def add_text(
        self,
        text: str,
        embedding: List[float],
        metadata: Optional[dict] = None,
        **kwargs: Any,
    ) -> str:
        """
        Add  texts through the embeddings and add to the vectorstore.

        Args:
          texts: text string to add to the jaguar vector store.
          embedding: embedding vector of the text, list of floats
          metadata: {'file_path': '../data/paul_graham/paul_graham_essay.txt',
                          'file_name': 'paul_graham_essay.txt',
                          'file_type': 'text/plain',
                          'file_size': 75042,
                          'creation_date': '2023-12-24',
                          'last_modified_date': '2023-12-24',
                          'last_accessed_date': '2023-12-28'}
          kwargs: vector_index=name_of_vector_index
                  file_column=name_of_file_column
                  metadata={...}

        Returns:
            id from adding the text into the vectorstore

        """
        text = self._sanitize_input(text)
        vcol = self._vector_index
        filecol = kwargs.get("file_column", "")
        text_tag = kwargs.get("text_tag", "")

        if text_tag != "":
            text = text_tag + " " + text

        podstorevcol = self._pod + "." + self._store + "." + vcol
        q = "textcol " + podstorevcol
        js = self.run(q)
        if js == "":
            return ""
        textcol = js["data"]

        zid = ""
        if metadata is None:
            ### no metadata and no files to upload
            str_vec = [str(x) for x in embedding]
            values_comma = self._sanitize_input(",".join(str_vec))
            podstore = self._pod + "." + self._store
            q = "insert into " + podstore + " ("
            q += vcol + "," + textcol + ") values ('" + values_comma
            q += "','" + text + "')"
            js = self.run(q, False)
            zid = js["zid"]
        else:
            str_vec = [str(x) for x in embedding]
            nvec, vvec, filepath = self._parseMeta(metadata, filecol)
            if filecol != "":
                rc = self._jag.postFile(self._token, filepath, 1)
                if not rc:
                    return ""
            names_comma = ",".join(nvec)
            names_comma += "," + vcol
            names_comma = self._sanitize_input(names_comma)
            ## col1,col2,col3,vecl

            if vvec is not None and len(vvec) > 0:
                values_comma = "'" + "','".join(vvec) + "'"
            else:
                values_comma = "'" + "','".join(vvec) + "'"

            ### 'va1','val2','val3'
            values_comma += ",'" + ",".join(str_vec) + "'"
            values_comma = self._sanitize_input(values_comma)
            ### 'v1,v2,v3'
            podstore = self._pod + "." + self._store
            q = "insert into " + podstore + " ("
            q += names_comma + "," + textcol + ") values (" + values_comma
            q += ",'" + text + "')"
            if filecol != "":
                js = self.run(q, True)
            else:
                js = self.run(q, False)
            zid = js["zid"]

        return zid

        Add  texts through the embeddings and add to the vectorstore.

        Args:
          texts: text string to add to the jaguar vector store.
          embedding: embedding vector of the text, list of floats
          metadata: {'file_path': '../data/paul_graham/paul_graham_essay.txt',
                          'file_name': 'paul_graham_essay.txt',
                          'file_type': 'text/plain',
                          'file_size': 75042,
                          'creation_date': '2023-12-24',
                          'last_modified_date': '2023-12-24',
                          'last_accessed_date': '2023-12-28'}
          kwargs: vector_index=name_of_vector_index
                  file_column=name_of_file_column
                  metadata={...}

        Returns:
            id from adding the text into the vectorstore

        
llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/llama_index/vector_stores/jaguar/base.py