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Choose the LLM Provider

This section of the documentation covers the methods and functionalities provided by SimplerLLM to interact with the different Large Language Models (LLMs).

The library simplifies the process of integrating AI capabilities into your projects, making it straightforward to send prompts, receive responses, and handle data from different LLM providers.

1. Creating an LLM Instance and Configuring API Keys​

To interact with an LLM, you first need to create an instance of the model. This process involves specifying which LLM provider you're using and which model you want to interact with.

Here's an example usage of the OpenAI API leveraging the gpt-3.5-turbo model.

from SimplerLLM.language.llm import LLM, LLMProvider

# Create an instance of an LLM
llm_instance = LLM.create(provider=LLMProvider.OPENAI, model_name="gpt-3.5-turbo")

However, we'll need to setup our API keys to use the LLM. We have 2 ways:

Add them to a .env file (Better Approach):​

Set up a a .env file at the root of your project. This file should include your API keys for various LLM providers and can also be used to customize default operational settings such as maximum retries and retry delays.

As an example, here's how the .env file would look like if we're planning to use the OpenAI LLM:

OPENAI_API_KEY="your_openai_api_key_here"

The script will automatically load these enviromental variables and use them when you want to generate anything.

Pass them as a function parameter:​

If you don't want to create a .env file you can simply pass your API key as a parameter when creating an LLM instance which would look like this:

from SimplerLLM.language.llm import LLM, LLMProvider

# Create an instance of an LLM
llm_instance = LLM.create(provider=LLMProvider.OPENAI, model_name="gpt-3.5-turbo", api_key="your_api_key")

2. Generating Responses​

Once you have an LLM instance, you can generate responses by providing prompts. Here's how to use the generate_response function:

response = llm_instance.generate_response(
    prompt="Explain the theory of relativity",
    messages=None,
    system_prompt="You are a helpful AI Assistant",
    temperature=0.6,
    max_tokens=500,
    top_p=0.8,
    full_response=False
)
print(response)

Function Parameters​

  • prompt: The question or statement to send to the LLM.
  • messages: (optional) List of message objects for structured conversations. For example, when creating a chatbot you'll need to save the chat history so everytime you add the new response of the api to the messages and send it with the new api call.
  • system_prompt (optional): Guides the model's tone and approach in generating responses.
  • max_tokens (optional): The maximum number of tokens to generate.
  • temperature (optional): Adjusts the diversity and predictability of the model's responses.
  • top_p (optional): The close to 0 the more the randomness of the response, where it limits the selection of the top probable words.
  • full_response (optional): If set to True, returns the complete response object from the API, including additional metadata.

If you don't set any of the optional parameters, they'll take the default values set in the library which are:

  • messages= None
  • system_prompt= "You are a helpful AI Assistant"
  • max_tokens= 300
  • temperature= 0.7
  • top_p= 1.0
  • full_response= False

3. Handling Retries​

If you want to handle how many time's the function retries after a failed api call, and the delay between each retry you'll need to adjust the enviromental variables that handle this.

The MAX_RETRIES and RETRY_DELAY are by default set to 3 and 2 respectively, so if you plan to change them you'll need to go to your .env file:

MAX_RETRIES=5
RETRY_DELAY=3

Now, if an API request fails, the library will retry the request a maximum of 5 times if it keeps failing with a delay of 3 seconds between each retry.

4. API call for each LLM provider​

Each LLM provider requires a slight change in the parameters when creating the llm instance, but the most important thing is adding the API key correctly in the .env file. So, when you plan on using multiple providers make sure you set all the keys needed in a correct format

Anyways, here's a brief overview and example usage for each provider:

OpenAI​

from SimplerLLM.language.llm import LLM, LLMProvider

llm_instance = LLM.create(provider=LLMProvider.OPENAI, model_name="gpt-3.5-turbo")

response = llm_instance.generate_response(prompt="generate a 5 words sentence")
print(response)

Google Gemini​

from SimplerLLM.language.llm import LLM, LLMProvider

llm_instance = LLM.create(provider=LLMProvider.GEMINI, model_name="gemini-1.5-flash")

response = llm_instance.generate_response(prompt="generate a 5 words sentence")
print(response)

Anthropic Claude​

from SimplerLLM.language.llm import LLM, LLMProvider

llm_instance = LLM.create(provider=LLMProvider.ANTHROPIC, model_name="claude-3-5-sonnet-20240620")

response = llm_instance.generate_response(prompt="generate a 5 words sentence")
print(response)

For Ollama (Local Model) - The Phi Model​

from SimplerLLM.language.llm import LLM, LLMProvider

llm_instance = LLM.create(provider=LLMProvider.OLLAMA, model_name="phi")

response = llm_instance.generate_response(prompt="generate a 5 words sentence")
print(response)

For Playground on LearnWithHasan (Exclusive for Power Memebers)​

from SimplerLLM.language.llm import LLM, LLMProvider

llm_instance = LLM.create(provider=LLMProvider.LWH, model_name="gpt-3.5-turbo")

response = llm_instance.generate_response(prompt="generate a 5 words sentence")
print(response)

Make sure you add your API key and user ID in the .env file in the correct format . You can get them by navigating to the Account Page on the website, where you'll find both values in the API section.