Building a Legal AI Chatbot: A Step-by-Step Guide Using bigscience/T0pp LLM, Open-Source NLP Models, Streamlit, PyTorch, and Hugging Face Transformers

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Building an Efficient Legal AI Chatbot

Introduction

This guide aims to help you create a practical Legal AI Chatbot using open-source tools. By leveraging the capabilities of bigscience/T0pp LLM, Hugging Face Transformers, and PyTorch, you can develop an accessible AI-powered legal assistant.

Setting Up Your Model

Begin by loading the bigscience/T0pp model and initializing a tokenizer for text preprocessing. This step enables the chatbot to understand and respond to legal queries effectively.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "bigscience/T0pp"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Text Preprocessing

Utilize spaCy and regular expressions to clean and structure legal text inputs. This process enhances the efficiency of your chatbot’s responses.

import spacy
import re

nlp = spacy.load("en_core_web_sm")

def preprocess_legal_text(text):
    text = text.lower()
    text = re.sub(r's+', ' ', text)
    text = re.sub(r'[^a-zA-Z0-9s]', '', text)
    doc = nlp(text)
    tokens = [token.lemma_ for token in doc if not token.is_stop]
    return " ".join(tokens)

sample_text = "The contract is valid for 5 years, terminating on December 31, 2025."
print(preprocess_legal_text(sample_text))

Extracting Legal Entities

Implement Named Entity Recognition (NER) to identify key entities such as organizations and dates within legal documents.

def extract_legal_entities(text):
    doc = nlp(text)
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    return entities

sample_text = "Apple Inc. signed a contract with Microsoft on June 15, 2023."
print(extract_legal_entities(sample_text))

Document Retrieval System

Use FAISS for efficient semantic searches of legal documents. This enables quick retrieval of relevant information based on user queries.

import faiss
import numpy as np
import torch
from transformers import AutoModel, AutoTokenizer

embedding_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
embedding_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

def embed_text(text):
    inputs = embedding_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        output = embedding_model(**inputs)
    embedding = output.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
    return embedding

legal_docs = [
    "A contract is legally binding if signed by both parties.",
    "An NDA prevents disclosure of confidential information.",
    "A non-compete agreement prohibits working for a competitor."
]

doc_embeddings = np.array([embed_text(doc) for doc in legal_docs])
index = faiss.IndexFlatL2(doc_embeddings.shape[1])
index.add(doc_embeddings)

query = "What happens if I break an NDA?"
query_embedding = embed_text(query).reshape(1, -1)
_, retrieved_indices = index.search(query_embedding, 1)

print(f"Best matching legal text: {legal_docs[retrieved_indices[0][0]]}")

Creating the Legal Chatbot

Define the chatbot function that generates responses to legal queries using the pre-trained language model.

def legal_chatbot(query):
    inputs = tokenizer(query, return_tensors="pt", padding=True, truncation=True)
    output = model.generate(**inputs, max_length=100)
    return tokenizer.decode(output[0], skip_special_tokens=True)

query = "What happens if I break an NDA?"
print(legal_chatbot(query))

Conclusion

By integrating these tools, you’ve established a scalable Legal AI Chatbot. This project lays the groundwork for developing AI-powered legal solutions, making legal assistance more efficient and accessible.

Get In Touch

If you seek guidance on implementing AI in your business, feel free to contact us at hello@itinai.ru or connect with us on Telegram, X, or LinkedIn.

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