749 lines
34 KiB
Python
749 lines
34 KiB
Python
import requests
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import json
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import time
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import base64
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import io
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import numpy as np
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import torch
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import tiktoken
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from PIL import Image
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import hashlib # Added for hashing PDF bytes in IS_CHANGED
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import os
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from .chat_manager import ChatSessionManager
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# Define a placeholder type name for PDF data.
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# The actual input connection will accept '*' but we check the structure.
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# Expecting a dictionary: {"filename": str, "bytes": bytes}
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PDF_DATA_TYPE = "*" # Use '*' to accept any type, check structure later
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class OpenRouterNode:
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"""
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A node for interacting with OpenRouter's chat/completion API.
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Supports text, images, and PDFs as input.
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Returns three outputs:
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1) "Output": the text response from the LLM
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2) "Stats": a string detailing tokens per second, input tokens, and output tokens
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3) "Credits": a string showing your remaining OpenRouter account balance
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"""
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models_cache = None
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last_fetch_time = 0
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cache_duration = 3600 # Cache duration in seconds (1 hour)
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default_request_timeout = 120
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min_request_timeout = 1
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max_request_timeout = 3600
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reasoning_effort_options = ("auto", "none", "minimal", "low", "medium", "high", "xhigh")
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default_reasoning_effort = "auto"
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def __init__(self):
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self.chat_manager = ChatSessionManager()
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@staticmethod
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def get_api_key(api_key_ui):
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"""
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Resolves the API key from:
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1. UI input field (if not empty)
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2. Environment variable 'LLM_KEY'
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3. config file 'openrouter_api_key.json' in node directory
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"""
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if api_key_ui and api_key_ui.strip():
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return api_key_ui.strip()
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# Check environment variable
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env_key = os.environ.get("LLM_KEY")
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if env_key and env_key.strip():
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return env_key.strip()
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# Check JSON file
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config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "openrouter_api_key.json")
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if os.path.exists(config_path):
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try:
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with open(config_path, 'r') as f:
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config = json.load(f)
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file_key = config.get("api_key")
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if file_key and file_key.strip():
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return file_key.strip()
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except Exception as e:
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print(f"Error reading openrouter_api_key.json: {e}")
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return ""
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Defines the input specification for this node.
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Includes optional inputs for image and PDF data.
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"""
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return {
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"required": {
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"api_key": ("STRING", {
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"multiline": False,
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"default": ""
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}),
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"system_prompt": ("STRING", {
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"multiline": True,
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"default": "You are a helpful assistant."
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}),
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"user_message_box": ("STRING", {
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"multiline": True,
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"default": "Hello, how are you?"
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}),
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"model": (cls.fetch_openrouter_models(),),
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"web_search": ("BOOLEAN", {"default": False}),
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"cheapest": ("BOOLEAN", {"default": True}),
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"fastest": ("BOOLEAN", {"default": False}),
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"aspect_ratio": ([
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"auto",
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"1:1 (1024x1024)",
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"2:3 (832x1248)",
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"3:2 (1248x832)",
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"3:4 (864x1184)",
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"4:3 (1184x864)",
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"4:5 (896x1152)",
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"5:4 (1152x896)",
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"9:16 (768x1344)",
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"16:9 (1344x768)",
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"21:9 (1536x672)",
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"1:4 (google/gemini-3.1-flash-image-preview (Nano Banana 2) only)",
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"4:1 (google/gemini-3.1-flash-image-preview (Nano Banana 2) only)",
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"1:8 (google/gemini-3.1-flash-image-preview (Nano Banana 2) only)",
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"8:1 (google/gemini-3.1-flash-image-preview (Nano Banana 2) only)",
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], {"default": "auto"}),
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"image_resolution": (["1K", "2K", "4K"], {"default": "1K"}),
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"reasoning_effort": (list(cls.reasoning_effort_options), {"default": cls.default_reasoning_effort}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": "fixed"}),
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"temperature": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 2.0,
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"step": 0.01,
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"display": "slider",
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"round": 0.01,
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}),
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"pdf_engine": (["auto", "mistral-ocr", "pdf-text"], {"default": "auto"}),
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"chat_mode": ("BOOLEAN", {"default": False}),
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"request_timeout": ("INT", {
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"default": cls.default_request_timeout,
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"min": cls.min_request_timeout,
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"max": cls.max_request_timeout,
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"step": 1,
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"display": "number",
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}),
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},
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"optional": {
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"pdf_data": (PDF_DATA_TYPE,), # Use '*' and check structure in generate_response
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"user_message_input": ("STRING", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("STRING", "IMAGE", "STRING", "STRING",)
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RETURN_NAMES = ("Output", "image", "Stats", "Credits")
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FUNCTION = "generate_response"
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CATEGORY = "LLM"
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@classmethod
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def fetch_openrouter_models(cls):
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"""
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Fetches a list of model IDs from the OpenRouter API, caching them.
