()
            self._neo4j_vector_db = None

    def close(self):
        """
        Close the Neo4j driver.
        """
        if self._neo4j_graph_db is not None:
            self._neo4j_graph_db._driver.close()
        if self._neo4j_vector_db is not None:
            self._neo4j_vector_db._driver.close()