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Ref #42053 This is the first PR for optimizing `LIKE` with ngram inverted index. Now, only VARCHAR data type is supported and only InnerMatch LIKE (%xxx%) query is supported. How to use it: ``` milvus_client = MilvusClient("http://localhost:19530") schema = milvus_client.create_schema() ... schema.add_field("content_ngram", DataType.VARCHAR, max_length=10000) ... index_params = milvus_client.prepare_index_params() index_params.add_index(field_name="content_ngram", index_type="NGRAM", index_name="ngram_index", min_gram=2, max_gram=3) milvus_client.create_collection(COLLECTION_NAME, ...) ``` min_gram and max_gram controls how we tokenize the documents. For example, for min_gram=2 and max_gram=4, we will tokenize each document with 2-gram, 3-gram and 4-gram. --------- Signed-off-by: SpadeA <tangchenjie1210@gmail.com> Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>