Intelli 0.4.2

New Features 🌟

• Update the agent to support the Llama 3.1 offline model.
• Add offline model capability to the chatbot.
• Unify Keras loader under a dedicated wrapper KerasWrapper.

Using the New Features 💻

• Gemma 2 chatbot with RAG.
• Llama 3 chatbot with RAG.
• Mistral chatbot with RAG.

Intelli v0.2.3

New Features 🌟

• Support for ANTHROPIC Models: Our chatbot integration now supports advanced ANTHROPIC models, including those with large context windows.
• Chatbot Provider Enumeration: The selection of AI providers has been simplified through the use of enumerators.
• Minor Bug Fixes: Adjust the parameter order for the controllers.

Using the New Features 💻

• ChatProvider enum simplifies the selecting providers.

from intelli.function.chatbot import ChatProvider

# check available chatbot providers
for provider in ChatProvider:
    print(provider.name)

• Check the chatbot documentation to use claude-3 model.

Contributors

• @gutyoh
• @Barqawiz

Intelli V0.2.0

New Features 🌟

• Add Keras Agents: Intelli now supports the loading of offline open-source models using KerasAgent.
• Supported Offline Models: gemma_2b_en, gemma_instruct_2b_en, gemma_7b_en, gemma_instruct_7b_en, mistral_7b_en, mistral_instruct_7b_en.

Using the New Features 💻

To use the new Keras Agent, instantiate the KerasAgent class with the appropriate parameters:

from intelli.flow.agents.kagent import KerasAgent

# Setting up a Gemma agent
gemma_params = {
    "model": "gemma_instruct_2b_en",
    "max_length": 200
}
gemma_agent = KerasAgent(agent_type="text",
                         mission="writing assistant",
                         model_params=gemma_params,
                         log=True)

Prepare the tasks with the user instructions:

from intelli.flow.input.task_input import TextTaskInput
from intelli.flow.tasks.task import Task

# Sample task to write blog post
task1 = Task(
    TextTaskInput("write blog post about electric cars"), gemma_agent, log=True
)

# Create more tasks as needed

Execute tasks using SequenceFlow. The example below shows a single task, but you can include additional tasks for text, image, or vision:

from intelli.flow.sequence_flow import SequenceFlow

# Start SequenceFlow
flow = SequenceFlow([task1], log=True)
final_result = flow.start()

Fore more details check the docs.

Intelli V0.1.5

What's New 🌟

• Add a function to generate a visual image for the flow.

flow.generate_graph_img()

• Add remote speech model, allowing to generate synthesised speeches from openai or google models.

• Fix a minor bug in the semantic search functionality.

Intelli V0.0.8

What's New 🌟

• Add vision controller, to switch between openai and gemini image to text engine.
• Update the flow to support vision controller, allowing to build advanced use cases like explanation of flowchart for a coder agent.
• Add cohere chatbot model.

Contributors

@basilmusa @Barqawiz

Intelli V0.0.6

What's New 🌟

Simplified Chatbot Creation

Create chatbots capable of utilizing various AI backends without altering your core codebase. This feature supports OpenAI, Mistral, and Gemini, simplifying the process of integrating intelligent conversational agents into your applications.

Enhanced Document Interaction

Enable your applications to interact with documents through chat.

Streamlined AI Flows

Create and manage flows of tasks executed by different AI models, enhancing automation and efficiency.

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