⚪ SimplerLLM (Beta)

⚡ Your Easy Pass to Advanced AI ⚡

🤔 What is SimplerLLM?

SimplerLLM is an open-source Python library designed to simplify interactions with Large Language Models (LLMs) for researchers and beginners. It offers a unified interface for different LLM providers and a suite of tools to enhance language model capabilities and make it Super easy for anyone to develop AI-powered tools and apps.

Easy Installation

With pip:

pip install simplerllm

Features

• Unified LLM Interface: Define an LLM instance in one line for providers like OpenAI and Google Gemini. Future versions will support more APIs and LLM providers.
• Generic Text Loader: Load text from various sources like DOCX, PDF, TXT files, YouTube scripts, or blog posts.
• RapidAPI Connector: Connect with AI services on RapidAPI.
• SERP Integration: Perform searches using DuckDuckGo, with more search engines coming soon.
• Prompt Template Builder: Easily create and manage prompt templates. And Much More Coming Soon!

Creating an LLM Instance

from SimplerLLM.langauge.llm import LLM, LLMProvider

# For OpenAI
llm_instance = LLM.create(provider=LLMProvider.OPENAI)
# For Google Gemini
gemini_instance = LLM.create(provider=LLMProvider.GEMINI,model_name="gemini-pro")

response = llm_instance.generate_text(user_prompt="generate a 5 words sentence")

Using Tools

SERP

from SimplerLLM.tools.serp import search_with_duck_duck_go

search_results = search_with_duck_duck_go("penut",3)

# use the search results the way you want!

Generic Text Loader

from SimplerLLM.tools.generic_loader import load_content

text_file = load_content("file.txt")

print(text_file.content)

Calling any RapidAPI API

from  SimplerLLM.tools.rapid_api import RapidAPIClient

api_url = "https://domain-authority1.p.rapidapi.com/seo/get-domain-info"
api_params = {
    'domain': 'learnwithhasan.com',
}

api_client = RapidAPIClient()  # API key read from environment variable
response = api_client.call_api(api_url, method='GET', params=api_params)

Prompt Template Builder

from SimplerLLM.prompts.prompt_builder import create_multi_value_prompts,create_prompt_template

basic_prompt = "Generate 5 titles for a blog about {topic} and {style}"

prompt_template = pr.create_prompt_template(basic_prompt)

prompt_template.assign_parms(topic = "marketing",style = "catchy")

print(prompt_template.content)


## working with multiple value prompts
multi_value_prompt_template = """Hello {name}, your next meeting is on {date}.
 and bring a {object} wit you"""

params_list = [
     {"name": "Alice", "date": "January 10th", "object" : "dog"},
     {"name": "Bob", "date": "January 12th", "object" : "bag"},
     {"name": "Charlie", "date": "January 15th", "object" : "pen"} 
]


multi_value_prompt = create_multi_value_prompts(multi_value_prompt_template)
generated_prompts = multi_value_prompt.generate_prompts(params_list)

print(generated_prompts[0])

Next Updates

• Adding More Tools
• Interacting With Local LLMs
• Prompt Optimization
• Response Evaluation
• GPT Trainer
• Document Chunker
• Advanced Document Loader
• Integration With More Providers