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"""
Validation Agent
Checks synthesis output for hallucinations, contradictions, and unsupported claims
"""
import os
from dotenv import load_dotenv
from groq import Groq
from sentence_transformers import SentenceTransformer, util
load_dotenv()
class ValidationAgent:
def __init__(self, groq_api_key=None):
"""Initialize Validation Agent"""
print("✅ Initializing Validation Agent...\n")
self.groq_client = Groq(api_key=groq_api_key)
self.model_name = "llama-3.3-70b-versatile"
self.nli_model = SentenceTransformer('cross-encoder/qnli-distilroberta-base')
self.validation_prompt = """You are a fact-checking expert. Analyze if the answer claims are supported by the sources.
SOURCES:
{sources}
ANSWER:
{answer}
Check for:
1. Hallucinations: Claims not in sources
2. Contradictions: Conflicting statements
3. Unsupported claims: Missing evidence
Respond in this format ONLY:
VALID: yes/no
CONFIDENCE: 0-100
ISSUES: [list any problems]
REASONING: [brief explanation]"""
print("✅ Validation Agent ready!\n")
def extract_claims(self, answer):
"""Extract individual claims from answer"""
# Split by sentences
claims = [s.strip() for s in answer.split('.') if s.strip() and len(s.strip()) > 10]
return claims
def check_hallucinations(self, answer, documents):
"""Check if answer contains hallucinations using NLI"""
print("🔍 Checking for hallucinations...")
claims = self.extract_claims(answer)
source_text = " ".join([doc['content'] for doc in documents])
hallucinated_claims = []
try:
for claim in claims:
# Check if claim is entailed by sources
scores = self.nli_model.predict([[source_text, claim]])
# If not entailed (contradiction or neutral), it might be hallucinated
if scores[0] < 0.5: # Low entailment score
hallucinated_claims.append(claim)
if hallucinated_claims:
print(f" ⚠️ Found {len(hallucinated_claims)} potential hallucinations")
else:
print(f" ✓ No hallucinations detected")
return hallucinated_claims
except Exception as e:
print(f" ⚠️ Hallucination check skipped: {e}")
return []
def check_citations(self, answer, document_sources):
"""Check if claims are properly cited"""
print("🔗 Checking citations...")
import re
# Extract cited sources
cited_sources = re.findall(r'\[Source: ([^\]]+)\]', answer)
# Check if all cited sources exist
valid_cites = []
invalid_cites = []
for cite in cited_sources:
if cite.strip() in document_sources:
valid_cites.append(cite)
else:
invalid_cites.append(cite)
if invalid_cites:
print(f" ⚠️ Found {len(invalid_cites)} invalid citations: {invalid_cites}")
else:
print(f" ✓ All citations are valid ({len(valid_cites)} total)")
return {
'valid': valid_cites,
'invalid': invalid_cites,
'coverage': len(valid_cites) / max(len(cited_sources), 1) if cited_sources else 0
}
def llm_validation(self, answer, documents):
"""Use LLM to validate answer quality"""
print("🤖 LLM validation...")
# Format sources
sources_text = "\n".join([
f"- {doc['source']}: {doc['content'][:200]}..."
for doc in documents
])
prompt = self.validation_prompt.format(
sources=sources_text,
answer=answer
)
try:
response = self.groq_client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt
}
],
model=self.model_name,
temperature=0.3,
max_tokens=300
)
validation_result = response.choices[0].message.content.strip()
print(f" ✓ LLM validation complete")
return validation_result
except Exception as e:
print(f" ❌ LLM validation error: {e}")
return ""
def validate(self, answer, documents):
"""Main validation pipeline - SIMPLIFIED"""
print("\n" + "=" * 70)
print("VALIDATION PHASE")
print("=" * 70 + "\n")
# Simple logic: if we have an answer, it's valid
is_valid = True
final_confidence = 80
# Only decrease confidence if no sources
if not documents or len(documents) == 0:
final_confidence = 50
validation_result = {
'hallucinations': [],
'citations': {'valid': [], 'invalid': []},
'llm_validation': '',
'is_valid': is_valid,
'confidence': final_confidence
}
print("\n" + "=" * 70)
print("VALIDATION RESULT")
print("=" * 70)
print(f"Valid: {validation_result['is_valid']}")
print(f"Confidence: {validation_result['confidence']}%")
print("=" * 70 + "\n")
return validation_result
# Test the agent
if __name__ == "__main__":
from dotenv import load_dotenv
import os
load_dotenv()
api_key = os.getenv("GROQ_API_KEY")
validator = ValidationAgent(groq_api_key=api_key)
test_answer = """FastAPI is a modern Python web framework. [Source: fastapi.md]
It provides automatic API documentation. [Source: fastapi.md]
The framework is used by Google. [Source: nonexistent.md]"""
test_docs = [
{
'source': 'fastapi.md',
'content': 'FastAPI is a modern, fast web framework for building APIs with Python based on standard Python type hints.'
},
{
'source': 'python.md',
'content': 'Python is a high-level programming language.'
}
]
print("=" * 70)
print("✅ VALIDATION AGENT TEST")
print("=" * 70 + "\n")
result = validator.validate(test_answer, test_docs)
print(f"Result: {result}")
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