Method query

embedding/embeddingHelper.py:90–128

(self, text, topK=5, where=None, where_document=None)

            self.redisCaching = RedisCaching()

    def query(self, text, topK=5, where=None, where_document=None):
        # where={"metadata_field": "is_equal_to_this"},
        # where_document={"$contains": "search_string"}
        if self.need_redis:
            starttime = time.time()
            res = self.query_preprocessing(text, top_k=topK)
            if res is not None:
                if len(res) >= 1:
                    return res
        if where is None:
            if where_document is None:
                result = self.collection.query(
                    query_texts=[text],
                    n_results=topK
                )
            else:
                result = self.collection.query(
                    query_texts=[text],
                    n_results=topK,
                )
        else:
            if where_document is None:
                result = self.collection.query(
                    query_texts=[text],
                    n_results=topK,
                    where=where
                )
            else:
                result = self.collection.query(
                    query_texts=[text],
                    n_results=topK,
                    where=where
                )

        result = self.processResult(query=[text], result=result)
        """
        result : [[]] 第一层为查询的数量，现在为1，第二层为每一次查询的相似度结果
        """
        return result

    def batchQuery(self, texts, topK=5, where=None, where_document=None):
        # where={"metadata_field": "is_equal_to_this"},