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e77665f | 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import os
from dotenv import load_dotenv
from openai import OpenAI
from configs.logging_config import setup_logger
from configs.settings import config
load_dotenv()
logger = setup_logger("llm_agent")
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
MODEL_NAME = config["llm"]["model"]
class LLMAgent:
def __init__(self):
self.prompts = {
"v1": self.prompt_v1,
"v2": self.prompt_v2,
"v3": self.prompt_v3
}
def prompt_v1(self, query):
return f"""
You are a business analyst. Answer the question.
Question: {query}
"""
def prompt_v2(self, query):
return f"""
You are an operations analyst.
Analyze the user query and provide:
1. Observations
2. Possible reasons
3. Suggested next steps
Question: {query}
"""
def prompt_v3(self, query):
return f"""
You are an AI Operations Copilot.
Rules:
- Do NOT make up data
- If unsure, say "I don't have enough data"
- Provide structured reasoning
Output format:
- Summary
- Possible Causes
- Confidence Level (High/Medium/Low)
- Suggested Actions
Question: {query}
"""
def run(self, query, version="v1"):
prompt = self.prompts[version](query)
logger.info(f"Prompt Version: {version}")
logger.info(f"User Query: {query}")
response = client.chat.completions.create(
model=MODEL_NAME,
messages=[
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": prompt}
]
)
output = response.choices[0].message.content
logger.info(f"Response: {output}")
return output
def run_cli():
agent = LLMAgent()
print("=== LLM Agent ===")
print("Type 'exit' to quit\n")
while True:
query = input("Enter your query: ")
if query.lower() == "exit":
break
version = input("Choose prompt version (v1/v2/v3): ")
result = agent.run(query, version)
print(f"\nAgent Response:\n{result}\n")
if __name__ == "__main__":
run_cli() |