README.md

metadata

title: OSH Agentic RAG
emoji: 🤖
colorFrom: purple
colorTo: blue
sdk: docker
pinned: false
tags:
  - ml-intern

🤖 OSH Agentic RAG

An agentic RAG system that uses tool-calling to answer legal questions about Kenyan OSH law.

How it works

Unlike traditional RAG (retrieve → generate), this agent decides what to do:

1. Receives question → analyzes what information is needed
2. Calls tools → search_documents or search_specific_act
3. Reviews results → decides if it has enough context
4. Generates answer → with citations to specific Acts/sections

Architecture

Component	Role	Size
Qwen3 1.7B (Q4_K_M)	Agent brain with tool-calling	1.1 GB
BGE-small-en-v1.5	Semantic search embeddings	130 MB
FAISS	Vector similarity search	—

Speed

Expected ~10-20s per response on free HF CPU (vs 75-90s for the non-agentic version).

API

from gradio_client import Client
client = Client("Rofati/osh-agentic-rag")
result = client.predict("What are noise exposure limits?", api_name="/ask")