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import os
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag.utils import setup_logger
setup_logger("lightrag", level="INFO")
WORKING_DIR = "./all_modes_demo"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def initialize_rag():
# Initialize LightRAG with a base model (gpt-4o-mini)
rag = LightRAG(
working_dir=WORKING_DIR,
embedding_func=openai_embed,
llm_model_func=gpt_4o_mini_complete, # Default model for most queries
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
# Load the data
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
# Example query
query_text = "What are the main themes in this story?"
# Demonstrate using default model (gpt-4o-mini) for all modes
print("\n===== Default Model (gpt-4o-mini) =====")
for mode in ["local", "global", "hybrid", "naive", "mix"]:
print(f"\n--- {mode.upper()} mode with default model ---")
response = rag.query(
query_text,
param=QueryParam(mode=mode)
)
print(response)
# Demonstrate using custom model (gpt-4o) for all modes
print("\n===== Custom Model (gpt-4o) =====")
for mode in ["local", "global", "hybrid", "naive", "mix"]:
print(f"\n--- {mode.upper()} mode with custom model ---")
response = rag.query(
query_text,
param=QueryParam(
mode=mode,
model_func=gpt_4o_complete # Override with more capable model
)
)
print(response)
# Mixed approach - use different models for different modes
print("\n===== Strategic Model Selection =====")
# Complex analytical question
complex_query = "How does the character development in the story reflect Victorian-era social values?"
# Use default model for simpler modes
print("\n--- NAIVE mode with default model (suitable for simple retrieval) ---")
response1 = rag.query(
complex_query,
param=QueryParam(mode="naive") # Use default model for basic retrieval
)
print(response1)
# Use more capable model for complex modes
print("\n--- HYBRID mode with more capable model (for complex analysis) ---")
response2 = rag.query(
complex_query,
param=QueryParam(
mode="hybrid",
model_func=gpt_4o_complete # Use more capable model for complex analysis
)
)
print(response2)
if __name__ == "__main__":
main() |