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"""
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current_time = time.time()
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if (cls.models_cache is None) or (current_time - cls.last_fetch_time > cls.cache_duration):
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url = "https://openrouter.ai/api/v1/models"
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try:
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response = requests.get(url, timeout=cls.default_request_timeout)
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response.raise_for_status()
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models = response.json()["data"]
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# Filter for models that support chat completions if needed, but API handles this
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model_list = sorted([model['id'] for model in models])
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cls.models_cache = model_list
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cls.last_fetch_time = current_time
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except requests.exceptions.RequestException as e:
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print(f"Error fetching models: {e}")
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# Provide a default list or indicate error if cache is empty
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if cls.models_cache is None:
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cls.models_cache = ["error_fetching_models", "google/gemma-3-27b-it", "openai/gpt-4o"] # Example fallbacks
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return cls.models_cache if cls.models_cache else ["error_fetching_models"] # Ensure it's never empty
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def validate_temperature(self, temperature):
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"""
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Validates and converts temperature value to float within acceptable range.
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"""
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try:
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temp = float(temperature)
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return max(0.0, min(2.0, temp)) # Clamp between 0.0 and 2.0
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except (ValueError, TypeError):
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return 1.0 # Return default if conversion fails
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def validate_request_timeout(self, request_timeout):
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"""
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Validates and converts request timeout to seconds within an acceptable range.
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"""
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try:
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timeout = int(request_timeout)
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return max(self.min_request_timeout, min(self.max_request_timeout, timeout))
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except (ValueError, TypeError):
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return self.default_request_timeout
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@classmethod
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def validate_reasoning_effort(cls, reasoning_effort):
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"""
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Validates OpenRouter reasoning effort. "auto" means do not send a
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reasoning override and let OpenRouter/model defaults apply.
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"""
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if isinstance(reasoning_effort, str):
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normalized_effort = reasoning_effort.strip().lower()
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if normalized_effort in cls.reasoning_effort_options:
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return normalized_effort
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return cls.default_reasoning_effort
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def fetch_credits(self, api_key, timeout=None):
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"""
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Fetches the user's credits information from the OpenRouter API.
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Returns a formatted string with remaining credits.
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"""
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api_key = self.get_api_key(api_key)
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if not api_key:
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return "API Key not provided."
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url = "https://openrouter.ai/api/v1/credits"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"HTTP-Referer": "https://github.com/yourusername/comfyui-openrouter",
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"X-Title": "ComfyUI OpenRouter LLM Node",
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}
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try:
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validated_timeout = self.validate_request_timeout(timeout)
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response = requests.get(url, headers=headers, timeout=validated_timeout)
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response.raise_for_status()
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result = response.json()
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# Check if 'data' and expected keys exist
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if "data" in result and "total_credits" in result["data"] and "total_usage" in result["data"]:
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total_credits = result["data"]["total_credits"]
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total_usage = result["data"]["total_usage"]
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remaining = total_credits - total_usage
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credits_text = f"Remaining: ${remaining:.3f}"
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else:
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credits_text = "Could not parse credit data from response."
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return credits_text
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except requests.exceptions.RequestException as e:
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# Provide more context about the error
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error_message = f"Error fetching credits: {str(e)}"
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if hasattr(e, 'response') and e.response is not None:
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error_message += f" | Status Code: {e.response.status_code} | Response: {e.response.text[:200]}" # Log part of response
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return error_message
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except json.JSONDecodeError:
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return "Error fetching credits: Could not decode JSON response."
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def generate_response(self, api_key, system_prompt, user_message_box, model,
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web_search, cheapest, fastest, temperature, pdf_engine, chat_mode,
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request_timeout=120, aspect_ratio="auto", image_resolution="1K", seed=0,
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pdf_data=None, user_message_input=None, reasoning_effort="auto", **kwargs):
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"""
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Sends a completion request to the OpenRouter chat completion endpoint.
