[Bug]: Gptcache server: with OpenAi embedding cache seems to be not working properly.

1) Copy the server.py from the gptcache_Server foler into a dir you want. 
2) Configure gptcache.yaml file:
embedding:
    openai
embedding_config:
    # Set embedding model params here
storage_config:
    data_dir:
        /Users/swathinarayanan/tolka_feedback_sep/gptdocker/gptcache_server/gptcache_data
    manager:
        sqlite,faiss
    vector_params:
        # Set vector storage related params here
evaluation:
    distance
evaluation_config:
    # Set evaluation metric kws here
pre_function:
    last_content
post_function:
    first
config:
    similarity_threshold: 0.8
    # Set other config here

3) Start the server:

python server.py -s 0.0.0.0 -p 8000 -of gptcache.yml -o True

4) Create a client program or API call and make request to gptcache server.

Example program:

import requests
import json
import time

def call_chat_completions_endpoint(base_url, api_key, user_question):
    # Endpoint URL
    url = f"{base_url}/v1/chat/completions"
    
    # Headers including the authorization token
    headers = {
        'Content-Type': 'application/json',
        'Authorization': f'Bearer {api_key}'
    }
    
    # Request payload
    payload = {
       
        'model': 'gpt-3.5-turbo',
        'messages': [{"role": "system", "content": "You are a helpful assistant."},
                     {'role': 'user', 'content': user_question}],
        'top_k': 10,
            
        
    }
    
    # Send POST request
    start_time = time.time()
    response = requests.post(url, headers=headers, data=json.dumps(payload))
    
    # Check if the request was successful
    if response.status_code == 200:
        # Process the successful response
        print("Success:", response.json())
        print("Time Consumed: {:.2f}s".format(time.time() - start_time))
    else:
        # Handle errors
        print(f"Error: {response.status_code}, Message: {response.text}")

# Example usage
if __name__ == "__main__":
    # Define the base URL of your FastAPI application
    BASE_URL = "http://localhost:8000"
    
    # Your API key for authorization (if needed)
    API_KEY = "****************************"
    
    # User question to be sent to the chat completions endpoint
    USER_QUESTION = "what is coral reef ?"
    
    call_chat_completions_endpoint(BASE_URL, API_KEY, USER_QUESTION)

MAC OS
M1 chip