semantic_configuration_name=semantic_configuration_name,
            fields=fields,
            vector_search=vector_search,
            semantic_settings=semantic_settings,
            scoring_profiles=scoring_profiles,
            default_scoring_profile=default_scoring_profile,
            default_fields=default_fields,
            user_agent=user_agent,
            cors_options=cors_options,
        )
        self.search_type = search_type
        self.semantic_configuration_name = semantic_configuration_name
        self.semantic_query_language = semantic_query_language
        self.fields = fields if fields else default_fields
    @property
    def embeddings(self) -> Optional[Embeddings]:
        # TODO: Support embedding object directly
        return None
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Add texts data to an existing index."""
        keys = kwargs.get("keys")
        ids = []
        # Write data to index
        data = []
        for i, text in enumerate(texts):
            # Use provided key otherwise use default key
            key = keys[i] if keys else str(uuid.uuid4())
            # Encoding key for Azure Search valid characters
            key = base64.urlsafe_b64encode(bytes(key, "utf-8")).decode("ascii")
            metadata = metadatas[i] if metadatas else {}
            # Add data to index
            # Additional metadata to fields mapping
            doc = {
                "@search.action": "upload",
                FIELDS_ID: key,
                FIELDS_CONTENT: text,
                FIELDS_CONTENT_VECTOR: np.array(
                    self.embedding_function(text), dtype=np.float32
                ).tolist(),
                FIELDS_METADATA: json.dumps(metadata),
            }