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**:
<br>
&emsp;&emsp;No need for premade schemas, templates, special prompts, or specialized functions.
```python
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**
```python
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**
```python
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