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Handles text, optional image, and optional PDF inputs.
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Returns four outputs:
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(1) Output: the LLM's text response
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(2) image: an image tensor if the response contains an image, else empty tensor
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(3) Stats: a string with tokens per second, prompt tokens, completion tokens
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(4) Credits: a string with the user's credit information
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"""
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# Create empty placeholder image
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placeholder_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
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# Resolve API key
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api_key = self.get_api_key(api_key)
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if not api_key:
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return ("Error: API Key not provided. Set LLM_KEY env var or use openrouter_api_key.json", placeholder_image, "Stats N/A", "Credits N/A")
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url = "https://openrouter.ai/api/v1/chat/completions"
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"HTTP-Referer": "https://github.com/yourusername/comfyui-openrouter",
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"X-Title": "ComfyUI OpenRouter LLM Node",
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}
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# Validate and convert temperature
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validated_temp = self.validate_temperature(temperature)
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validated_timeout = self.validate_request_timeout(request_timeout)
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validated_reasoning_effort = self.validate_reasoning_effort(reasoning_effort)
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# Decide whether to use user_message_input or user_message_box
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user_text = user_message_input if user_message_input is not None and user_message_input.strip() else user_message_box
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# Initialize session_path
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session_path = None
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# Handle chat mode
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if chat_mode:
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# Get or create a chat session
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session_path, messages = self.chat_manager.get_or_create_session(user_text, system_prompt)
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# Check if we need to update the system prompt (for existing sessions)
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if messages and messages[0]["role"] == "system" and messages[0]["content"] != system_prompt:
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# Update system prompt if it has changed
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messages[0]["content"] = system_prompt
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else:
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# Non-chat mode: Build the messages array, starting with a system prompt.
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messages = [
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{"role": "system", "content": system_prompt},
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]
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# --- Build the user message content ---
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user_content_blocks = []
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# 1. Add Text part (always present)
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user_content_blocks.append({
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"type": "text",
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"text": user_text
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})
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# 2. Add Image parts (optional) - support multiple images from kwargs
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# Process all image_N inputs from kwargs
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image_keys = sorted([k for k in kwargs.keys() if k.startswith('image_')],
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key=lambda x: int(x.split('_')[1]))
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for image_key in image_keys:
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if kwargs[image_key] is not None:
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try:
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img_str = self.image_to_base64(kwargs[image_key])
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user_content_blocks.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{img_str}"
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}
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})
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except Exception as e:
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print(f"Error processing {image_key}: {e}")
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return (f"Error processing {image_key}: {e}", placeholder_image, "Stats N/A", "Credits N/A")
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|
|
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# 3. Add PDF part (optional)
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pdf_filename = "document.pdf" # Default filename if not provided
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if pdf_data is not None:
|
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# Validate pdf_data structure (expecting dict with 'filename' and 'bytes')
|
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if isinstance(pdf_data, dict) and "bytes" in pdf_data and isinstance(pdf_data["bytes"], bytes):
|
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pdf_bytes = pdf_data["bytes"]
|
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# Use provided filename if available and valid, otherwise use default
|
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if "filename" in pdf_data and isinstance(pdf_data["filename"], str) and pdf_data["filename"].strip():
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pdf_filename = pdf_data["filename"]
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try:
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base64_pdf = base64.b64encode(pdf_bytes).decode('utf-8')
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data_url = f"data:application/pdf;base64,{base64_pdf}"
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user_content_blocks.append({
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"type": "file",
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"file": {
|
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"filename": pdf_filename,
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"file_data": data_url
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}
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})
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except Exception as e:
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print(f"Error encoding PDF: {e}")
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return (f"Error encoding PDF: {e}", placeholder_image, "Stats N/A", "Credits N/A")
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else:
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# Handle case where pdf_data is not in the expected format
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print(f"Warning: pdf_data input is not in the expected format (dict with 'filename' and 'bytes'). PDF not included.")
