import os
import warnings
from typing import Literal, Optional, Dict, Any

from openai import OpenAI

from mem0.configs.embeddings.base import BaseEmbedderConfig
from mem0.embeddings.base import EmbeddingBase


class OpenAIEmbedding(EmbeddingBase):
    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
        super().__init__(config)

        self.config.model = self.config.model or "text-embedding-3-small"
        self.config.embedding_dims = self.config.embedding_dims or 1536

        api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")
        base_url = (
            self.config.openai_base_url
            or os.getenv("OPENAI_API_BASE")
            or os.getenv("OPENAI_BASE_URL")
            or "https://api.openai.com/v1"
        )
        if os.environ.get("OPENAI_API_BASE"):
            warnings.warn(
                "The environment variable 'OPENAI_API_BASE' is deprecated and will be removed in the 0.1.80. "
                "Please use 'OPENAI_BASE_URL' instead.",
                DeprecationWarning,
            )

        self.client = OpenAI(api_key=api_key, base_url=base_url)

    def embed(
        self,
        text,
        memory_action: Optional[Literal["add", "search", "update"]] = None,
        context: Optional[Dict[str, Any]] = None,
    ):
        """
        Get the embedding for the given text using OpenAI.

        Args:
            text (str): The text to embed.
            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
            context (optional): Context information like user_id and visitor_type. Defaults to None.
        Returns:
            list: The embedding vector.
        """
        text = text.replace("\n", " ")

        # Build extra_headers from context
        extra_headers = {}
        if context:
            if context.get("user_id"):
                extra_headers["x-account-id"] = context["user_id"]
            if context.get("visitor_type"):
                extra_headers["x-account-type"] = context["visitor_type"]
        return (
            self.client.embeddings.create(
                input=[text],
                model=self.config.model,
                dimensions=self.config.embedding_dims,
                extra_headers=extra_headers,
            )
            .data[0]
            .embedding
        )
