{"repo":"tmasjc/deka-oss","free":true,"listed":false,"github":"https://github.com/tmasjc/deka-oss","clone":"git clone https://github.com/tmasjc/deka-oss.git","description":"Deka — Aligning Human Intuition with Semantic Space","language":"Python","stars":55,"topics":["semantic-search","vector-database"],"license":"MIT","category":"databases-storage","readme_excerpt":"### 🇨🇳 中文版本 README 有更详细介绍 Deka — Definition and Embedding Knowledge Alignment — is a human-in-the-loop workbench that escalates a domain expert’s intuitive notion of a query — \"find me content like this\" — into a precise, reproducible, and scalable set of labelled results drawn from a vector-search corpus. It proceeds in four phases: Phase What happens --- --- 1. Probe Interactively tune hybrid (dense + learned-sparse) retrieval until the operator’s relevance judgements converge. 2. Harvest Treat the validated FIT examples as a query and sweep the corpus for their embedding neighbourhood. 3. Refine Distil that geometric cohort into an explicit, auditable language rubric, then judge a stratified sample with an LLM. 4. Apply Train a low-cost classifier on the rubric-judged sample to label the full cohort at near-zero marginal cost. When to Use Deka is built for corpora too large to read, whose recurring questions are concept-shaped — patterns a domain expert recognises on sight but cannot specify as a filter. If your query can be expressed as a keyword search, Deka is overkill; If it seeks a factual answer, use RAG instead; Deka earns its cost when relevance exists only as tacit expert judgement that must be escalated into a definition applicable at corpus scale. Deliverables A finalised session leaves the operator with two things: - A labelled dataset — the Download Artifacts zip, containing merged.csv . The bundle is self-contained — conversation text is","default_branch":null,"files":null,"tree":[],"storefront":"/r/tmasjc","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/tmasjc/deka-oss/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}