#!/usr/bin/env python
# coding: utf-8

# # Basic Loading of the Kernel
# 

# To run the notebooks we recommend using Poetry and starting a shell with a virtual environment
# prepared to use SK.
# 
# See [DEV_SETUP.md](../../python/DEV_SETUP.md) for more information.
# 

# In[ ]:


get_ipython().system('python -m pip install semantic-kernel==1.0.3')


# In[ ]:


from semantic_kernel import Kernel, kernel

kernel = Kernel()


# ### Configuring API Keys and Endpoints
# 
# #### Option 1: using OpenAI
# 
# Add your [OpenAI Key](https://openai.com/product/) key to either your environment variables or to the `.env` file in the same folder (org Id only if you have multiple orgs):
# 
# ```
# OPENAI_API_KEY="sk-..."
# OPENAI_ORG_ID=""
# ```
# The environment variables names should match the names used in the `.env` file, as shown above.
# 
# If using the `.env` file, please configure the `env_file_path` parameter with a valid path when creating the ChatCompletion class:
# 
# ```
# chat_completion = OpenAIChatCompletion(service_id="test", env_file_path=<path_to_file>)
# ```
# 
# Use "keyword arguments" to instantiate an OpenAI Chat Completion service and add it to the kernel:
# 
# #### Option 2: using Azure OpenAI
# 
# Add your [Azure Open AI Service key](https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=programming-language-studio) settings to either your system's environment variables or to the `.env` file in the same folder:
# 
# ```
# AZURE_OPENAI_API_KEY="..."
# AZURE_OPENAI_ENDPOINT="https://..."
# AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
# AZURE_OPENAI_TEXT_DEPLOYMENT_NAME="..."
# AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="..."
# ```
# The environment variables names should match the names used in the `.env` file, as shown above.
# 
# If using the `.env` file, please configure the `env_file_path` parameter with a valid path when creating the ChatCompletion class:
# 
# ```
# chat_completion = AzureChatCompletion(service_id="test", env_file_path=<path_to_file>)
# ```
# 

# When using the kernel for AI requests, the kernel needs some settings like URL and credentials to the AI models.
# 
# The SDK currently supports OpenAI and Azure OpenAI, among other connectors.
# 
# If you need an Azure OpenAI key, go [here](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/quickstart?pivots=rest-api).
# 

# In[ ]:


from services import Service

# Select a service to use for this notebook (available services: OpenAI, AzureOpenAI, HuggingFace)
selectedService = Service.OpenAI


# In[ ]:


service_id = None
if selectedService == Service.OpenAI:
    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

    service_id = "oai_chat_gpt"
    kernel.add_service(
        OpenAIChatCompletion(service_id=service_id, ai_model_id="gpt-3.5-turbo-1106"),
    )
elif selectedService == Service.AzureOpenAI:
    from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

    service_id = "aoai_chat_completion"
    kernel.add_service(
        AzureChatCompletion(service_id=service_id),
    )


# Great, now that you're familiar with setting up the Semantic Kernel, let's see [how we can use it to run prompts](02-running-prompts-from-file.ipynb).
#