Semantic-dictionary – A Python dictionary with semantic lookup
The idea is simple: a Python dictionary that uses semantic similarity during lookup. Say you're working on user preference extraction for an AI agent. In some cases, the model may output something like: ``` { "preference": "Database", "value": "sqlite3" } ``` but it (or a different model) may later request the user preference using a subtly different key: ``` { "action": "get_user_preference", "preference": "Database System" } ``` If we parse these responses and store them in a standard dictionary, we'll miss the similarity between the two preference keys "Database" and "Database System",…
Semantic-dictionary – A Python dictionary with semantic lookup launched on June 30, 2025 with 6 votes and more than 75% of that year's launches. The site was not reachable when checked in September 2026.
What it does
In the maker’s words, at launch
The idea is simple: a Python dictionary that uses semantic similarity during lookup. Say you're working on user preference extraction for an AI agent. In some cases, the model may output something like: ``` { "preference": "Database", "value": "sqlite3" } ``` but it (or a different model) may later request the user…
Does a similar job
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All-pair similarity search on millions of sets in Python and on laptop2018 · github.com · ▲123All-pair set similarity search on millions of sets in Python and on a laptop - GitHub - ekzhu/SetSimilaritySearch: All-pair set similarity search on millions of sets in Python and on a laptop
Semantic Grep – A Word2Vec-powered search tool2024 · github.com · ▲356Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.
Machine Perception2022 · machineperception.co · ▲63Semantic and similarity search engine for image datasets
Semantic Calculator (king-man+woman=?)2025 · calc.datova.ai · ▲176I've been playing with embeddings and wanted to try out what results the embedding layer will produce based on just word-by-word input and addition / subtraction, beyond what many videos / papers mention (like the obvious king-man+woman=queen). So I built something that doesn't just give the first answer, but ranks the matches based on distance / cosine symmetry. I polished it a bit so that others can try it out, too. For now, I only have nouns (and some proper nouns) in the dataset, and pick the most common interpretation among the homographs. Also, it's case sensitive.
GitBook AI Lens2023 · gitbook.com · ▲229Semantic search for your technical documentation & knowledge

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