|
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# Optionally return an error or just proceed without the PDF
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# return ("Error: Invalid PDF data format.", "Stats N/A", "Credits N/A")
|
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|
|
|
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# Determine message format based on content type
|
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# Use simple string format for text-only requests to ensure compatibility
|
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# Use structured format only when we have multimodal content
|
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has_multimodal_content = len(user_content_blocks) > 1 or any(block.get("type") != "text" for block in user_content_blocks)
|
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|
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if has_multimodal_content:
|
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# Use structured format for multimodal content
|
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new_user_message = {
|
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"role": "user",
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"content": user_content_blocks
|
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}
|
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else:
|
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# Use simple string format for text-only requests
|
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new_user_message = {
|
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"role": "user",
|
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"content": user_text
|
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}
|
|
|
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if chat_mode:
|
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# In chat mode, append to existing conversation (but don't save yet - wait for response)
|
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messages.append(new_user_message)
|
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else:
|
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# In non-chat mode, messages array already has system prompt, just append user message
|
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messages.append(new_user_message)
|
|
|
|
# --- Apply model modifiers ---
|
|
modified_model = model
|
|
# Check if model already has modifiers to avoid duplication
|
|
if web_search and ":online" not in modified_model:
|
|
modified_model = f"{modified_model}:online"
|
|
if ":online" not in modified_model:
|
|
if cheapest and ":floor" not in modified_model:
|
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modified_model = f"{modified_model}:floor"
|
|
elif fastest and not cheapest and ":nitro" not in modified_model:
|
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modified_model = f"{modified_model}:nitro"
|
|
|
|
|
|
# --- Construct the final payload ---
|
|
data = {
|
|
"model": modified_model,
|
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"messages": messages,
|
|
"temperature": validated_temp,
|
|
"seed": seed
|
|
}
|
|
if validated_reasoning_effort != "auto":
|
|
data["reasoning"] = {"effort": validated_reasoning_effort}
|
|
|
|
print(f"Payload: model={modified_model}")
|
|
|
|
# Add plugins if a specific PDF engine is selected
|
|
if pdf_engine != "auto":
|
|
data["plugins"] = [
|
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{
|
|
"id": "file-parser",
|
|
"pdf": {
|
|
"engine": pdf_engine
|
|
}
|
|
}
|
|
]
|
|
|
|
# --- Pre-calculate text input tokens (rough estimate) ---
|
|
# Note: Actual token count depends on the model and includes parsed PDF/image data.
|
|
# Rely on the API response for accurate usage stats.
|
|
text_token_estimate = 0
|
|
try:
|
|
text_token_estimate = self.count_tokens(system_prompt, model) + self.count_tokens(user_text, model)
|
|
except Exception as e:
|
|
print(f"Warning: Token counting failed - {e}")
|
|
|
|
|
|
# --- Make API Call and Process Response ---
|
|
try:
|
|
start_time = time.time()
|
|
response = requests.post(url, headers=headers, json=data, timeout=validated_timeout)
|
|
response.raise_for_status() # Raises HTTPError for bad responses (4xx or 5xx)
|
|
end_time = time.time()
|
|
|
|
result = response.json()
|
|
# Debug: print truncated response to see what OpenRouter returned
|
|
debug_str = json.dumps(result, default=str)
|
|
print(f"API response ({len(debug_str)} chars): {debug_str[:500]}")
|
|
|
|
# --- Extract results and calculate stats ---
|
|
if not result.get("choices") or not result["choices"][0].get("message"):
|
|
raise ValueError("Invalid response format from API: 'choices' or 'message' missing.")
|
|
|
|
# Parse response for text and image content
|
|
message = result["choices"][0]["message"]
|
|
text_output = message.get("content", "")
|
|
image_tensor = placeholder_image
|
|
|
|
# Check for images in the separate images field (OpenRouter format)
|
|
if message.get("images"):
|
|
print(f"Found {len(message['images'])} image(s) in API response")
|
|
try:
|
|
# Get the first image from the images array
|
|
first_image = message["images"][0]
|
|
image_url = first_image["image_url"]["url"]
|
|
|
|
if image_url.startswith("data:image"):
|
|
base64_str = image_url.split(",", 1)[1]
|
|
try:
|
|
# Convert base64 to image tensor
|
|
image_tensor = self.base64_to_image(base64_str)
|
|
print(f"Successfully decoded image from API response")
|
|
except Exception as e:
|
|
print(f"Error decoding image: {e}")
|
|
else:
|
|
print(f"Image URL format not supported: {image_url[:50]}...")
