[CODE QUALITY]

playground/agents/mm_agent_example.py

@@ -4,14 +4,16 @@ load_dict = {"ImageCaptioning": "cuda"}
 
 node = MultiModalAgent(load_dict)
 
-text = node.run_text("What is your name? Generate a picture of yourself")
+text = node.run_text(
+    "What is your name? Generate a picture of yourself"
+)
 
 img = node.run_img("/image1", "What is this image about?")
 
 chat = node.chat(
     (
-        "What is your name? Generate a picture of yourself. What is this image"
-        " about?"
+        "What is your name? Generate a picture of yourself. What is"
+        " this image about?"
     ),
     streaming=True,
 )

playground/demos/accountant_team/account_team2.py

@@ -53,7 +53,8 @@ decision_making_support_agent = Agent(
 pdf_path = "bankstatement.pdf"
 fraud_detection_instructions = "Detect fraud in the document"
 summary_agent_instructions = (
-    "Generate an actionable summary of the document with action steps to take"
+    "Generate an actionable summary of the document with action steps"
+    " to take"
 )
 decision_making_support_agent_instructions = (
     "Provide decision making support to the business owner:"
@@ -77,5 +78,6 @@ summary_agent_output = summary_generator_agent.run(
 
 # Provide decision making support to the accountant
 decision_making_support_agent_output = decision_making_support_agent.run(
-    f"{decision_making_support_agent_instructions}: {summary_agent_output}"
+    f"{decision_making_support_agent_instructions}:"
+    f" {summary_agent_output}"
 )

playground/demos/accountant_team/accountant_team.py

@@ -81,7 +81,9 @@ class AccountantSwarms:
         super().__init__()
         self.pdf_path = pdf_path
         self.list_pdfs = list_pdfs
-        self.fraud_detection_instructions = fraud_detection_instructions
+        self.fraud_detection_instructions = (
+            fraud_detection_instructions
+        )
         self.summary_agent_instructions = summary_agent_instructions
         self.decision_making_support_agent_instructions = (
             decision_making_support_agent_instructions
@@ -98,7 +100,8 @@ class AccountantSwarms:
 
         # Generate an actionable summary of the document
         summary_agent_output = summary_generator_agent.run(
-            f"{self.summary_agent_instructions}: {fraud_detection_agent_output}"
+            f"{self.summary_agent_instructions}:"
+            f" {fraud_detection_agent_output}"
         )
 
         # Provide decision making support to the accountant
@@ -113,7 +116,9 @@ class AccountantSwarms:
 swarm = AccountantSwarms(
     pdf_path="tesla.pdf",
     fraud_detection_instructions="Detect fraud in the document",
-    summary_agent_instructions="Generate an actionable summary of the document",
+    summary_agent_instructions=(
+        "Generate an actionable summary of the document"
+    ),
     decision_making_support_agent_instructions=(
         "Provide decision making support to the business owner:"
     ),

playground/demos/ad_gen/ad_gen.py

@@ -30,15 +30,34 @@ class ProductAdConceptGenerator:
             "in a luxurious setting", "in a playful and colorful background", "in an ice cave setting",
             "in a serene and calm landscape"
         ]
+<<<<<<< HEAD
+=======
+        self.contexts = [
+            "high realism product ad (extremely creative)"
+        ]
+>>>>>>> 831147e ([CODE QUALITY])
 
     def generate_concept(self):
         theme = random.choice(self.themes)
         context = random.choice(self.contexts)
+<<<<<<< HEAD
         return f"An ad for {self.product_name} that embodies a {theme} theme {context}"
 
 # User input
 product_name = input("Enter a product name for ad creation (e.g., 'PS5', 'AirPods', 'Kirkland Vodka'): ")
 social_media_platform = input("Enter a social media platform (e.g., 'Facebook', 'Twitter', 'Instagram'): ")
+=======
+        return (
+            f"{theme} inside a {style} {self.product_name}, {context}"
+        )
+
+
+# User input
+product_name = input(
+    "Enter a product name for ad creation (e.g., 'PS5', 'AirPods',"
+    " 'Kirkland Vodka'): "
+)
+>>>>>>> 831147e ([CODE QUALITY])
 
 # Generate creative concept
 concept_generator = ProductAdConceptGenerator(product_name)
@@ -53,6 +72,16 @@ ad_copy_prompt = f"Write a compelling {social_media_platform} ad copy for a prod
 ad_copy = ad_copy_agent.run(task=ad_copy_prompt)
 
 # Output the results
+<<<<<<< HEAD
 print("Creative Concept:", creative_concept)
 print("Image Path:", image_paths[0] if image_paths else "No image generated")
 print("Ad Copy:", ad_copy)
+=======
+print("Creative Concept:", concept_result)
+print("Design Ideas:", design_result)
+print("Ad Copy:", copywriting_result)
+print(
+    "Image Path:",
+    image_paths[0] if image_paths else "No image generated",
+)
+>>>>>>> 831147e ([CODE QUALITY])

playground/demos/ai_research_team/main.py

@@ -48,7 +48,9 @@ paper_implementor_agent = Agent(
 
 paper = pdf_to_text(PDF_PATH)
 algorithmic_psuedocode_agent = paper_summarizer_agent.run(
-    "Focus on creating the algorithmic pseudocode for the novel method in this"
-    f" paper: {paper}"
+    "Focus on creating the algorithmic pseudocode for the novel"
+    f" method in this paper: {paper}"
+)
+pytorch_code = paper_implementor_agent.run(
+    algorithmic_psuedocode_agent
 )
-pytorch_code = paper_implementor_agent.run(algorithmic_psuedocode_agent)

