omnimodal agent

README.md

@@ -39,7 +39,8 @@ We're hiring: Engineers, Researchers, Interns And, salesprofessionals to work on
 ## Usage
 We have a small gallery of examples to run here, [for more check out the docs to build your own agent and or swarms!](https://docs.apac.ai)
 
-### ``
+### `MultiAgentDebate`
+
 - `MultiAgentDebate` is a simple class that enables multi agent collaboration.
 
 ```python

docs/swarms/agents/omni_agent.md

@@ -0,0 +1,61 @@
+# `OmniModalAgent` Documentation
+
+## Overview & Architectural Analysis
+The `OmniModalAgent` class is at the core of an architecture designed to facilitate dynamic interactions using various tools, through a seamless integration of planning, task execution, and response generation mechanisms. It encompasses multiple modalities including natural language processing, image processing, and more, aiming to provide comprehensive and intelligent responses.
+
+### Architectural Components:
+1. **LLM (Language Model)**: It acts as the foundation, underpinning the understanding and generation of language-based interactions.
+2. **Chat Planner**: This component drafts a blueprint for the steps necessary based on the user's input.
+3. **Task Executor**: As the name suggests, it's responsible for executing the formulated tasks.
+4. **Tools**: A collection of tools and utilities used to process different types of tasks. They span across areas like image captioning, translation, and more.
+
+## Structure & Organization
+
+### Table of Contents:
+1. Introduction
+2. Architectural Analysis
+3. Methods
+    - Initialization (`__init__`)
+    - Agent Runner (`run`)
+4. Usage Examples
+5. Error Messages & Exception Handling
+6. Summary
+
+### Methods
+
+#### Initialization (`__init__`):
+This method initializes the agent with a given language model and loads a plethora of tools.
+Parameters:
+- **llm (BaseLanguageModel)**: The language model for the agent.
+
+During initialization, various tools like "document-question-answering", "image-captioning", and more are loaded.
+
+#### Agent Runner (`run`):
+This method represents the primary operation of the OmniModalAgent. It takes an input, devises a plan using the chat planner, executes the plan with the task executor, and finally, the response generator crafts a response based on the tasks executed.
+
+Parameters:
+- **input (str)**: The input string provided by the user.
+
+Returns:
+- **response (str)**: The generated response after executing the plan.
+
+## Examples & Use Cases
+
+### Usage:
+```python
+from swarms import OmniModalAgent, OpenAIChat
+
+llm = OpenAIChat()
+agent = OmniModalAgent(llm)
+response = agent.run("Hello, how are you? Create an image of how you are doing!")
+print(response)
+```
+This example showcases the instantiation of the OmniModalAgent with a language model and then running the agent with a sample input.
+
+## Error Messages & Exception Handling
+Currently, the provided code does not specify particular errors or exceptions. However, future iterations might include error handling mechanisms to cater to issues like tool loading failures, task execution errors, etc.
+
+## Summary
+The `OmniModalAgent` is a robust framework designed to assimilate multiple tools and processes into a singular architecture. It aids in understanding, planning, executing, and responding to user inputs in a comprehensive manner. Developers aiming to integrate advanced interactions spanning multiple domains will find this class invaluable.
+
+For further details on the internal tools and modules like `BaseLanguageModel`, `TaskExecutor`, etc., refer to their respective documentation.

mkdocs.yml

@@ -85,6 +85,7 @@ nav:
       - Overview: "swarms/models/index.md"
       - HuggingFaceLLM: "swarms/models/hf.md"
       - Anthropic: "swarms/models/anthropic.md"
+    - OmniModalAgent: "swarms/agents/omni_agent.md"
 - Examples:
     - Overview: "examples/index.md"
     - Agents:

swarms/agents/omni_modal_agent.py

@@ -13,34 +13,6 @@ from langchain_experimental.autonomous_agents.hugginggpt.task_planner import (
 )
 from transformers import load_tool
 
-# from swarms.agents.multi_modal_workers.omni_agent.omni_chat import chat_huggingface
-# class OmniModalAgent:
-#     def __init__(
-#         self, 
-#         api_key, 
-#         api_endpoint, 
-#         api_type
-#     ):
-#         self.api_key = api_key
-#         self.api_endpoint = api_endpoint
-#         self.api_type = api_type
-
-#     def chat(
-#         self, 
-#         data
-#     ):
-#         """Chat with omni-modality model that uses huggingface to query for a specific model at run time. Translate text to speech, create images and more"""
-#         messages = data.get("messages")
-#         api_key = data.get("api_key", self.api_key)
-#         api_endpoint = data.get("api_endpoint", self.api_endpoint)
-#         api_type = data.get("api_type", self.api_type)
-
-#         if not(api_key and api_type and api_endpoint):
-# #             raise ValueError("Please provide api_key, api_type, and api_endpoint")
-        
-# #         response = chat_huggingface(messages, api_key, api_type, api_endpoint)
-# #         return response
-
 class Step:
     def __init__(
         self,