import os
import time
from lightrag import LightRAG, QueryParam
from lightrag.llm import ollama_model_complete, ollama_embedding
from lightrag.utils import EmbeddingFunc

# Working directory and the directory path for text files
WORKING_DIR = "./dickens"
TEXT_FILES_DIR = "/llm/mt"

# Create the working directory if it doesn't exist
if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)

# Initialize LightRAG
rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=ollama_model_complete,
    llm_model_name="qwen2.5:3b-instruct-max-context",
    embedding_func=EmbeddingFunc(
        embedding_dim=768,
        max_token_size=8192,
        func=lambda texts: ollama_embedding(texts, embed_model="nomic-embed-text"),
    ),
)

# Read all .txt files from the TEXT_FILES_DIR directory
texts = []
for filename in os.listdir(TEXT_FILES_DIR):
    if filename.endswith(".txt"):
        file_path = os.path.join(TEXT_FILES_DIR, filename)
        with open(file_path, "r", encoding="utf-8") as file:
            texts.append(file.read())


# Batch insert texts into LightRAG with a retry mechanism
def insert_texts_with_retry(rag, texts, retries=3, delay=5):
    for _ in range(retries):
        try:
            rag.insert(texts)
            return
        except Exception as e:
            print(
                f"Error occurred during insertion: {e}. Retrying in {delay} seconds..."
            )
            time.sleep(delay)
    raise RuntimeError("Failed to insert texts after multiple retries.")


insert_texts_with_retry(rag, texts)

# Perform different types of queries and handle potential errors
try:
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="naive")
        )
    )
except Exception as e:
    print(f"Error performing naive search: {e}")

try:
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="local")
        )
    )
except Exception as e:
    print(f"Error performing local search: {e}")

try:
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="global")
        )
    )
except Exception as e:
    print(f"Error performing global search: {e}")

try:
    print(
        rag.query(
            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
        )
    )
except Exception as e:
    print(f"Error performing hybrid search: {e}")


# Function to clear VRAM resources
def clear_vram():
    os.system("sudo nvidia-smi --gpu-reset")


# Regularly clear VRAM to prevent overflow
clear_vram_interval = 3600  # Clear once every hour
start_time = time.time()

while True:
    current_time = time.time()
    if current_time - start_time > clear_vram_interval:
        clear_vram()
        start_time = current_time
    time.sleep(60)  # Check the time every minute