# Nepal Legal RAG

> A CLI retrieval prototype for asking questions about the Constitution of Nepal.

Author: Nirdesh Pokharel

Canonical URL: https://nirdeshpokhrel.com.np/work/nepal-legal-rag

## Architecture

PDF → 800-character chunks / 100-character overlap → OpenAI embeddings → Qdrant → top-5 retrieval → streamed answer

## Problem

Explore question answering over a legal PDF by providing the language model with relevant source passages.

## Ingestion and retrieval

The indexing script extracts and splits PDF text, batches OpenAI embeddings, and stores vectors in Qdrant. The CLI retrieves five chunks for a question and streams an answer using the retrieved context.

## Response boundaries

The prompt requests article citations when they are available in context. The interface reports when nothing relevant is found, and conversation history lasts only for the current session.

## Limitations

This is a prototype rather than a production legal-advice service. Retrieval quality depends on the source PDF and extracted text. Re-indexing recreates the collection, and grounding does not guarantee that every generated answer is correct.

## Index lifecycle

The indexing script recreates the nepali-constitution collection and batches embeddings in groups of 150. Text chunks use 800 characters with 100 characters of overlap. Rebuilding simplifies this prototype, but replacing a collection is an operational limitation if serving queries continuously.

## Retrieval tradeoffs

The five retrieved chunks bound the context sent to the chat model. Overlap can preserve sentences near chunk boundaries, but it also repeats text in the index. A fixed chunk size and retrieval count do not establish recall or answer accuracy; those need evaluation against representative questions.

## Evaluation priorities

Useful checks include questions with direct source support, cross-article questions, irrelevant questions, and malformed PDF extraction. Inspect whether cited articles support each answer. Missing retrieval context, ambiguous wording, and model-generated mistakes should be evaluated separately.

## Source and maturity

The public repository contains shared configuration, indexing, a CLI chat loop, and a PDF inspection script. It is a small LangChain and Qdrant playground. There is no published production benchmark or claim that the tool can replace authoritative legal interpretation.

## Source

https://github.com/codernirdesh/nepal-legal-RAG