|
|
except Exception as e:
|
|
print(f"Error processing images from response: {e}")
|
|
else:
|
|
print("No images found in API response - this may be normal if the model doesn't support image generation or the prompt didn't request an image")
|
|
|
|
# Also handle legacy multimodal content format as fallback
|
|
if isinstance(text_output, list):
|
|
text_parts = []
|
|
for content in text_output:
|
|
if isinstance(content, dict):
|
|
if content.get("type") == "text":
|
|
text_parts.append(content.get("text", ""))
|
|
elif content.get("type") == "image_url":
|
|
# Extract base64 image data
|
|
image_url = content["image_url"]["url"]
|
|
if image_url.startswith("data:image"):
|
|
base64_str = image_url.split(",", 1)[1]
|
|
try:
|
|
# Convert base64 to image tensor
|
|
image_tensor = self.base64_to_image(base64_str)
|
|
except Exception as e:
|
|
print(f"Error decoding image: {e}")
|
|
text_output = "\n".join(text_parts)
|
|
|
|
response_ms = result.get("response_ms", None)
|
|
api_usage = result.get("usage", {})
|
|
prompt_tokens = api_usage.get("prompt_tokens", text_token_estimate) # Use API value if available
|
|
completion_tokens = api_usage.get("completion_tokens", 0)
|
|
if completion_tokens == 0 and text_output: # Estimate completion tokens if API doesn't provide them
|
|
try:
|
|
completion_tokens = self.count_tokens(text_output, model)
|
|
except Exception as e:
|
|
print(f"Warning: Completion token counting failed - {e}")
|
|
|
|
|
|
# Calculate tokens per second (TPS)
|
|
tps = 0
|
|
elapsed_time = end_time - start_time
|
|
if response_ms is not None:
|
|
server_elapsed_time = response_ms / 1000.0
|
|
if server_elapsed_time > 0:
|
|
tps = completion_tokens / server_elapsed_time
|
|
elif elapsed_time > 0:
|
|
# Use client-side timing as fallback, less accurate due to network latency
|
|
tps = completion_tokens / elapsed_time
|
|
# Optional: apply a heuristic correction factor if needed, but server time is better
|
|
# correction_factor = 1.28 # Example factor, might need tuning
|
|
# tps *= correction_factor
|
|
|
|
stats_text = (
|
|
f"TPS: {tps:.2f}, "
|
|
f"Prompt Tokens: {prompt_tokens}, "
|
|
f"Completion Tokens: {completion_tokens}, "
|
|
f"Temp: {validated_temp:.1f}, "
|
|
f"Model: {modified_model}" # Display the actual model used
|
|
)
|
|
if pdf_engine != "auto":
|
|
stats_text += f", PDF Engine: {pdf_engine}"
|
|
if validated_reasoning_effort != "auto":
|
|
stats_text += f", Reasoning: {validated_reasoning_effort}"
|
|
|
|
|
|
# Fetch credits information AFTER the main request
|
|
credits_text = self.fetch_credits(api_key, timeout=validated_timeout)
|
|
|
|
# Save conversation in chat mode
|
|
if chat_mode and session_path:
|
|
# Append assistant's response to the conversation
|
|
assistant_message = {
|
|
"role": "assistant",
|
|
"content": text_output
|
|
}
|
|
messages.append(assistant_message)
|
|
|
|
# Save the updated conversation
|
|
self.chat_manager.save_conversation(session_path, messages)
|
|
|
|
return (text_output, image_tensor, stats_text, credits_text)
|
|
|
|
except requests.exceptions.RequestException as e:
|
|
error_message = f"API Request Error: {str(e)}"
|
|
if hasattr(e, 'response') and e.response is not None:
|
|
try:
|
|
error_detail = e.response.json()
|
|
error_message += f" | Details: {error_detail}"
|
|
except json.JSONDecodeError:
|
|
error_message += f" | Status: {e.response.status_code} | Response: {e.response.text[:200]}"
|
|
else:
|
|
error_message += " (Network or connection issue)"
|
|
print(f"ERROR: {error_message}")
|
|
return (error_message, placeholder_image, "Stats N/A due to error", "Credits N/A due to error")
|
|
except Exception as e:
|
|
print(f"ERROR: Node Error: {str(e)}")
|
|
return (f"Node Error: {str(e)}", placeholder_image, "Stats N/A due to error", "Credits N/A due to error")
|
|
|
|
@staticmethod
|
|
def image_to_base64(image):
|
|
"""
|
|
Converts a ComfyUI IMAGE (torch.Tensor, BHWC, float 0-1)
|
|
into a base64-encoded PNG string.
