konaequity

konaequity / konash

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KONASH: Train knowledge agents that search, retrieve, and reason. Based on KARL (Databricks, 2026).

pypi.orgprojectkonash faiss fine-tuning knowledge-agents konash llm
125
0
100% credibility
Found Mar 14, 2026 at 123 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

KONASH is an open-source Python tool for training affordable AI agents to search, retrieve, and reason over personal or enterprise document collections using reinforcement learning.

How It Works

1
🔍 Discover KONASH

You hear about KONASH, a simple way to make your documents smarter with AI, and install it with one easy command.

2
🔑 Connect AI helpers

You quickly link free online AI services that power the thinking, taking just a couple of minutes.

3
📁 Add your documents

Point it to a folder of your files like reports, articles, or notes—it reads and prepares them automatically.

4
🚀 Train your smart assistant

Choose a scale like 'quick test' or 'full power', hit go, and watch it learn to search and answer from your docs in minutes to hours.

5
💬 Ask questions

Type natural questions like 'What caused the crisis?' and get clear, evidence-based answers drawn from your files.

✅ Your knowledge agent is ready

Now you have a personal expert that finds facts, reasons across docs, and saves you hours of searching—smarter every time you train more.

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AI-Generated Review

What is konash?

Konash lets you train knowledge agents on any document corpus using reinforcement learning, turning folders of PDFs, code, or reports into agents that learn to search, retrieve, and reason accurately. Built in Python, it's a CLI-first github train ai tool – run `pip install konash`, `konash setup` for API keys, then `konash train` on local files or benchmarks like BrowseComp-Plus, and query with `konash ask`. It handles github train lora fine-tunes via Together AI or local GPUs, outputting deployable models for vLLM.

Why is it gaining traction?

It stands out by delivering frontier-level grounded reasoning at ~$100 per iteration versus $100K+ alternatives, thanks to off-policy RL that teaches efficient multi-step search over facts. Developers dig the zero-lock-in workflow: ingest corpus, synthesize QA via agent loops, filter pass-rates, train – all in hours with parallel rollouts for reliable answers. For github train ai model users, the value-guided search and LoRA hot-swapping beat basic RAG kits.

Who should use this?

ML engineers customizing RAG for enterprise docs like SEC filings or tech stacks, where standard retrievers fail on multi-hop queries. Finance teams training on FinanceBench-style data, or docs-heavy startups needing github train model agents without massive infra. Skip if you're just fine-tuning LLMs from scratch sans search focus.

Verdict

Worth a spin for github train lora on knowledge tasks – CLI and presets make prototyping fast, docs cover edge cases well. At 123 stars and 1.0% credibility, it's pre-maturity; validate on your data before commit, but KARL impl quality signals strong upside.

(198 words)

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