import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
import asyncio
import nest_asyncio

nest_asyncio.apply()
from lightrag.kg.shared_storage import initialize_pipeline_status

# WorkingDir
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)
print(f"WorkingDir: {WORKING_DIR}")

# mongo
os.environ["MONGO_URI"] = "mongodb://root:root@localhost:27017/"
os.environ["MONGO_DATABASE"] = "LightRAG"

# neo4j
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "neo4j"

# milvus
os.environ["MILVUS_URI"] = "http://localhost:19530"
os.environ["MILVUS_USER"] = "root"
os.environ["MILVUS_PASSWORD"] = "root"
os.environ["MILVUS_DB_NAME"] = "lightrag"


async def initialize_rag():
    rag = LightRAG(
        working_dir=WORKING_DIR,
        llm_model_func=ollama_model_complete,
        llm_model_name="qwen2.5:14b",
        llm_model_max_async=4,
        llm_model_max_token_size=32768,
        llm_model_kwargs={
            "host": "http://127.0.0.1:11434",
            "options": {"num_ctx": 32768},
        },
        embedding_func=EmbeddingFunc(
            embedding_dim=1024,
            max_token_size=8192,
            func=lambda texts: ollama_embed(
                texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
            ),
        ),
        kv_storage="MongoKVStorage",
        graph_storage="Neo4JStorage",
        vector_storage="MilvusVectorDBStorage",
    )

    await rag.initialize_storages()
    await initialize_pipeline_status()

    return rag


def main():
    # Initialize RAG instance
    rag = asyncio.run(initialize_rag())

    # Insert example text
    with open("./book.txt", "r", encoding="utf-8") as f:
        rag.insert(f.read())

    # Test different query modes
    print("\nNaive Search:")
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="naive")
        )
    )

    print("\nLocal Search:")
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="local")
        )
    )

    print("\nGlobal Search:")
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="global")
        )
    )

    print("\nHybrid Search:")
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
        )
    )


if __name__ == "__main__":
    main()
