karakeep-semantic

Semantic search for bookmarks converts bookmark content into vector embeddings, letting you find articles by conceptual meaning. You can use natural language queries to retrieve content based on the underlying topic rather than relying on exact keyword matches.

Yes, semantic search allows you to find bookmarks using conversational phrases based on vague memories. It retrieves articles by understanding the conceptual meaning of your query, so you can search for concepts like getting things done without knowing the exact title.

Vector search works by converting your bookmark content into vector embeddings and storing them in a vector database. When you query, it matches the semantic meaning of your search phrase against these embeddings to retrieve the most conceptually relevant results.

Yes, enabling semantic search requires integrating with an external embedding model and a vector database. These components process and store the conceptual representations of your bookmark content to facilitate efficient meaning-based querying.

The best way to query bookmark content is using natural language phrases that describe the article's topic. Semantic search interprets the conceptual meaning of conversational queries to retrieve relevant bookmarks, such as searching for articles about building startups with a specified result limit.

Semantic search depends on the quality of the external embedding model and vector database integration. If bookmarks are not properly converted into vector embeddings, the system cannot match conceptual meanings, limiting its ability to retrieve articles based on natural language queries.