642 lines
29 KiB
Python
642 lines
29 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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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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def __init__(self):
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self.chat_manager = ChatSessionManager()
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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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"image_generation": ("BOOLEAN", {"default": False}),
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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": 0.1,
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"display": "slider",
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"round": 1,
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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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},
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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)
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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 fetch_credits(self, api_key):
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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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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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response = requests.get(url, headers=headers)
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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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image_generation=False, pdf_data=None, user_message_input=None, **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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if not api_key:
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return ("Error: API Key not provided.", 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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# 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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# 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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# 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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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)
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# --- Apply model modifiers ---
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modified_model = model
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# Check if model already has modifiers to avoid duplication
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if web_search and ":online" not in modified_model:
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modified_model = f"{modified_model}:online"
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# Apply :floor/:nitro only if no other modifier is present or relevant
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# Note: :floor and :nitro might conflict with :online. Check OpenRouter docs for precedence.
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# Assuming :online takes precedence if specified.
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if ":online" not in modified_model:
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if cheapest and ":floor" not in modified_model:
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modified_model = f"{modified_model}:floor"
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elif fastest and not cheapest and ":nitro" not in modified_model: # Only apply fastest if cheapest is false
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modified_model = f"{modified_model}:nitro"
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# --- Construct the final payload ---
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data = {
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"model": modified_model,
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"messages": messages,
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"temperature": validated_temp
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# Omitting max_tokens lets the model decide (usually preferred)
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}
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# Only add modalities parameter if explicitly requested by user
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# This prevents "Multi-modal output is not supported" errors on text-only models
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if image_generation:
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data["modalities"] = ["image", "text"]
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print(f"Image generation enabled by user setting")
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# Add plugins if a specific PDF engine is selected
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if pdf_engine != "auto":
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data["plugins"] = [
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{
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"id": "file-parser",
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"pdf": {
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"engine": pdf_engine
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}
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}
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]
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# --- Pre-calculate text input tokens (rough estimate) ---
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# Note: Actual token count depends on the model and includes parsed PDF/image data.
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# Rely on the API response for accurate usage stats.
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text_token_estimate = 0
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try:
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text_token_estimate = self.count_tokens(system_prompt, model) + self.count_tokens(user_text, model)
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except Exception as e:
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print(f"Warning: Token counting failed - {e}")
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# --- Make API Call and Process Response ---
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try:
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start_time = time.time()
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response = requests.post(url, headers=headers, json=data)
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response.raise_for_status() # Raises HTTPError for bad responses (4xx or 5xx)
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end_time = time.time()
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result = response.json()
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# --- Extract results and calculate stats ---
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if not result.get("choices") or not result["choices"][0].get("message"):
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raise ValueError("Invalid response format from API: 'choices' or 'message' missing.")
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# Parse response for text and image content
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message = result["choices"][0]["message"]
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text_output = message.get("content", "")
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image_tensor = placeholder_image
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# Check for images in the separate images field (OpenRouter format)
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if message.get("images"):
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print(f"Found {len(message['images'])} image(s) in API response")
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try:
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# Get the first image from the images array
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first_image = message["images"][0]
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image_url = first_image["image_url"]["url"]
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if image_url.startswith("data:image"):
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base64_str = image_url.split(",", 1)[1]
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try:
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# Convert base64 to image tensor
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image_tensor = self.base64_to_image(base64_str)
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print(f"Successfully decoded image from API response")
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except Exception as e:
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print(f"Error decoding image: {e}")
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else:
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print(f"Image URL format not supported: {image_url[:50]}...")
