We have presented a graph-text contrastive learning of inorganic crystal structure (CLICS) for grasping geometric concepts composing crystal structures. CLICS breaks down chemical entities into the geometric concepts and integrally embeds them in the feature space. The contextual patterns of the local atomic environments, their connected geometries, and the space groups were represented in texts, and contrasted with the graphs. Throughout experiments, we have demonstrated that CLICS has integrally embedded the geometric concepts and is aware of the meanings of the geometries. Experimental results of similar structure search and classification tasks under challenging situations also suggest that similar crystal structures have got closer based on the geometric concepts. CLICS could provide a first step toward developing a geometric foundation model of inorganic materials. Incorporating the chemical properties of elements in addition to the geometric concepts is a research direction. As possible broader impacts, property prediction would be improved by CLICS-pretraining, and the feature space would also be useful for materials exploration as well as a soft constraint for generative models.