multi agent docs + playground code quality

example.py

@@ -6,7 +6,6 @@ from dotenv import load_dotenv
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 
-<<<<<<< HEAD
 # Load the environment variables
 load_dotenv()
 
@@ -19,15 +18,9 @@ llm = OpenAIChat(
     model_name="gpt-4",
     openai_api_key=api_key,
     max_tokens=1000,
-=======
-# Initialize the language model
-llm = OpenAIChat(
-    temperature=0.5,
->>>>>>> 4ae59df8 (tools fix, parse docs, inject tools docs into prompts, and attempt to execute tools, display markdown)
 )
 
 ## Initialize the workflow
-<<<<<<< HEAD
 agent = Agent(
     llm=llm,
     max_loops=1,
@@ -38,10 +31,3 @@ agent = Agent(
 # Run the workflow on a task
 out = agent.run("Generate a 10,000 word blog on health and wellness.")
 print(out)
-=======
-flow = Flow(llm=llm, max_loops=1, dashboard=True)
-
-# Run the workflow on a task
-out = flow.run("Generate a 10,000 word blog on health and wellness.")
-
->>>>>>> 4ae59df8 (tools fix, parse docs, inject tools docs into prompts, and attempt to execute tools, display markdown)

multi_agent_debate.py

@@ -0,0 +1,31 @@
+import os
+
+from dotenv import load_dotenv
+
+from swarms.models import OpenAIChat
+from swarms.structs import Flow
+from swarms.swarms.multi_agent_collab import MultiAgentCollaboration
+
+load_dotenv()
+
+api_key = os.environ.get("OPENAI_API_KEY")
+
+# Initialize the language model
+llm = OpenAIChat(
+    temperature=0.5,
+    openai_api_key=api_key,
+)
+
+
+## Initialize the workflow
+flow = Flow(llm=llm, max_loops=1, dashboard=True)
+flow2 = Flow(llm=llm, max_loops=1, dashboard=True)
+flow3 = Flow(llm=llm, max_loops=1, dashboard=True)
+
+
+swarm = MultiAgentCollaboration(
+    agents=[flow, flow2, flow3],
+    max_iters=4,
+)
+
+swarm.run("Generate a 10,000 word blog on health and wellness.")

playground/demos/autotemp/autotemp.py

@@ -1,19 +1,24 @@
 import re
 from swarms.models.openai_models import OpenAIChat
 
+
 class AutoTemp:
     """
     AutoTemp is a tool for automatically selecting the best temperature setting for a given task.
     It generates responses at different temperatures, evaluates them, and ranks them based on quality.
     """
 
-    def __init__(self, api_key, default_temp=0.0, alt_temps=None, auto_select=True, max_workers=6):
+    def __init__(
+        self, api_key, default_temp=0.0, alt_temps=None, auto_select=True, max_workers=6
+    ):
         self.api_key = api_key
         self.default_temp = default_temp
         self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
         self.auto_select = auto_select
         self.max_workers = max_workers
-        self.llm = OpenAIChat(openai_api_key=self.api_key, temperature=self.default_temp)
+        self.llm = OpenAIChat(
+            openai_api_key=self.api_key, temperature=self.default_temp
+        )
 
     def evaluate_output(self, output, temperature):
         print(f"Evaluating output at temperature {temperature}...")
@@ -34,12 +39,16 @@ class AutoTemp:
         ---
         """
         score_text = self.llm(eval_prompt, temperature=0.5)
-        score_match = re.search(r'\b\d+(\.\d)?\b', score_text)
+        score_match = re.search(r"\b\d+(\.\d)?\b", score_text)
         return round(float(score_match.group()), 1) if score_match else 0.0
 
     def run(self, prompt, temperature_string):
         print("Starting generation process...")
-        temperature_list = [float(temp.strip()) for temp in temperature_string.split(',') if temp.strip()]
+        temperature_list = [
+            float(temp.strip())
+            for temp in temperature_string.split(",")
+            if temp.strip()
+        ]
         outputs = {}
         scores = {}
         for temp in temperature_list:

playground/demos/nutrition/nutrition.py

@@ -11,12 +11,16 @@ openai_api_key = os.getenv("OPENAI_API_KEY")
 
 # Define prompts for various tasks
 MEAL_PLAN_PROMPT = "Based on the following user preferences: dietary restrictions as vegetarian, preferred cuisines as Italian and Indian, a total caloric intake of around 2000 calories per day, and an exclusion of legumes, create a detailed weekly meal plan. Include a variety of meals for breakfast, lunch, dinner, and optional snacks."
-IMAGE_ANALYSIS_PROMPT = "Identify the items in this fridge, including their quantities and condition."
+IMAGE_ANALYSIS_PROMPT = (
+    "Identify the items in this fridge, including their quantities and condition."
+)
+
 
 # Function to encode image to base64
 def encode_image(image_path):
     with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
 
