“Because your grandma’s tea deserves LLM-level respect.”
A few weeks ago, while sipping ginger tea during a coding session (thanks, Grandma!), a curious thought struck me:
What if ancient herbal remedies could be queried like ChatGPT?
What if we could combine AI, semantic search, and knowledge graphs to revive traditional wisdom in a modern, developer-friendly way?
That's how Herbal Remedy Advisor was born. 💡
🔮 Meet the App
Herbal Remedy Advisor is an AI-powered herbal medicine search engine. It's like if ChatGPT trained with your grandma and also learned SQL.
It lets you:
🧠 Ask questions like “what helps with a sore throat?” and get meaningful, filtered results.
🌿 Browse a full knowledge base of natural remedies with safety and usage info.
➕ Add your own remedies—because healing wisdom shouldn’t retire.
🤖 Chat with a helpful agent powered by Gemini and Ollama, trained on herbal context.
⚡ Enjoy fast semantic queries with vector-powered SQL magic via MindsDB.
🧠 Under the Hood
I didn’t want to just throw another Flask app into the wild. I wanted this to be smooth, fast, and hackable.
Stack Highlights:
Layer What I Used
LLM Agent gemini-2.0-flash
Embeddings deepseek-r1:1.5b via Ollama
AI Database MindsDB + native Knowledge Base
Backend Flask + Jinja2
UI Bootstrap 5 (quick and clean)
Dev Tooling uv (because pip deserves better)
💻 Dev Magic – Fast Setup
I wanted the setup to be beginner-friendly but still "cool dev-approved".
bash
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clone the repo
git clone https://github.com/Zedoman/Herbal.git
cd Herbal
install with uv
uv venv
uv pip install . # or compile with pyproject.toml
run MindsDB and Ollama locally
docker run -p 47334:47334 mindsdb/mindsdb
ollama run deepseek-r1:1.5b
Then just run the Flask app and boom — you’re in herbal heaven.
🌱 Features I Loved Building
🔍 Semantic Search via SQL — semantic_search('cold remedy', content) — yep, it's a real thing.
🛡️ Safety filters — because not everything natural is safe for everyone.
🤖 Agent mode — ask about pregnancy-safe remedies, and it checks context from the KB.
📦 Auto init — first run sets up everything: knowledge base, LLM engine, sample data.
📸 A Peek into the UI
Responsive cards, clear safety info, and minimal fuss.
🧪 SQL, but Cool
Want to find a remedy that helps with "headache", is marked safe, and feels semantic?
sql
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SELECT *
FROM herbal_remedy_kb
WHERE semantic_search('headache relief', content)
AND symptom = 'Headache'
AND safety LIKE '%Safe%'
LIMIT 20;
LLM power, SQL-style. 😎
🙏 Shoutouts
MindsDB – ML meets SQL without the drama.
Ollama – Local models that just work.
uv – My new favorite Python package manager.
🚀 What’s Next?
Add user accounts and favorites
More detailed interaction metadata (e.g., drug interactions)
Support for Ayurveda & TCM
Maybe even turn this into a mobile app?
🧝♂️ Final Thought
If you're into AI, dev tooling, or you’ve ever been cured by a cup of clove tea—
you’ll enjoy building on this. 🌿
Check it out on GitHub →
🔗 github.com/Zedoman/Herbal
Let me know your thoughts, feature ideas, or which remedy you’d love to see next!
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