import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.llama_index_impl import (
    llama_index_complete_if_cache,
    llama_index_embed,
)
from lightrag.utils import EmbeddingFunc
from llama_index.llms.litellm import LiteLLM
from llama_index.embeddings.litellm import LiteLLMEmbedding
import asyncio

# Configure working directory
WORKING_DIR = "./index_default"
print(f"WORKING_DIR: {WORKING_DIR}")

# Model configuration
LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4")
print(f"LLM_MODEL: {LLM_MODEL}")
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")

# LiteLLM configuration
LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000")
print(f"LITELLM_URL: {LITELLM_URL}")
LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-1234")

if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)


# Initialize LLM function
async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
    try:
        # Initialize LiteLLM if not in kwargs
        if "llm_instance" not in kwargs:
            llm_instance = LiteLLM(
                model=f"openai/{LLM_MODEL}",  # Format: "provider/model_name"
                api_base=LITELLM_URL,
                api_key=LITELLM_KEY,
                temperature=0.7,
            )
            kwargs["llm_instance"] = llm_instance

        response = await llama_index_complete_if_cache(
            kwargs["llm_instance"],
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            **kwargs,
        )
        return response
    except Exception as e:
        print(f"LLM request failed: {str(e)}")
        raise


# Initialize embedding function
async def embedding_func(texts):
    try:
        embed_model = LiteLLMEmbedding(
            model_name=f"openai/{EMBEDDING_MODEL}",
            api_base=LITELLM_URL,
            api_key=LITELLM_KEY,
        )
        return await llama_index_embed(texts, embed_model=embed_model)
    except Exception as e:
        print(f"Embedding failed: {str(e)}")
        raise


# Get embedding dimension
async def get_embedding_dim():
    test_text = ["This is a test sentence."]
    embedding = await embedding_func(test_text)
    embedding_dim = embedding.shape[1]
    print(f"embedding_dim={embedding_dim}")
    return embedding_dim


# Initialize RAG instance
rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=llm_model_func,
    embedding_func=EmbeddingFunc(
        embedding_dim=asyncio.run(get_embedding_dim()),
        max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
        func=embedding_func,
    ),
)

# 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"))
)