Method handle

apps/knowledge/vector/pg_vector.py:250–270

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

class EmbeddingSearch(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(
            {"embedding_query": query_set},
            select_string=get_file_content(
                os.path.join(PROJECT_DIR, "apps", "knowledge", "sql", "embedding_search.sql")
            ),
            with_table_name=True,
        )
        embedding_model = select_list(
            exec_sql, [len(query_embedding), json.dumps(query_embedding), *exec_params, len(query_embedding), json.dumps(query_embedding), top_number, similarity, top_number]
        )
        return embedding_model

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