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import logging | |
import os | |
from openai import OpenAI | |
from openai import OpenAIError, RateLimitError | |
from config import OPENAI_API_KEY | |
# Initialize the OpenAI client with the API key from the environment variable | |
#api_key = os.getenv('OPENAI_API_KEY') | |
client = OpenAI(api_key=OPENAI_API_KEY) | |
def generate_rag_response(json_output, user_query): | |
logging.info("Generating RAG response") | |
# Extract text from the JSON output | |
context_texts = [hit['chunk_text'] for hit in json_output] | |
# Create the context for the prompt | |
context = "\n".join(context_texts) | |
prompt = f"Based on the given context, answer the user query: {user_query}\nContext:\n{context}" | |
main_prompt = [ | |
{"role": "system", "content": "You are a helpful assistant."}, | |
{"role": "user", "content": prompt} | |
] | |
try: | |
# Create a chat completion request | |
chat_completion = client.chat.completions.create( | |
messages=main_prompt, | |
model="gpt-4o-mini", # Use the gpt-4o-mini model | |
timeout=10 | |
) | |
# Log the response from the model | |
logging.info("RAG response generation completed") | |
logging.info(f"RAG response: {chat_completion.choices[0].message.content}") | |
return chat_completion.choices[0].message.content, None | |
except RateLimitError as e: | |
logging.error(f"Rate limit exceeded: {e}") | |
return None, "Rate limit exceeded. Please try again later." | |
except OpenAIError as e: | |
logging.error(f"OpenAI API error: {e}") | |
return None, f"An error occurred: {str(e)}" | |
except Exception as e: | |
logging.error(f"Unexpected error: {e}") | |
return None, str(e) | |