  In recent years, the use of open-source models has gained immense popularity
in various fields, including legal language modelling and analysis. These
models have proven to be highly effective in tasks such as summarizing legal
documents, extracting key information, and even predicting case outcomes. This
has revolutionized the legal industry, enabling lawyers, researchers, and
policymakers to quickly access and analyse vast amounts of legal text, saving
time and resources. This paper presents a novel approach to legal language
modeling (LLM) and analysis using open-source models from Hugging Face. We
leverage Hugging Face embeddings via LangChain and Sentence Transformers to
develop an LLM tailored for legal texts. We then demonstrate the application of
this model by extracting insights from the official Constitution of India. Our
methodology involves preprocessing the data, splitting it into chunks, using
ChromaDB and LangChainVectorStores, and employing the Google/Flan-T5-XXL model
for analysis. The trained model is tested on the Indian Constitution, which is
available in PDF format. Our findings suggest that our approach holds promise
for efficient legal language processing and analysis.
