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from openai import OpenAI
from params import OPENAI_MODEL, OPENAI_API_KEY
import llamanet
# Create an instance of the OpenAI class
client = OpenAI(api_key=OPENAI_API_KEY)
# Initialize LlamaNet client
llamanet_client = llamanet.Client()
def send_to_chatgpt(msg_list):
try:
completion = client.chat.completions.create(
model=OPENAI_MODEL,
messages=msg_list,
temperature=0.6,
stream=True
)
chatgpt_response = ""
for chunk in completion:
if chunk.choices[0].delta.content is not None:
chatgpt_response += chunk.choices[0].delta.content
# Note: Usage information might not be available with LlamaNet
chatgpt_usage = None
return chatgpt_response, chatgpt_usage
except Exception as e:
print(f"Error in send_to_chatgpt: {str(e)}")
return f"Error: {str(e)}", None
def send_to_llamanet(msg_list):
try:
# Convert msg_list to the format expected by LlamaNet
llamanet_messages = [{"role": msg["role"], "content": msg["content"]} for msg in msg_list]
# Send request to LlamaNet
response = llamanet_client.chat.completions.create(
model="llamanet",
messages=llamanet_messages,
stream=True
)
llamanet_response = ""
for chunk in response:
if chunk.choices[0].delta.content is not None:
llamanet_response += chunk.choices[0].delta.content
# LlamaNet doesn't provide usage information
llamanet_usage = None
return llamanet_response, llamanet_usage
except Exception as e:
print(f"Error in send_to_llamanet: {str(e)}")
return f"Error: {str(e)}", None
def send_to_llm(provider, msg_list):
if provider == "llamanet":
return send_to_llamanet(msg_list)
elif provider == "openai":
return send_to_chatgpt(msg_list)
else:
raise ValueError(f"Unknown provider: {provider}")
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