Method handle

apps/knowledge/vector/pg_vector.py:277–302

(
        self,
        query_set,
        query_text,
        query_embedding,
        top_number: int,
        similarity: float,
        search_mode: SearchMode,
        knowledge_id_list: list[str] = None,
    )

class KeywordsSearch(ISearch):
    def handle(
        self,
        query_set,
        query_text,
        query_embedding,
        top_number: int,
        similarity: float,
        search_mode: SearchMode,
        knowledge_id_list: list[str] = None,
    ):
        exec_sql, exec_params = generate_sql_by_query_dict(
            {"keywords_query": query_set},
            select_string=get_file_content(
                os.path.join(PROJECT_DIR, "apps", "knowledge", "sql", "keywords_search.sql")
            ),
            with_table_name=True,
        )
        terms = (
            list(QuerySet(Termbase).filter(knowledge_id__in=knowledge_id_list).values_list("content", flat=True))
            if knowledge_id_list
            else None
        )
        embedding_model = select_list(
            exec_sql, [to_query(query_text, user_words=terms), *exec_params, to_query(query_text, user_words=terms), similarity, top_number]
        )
        return embedding_model

    def support(self, search_mode: SearchMode):
        return search_mode.value == SearchMode.keywords.value