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import os
# from llama_index.llms.openllm import OpenLLM
from llama_index.llms.nebius import NebiusLLM
# ...existing environment variable loading logic...
from dotenv import load_dotenv
load_dotenv()
LLM_PROVIDER = os.environ.get("LLM_PROVIDER", "openllm").lower()
LLM_API_URL = os.environ.get("LLM_API_URL")
LLM_API_KEY = os.environ.get("LLM_API_KEY")
NEBIUS_API_KEY = os.environ.get("NEBIUS_API_KEY", "")
OPENLLM_MODEL = os.environ.get("OPENLLM_MODEL")
NEBIUS_MODEL = os.environ.get("NEBIUS_MODEL")
if LLM_PROVIDER == "nebius":
llm = NebiusLLM(
api_key=NEBIUS_API_KEY,
model=NEBIUS_MODEL
)
else:
pass
# llm = OpenLLM(
# model=OPENLLM_MODEL,
# api_base=LLM_API_URL,
# api_key=LLM_API_KEY,
# max_new_tokens=2048,
# temperature=0.7,
# )
import re
def call_llm_api(messages):
"""
Calls the LLM API endpoint with the conversation messages using OpenLLM or NebiusLLM.
Args:
messages (list): List of dicts with 'role' and 'content' for each message.
Returns:
str: The assistant's reply as a string.
"""
from llama_index.core.llms import ChatMessage
chat_messages = [ChatMessage(role=m["role"], content=m["content"]) for m in messages]
response = llm.chat(chat_messages)
return response.message.content
def is_stage_complete(llm_reply):
"""
Heuristic to determine if the current stage is complete based on LLM reply.
Args:
llm_reply (str): The assistant's reply.
Returns:
bool: True if the stage is considered complete, False otherwise.
"""
triggers = [
"stage complete",
"let's move to the next stage",
"moving to the next stage",
"next stage",
"you have completed this stage"
]
return any(trigger in llm_reply.lower() for trigger in triggers)
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