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Soma is a local knowledge-base search engine developed by AwesomeDog, designed to help users understand and retrieve their own materials through intelligent, locally executed search. The software operates by scanning multimedia files from configured projects, storing the collected items in a local database, and then applying local AI to construct a semantic fingerprint for each individual file. This fingerprinting approach means that searches are not limited to literal text matches; users can query their materials using natural language phrasing when they remember concepts rather than exact terms, or they can fall back on exact keyword searching when precision is required. Because the database, the AI processing, and the search indexes all remain on the user's own machine, Soma fits squarely into the local-first productivity and knowledge management category, appealing to users who prefer not to send their files or queries to external services. Typical use cases include organizing and rediscovering accumulated research materials, indexing collections of multimedia content attached to ongoing projects, and building a searchable personal knowledge base that can be interrogated conversationally. The software offers three distinct access methods to suit different workflows: a command-line interface for users who work primarily in the terminal or wish to script their searches, an HTTP service that allows other applications to integrate with the search engine programmatically, and a built-in web interface for those who prefer a browser-based experience. This multi-interface design makes Soma adaptable both as an interactive everyday tool and as a backend component in a larger local toolchain. The current release of Soma is version 0.10.0, and the software has been published in four versions to date, reflecting an active development history under the AwesomeDog publisher. Users considering Soma can expect a focused, locally hosted solution whose purpose is to make personal project materials genuinely searchable through both semantic understanding and traditional keyword lookup.
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