Korean Tax AI with Graph-RAG

from taxia import TaxiaEngine, TaxiaConfig
from taxia.types import QueryResponse

# Configure with custom settings
config = TaxiaConfig(
    llm_provider="claude",  # or "openai"
    model_name="claude-3-5-sonnet-20241022",
    temperature=0.0,  # Deterministic for legal
    max_tokens=4096,
    retrieval_k=5,  # Top-5 similar documents
)

# Initialize engine
engine = TaxiaEngine(config)

# Execute semantic query
response: QueryResponse = engine.query(
    "2024년 법인세 세율은 과세표준에 따라 어떻게 달라지나요?"
)

# Access structured response
print(f"Answer: {response.answer}")
print(f"Citations: {response.citations}")  # List[Citation]
print(f"Confidence: {response.metadata.confidence}")
print(f"Retrieval Time: {response.metadata.retrieval_time_ms}ms")

# Error handling
try:
    result = engine.query("복잡한 세법 질의")
except ValidationError as e:
    print(f"Invalid query: {e}")
except TimeoutError:
    print("LLM timeout (>30s)")