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一个AI基础代码工具包

liangdabiao
liangdabiao

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开发微型SaaS,一定要快，把基础重复的部分打包好，每次直接利用就可以了，例如 对接大模型，向量数据库，请求第三方api，保证json返回，和wordpress的整合等等。

介绍代码包： https://github.com/hassancs91/SimplerLLM

Creating an LLM Instance

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")

Using Tools

SERP

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!

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 09/19/2024.
 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])

Chunking Functions

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.

chunk_by_max_chunk_size

This function splits text into chunks with a maximum size, optionally preserving sentence structure.

chunk_by_sentences

This function splits the text into chunks based on sentences.

chunk_by_paragraphs

This function splits text into chunks based on paragraphs.

chunk_by_semantics

This functions splits text into chunks based on semantics.

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)

Next Updates

  • Adding More Tools
  • Interacting With Local LLMs
  • Prompt Optimization
  • Response Evaluation
  • GPT Trainer
  • Document Chunker
  • Advanced Document Loader
  • Integration With More Providers
  • Simple RAG With SimplerVectors
  • Integration with Vector Databases
  • Agent Builder
  • LLM Server