Conventional database selection algorithms are very helpful in improving search results and reducing network overhead and computation time by cutting off databases that are irrelevant to the queries. However, they have not been designed to adapt to the changing interest and intentions of users in the document retrieval process. In this paper, we propose an adaptive search system using heterogeneous document vector spaces. Our system provides a dynamic construction function of search engine components based on a vector space model. In order to reflect the user's current working contexts in the components of the search engine, our system selects adaptively pre-defined sets of feature terms with different search domains depending on the user's currently-viewed documents. By exploiting such adaptive and heterogeneous document vector spaces with specific domains, our system allows users to retrieve proper documents that match their current working contexts. Also, our system implements a conventional database selection method to reduce the computation time of the similarity calculations in each document vector space. In this study, we confirm that our adaptive search system can improve the precision rates of search results as well as the scalability of computation times, by several experiments using the experimental retrieval system implemented in the desktop environment.