Awesome

Intelli

Create chatbots and AI agent work flows. It allows to connect your data with multiple AI models like OpenAI, Gemini, and Mistral through a unified access layer.

Install

pip install intelli

For detailed usage instructions, refer to the documentation.

Code Examples

Create Chatbot

Switch between multiple chatbot providers without changing your code.

from intelli.function.chatbot import Chatbot, ChatProvider
from intelli.model.input.chatbot_input import ChatModelInput

def call_chatbot(provider, model=None):
    # prepare common input 
    input = ChatModelInput("You are a helpful assistant.", model)
    input.add_user_message("What is the capital of France?")

    # creating chatbot instance
    openai_bot = Chatbot(YOUR_API_KEY, provider)
    response = openai_bot.chat(input)

    return response

# call chatGPT
call_chatbot(ChatProvider.OPENAI, "gpt-4")

# call mistralai
call_chatbot(ChatProvider.MISTRAL, "mistral-medium")

# call claude3
call_chatbot(ChatProvider.ANTHROPIC, "claude-3-sonnet-20240229")

# call google gemini
call_chatbot(ChatProvider.GEMINI)

Create AI Flows

You can create a flow of tasks executed by different AI models. Here's an example of creating a blog post flow:

from intelli.flow import Agent, Task, SequenceFlow, TextTaskInput, TextProcessor


# define agents
blog_agent = Agent(agent_type='text', provider='openai', mission='write blog posts', model_params={'key': YOUR_OPENAI_API_KEY, 'model': 'gpt-4'})
copy_agent = Agent(agent_type='text', provider='gemini', mission='generate description', model_params={'key': YOUR_GEMINI_API_KEY, 'model': 'gemini'})
artist_agent = Agent(agent_type='image', provider='stability', mission='generate image', model_params={'key': YOUR_STABILITY_API_KEY})

# define tasks
task1 = Task(TextTaskInput('blog post about electric cars'), blog_agent, log=True)
task2 = Task(TextTaskInput('Generate short image description for image model'), copy_agent, pre_process=TextProcessor.text_head, log=True)
task3 = Task(TextTaskInput('Generate cartoon style image'), artist_agent, log=True)

# start sequence flow
flow = SequenceFlow([task1, task2, task3], log=True)
final_result = flow.start()

Graph-Based AI Flows

To build async flows with multiple paths, refer to the flow tutorial.

Connect Your Docs With Chatbot

IntelliPy allows you to chat with your docs using multiple LLMs. To connect your data, visit the IntelliNode App, start a project using the Document option, upload your documents or images, and copy the generated One Key. This key will be used to connect the chatbot to your uploaded data.

# creating chatbot with the intellinode one key
bot = Chatbot(YOUR_OPENAI_API_KEY, "openai", {"one_key": YOUR_ONE_KEY})

input = ChatModelInput("You are a helpful assistant.", "gpt-3.5-turbo")
input.add_user_message("What is the procedure for requesting a refund according to the user manual?")
# optional to returne the searched file name
input.attach_reference = True

response = bot.chat(input)

Generate Images

Use the image controller to generate arts from multiple models with minimum code change:

from intelli.controller.remote_image_model import RemoteImageModel
from intelli.model.input.image_input import ImageModelInput

# model details - change only two words to switch
provider = "openai"
model_name = "dall-e-3"

# prepare the input details
prompts = "cartoonishly-styled solitary snake logo, looping elegantly to form both the body of the python and an abstract play on data nodes."
image_input = ImageModelInput(prompt=prompt, width=1024, height=1024, model=model_name)

# call the model openai/stability
wrapper = RemoteImageModel(your_api_key, provider)
results = wrapper.generate_images(image_input)

Keras Agent

Load gemma or mistral models offline using keras agent, check the docs.

Repository Setup

1. Install the requirements.

pip install -r requirements.txt

2. Rename .example.env to .env and fill the keys.

3. Run the test cases, examples below.

# images
python3 -m unittest intelli.test.integration.test_remote_image_model

# chatbot
python3 -m unittest intelli.test.integration.test_chatbot

# mistral
python3 -m unittest intelli.test.integration.test_mistralai_wrapper

# ai flows
python3 -m unittest intelli.test.integration.test_flow_sequence

Pillars

• The wrapper layer provides low-level access to the latest AI models.
• The controller layer offers a unified input to any AI model by handling the differences.
• The function layer provides abstract functionality that extends based on the app's use cases.
• Flows: create a flow of ai agents working toward user tasks.