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__init__.py

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+from .node import *
+
+NODE_CLASS_MAPPINGS = {
+                        "RH_LLMAPI_NODE": RH_LLMAPI_Node,
+                    }
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RH_LLMAPI_NODE": "Runninghub LLM API Node",
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

node.py

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+from openai import OpenAI
+import time
+from PIL import Image
+import numpy as np
+import base64
+import os
+
+def encode_image_b64(ref_image):
+    i = 255. * ref_image.cpu().numpy()[0]
+    img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+
+    lsize = np.max(img.size)
+    factor = 1
+    while lsize / factor > 2048:
+        factor *= 2
+    img = img.resize((img.size[0] // factor, img.size[1] // factor))
+
+    image_path = f'{time.time()}.webp'
+    img.save(image_path, 'WEBP')
+
+    with open(image_path, "rb") as image_file:
+        base64_image = base64.b64encode(image_file.read()).decode('utf-8')
+
+    # print(img_base64)
+    os.remove(image_path)
+    return base64_image
+
+class RH_LLMAPI_Node():
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "api_baseurl": ("STRING", {"multiline": True}),
+                "api_key": ("STRING", {"default": ""}),
+                "model": ("STRING", {"default": ""}),
+                "role": ("STRING", {"multiline": True, "default": "You are a helpful assistant"}),
+                "prompt": ("STRING", {"multiline": True, "default": "Hello"}),
+                "temperature": ("FLOAT", {"default": 0.6}),
+                "seed": ("INT", {"default": 100}),
+            },
+            "optional": {
+                "ref_image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("describe",)
+    FUNCTION = "rh_run_llmapi"
+    CATEGORY = "Runninghub"
+
+    def rh_run_llmapi(self, api_baseurl, api_key, model, role, prompt, temperature, seed, ref_image=None):
+
+        client = OpenAI(api_key=api_key, base_url=api_baseurl)
+        if ref_image is None:
+            messages = [
+                {'role': 'system', 'content': f'{role}'},
+                {'role': 'user', 'content': f'{prompt}'},
+            ]
+        else:
+            base64_image = encode_image_b64(ref_image)
+            messages = [
+                {'role': 'system', 'content': f'{role}'},
+                {'role': 'user', 
+                 'content': [
+                        {
+                            "type": "text",
+                            "text": f"{prompt}"
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{base64_image}"
+                            }
+                        },
+                    ]},
+            ]
+        completion = client.chat.completions.create(model=model, messages=messages, temperature=temperature)
+        if completion is not None and hasattr(completion, 'choices'):
+            prompt = completion.choices[0].message.content
+        else:
+            prompt = 'Error'
+        return (prompt,)