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import json
from typing import List, Dict, Any, Union
from gradio.components.chatbot import ChatMessage
from llm import LLM_Client
import os
from config import session_keys
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(f"🤖 {__name__}")
class LLMNode:
def __init__(self, session_id):
self.session_id = session_id
settings = session_keys.get(session_id, {})
self.provider = settings.get("provider", "OpenAI")
self.tool_model = settings.get("tool_call_model", "gpt-4o-mini")
self.response_model = settings.get("response_model", "gpt-4o-mini")
logger.info(f"Provider: {self.provider}")
logger.info(f"Tool_model: {self.tool_model}")
logger.info(f"Response_model: {self.response_model}")
self.tool_selector = LLM_Client(session_id, sourceAI="openai")
self.generator = None
if self.provider.lower() == "openai":
if settings.get("OPENAI_API_KEY"):
self.generator = LLM_Client(session_id, sourceAI="openai")
elif self.provider.lower() == "nebius":
if settings.get("NEBIUS_API_KEY"):
self.generator = LLM_Client(session_id, sourceAI="nebius")
def build_prompt(
self,
history: List[Union[Dict[str, Any], ChatMessage]],
message: str,
image_base64: str = None,
vision_enabled: bool = False,
type: str = "generate",
encryptId: str = None,
history_len = 20,
face_data: dict = None,
color_season: str = None
) -> List[Dict[str, Any]]:
prompts = []
chat_prompt = []
for msg in history:
if isinstance(msg, ChatMessage):
role, content = msg.role, msg.content
else:
role, content = msg.get("role"), msg.get("content")
if role in ["user", "assistant", "system"]:
chat_prompt.append({"role": role, "content": content})
user_data_str = ""
if face_data:
user_data_str += f"User facial parts' color: {face_data}.\n"
if color_season:
user_data_str += f"User color season: {color_season}\n"
if encryptId:
user_data_str += f"tmp_id: {encryptId}"
user_message = ""
if user_data_str != "":
user_message += f"UserData info: {user_data_str}\n\n"
user_message += message
if vision_enabled:
if image_base64:
chat_prompt.append({
"role": "user",
"content": [
{"type": "text", "text": user_message},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}
]
})
else:
chat_prompt.append({
"role": "user",
"content": [
{"type": "text", "text": user_message}
]
})
else:
chat_prompt.append({"role": "user", "content": message})
# Construct Main System Prompt
text_prompt_path = "promptsDB"
with open(os.path.join(text_prompt_path, "system_prompt.txt"), "r", encoding="utf-8") as f:
system_prompt_str = f.read().strip()
system_prompt = {
"role": "system",
"content": system_prompt_str
}
prompts.append(system_prompt)
# Construct User Data
logger.info(f"User Message: \n{user_message}")
logger.info(f"Image Exist: {True if image_base64 else False}")
chat_prompt
prompts = prompts + chat_prompt[-history_len:]
return prompts
def call_tool_step(
self,
messages: List[Union[Dict[str, str], Dict[str, Any]]],
tools: List[Dict[str, Any]]
) -> Dict[str, Any]:
return self.tool_selector.get_completion(
model=self.tool_model,
max_tokens=300,
messages=messages,
tools=tools,
tool_choice="auto"
)
def call_final_step(self, messages: List[Dict[str, str]]) -> Dict[str, Any]:
return self.generator.get_completion(
model=self.response_model,
max_tokens=300,
messages=messages,
tool_choice="none"
)
def sanitize_messages_for_nebius(self, messages: List[Dict[str, Any]]) -> List[Dict[str, str]]:
sanitized = []
for msg in messages:
if msg["role"] in ["user", "assistant", "system"]:
sanitized.append({"role": msg["role"], "content": msg.get("content", "")})
elif msg["role"] == "tool":
content = msg.get("content", "")
sanitized.append({"role": "assistant", "content": f"Tool '{msg['name']}' returned:\n{content}"})
return sanitized
def call_generation_step(
self,
message: str,
history: List[Union[Dict[str, Any], ChatMessage]],
tool_result: str = None,
face_data: dict = None,
color_season: str = None
) -> List[Dict[str, Any]]:
messages = self.build_prompt(
history=history,
message=message,
image_base64=None,
vision_enabled=False,
face_data=face_data,
color_season=color_season
)
if tool_result:
messages.append({
"role": "assistant",
"content": f"Information (in your system) - use this if you required to answer that: \n\n{tool_result}"
})
step2 = self.call_final_step(messages)
return [{"role": "assistant", "content": step2["choices"][0]["message"]["content"]}]
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