class embeddingSearch:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "Embedding Search": ("STRING", {
                    "tooltip": "Searchg For A Specific Embedding By Name Or Civit AIR Code (eg: EasyNegative).",
                }),
                "Model Architecture": ([
                        "All",
                        "FLUX.1-Schnell",
                        "FLUX.1-Dev",
                        "Pony",
                        "SD 1.5",
                        "SD 1.5 Hyper",
                        "SD 1.5 LCM",
                        "SD 3",
                        "SDXL 1.0",
                        "SDXL 1.0 LCM",
                        "SDXL Distilled",
                        "SDXL Hyper",
                        "SDXL Lightning",
                        "SDXL Turbo",
                    ], {
                    "tooltip": "Choose Embedding Model Architecture To Filter Out The Results.",
                    "default": "SD 1.5",
                }),
                "EmbeddingList": ([
                        "civitai:7808@9208 (EasyNegative)",
                        "civitai:4629@5637 (Deep Negative V1.x)",
                        "civitai:56519@60938 (negative_hand Negative Embedding)",
                        "civitai:72437@77169 (BadDream + UnrealisticDream (Negative Embeddings))",
                        "civitai:11772@25820 (veryBadImageNegative)",
                        "civitai:71961@94057 (Fast Negative Embedding (+ FastNegativeV2))",
                    ], {
                    "tooltip": "Embedding Results Will Show UP Here So You Could Choose From.",
                    "default": "civitai:7808@9208 (EasyNegative)",
                }),
                "weight": ("FLOAT", {
                    "tooltip": "Defines the strength or influence of the Embedding on the generation process.",
                    "default": 1.0,
                    "min": 0,
                    "max": 1,
                    "step": 0.1,
                }),
            },
        }

    DESCRIPTION = "Directly Search and Connect Embeddings to Runware Inference Nodes In ComfyUI."
    FUNCTION = "embeddingSearch"
    RETURN_TYPES = ("RUNWAREEMBEDDING",)
    RETURN_NAMES = ("Runware Embedding",)
    CATEGORY = "Runware"

    @classmethod
    def VALIDATE_INPUTS(cls, EmbeddingList):
        return True

    def embeddingSearch(self, **kwargs):
        currentModel = kwargs.get("EmbeddingList")
        embeddingWeight = kwargs.get("weight")
        embeddingAIRCode = currentModel.split(" ")[0]
        return ({
            "model": embeddingAIRCode,
            "weight": round(embeddingWeight, 2),
        },)