import os
from functools import lru_cache

from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
from langchain.chat_models.base import BaseChatModel
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import Document
from langchain.vectorstores import Qdrant, VectorStore

from edu_assistant.utils.qdrant_utils import load_qdrant_client


@lru_cache(maxsize=1)
def load_llm() -> BaseChatModel:
    if os.environ.get("AZURE_OPENAI"):
        llm = AzureChatOpenAI(
            openai_api_type="azure",
            openai_api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
            openai_api_base=os.environ.get("AZURE_OPENAI_API_BASE"),
            openai_api_version="2023-05-15",
            deployment_name=os.environ.get("AZURE_OPENAI_DEPLOYMENT_ID", "gpt-35-turbo"),
            model="gpt-3.5-turbo",
            temperature=0,
        )
    else:
        llm = ChatOpenAI(
            openai_api_key=os.environ.get("OPENAI_API_KEY"),
            openai_proxy=os.environ.get("OPENAI_PROXY", ""),
            model="gpt-3.5-turbo",
        )

    return llm


@lru_cache(maxsize=1)
def load_gpt4_llm() -> BaseChatModel:
    llm = ChatOpenAI(
        openai_api_key=os.environ.get("OPENAI_API_KEY"),
        openai_proxy=os.environ.get("OPENAI_PROXY", ""),
        model="gpt-4",
    )

    return llm


@lru_cache(maxsize=1)
def load_gpt4_flag() -> bool:
    return os.environ.get("CODEDOG_ENABLE_GPT4") is not None


@lru_cache(maxsize=1)
def load_embeddings():
    if os.environ.get("AZURE_OPENAI"):
        embeddings = OpenAIEmbeddings(
            openai_api_type="azure",
            openai_api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
            openai_api_base=os.environ.get("AZURE_OPENAI_API_BASE"),
            openai_api_version="2023-05-15",
            deployment=os.environ.get("AZURE_OPENAI_EMBEDDING_DEP_ID", ""),
        )
    else:
        embeddings = OpenAIEmbeddings(
            openai_api_key=os.environ.get("OPENAI_API_KEY"),
            openai_proxy=os.environ.get("OPENAI_PROXY", ""),
        )

    return embeddings


@lru_cache(maxsize=10)
def load_vectorstore(collection_name: str = "default") -> VectorStore:
    if os.environ.get("QDRANT_API"):
        client = load_qdrant_client()
        embeddings = load_embeddings()
        doc_store = Qdrant(client=client, collection_name=collection_name, embeddings=embeddings)

        return doc_store

    return None


@lru_cache(maxsize=20)
def escape_for_prompt(text: str) -> str:
    """escape cruly brackets in text for generate prompt.

    Args:
        text (str): input string.

    Returns:
        str: escaped string.
    """
    return text.replace("{", "{{").replace("}", "}}")


def shrink_docs(docs: list[Document], max_size=50):
    """shrink source docs content size for display.

    Args:
        docs (dict): Retrieval Chain returned docs.
    """
    for doc in docs:
        doc.page_content = doc.page_content[:max_size] + ".."
    return docs