Word embeddings encode a lot of semantic as well as syntactic features and therefore are useful in many tasks especially in Natural Language Processing and Information Retrieval. FREDDY (Fast woRd EmbedDings Database sYstems), an extended PostgreSQL database system, allowing the user to analyze structured knowledge in the database relations together with unstructured text corpora encoded as word embedding by introducing novel operations for similarity calculation and analogy inference. Approximation techniques support these operations to perform fast similarity computations on high-dimensional vector spaces. This demo allows exploring the powerful query capabilities of FREDDY on different database schemes and a variety of word embeddings generated on different text corpora. From a systems perspective, the user is able to examine the impact of multiple approximation techniques and their parameters for similarity search on query execution time and precision.