⚡ Your Easy Pass to Advanced AI ⚡
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.
With pip:
pip install simplerllm
- 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!
To use this library, you need to set several API keys in your environment. Start by creating a .env file in the root directory of your project and adding your API keys there.
🔴 This file should be kept private and not committed to version control to protect your keys.
Here is an example of what your .env file should look like:
OPENAI_API_KEY="your_openai_api_key_here"
GEMENI_API_KEY="your_gemeni_api_key_here"
CLAUDE_API_KEY="your_claude_api_key_here"
RAPIDAPI_API_KEY="your_rapidapi_key_here" # for accessing APIs on RapidAPI
VALUE_SERP_API_KEY="your_value_serp_api_key_here" #for Google search
SERPER_API_KEY="your_serper_api_key_here" #for Google search
STABILITY_API_KEY="your_stability_api_key_here" #for image generation
from SimplerLLM.language.llm import LLM, LLMProvider
# For OpenAI
llm_instance = LLM.create(provider=LLMProvider.OPENAI, model_name="gpt-3.5-turbo")
# For Google Gemini
#llm_instance = LLM.create(provider=LLMProvider.GEMINI,model_name="gemini-pro")
# For Anthropic Claude
#llm_instance = LLM.create(LLMProvider.ANTHROPIC, model_name="claude-3-opus-20240229")
response = llm_instance.generate_response(prompt="generate a 5 words sentence")
from SimplerLLM.tools.serp import search_with_serper_api
search_results = search_with_serper_api("your search query", num_results=3)
# use the search results the way you want!
from SimplerLLM.tools.generic_loader import load_content
text_file = load_content("file.txt")
print(text_file.content)
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)
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])
We have introduced new functions to help you split texts into manageable chunks based on different criteria. These functions are part of the chunker tool.
This function splits text into chunks with a maximum size, optionally preserving sentence structure.
This function splits the text into chunks based on sentences.
This function splits text into chunks based on paragraphs.
Example
from SimplerLLM.tools import text_chunker as chunker
blog_url = "https://www.semrush.com/blog/digital-marketing/"
blog_post = loader.load_content(blog_url)
text = blog_post.content
chunks = chunker.chunk_by_max_chunk_size(text, 100, True)
- Adding More Tools
- Interacting With Local LLMs
- Prompt Optimization
- Response Evaluation
- GPT Trainer
- Document Chunker
- Advanced Document Loader
- Integration With More Providers