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968e24d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import torch
import faiss
import json
import sqlite3
import re
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
class LegalQAEngine:
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {self.device}")
# ---- Load QA model ----
self.tokenizer = AutoTokenizer.from_pretrained("outputs/qa_model/final")
self.qa_model = AutoModelForQuestionAnswering.from_pretrained(
"outputs/qa_model/final"
).to(self.device)
self.qa_model.eval()
# ---- Load retriever ----
self.embedder = SentenceTransformer("BAAI/bge-base-en-v1.5", device=self.device)
self.index = faiss.read_index("data/processed/faiss/faiss_index.bin")
with open("data/processed/embeddings/paragraph_ids.json", encoding="utf-8") as f:
self.para_ids = json.load(f)
self.db = sqlite3.connect("data/processed/indexed/paragraphs.db")
self.cursor = self.db.cursor()
print("✓ Enhanced QA inference system ready")
# ------------------------------------------------------------------
# TEXT NORMALIZATION (critical for PDF artifacts)
# ------------------------------------------------------------------
def _normalize(self, text: str) -> str:
text = text.lower()
text = re.sub(r"\s+", " ", text)
return text.strip()
# ------------------------------------------------------------------
# REFUTED CLAUSE DETECTION (Article 21 FIX)
# ------------------------------------------------------------------
def _is_refuted_clause(self, answer_text, paragraph_text):
para = self._normalize(paragraph_text)
ans = self._normalize(answer_text)
# Patterns like:
# "it is not correct to say, ..., that X"
# "it cannot be said, ..., that X"
refutation_regexes = [
r"not correct to say.*?that\s+(.*?)(?:\.|,)",
r"cannot be said.*?that\s+(.*?)(?:\.|,)",
]
for pattern in refutation_regexes:
matches = re.findall(pattern, para)
for refuted_prop in matches:
# If answer is part of the refuted proposition → block
if ans in refuted_prop:
return True
return False
# ------------------------------------------------------------------
# RETRIEVAL
# ------------------------------------------------------------------
def retrieve_paragraphs(self, question, top_k=8):
q_emb = self.embedder.encode(
[question], normalize_embeddings=True, convert_to_numpy=True
)
scores, indices = self.index.search(q_emb, top_k)
results = []
for score, idx in zip(scores[0], indices[0]):
para_id = self.para_ids[idx]
self.cursor.execute(
"SELECT judgment_id, page_no, text FROM paragraphs WHERE id = ?",
(para_id,),
)
row = self.cursor.fetchone()
if row:
judgment_id, page_no, text = row
results.append(
{
"judgment_id": judgment_id,
"page_no": page_no,
"text": text,
"retrieval_score": float(score),
}
)
return results
# ------------------------------------------------------------------
# ANSWERING
# ------------------------------------------------------------------
def answer_question(self, question, top_k=8, max_answers=2):
paragraphs = self.retrieve_paragraphs(question, top_k)
candidates = []
for para in paragraphs:
inputs = self.tokenizer(
question,
para["text"],
return_tensors="pt",
truncation=True,
max_length=512,
).to(self.device)
with torch.no_grad():
outputs = self.qa_model(**inputs)
start_logits = outputs.start_logits[0]
end_logits = outputs.end_logits[0]
token_type_ids = inputs["token_type_ids"][0].tolist()
question_end = token_type_ids.index(1)
top_starts = torch.topk(start_logits, k=5).indices
top_ends = torch.topk(end_logits, k=5).indices
for s in top_starts:
for e in top_ends:
if e < s or (e - s) > 80:
continue
# ❌ Block question echo
if s < question_end:
continue
answer_tokens = inputs["input_ids"][0][s : e + 1]
answer_text = self.tokenizer.decode(
answer_tokens, skip_special_tokens=True
).strip()
words = answer_text.split()
if len(words) < 8:
continue
# ❌ Block refuted propositions
if self._is_refuted_clause(answer_text, para["text"]):
continue
score = start_logits[s].item() + end_logits[e].item()
# Doctrinal boost
if any(
k in answer_text.lower()
for k in ["the court", "held that", "it is clear that", "the law"]
):
score += 1.5
candidates.append(
{
"answer": answer_text,
"confidence": score,
"judgment_id": para["judgment_id"],
"page_no": para["page_no"],
"paragraph": para["text"],
"retrieval_score": para["retrieval_score"],
}
)
# ---- Deduplicate answers ----
seen = set()
final = []
for c in sorted(candidates, key=lambda x: x["confidence"], reverse=True):
key = self._normalize(c["answer"])
if key not in seen:
seen.add(key)
final.append(c)
return final[:max_answers]
# ----------------------------------------------------------------------
# DEMO
# ----------------------------------------------------------------------
if __name__ == "__main__":
qa = LegalQAEngine()
questions = [
"What is the scope of Article 21?",
"What are the conditions for granting anticipatory bail?",
"What is the burden of proof in criminal law?",
]
for q in questions:
print("\n" + "=" * 90)
print(f"QUESTION: {q}")
print("=" * 90)
answers = qa.answer_question(q)
for i, ans in enumerate(answers, 1):
print(f"\nANSWER {i}:")
print(ans["answer"])
print(
f"\nSOURCE: {ans['judgment_id']} | Page: {ans['page_no']}"
)
print(f"Retrieval score: {ans['retrieval_score']:.3f}")
print(f"Confidence score: {ans['confidence']:.2f}")
print("\nPARAGRAPH:")
print(ans["paragraph"][:700] + "...")
|