October 25, 2024

Overview

The Semantic Search–Powered Legal Chatbot is an AI-driven application that enables users to interactively query the Indian Penal Code (IPC) using natural language. Unlike traditional keyword-based search tools, this chatbot leverages semantic similarity to retrieve relevant legal sections, summaries, and interpretations — ensuring responses are contextually accurate and human-like. Designed with an intuitive Gradio interface, the system allows users to ask legal questions conversationally and receive clear, reliable answers backed by structured legal data. This project bridges the gap between law and AI, democratizing access to legal knowledge for students, professionals, and the general public.

Key Features

• 🧠 Semantic Understanding of Queries
  Utilizes sentence embeddings and transformer-based language models to interpret user intent beyond keywords.
• ⚖️ Contextual Retrieval of IPC Sections
  Retrieves the most relevant sections and clauses of the Indian Penal Code using vector similarity search (cosine similarity) instead of plain text lookup.
• 💬 Interactive Gradio Interface
  A clean, conversational UI built with Gradio for seamless question–answer interactions and real-time feedback.
• 📚 Structured Legal Dataset
  Preprocessed IPC dataset containing section numbers, descriptions, and legal explanations for fast semantic search.
• 🔍 Intelligent Response Generation
  Combines retrieved legal text with summarization logic to generate clear, simplified explanations for users.
• 📈 Scalable and Extensible Design
  Can easily be extended to cover additional acts (like CrPC, IT Act, etc.) or connected to external databases and legal APIs.

Technologies Used

• Frontend: Gradio (Python Web UI)
• Backend / NLP: Python, Sentence-Transformers (e.g., nomic-ai/nomic-embed-text-v1), FAISS
• Data Source: Structured Indian Penal Code dataset (.pdf)
• Semantic Search: Cosine Similarity on Embedding Vectors
• Models : Hugging Face Transformers (mistralai/Mistral-7B-Instruct-v0.2)
• Deployment: Hugging Face Spaces

Architecture Overview

1. Data Preparation Layer

• Parsed and structured IPC sections into a pickle file.
• Generated embeddings for all IPC clauses using a transformer model.

2. Query Understanding & Retrieval

• When a user submits a query, it is embedded using the same model.
• The system computes semantic similarity between the query embedding and all IPC embeddings.
• The top-matching sections are retrieved and ranked by relevance score.

3. Response Generation Layer

• Synthesizes a clear, concise explanation combining the retrieved section and relevant context.
• Returns the answer through the Gradio interface with a link to the IPC section reference.

4. Interface Layer

• Gradio app handles user inputs, displays responses, and enables conversational flow.

Challenges and Learnings

• Legal Language Complexity:
  Handling formal and archaic legal wording required extensive preprocessing, including lemmatization and abbreviation normalization.
• Semantic Ambiguity:
  Many legal queries overlap conceptually (e.g., theft vs. robbery). Implementing cosine similarity thresholding helped improve precision.
• Model Performance:
  Experimented with different transformer models for optimal balance between accuracy and latency for real-time responses.
• Explainability:
  Ensured responses included section references and source text to maintain trust and transparency.

Outcome

• Successfully developed a legal chatbot that delivers accurate, understandable, and reference-backed answers to legal questions.
• Demonstrated how semantic search and NLP can make complex domains like law accessible and conversational.
• Achieved high user satisfaction in tests with law students and paralegals who found it faster and more intuitive than manual lookup.

Conclusion

This project showcases your ability to integrate semantic AI, natural language processing, and intuitive UI design into a real-world legal application.
It reflects strong skills in retrieval-based NLP systems, knowledge graph construction, and AI-driven user interaction design, making it a valuable asset in both AI and legal-tech domains.