llm-axe Goal llm-axe is meant to be a flexible toolkit that provides simple abstractions for commonly used functions related to LLMs. It's not meant to intrude in your development workflow as other larger frameworks often do. It has functions for automatic schema generation , pre-made agents with self-tracking chat history and fully customizable agents . Have feedback/questions? Join the Discord Installation pip install llm-axe Example Snippets Online Chat Demo : Demo chat app showcasing an LLM with internet access Custom Agent llm = OllamaChat ( model = "llama3:instruct" ) agent = Agent ( llm , custom_system_prompt = "Always respond with the word LLAMA, no matter what" ) resp = agent . ask ( "What is the meaning of life?" ) print ( resp ) # Output # LLAMA Function Calling ??A function calling LLM can be created with just 3 lines of code : ??No need for premade schemas, templates, special prompts, or specialized functions. prompt = "I have 500 coins, I just got 200 more. How many do I have?" llm = OllamaChat ( model = "llama3:instruct" ) fc = FunctionCaller ( llm , [ get_time , get_date , get_location , add , multiply ]) result = fc . get_function ( prompt ) Online Agent prompt = "Tell me a bit about this website: https://toscrape.com/?" llm = OllamaChat ( model = "llama3:instruct" ) searcher = OnlineAgent ( llm ) resp = searcher . search ( prompt ) #output: Based on information from the internet, it appears that https://toscrape.com/ is a website dedicated to web scraping. # It provides a sandbox environment for beginners and developers to learn and validate their web scraping technologies... PDF Reader llm = OllamaChat ( model = "llama3:instruct" ) files = [ "../FileOne.pdf" , "../FileTwo.pdf" ] agent = PdfReader ( llm ) resp = agent . ask ( "Summarize these documents for me" , files ) Data Extractor llm = OllamaChat ( model = "llama3:instruct" ) info = read_pdf ( "../Example.pdf" ) de = DataExtractor ( llm , reply_as_json = True ) resp = de . ask ( info , [ "name" , "email" , "phone" , "address" ]) #output: {'Name': 'Frodo Baggins', 'Email': 'frodo@gmail.com', 'Phone': '555-555-5555', 'Address': 'Bag-End, Hobbiton, The Shire'} Object Detector llm = OllamaChat ( model = "llava:7b" ) detector = ObjectDetectorAgent ( llm , llm ) resp = detector . detect ( images = [ "../img2.jpg" ], objects = [ "sheep" , "chicken" , "cat" , "dog" ]) #{ # "objects": [ # { "label": "Sheep", "location": "Field", "description": "White, black spots" }, # { "label": "Dog", "location": "Barn", "description": "Brown, white spots" } # ] #} See more complete examples How to setup llm-axe with your own LLM Features Local LLM internet access with Online Agent PDF Document Reader Agent Premade utility Agents for common tasks Compatible with any LLM, local or externally hosted Built-in support for Ollama Important Notes The results you get from the agents are highly dependent on the capability of your LLM. An inadequate LLM will not be able to provide results that are usable with llm-axe Testing in development was done using llama3 8b:instruct 4 bit quant
