Hashgrid DNA

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AI 工具 DNA is a search engine that learns from feedback. Bring your own items and any embedding model, score results with an LLM or user clicks, and each search improves while requiring fewer LLM calls.

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DNA stands out by separating embedding and judging from the search layer: you supply vectors from any model and scores from an LLM or real users, while training happens on Hashgrid's side. The pitch is lower cost than RAG plus a reranker, with counters showing LLM calls saved. Best suited to teams w

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Hashgrid DNA is positioned as a feedback-driven search engine rather than a hosted vector database. You bring your own items and generate one embedding per item with any model you like, then query through a simple HTTP API. The system proposes results between your searches and your items, and results confirmed by feedback are reinforced over time.

The core loop is deliberately minimal: send embeddings, run a search, and return a score when you have one. Scoring can come from an LLM, a small model, or your users’ clicks. DNA trains on its own side, so you do not host or tune a model yourself, and the page advertises savings in LLM calls compared with a RAG plus reranker setup.

It fits developers and product teams that already have a catalog of nodes such as products, documents, tools, or people, along with some source of relevance feedback. Because any embedding model works and the integration is a few API calls, it can slot into existing retrieval pipelines without a migration.

A practical approach is to start with a small item set and a single feedback signal, watch the top-results and LLM-calls-saved counters, then expand scoring sources once relevance stabilizes. Access requires an API key, so evaluate it against your current retrieval stack before committing.