chore: sync

playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent.py

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 from swarms.structs import Agent
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 
@@ -15,3 +16,35 @@ agent = Agent(
 )
 
 agent.run(task=task, img=img)
+=======
+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)
+>>>>>>> fa52e094 (CLEAN UP: Flow and demo layouts)

swarms/structs/non_linear_workflow.py

@@ -0,0 +1,79 @@
+from swarms.models import OpenAIChat
+from swarms.structs.flow import Flow
+
+import concurrent.futures
+from typing import Callable, List, Dict, Any, Sequence
+
+
+class Task:
+    def __init__(self, id: str, task: str, flows: Sequence[Flow], dependencies: List[str] = []):
+        self.id = id
+        self.task = task
+        self.flows = flows
+        self.dependencies = dependencies
+        self.results = []
+
+    def execute(self, parent_results: Dict[str, Any]):
+        args = [parent_results[dep] for dep in self.dependencies]
+        for flow in self.flows:
+            result = flow.run(self.task, *args)
+            self.results.append(result)
+            args = [result]  # The output of one flow becomes the input to the next
+
+
+class Workflow:
+    def __init__(self):
+        self.tasks: Dict[str, Task] = {}
+        self.executor = concurrent.futures.ThreadPoolExecutor()
+
+    def add_task(self, task: Task):
+        self.tasks[task.id] = task
+
+    def run(self):
+        completed_tasks = set()
+        while len(completed_tasks) < len(self.tasks):
+            futures = []
+            for task in self.tasks.values():
+                if task.id not in completed_tasks and all(
+                    dep in completed_tasks for dep in task.dependencies
+                ):
+                    future = self.executor.submit(
+                        task.execute,
+                        {dep: self.tasks[dep].results for dep in task.dependencies},
+                    )
+                    futures.append((future, task.id))
+
+            for future, task_id in futures:
+                future.result()  # Wait for task completion
+                completed_tasks.add(task_id)
+
+    def get_results(self):
+        return {task_id: task.results for task_id, task in self.tasks.items()}
+
+
+# create flows
+llm = OpenAIChat(openai_api_key="sk-")
+
+flow1 = Flow(llm, max_loops=1)
+flow2 = Flow(llm, max_loops=1)
+flow3 = Flow(llm, max_loops=1)
+flow4 = Flow(llm, max_loops=1)
+
+
+# Create tasks with their respective Flows and task strings
+task1 = Task("task1", "Generate a summary on Quantum field theory", [flow1])
+task2 = Task("task2", "Elaborate on the summary of topic X", [flow2, flow3], dependencies=["task1"])
+task3 = Task("task3", "Generate conclusions for topic X", [flow4], dependencies=["task1"])
+
+# Create a workflow and add tasks
+workflow = Workflow()
+workflow.add_task(task1)
+workflow.add_task(task2)
+workflow.add_task(task3)
+
+# Run the workflow
+workflow.run()
+
+# Get results
+results = workflow.get_results()
+print(results)