 # Initialize Language Model (LLM)
 llm = OpenAIChat(
@@ -24,12 +28,13 @@ llm = OpenAIChat(
     max_tokens=3000,
 )
 
+
 # Function to handle vision tasks
 def create_vision_agent(image_path):
     base64_image = encode_image(image_path)
     headers = {
         "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}"
+        "Authorization": f"Bearer {openai_api_key}",
     }
     payload = {
         "model": "gpt-4-vision-preview",
@@ -38,28 +43,39 @@ def create_vision_agent(image_path):
                 "role": "user",
                 "content": [
                     {"type": "text", "text": IMAGE_ANALYSIS_PROMPT},
-                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
-                ]
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
+                    },
+                ],
             }
         ],
-        "max_tokens": 300
+        "max_tokens": 300,
     }
-    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+    response = requests.post(
+        "https://api.openai.com/v1/chat/completions", headers=headers, json=payload
+    )
     return response.json()
 
+
 # Function to generate an integrated shopping list considering meal plan and fridge contents
-def generate_integrated_shopping_list(meal_plan_output, image_analysis, user_preferences):
+def generate_integrated_shopping_list(
+    meal_plan_output, image_analysis, user_preferences
+):
     # Prepare the prompt for the LLM
-    fridge_contents = image_analysis['choices'][0]['message']['content']
-    prompt = (f"Based on this meal plan: {meal_plan_output}, "
+    fridge_contents = image_analysis["choices"][0]["message"]["content"]
+    prompt = (
+        f"Based on this meal plan: {meal_plan_output}, "
         f"and the following items in the fridge: {fridge_contents}, "
         f"considering dietary preferences as vegetarian with a preference for Italian and Indian cuisines, "
-              f"generate a comprehensive shopping list that includes only the items needed.")
+        f"generate a comprehensive shopping list that includes only the items needed."
+    )
 
     # Send the prompt to the LLM and return the response
     response = llm(prompt)
     return response  # assuming the response is a string
 
+
 # Define agent for meal planning
 meal_plan_agent = Flow(
     llm=llm,
@@ -74,19 +90,19 @@ user_preferences = {
     "dietary_restrictions": "vegetarian",
     "preferred_cuisines": ["Italian", "Indian"],
     "caloric_intake": 2000,
-    "other notes": "Doesn't eat legumes"
+    "other notes": "Doesn't eat legumes",
 }
 
 # Generate Meal Plan
-meal_plan_output = meal_plan_agent.run(
-    f"Generate a meal plan: {user_preferences}"
-)
+meal_plan_output = meal_plan_agent.run(f"Generate a meal plan: {user_preferences}")
 
 # Vision Agent - Analyze an Image
 image_analysis_output = create_vision_agent("full_fridge.jpg")
 
 # Generate Integrated Shopping List
-integrated_shopping_list = generate_integrated_shopping_list(meal_plan_output, image_analysis_output, user_preferences)
+integrated_shopping_list = generate_integrated_shopping_list(
+    meal_plan_output, image_analysis_output, user_preferences
+)
 
 # Print and save the outputs
 print("Meal Plan:", meal_plan_output)

swarms/swarms/multi_agent_collab.py

@@ -56,7 +56,7 @@ class MultiAgentCollaboration:
 
     Usage:
     >>> from swarms.models import OpenAIChat
-    >>> from swarms.structs import Agent
+    >>> from swarms.structs import Flow
     >>> from swarms.swarms.multi_agent_collab import MultiAgentCollaboration
     >>>
     >>> # Initialize the language model
@@ -66,14 +66,14 @@ class MultiAgentCollaboration:
     >>>
     >>>
     >>> ## Initialize the workflow
-    >>> agent = Agent(llm=llm, max_loops=1, dashboard=True)
+    >>> flow = Flow(llm=llm, max_loops=1, dashboard=True)
     >>>
     >>> # Run the workflow on a task
-    >>> out = agent.run("Generate a 10,000 word blog on health and wellness.")
+    >>> out = flow.run("Generate a 10,000 word blog on health and wellness.")
     >>>
     >>> # Initialize the multi-agent collaboration
     >>> swarm = MultiAgentCollaboration(
-    >>>     agents=[agent],
+    >>>     agents=[flow],
     >>>     max_iters=4,
     >>> )
     >>>
@@ -87,7 +87,7 @@ class MultiAgentCollaboration:
 
     def __init__(
         self,
-        agents: List[Agent],
+        agents: List[Flow],
         selection_function: callable = None,
         max_iters: int = 10,
         autosave: bool = True,
@@ -200,15 +200,11 @@ class MultiAgentCollaboration:
             print("\n")
             n += 1
 
-    def select_next_speaker_roundtable(
-        self, step: int, agents: List[Agent]
-    ) -> int:
+    def select_next_speaker_roundtable(self, step: int, agents: List[Flow]) -> int:
         """Selects the next speaker."""
         return step % len(agents)
 
-    def select_next_speaker_director(
-        step: int, agents: List[Agent], director
-    ) -> int:
+    def select_next_speaker_director(step: int, agents: List[Flow], director) -> int:
         # if the step if even => director
         # => director selects next speaker
         if step % 2 == 1:
@@ -262,9 +258,7 @@ class MultiAgentCollaboration:
         for _ in range(self.max_iters):
             for agent in self.agents:
                 result = agent.run(conversation)
-                self.results.append(
-                    {"agent": agent, "response": result}
-                )
+                self.results.append({"agent": agent, "response": result})
                 conversation += result
 
                 if self.autosave: