jackyliang

jackyliang / jit-semantic-search

Public

Semantic search without pre-processing. Query any text corpus instantly — no embeddings, no vector store, no index.

11
0
100% credibility
Found Mar 22, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python package for performing semantic searches on lists of text documents just-in-time without needing databases or preprocessing.

How It Works

1
📖 Discover smart search

You learn about a simple tool that finds meaningful matches in your texts, like an upgraded search function without any setup hassle.

2
💻 Set it up easily

You add the tool to your computer with one quick command, and it's ready to use right away.

3
📝 Gather your texts

You collect the messages, descriptions, or listings you want to search through into a simple list.

4
🔍 Ask your question

You type what you're looking for, like 'waterproof speaker under 50 dollars', and run the search on your texts.

5
⚡ Pick a search style

You choose from fast keyword, smart meaning, or balanced options to fit your needs.

6
📋 See top matches

Instantly get a list of the best matching texts, ranked with confidence scores.

🎉 Find what you need

You quickly pinpoint the perfect results from your data, saving time without building anything extra.

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AI-Generated Review

What is jit-semantic-search?

jit-semantic-search delivers semantic search python on any text corpus without embeddings, vector stores, or indexes—just pass documents and a query for instant ranked results. Built in Python with a clean API, CLI (like `jit-search "query" --file data.json`), and FastAPI server for semantic search rag workflows. It's grep for semantics: query fetched data from APIs or files on the fly, no setup.

Why is it gaining traction?

Skips the preprocessing grind of tools like semantic search elasticsearch or pgvector, delivering first results in 200ms via cascade strategy (0.956 NDCG@10 on benchmarks). Handles structured objects by concatenating fields, beats indexed alternatives on time-to-first-result for one-offs, and offers strategies from lexical (0.4ms) to neural for speed-quality tradeoffs. Developers grab it for zero-infra semantic search meaning on dynamic corpora.

Who should use this?

AI agents pulling product listings from supplier APIs or arXiv papers to match specs. Support bots querying Slack, Intercom, and email feedback for issues. Data pipelines scanning customer logs without semantic search azure overhead—perfect for devs ditching embeddings for ad-hoc corpus searches.

Verdict

Solid experiment for semantic search core, with strong benchmarks and easy CLI/server entrypoints, but 11 stars and 1.0% credibility score flag it as research-only—not production-ready. Test on your data; contribute if JIT fits your github semantic commits or rag needs.

(198 words)

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