multi modal auto agent + removed workflow.py

mkdocs.yml

@@ -114,6 +114,11 @@ nav:
     - Worker: 
       - Basic: "examples/worker.md"
       - StackedWorker: "examples/stacked_worker.md"
+- Applications:
+    - CustomerSupport: 
+      - Overview: "applications/customer_support.md"
+    - Marketing: 
+      - Overview: "applications/marketing_agencies.md" 
 - Corporate:
     - FAQ: "faq.md"
     - Purpose: "purpose.md"
@@ -128,9 +133,4 @@ nav:
     - Demos: "demos.md"
     - Architecture: "architecture.md"
     - Checklist: "checklist.md"
-    - Hiring: "hiring.md"
-- Applications:
-    - CustomerSupport: 
-      - Overview: "applications/customer_support.md"
-    - Marketing: 
-      - Overview: "applications/marketing_agencies.md" 
+    - Hiring: "hiring.md"

multi_modal_auto_agent.py

@@ -0,0 +1,30 @@
+from swarms.structs import Flow
+from swarms.models import Idefics
+
+# Multi Modality Auto Agent
+llm = Idefics(max_length=2000)
+
+task = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+
+## Initialize the workflow
+flow = Flow(
+    llm=llm,
+    max_loops=2,
+    dashboard=True,
+    # stopping_condition=None,  # You can define a stopping condition as needed.
+    # loop_interval=1,
+    # retry_attempts=3,
+    # retry_interval=1,
+    # interactive=False,  # Set to 'True' for interactive mode.
+    # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
+)
+
+# out = flow.load_state("flow_state.json")
+# temp = flow.dynamic_temperature()
+# filter = flow.add_response_filter("Trump")
+out = flow.run(task)
+# out = flow.validate_response(out)
+# out = flow.analyze_feedback(out)
+# out = flow.print_history_and_memory()
+# # out = flow.save_state("flow_state.json")
+# print(out)

swarms/structs/base.py

@@ -0,0 +1,5 @@
+"""
+Base Structure for all Swarm Structures
+    
+
+"""

swarms/structs/flow.py

@@ -111,10 +111,10 @@ class Flow:
         interactive: bool = False,
         dashboard: bool = False,
         name: str = "Flow agent",
-        system_message: str = FLOW_SYSTEM_PROMPT,
+        system_prompt: str = FLOW_SYSTEM_PROMPT,
         # tools: List[BaseTool] = None,
         dynamic_temperature: bool = False,
-        saved_state: Optional[str] = None,
+        saved_state_path: Optional[str] = "flow_state.json",
         autosave: bool = False,
         **kwargs: Any,
     ):
@@ -133,9 +133,9 @@ class Flow:
         self.dashboard = dashboard
         self.dynamic_temperature = dynamic_temperature
         # self.tools = tools
-        self.system_message = system_message
+        self.system_prompt = system_prompt
         self.name = name
-        self.saved_state = saved_state
+        self.saved_state_path = saved_state_path
         self.autosave = autosave
         self.response_filters = []
 
@@ -206,7 +206,7 @@ class Flow:
 
                 Flow Configuration:
                     Name: {self.name}
-                    System Prompt: {self.system_message}
+                    System Prompt: {self.system_prompt}
                     Task: {task}
                     Max Loops: {self.max_loops}
                     Stopping Condition: {self.stopping_condition}
@@ -317,8 +317,10 @@ class Flow:
             time.sleep(self.loop_interval)
         self.memory.append(history)
 
-        # if self.autosave:
-        #     self.save_state("flow_state.json")
+        if self.autosave:
+            save_path = self.saved_state_path or "flow_state.json"
+            print(colored(f"Autosaving flow state to {save_path}", "green"))
+            self.save_state(save_path)
 
         return response  # , history
 
@@ -422,7 +424,7 @@ class Flow:
         Returns:
             str: The agent history prompt
         """
-        system_prompt = system_prompt or self.system_message
+        system_prompt = system_prompt or self.system_prompt
         agent_history_prompt = f"""
             SYSTEM_PROMPT: {system_prompt}
 
@@ -736,7 +738,7 @@ class Flow:
         """
         prompt = f"""
 
-        SYSTEM_PROMPT: {self.system_message}
+        SYSTEM_PROMPT: {self.system_prompt}
 
         History: {history}
 
@@ -745,6 +747,6 @@ class Flow:
         response = self.llm(prompt, **kwargs)
         return {"role": self.name, "content": response}
 
-    def update_system_message(self, system_message: str):
+    def update_system_prompt(self, system_prompt: str):
         """Upddate the system message"""
-        self.system_message = system_message
+        self.system_prompt = system_prompt

swarms/swarms/base.py

@@ -78,8 +78,6 @@ class AbstractSwarm(ABC):
         Scale down the number of workers
 
 
-
-
     """
 
     # TODO: Pass in abstract LLM class that can utilize Hf or Anthropic models, Move away from OPENAI

workflow.py

@@ -1,11 +0,0 @@
-from swarms.models import OpenAIChat
-from swarms.structs import Workflow
-
-
-llm = OpenAIChat(openai_api_key="")
-
-workflow = Workflow(llm)
-
-workflow.add("What's the weather in miami")
-
-workflow.run()