At query time, embedding_search embeds the incoming query using the same model — this is important, the query and the chunks must live in the same vector space — then computes cosine similarity between the query vector and every stored chunk vector. Cosine similarity measures the angle between two vectors: a score of 1 means identical direction, 0 means completely unrelated, and negative values mean opposite meaning. The chunks are then ranked by this score and the top-k are returned. The same sanity check query from the BM25 section runs here too, so you can see the first direct comparison between the two approaches on identical input.
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If you're a Google Workspace user, Google has now released a command-line interface (CLI) that officially gives developers a way to integrate third-party AI agents like OpenClaw into the Workspace platform. This means that OpenClaw and other AI assistants can connect to a user's Google Workspace services such as Gmail and Google Drive.
Enter lambda terms, click subterms to reduce them, and explore different reduction paths through a branching derivation tree.
the O(1) computation of a memory address