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905e3b7 | 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 | """Validation node for DocIntel."""
from app.agents.state import AgentState
from app.config import settings
def validate_response(state: AgentState) -> AgentState:
"""Validate response and calculate confidence"""
print("🔍 Validating response...")
response = state.get("response", "")
documents = state.get("documents", [])
current_confidence = state.get("confidence", 0.0)
if not response:
state["validated"] = False
state["confidence"] = 0.0
print("❌ No response to validate")
return state
# Factor 1: Document relevance (40% weight)
doc_scores = [d.get("score", 0) for d in documents[:3]]
avg_doc_score = sum(doc_scores) / len(doc_scores) if doc_scores else 0
# Factor 2: Response length (20% weight) - longer responses indicate more content
length_score = min(1.0, len(response) / 500) # Cap at 500 chars
# Factor 3: Number of sources (20% weight)
source_score = min(1.0, len(documents) / settings.TOP_K) if documents else 0
# Factor 4: Confidence from retrieval (20% weight)
retrieval_score = min(1.0, current_confidence * 2) # Scale up
# Calculate weighted confidence
confidence = (
avg_doc_score * 0.40 +
length_score * 0.20 +
source_score * 0.20 +
retrieval_score * 0.20
)
# Cap at 0.95
confidence = min(0.95, confidence)
# If no documents, low confidence
if not documents:
confidence = 0.1
state["confidence"] = confidence
state["validated"] = confidence > 0.3 # Threshold for validation
print(f"✅ Confidence: {confidence:.3f} (doc: {avg_doc_score:.2f}, len: {length_score:.2f}, src: {source_score:.2f}, ret: {retrieval_score:.2f})")
return state |