The self-organizing workspace
We leverage both OpenAI embeddings models and Pinecone vector search as fundamental pillars of Mem X. These technologies power features such as similar mems and smart results, among others. Similar mems surfaces documents that are semantically similar to the document a user is viewing, allowing users to discover knowledge from across their team, re-discover knowledge they forgot they had, and make new connections between pieces of information they might not have otherwise seen. Smart results allows users to ask Mem questions as though it were a person – e.g., “How many people did we add to the Mem X waitlist in March?”. With smart results, Mem understands the semantic meaning of a user's search query and then finds the most relevant results.
OpenAI offers different embeddings models specialized for different functionalities. We use the text similarity and text search models. The similarity embeddings are good at capturing semantic similarity between multiple pieces of text, and the text search embeddings are trained to measure whether long documents are relevant to a short search query.