How to use the litecli.packages.parseutils.last_word function in litecli

return [{"type": "keyword"}]
            else:
                return column_suggestions

        # Get the token before the parens
        idx, prev_tok = p.token_prev(len(p.tokens) - 1)
        if prev_tok and prev_tok.value and prev_tok.value.lower() == "using":
            # tbl1 INNER JOIN tbl2 USING (col1, col2)
            tables = extract_tables(full_text)

            # suggest columns that are present in more than one table
            return [{"type": "column", "tables": tables, "drop_unique": True}]
        elif p.token_first().value.lower() == "select":
            # If the lparen is preceeded by a space chances are we're about to
            # do a sub-select.
            if last_word(text_before_cursor, "all_punctuations").startswith("("):
                return [{"type": "keyword"}]
        elif p.token_first().value.lower() == "show":
            return [{"type": "show"}]

        # We're probably in a function argument list
        return [{"type": "column", "tables": extract_tables(full_text)}]
    elif token_v in ("set", "by", "distinct"):
        return [{"type": "column", "tables": extract_tables(full_text)}]
    elif token_v == "as":
        # Don't suggest anything for an alias
        return []
    elif token_v in ("show"):
        return [{"type": "show"}]
    elif token_v in ("to",):
        p = sqlparse.parse(text_before_cursor)[0]
        if p.token_first().value.lower() == "change":

def suggest_type(full_text, text_before_cursor):
    """Takes the full_text that is typed so far and also the text before the
    cursor to suggest completion type and scope.

    Returns a tuple with a type of entity ('table', 'column' etc) and a scope.
    A scope for a column category will be a list of tables.
    """

    word_before_cursor = last_word(text_before_cursor, include="many_punctuations")

    identifier = None

    # here should be removed once sqlparse has been fixed
    try:
        # If we've partially typed a word then word_before_cursor won't be an empty
        # string. In that case we want to remove the partially typed string before
        # sending it to the sqlparser. Otherwise the last token will always be the
        # partially typed string which renders the smart completion useless because
        # it will always return the list of keywords as completion.
        if word_before_cursor:
            if word_before_cursor.endswith("(") or word_before_cursor.startswith("\\"):
                parsed = sqlparse.parse(text_before_cursor)
            else:
                parsed = sqlparse.parse(text_before_cursor[: -len(word_before_cursor)])

punctuations="most_punctuations",
    ):
        """Find completion matches for the given text.

        Given the user's input text and a collection of available
        completions, find completions matching the last word of the
        text.

        If `start_only` is True, the text will match an available
        completion only at the beginning. Otherwise, a completion is
        considered a match if the text appears anywhere within it.

        yields prompt_toolkit Completion instances for any matches found
        in the collection of available completions.
        """
        last = last_word(text, include=punctuations)
        text = last.lower()

        completions = []

        if fuzzy:
            regex = ".*?".join(map(escape, text))
            pat = compile("(%s)" % regex)
            for item in sorted(collection):
                r = pat.search(item.lower())
                if r:
                    completions.append((len(r.group()), r.start(), item))
        else:
            match_end_limit = len(text) if start_only else None
            for item in sorted(collection):
                match_point = item.lower().find(text, 0, match_end_limit)
                if match_point >= 0: