🧑‍⚖️ Building a Saudi Labor Law AI Assistant — Bilingual, Semantic, and Context-Aware

This content originally appeared on DEV Community and was authored by Zainalabdeen

The Saudi Labor Law is a complex and evolving legal framework. For HR teams, employers, and employees, understanding its details — from leave entitlements to termination rules — often means scrolling through dozens of pages, interpreting legal text, and trying to connect articles to real-world cases.

I wanted to change that.
So I built Saudi Labor Law AI Assistant — an intelligent, bilingual chatbot that answers legal questions instantly, explains relevant articles, and even analyzes employee-specific scenarios — all powered by vector search, LLMs, and semantic retrieval.

Why This Project Matters

The challenges were clear:

The official English translation of the law is outdated — the Arabic version is the authoritative reference.

Searching manually across legal PDFs is slow and error-prone.

HR teams need contextual interpretations, not just raw text.

The solution? Combine document parsing, embeddings, vector databases, translation, and LLM reasoning into one end-to-end system that delivers article-backed, trustworthy answers in Arabic or English.

What the AI Assistant Can Do

Here’s what the system offers today:

Ask legal questions in Arabic or English — answers come in the same language.

Analyze real employee cases — like leave eligibility, overtime pay, or termination compensation.

Retrieve the exact legal articles that support every answer.

Integrate employee data (age, salary, service years) into the reasoning process for personalized results.

Handle bilingual queries with automatic translation and context matching.

How It Works

The assistant is built on a robust NLP and retrieval pipeline:

PDF Parsing – The official Arabic labor law is parsed with PyMuPDF, preserving RTL text and diacritics.

Structured Splitting – The document is split into parts, chapters, and articles with metadata.

Translation – Each article is translated to English using Helsinki-NLP/opus-mt-ar-en for bilingual support.

Vectorization – Both Arabic and English texts are embedded using intfloat/multilingual-e5-base and stored in a Qdrant vector database.

Retrieval + Reasoning – A VectorIndexRetriever fetches the most relevant articles, which are then passed to GPT-4o-mini for grounded, human-readable answers.

Hybrid Search Evaluation – After testing semantic and hybrid retrieval methods on 1,245 queries, hybrid search proved superior and is used by default.

Context-Aware Legal Reasoning

One of the most powerful features is employee-specific reasoning.
For example:

“Is this employee eligible for 30 days of annual leave if he has worked for 6 years?”

The chatbot uses employee metadata (service years, salary, leave days, etc.) to reason about the law in context, delivering precise, actionable answers — always citing the original legal article.

Streamlit Interface

The frontend is built with Streamlit to make the experience intuitive and user-friendly:

Auto-detect Arabic or English queries.

Optional employee data input.

Expandable references with similarity scores.

Source tracing from Part → Chapter → Article.

Example in Action

Arabic Example:

: ما هي مدة الإجازة السنوية بعد خمس سنوات من الخدمة؟
: يستحق العامل ثلاثين يوماً من الإجازة السنوية…
: استنادًا إلى المادة التاسعة بعد المائة

English Example:

: What are the sick leave entitlements for an employee?
: The employee is entitled to paid sick leave for a specific duration…
: Based on Article 117 – Chapter Four

What’s Next

The project is just getting started. Planned enhancements include:

PDF export of Q&A with references

HR calculators (end-of-service, overtime, vacation accrual)

Arabic voice interaction

HR analytics dashboard

Tech Stack

Saudi Labor Law AI Assistant is open-source and licensed under MIT. It’s built to make labor law understandable, accessible, and actionable — for HR teams, companies, and employees across Saudi Arabia.

Explore the Project

GitHub Repository
I build This Project as Final Project Of learning LLm-ZoomCamp Course

This content originally appeared on DEV Community and was authored by Zainalabdeen