Merge branch 'kyegomez:master' into master

multi_modal_auto_agent.py

@@ -26,7 +26,7 @@ agent = Agent(
     max_loops="auto",
     autosave=True,
     dashboard=True,
-    multi_modal=True
+    multi_modal=True,
 )
 
 # Run the workflow on a task

swarms/models/__init__.py

@@ -17,7 +17,9 @@ from swarms.models.wizard_storytelling import (
 from swarms.models.mpt import MPT7B  # noqa: E402
 
 # MultiModal Models
-from swarms.models.base_multimodal_model import BaseMultiModalModel # noqa: E402
+from swarms.models.base_multimodal_model import (
+    BaseMultiModalModel,
+)  # noqa: E402
 from swarms.models.idefics import Idefics  # noqa: E402
 from swarms.models.vilt import Vilt  # noqa: E402
 from swarms.models.nougat import Nougat  # noqa: E402

swarms/models/base_multimodal_model.py

@@ -1,8 +1,8 @@
-from abc import abstractmethod
 import asyncio
 import base64
 import concurrent.futures
 import time
+from abc import abstractmethod
 from concurrent.futures import ThreadPoolExecutor
 from io import BytesIO
 from typing import List, Optional, Tuple

swarms/prompts/agent_system_prompts.py

@@ -65,3 +65,16 @@ def agent_system_prompt_2(name: str):
     If the user asks you to write code return the response in markdown inside of 6 backticks to render it as code. Write the code in the language specified by the user in the prompt.
     """
     return AGENT_SYSTEM_PROMPT_2
+
+
+def agent_system_prompt_3(agent_name: str = None, sop: str = None):
+    AGENT_SYSTEM_PROMPT_3 = f"""
+    You are {agent_name}, an fully autonomous agent LLM backed agent.
+    for a specific use case. Agent's use custom instructions, capabilities, 
+    and data to optimize LLMs for a more narrow set of tasks. You yourself are an agent created by a user, 
+    and your name is {agent_name}.
+    
+    Here are instructions from the user outlining your goals and how you should respond:
+    {sop}
+    """
+    return AGENT_SYSTEM_PROMPT_3