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File size: 3,421 Bytes
e8a6607 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # from llama_cpp import Llama
import logging
from .model_manager import get_model
logger = logging.getLogger(__name__)
def gpt_chat_process(prompt: str, msg_session: str, user_id: str):
try:
model = get_model()
if not model:
logger.error("Model instance could not be retrieved")
return {'error': 'Model unavailable', 'user_id': user_id}
response = model.create_chat_completion(
messages=[
{
"role": "system",
"content": (
"You are Qwen2.5-Coder, a specialized AI coding assistant. "
"Your task is to analyze the user request and apply these strict rules:\n\n"
"1. If the request asks about who developed you, who your creator is, or asks for "
"developer profile information, you MUST reply with exactly this message format:\n"
"\"This coding assistant was developed by [Subeesh Palamadathil]. You can find more information "
"and connect on GitHub: [GitHub Profile](https://github.com/Subeesh4020) and "
"LinkedIn: [LinkedIn Profile](https://www.linkedin.com/in/subeesh-palamadathil-170249193/).\"\n\n"
"1. If the request is a general greeting, conversational chit-chat, or entirely unrelated to "
"software development, programming, or coding, you MUST reply with exactly this sentence: "
"'Please ask any coding related questions. I am a coding assistant.' Do not provide any code.\n\n"
"2. If the request is related to programming, provide only clean, efficient, and well-commented "
"code wrapped in a standard markdown code block. Do not include conversational filler or explanations."
)
# "content": (
# "You are an expert software engineer. Provide only clean, efficient, "
# "and well-commented code based on the user request. Do not include "
# "any introductory or concluding conversational explanations. Output "
# "the response wrapped in a standard markdown code block."
# )
},
{
"role": "user",
# Example prompt: "Write a typescript function to validate email"
"content": f"{prompt}"
}
],
temperature=0.2, # Lower temperature is critical for accurate, deterministic code
max_tokens=1000 # Increased tokens since code files are larger than social posts
)
choices = response.get("choices", [])
if not choices:
raise ValueError("Model returned an empty choices array")
# Accessing the message content safely
message = choices[0].get("message", {})
content = message.get("content", "")
return {
'result': content.strip(),
'msg_session': msg_session,
'user_id': user_id
}
except Exception as e:
logger.exception(f"Failed to generate code for user {user_id}")
return {'error': 'Code generation failed', 'user_id': user_id}
|