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except Exception as e:
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print(f"Error processing images from response: {e}")
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else:
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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")
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# Also handle legacy multimodal content format as fallback
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if isinstance(text_output, list):
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text_parts = []
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for content in text_output:
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if isinstance(content, dict):
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if content.get("type") == "text":
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text_parts.append(content.get("text", ""))
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elif content.get("type") == "image_url":
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# Extract base64 image data
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image_url = content["image_url"]["url"]
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if image_url.startswith("data:image"):
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base64_str = image_url.split(",", 1)[1]
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try:
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# Convert base64 to image tensor
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image_tensor = self.base64_to_image(base64_str)
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except Exception as e:
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print(f"Error decoding image: {e}")
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text_output = "\n".join(text_parts)
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response_ms = result.get("response_ms", None)
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api_usage = result.get("usage", {})
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prompt_tokens = api_usage.get("prompt_tokens", text_token_estimate) # Use API value if available
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completion_tokens = api_usage.get("completion_tokens", 0)
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if completion_tokens == 0 and text_output: # Estimate completion tokens if API doesn't provide them
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try:
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completion_tokens = self.count_tokens(text_output, model)
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except Exception as e:
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print(f"Warning: Completion token counting failed - {e}")
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# Calculate tokens per second (TPS)
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tps = 0
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elapsed_time = end_time - start_time
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if response_ms is not None:
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server_elapsed_time = response_ms / 1000.0
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if server_elapsed_time > 0:
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tps = completion_tokens / server_elapsed_time
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elif elapsed_time > 0:
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# Use client-side timing as fallback, less accurate due to network latency
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tps = completion_tokens / elapsed_time
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# Optional: apply a heuristic correction factor if needed, but server time is better
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# correction_factor = 1.28 # Example factor, might need tuning
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# tps *= correction_factor
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stats_text = (
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f"TPS: {tps:.2f}, "
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f"Prompt Tokens: {prompt_tokens}, "
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f"Completion Tokens: {completion_tokens}, "
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f"Temp: {validated_temp:.1f}, "
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f"Model: {modified_model}" # Display the actual model used
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)
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if pdf_engine != "auto":
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stats_text += f", PDF Engine: {pdf_engine}"
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# Fetch credits information AFTER the main request
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credits_text = self.fetch_credits(api_key)
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# Save conversation in chat mode
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if chat_mode and session_path:
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# Append assistant's response to the conversation
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assistant_message = {
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"role": "assistant",
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"content": text_output
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}
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messages.append(assistant_message)
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# Save the updated conversation
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self.chat_manager.save_conversation(session_path, messages)
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return (text_output, image_tensor, stats_text, credits_text)
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except requests.exceptions.RequestException as e:
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error_message = f"API Request Error: {str(e)}"
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if hasattr(e, 'response') and e.response is not None:
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try:
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error_detail = e.response.json() # Try to get JSON error detail
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error_message += f" | Details: {error_detail}"
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except json.JSONDecodeError:
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error_message += f" | Status: {e.response.status_code} | Response: {e.response.text[:200]}" # Show raw text if not JSON
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else:
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error_message += " (Network or connection issue)" # Generic network error
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return (error_message, placeholder_image, "Stats N/A due to error", "Credits N/A due to error")
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except Exception as e: # Catch other potential errors (e.g., JSON parsing, value errors)
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return (f"Node Error: {str(e)}", placeholder_image, "Stats N/A due to error", "Credits N/A due to error")
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@staticmethod
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def image_to_base64(image):
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"""
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Converts a ComfyUI IMAGE (torch.Tensor, BHWC, float 0-1)
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into a base64-encoded PNG string.
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"""
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if not isinstance(image, torch.Tensor):
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raise TypeError("Input 'image' is not a torch.Tensor")
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|
|
# 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,
|
|
image_generation=False, pdf_data=None, user_message_input=None, **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
|
|
|
|
|
|
# Combine all relevant inputs into a tuple for comparison
|
|
# Use primitive types where possible for reliable hashing/comparison
|
|
return (api_key, system_prompt, user_message_box, model,
|
|
web_search, cheapest, fastest, temp_float, pdf_engine, chat_mode,
|
|
image_generation, 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
|
|
}
|