|
|
"""
|
|
if not isinstance(image, torch.Tensor):
|
|
raise TypeError("Input 'image' is not a torch.Tensor")
|
|
|
|
# Remove batch dimension if present
|
|
if image.ndim == 4:
|
|
if image.shape[0] != 1:
|
|
print(f"Warning: Image batch size is {image.shape[0]}, using only the first image.")
|
|
image = image.squeeze(0) # Shape HWC
|
|
|
|
if image.ndim != 3:
|
|
raise ValueError(f"Unexpected image dimensions: {image.shape}. Expected HWC.")
|
|
|
|
# Convert float tensor (0-1) to numpy array (0-255, uint8)
|
|
image_np = image.cpu().numpy()
|
|
if image_np.dtype != np.uint8:
|
|
if image_np.min() < 0 or image_np.max() > 1:
|
|
print("Warning: Image tensor values outside [0, 1] range. Clamping.")
|
|
image_np = np.clip(image_np, 0, 1)
|
|
image_np = (image_np * 255).astype(np.uint8)
|
|
|
|
# Convert numpy array to PIL Image
|
|
pil_image = Image.fromarray(image_np, 'RGB') # Assuming RGB, adjust if needed
|
|
|
|
# Save PIL Image to a bytes buffer as PNG
|
|
buffered = io.BytesIO()
|
|
pil_image.save(buffered, format="PNG")
|
|
|
|
# Encode the bytes buffer to base64 string
|
|
return base64.b64encode(buffered.getvalue()).decode('utf-8')
|
|
|
|
@staticmethod
|
|
def base64_to_image(base64_str: str) -> torch.Tensor:
|
|
"""
|
|
Converts a base64 image string to a ComfyUI image tensor
|
|
Returns tensor in [1, H, W, 3] format with values in [0, 1]
|
|
"""
|
|
try:
|
|
# Decode base64 string to image
|
|
img_data = base64.b64decode(base64_str)
|
|
img = Image.open(io.BytesIO(img_data))
|
|
img = img.convert("RGB")
|
|
|
|
# Convert to numpy array and normalize to [0, 1]
|
|
img_array = np.array(img).astype(np.float32) / 255.0
|
|
|
|
# Add batch dimension: [1, H, W, 3]
|
|
img_tensor = torch.from_numpy(img_array).unsqueeze(0)
|
|
|
|
print(f"Successfully converted base64 to image tensor: {img_tensor.shape}")
|
|
return img_tensor
|
|
|
|
except Exception as e:
|
|
print(f"Error in base64_to_image: {e}")
|
|
# Return a small placeholder image instead of failing
|
|
return torch.zeros((1, 64, 64, 3), dtype=torch.float32)
|
|
|
|
@staticmethod
|
|
def count_tokens(text, model):
|
|
"""
|
|
Count tokens for a given text using tiktoken.
|
|
Uses model-specific encodings where possible, falls back to cl100k_base.
|
|
Handles potential errors during encoding.
|
|
"""
|
|
if not text or not isinstance(text, str):
|
|
return 0
|
|
|
|
# Strip any model modifiers like :floor, :nitro, :online
|
|
base_model = model.split(':')[0] if ':' in model else model
|
|
|
|
# Simplified mapping, cl100k_base is common for many recent models
|
|
encoding_name = "cl100k_base"
|
|
try:
|
|
# List known models/prefixes that definitely use cl100k_base
|
|
# Add others if known, but cl100k_base is a safe default for many
|
|
cl100k_models = [
|
|
"openai/gpt-4", "openai/gpt-3.5", "openai/gpt-4o",
|
|
"anthropic/claude",
|
|
"google/gemini",
|
|
"meta-llama/llama-2", "meta-llama/llama-3",
|
|
"mistralai/mistral", "mistralai/mixtral",
|
|
]
|
|
# Check if the base_model or its prefix matches known cl100k models
|
|
is_cl100k = any(base_model.startswith(prefix) for prefix in cl100k_models)
|
|
|
|
if is_cl100k:
|
|
encoding_name = "cl100k_base"
|
|
# else: # Add logic for other encodings if needed, e.g., p50k_base for older models
|
|
# pass # Stick with cl100k_base as default for now
|
|
|
|
encoding = tiktoken.get_encoding(encoding_name)
|
|
token_count = len(encoding.encode(text, disallowed_special=())) # Allow special tokens
|
|
return token_count
|
|
|
|
except Exception as e:
|
|
print(f"Warning: Tiktoken error for model '{model}' (base: '{base_model}', encoding: '{encoding_name}'): {e}. Falling back to estimation.")
|
|
# Fallback: Estimate tokens based on characters (rough approximation)
|
|
# Average ~4 chars per token is a common heuristic
|
|
return max(1, round(len(text) / 4))
|
|
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, api_key, system_prompt, user_message_box, model,
|
|
web_search, cheapest, fastest, temperature, pdf_engine, chat_mode,
|
|
request_timeout=120, aspect_ratio="auto", image_resolution="1K", seed=0,
|
|
pdf_data=None, user_message_input=None, reasoning_effort="auto", **kwargs):
|
|
"""
|
|
Check if any input that affects the output has changed.
|
|
Includes hashing image and PDF data.
|
|
"""
|
|
# Hash image data if present - handle multiple images from kwargs
|
|
image_hashes = []
|
|
image_keys = sorted([k for k in kwargs.keys() if k.startswith('image_')],
|
|
key=lambda x: int(x.split('_')[1]))
|
|
|
|
for image_key in image_keys:
|
|
if kwargs[image_key] is not None:
|
|
image = kwargs[image_key]
|
|
if isinstance(image, torch.Tensor):
|
|
try:
|
|
hasher = hashlib.sha256()
|
|
hasher.update(image.cpu().numpy().tobytes())
|
|
image_hashes.append(hasher.hexdigest())
|
|
except Exception as e:
|
|
print(f"Warning: Could not hash {image_key} data for IS_CHANGED: {e}")
|
|
image_hashes.append(f"{image_key}_hashing_error")
|
|
else:
|
|
image_hashes.append(None)
|
|
|
|
|
|
# Hash PDF data if present and valid
|
|
pdf_hash = None
|
|
if pdf_data is not None and isinstance(pdf_data, dict) and "bytes" in pdf_data and isinstance(pdf_data["bytes"], bytes):
|
|
try:
|
|
hasher = hashlib.sha256()
|
|
hasher.update(pdf_data["bytes"])
|
|
pdf_hash = hasher.hexdigest()
|
|
# Optionally include filename in hash if it affects processing?
|
|
# if "filename" in pdf_data: hasher.update(pdf_data["filename"].encode())
|
|
except Exception as e:
|
|
print(f"Warning: Could not hash pdf data for IS_CHANGED: {e}")
|
|
pdf_hash = "pdf_hashing_error" # Use a placeholder on error
|
|
elif pdf_data is not None:
|
|
# Handle cases where pdf_data is present but not in the expected format
|
|
pdf_hash = "invalid_pdf_data_format"
|
|
|
|
|
|
# Ensure temperature is consistently represented (e.g., as float)
|
|
try:
|
|
temp_float = float(temperature) if isinstance(temperature, (str, int, float)) else 1.0
|
|
temp_float = max(0.0, min(2.0, temp_float))
|
|
except (ValueError, TypeError):
|
|
temp_float = 1.0
|
|
|
|
try:
|
|
timeout_int = int(request_timeout)
|
|
timeout_int = max(cls.min_request_timeout, min(cls.max_request_timeout, timeout_int))
|
|
except (ValueError, TypeError):
|
|
timeout_int = cls.default_request_timeout
|
|
|
|
validated_reasoning_effort = cls.validate_reasoning_effort(reasoning_effort)
|
|
|
|
|
|
# Combine all relevant inputs into a tuple for comparison
|
|
# Use primitive types where possible for reliable hashing/comparison
|
|
# Note: api_key here is the UI value only. Keys resolved from the LLM_KEY
|
|
# env var or openrouter_api_key.json are intentionally NOT part of the
|
|
# cache key — they're treated as user environment, not workflow inputs.
|
|
return (api_key, system_prompt, user_message_box, model,
|
|
web_search, cheapest, fastest, temp_float, pdf_engine, chat_mode,
|
|
timeout_int, aspect_ratio, image_resolution, seed, validated_reasoning_effort,
|
|
tuple(image_hashes), pdf_hash, user_message_input)
|
|
|
|
# Node class mappings
|
|
NODE_CLASS_MAPPINGS = {
|
|
"OpenRouterNode": OpenRouterNode
|
|
}
|
|
|
|
# Node display name mappings
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"OpenRouterNode": "OpenRouter LLM Node (Text/Multi-Image/PDF/Chat)" # Updated name
|
|
}
|