playground/demos/assembly/assembly.py

@@ -7,10 +7,10 @@ from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
 llm = GPT4VisionAPI()
 
 task = (
-    "Analyze this image of an assembly line and identify any issues such as"
-    " misaligned parts, defects, or deviations from the standard assembly"
-    " process. IF there is anything unsafe in the image, explain why it is"
-    " unsafe and how it could be improved."
+    "Analyze this image of an assembly line and identify any issues"
+    " such as misaligned parts, defects, or deviations from the"
+    " standard assembly process. IF there is anything unsafe in the"
+    " image, explain why it is unsafe and how it could be improved."
 )
 img = "assembly_line.jpg"
 

playground/demos/autobloggen.py

@@ -9,7 +9,8 @@ from swarms.prompts.autobloggen import (
 
 # Prompts
 topic_selection_task = (
-    "Generate 10 topics on gaining mental clarity using ancient practices"
+    "Generate 10 topics on gaining mental clarity using ancient"
+    " practices"
 )
 
 
@@ -54,7 +55,9 @@ class AutoBlogGenSwarm:
     ):
         self.llm = llm()
         self.topic_selection_task = topic_selection_task
-        self.topic_selection_agent_prompt = topic_selection_agent_prompt
+        self.topic_selection_agent_prompt = (
+            topic_selection_agent_prompt
+        )
         self.objective = objective
         self.iterations = iterations
         self.max_retries = max_retries
@@ -90,7 +93,9 @@ class AutoBlogGenSwarm:
 
     def step(self):
         """Steps through the task"""
-        topic_selection_agent = self.llm(self.topic_selection_agent_prompt)
+        topic_selection_agent = self.llm(
+            self.topic_selection_agent_prompt
+        )
         topic_selection_agent = self.print_beautifully(
             "Topic Selection Agent", topic_selection_agent
         )
@@ -100,7 +105,9 @@ class AutoBlogGenSwarm:
 
         # Agent that reviews the draft
         review_agent = self.llm(self.get_review_prompt(draft_blog))
-        review_agent = self.print_beautifully("Review Agent", review_agent)
+        review_agent = self.print_beautifully(
+            "Review Agent", review_agent
+        )
 
         # Agent that publishes on social media
         distribution_agent = self.llm(
@@ -119,7 +126,11 @@ class AutoBlogGenSwarm:
             except Exception as error:
                 print(
                     colored(
-                        f"Error while running AutoBlogGenSwarm {error}", "red"
+                        (
+                            "Error while running AutoBlogGenSwarm"
+                            f" {error}"
+                        ),
+                        "red",
                     )
                 )
                 if attempt == self.retry_attempts - 1:

playground/demos/autotemp/autotemp.py

@@ -47,7 +47,11 @@ class AutoTemp:
         """
         score_text = self.llm(eval_prompt, temperature=0.5)
         score_match = re.search(r"\b\d+(\.\d)?\b", score_text)
-        return round(float(score_match.group()), 1) if score_match else 0.0
+        return (
+            round(float(score_match.group()), 1)
+            if score_match
+            else 0.0
+        )
 
     def run(self, prompt, temperature_string):
         print("Starting generation process...")

playground/demos/autotemp/blog_gen.py

@@ -12,7 +12,9 @@ class BlogGen:
         blog_topic,
         temperature_range: str = "0.4,0.6,0.8,1.0,1.2",
     ):  # Add blog_topic as an argument
-        self.openai_chat = OpenAIChat(openai_api_key=api_key, temperature=0.8)
+        self.openai_chat = OpenAIChat(
+            openai_api_key=api_key, temperature=0.8
+        )
         self.auto_temp = AutoTemp(api_key)
         self.temperature_range = temperature_range
         self.workflow = SequentialWorkflow(max_loops=5)
@@ -52,11 +54,15 @@ class BlogGen:
             )
 
             chosen_topic = topic_output.split("\n")[0]
-            print(colored("Selected topic: " + chosen_topic, "yellow"))
+            print(
+                colored("Selected topic: " + chosen_topic, "yellow")
+            )
 
             # Initial draft generation with AutoTemp
-            initial_draft_prompt = self.DRAFT_WRITER_SYSTEM_PROMPT.replace(
-                "{{CHOSEN_TOPIC}}", chosen_topic
+            initial_draft_prompt = (
+                self.DRAFT_WRITER_SYSTEM_PROMPT.replace(
+                    "{{CHOSEN_TOPIC}}", chosen_topic
+                )
             )
             auto_temp_output = self.auto_temp.run(
                 initial_draft_prompt, self.temperature_range
@@ -89,13 +95,17 @@ class BlogGen:
             )
 
             # Distribution preparation using OpenAIChat
-            distribution_prompt = self.DISTRIBUTION_AGENT_SYSTEM_PROMPT.replace(
-                "{{ARTICLE_TOPIC}}", chosen_topic
+            distribution_prompt = (
+                self.DISTRIBUTION_AGENT_SYSTEM_PROMPT.replace(
+                    "{{ARTICLE_TOPIC}}", chosen_topic
+                )
             )
             distribution_result = self.openai_chat.generate(
                 [distribution_prompt]
             )
-            distribution_output = distribution_result.generations[0][0].text
+            distribution_output = distribution_result.generations[0][
+                0
+            ].text
             print(
                 colored(
                     (

playground/demos/autotemp/blog_gen_example.py

@@ -5,7 +5,9 @@ from blog_gen import BlogGen
 def main():
     api_key = os.getenv("OPENAI_API_KEY")
     if not api_key:
-        raise ValueError("OPENAI_API_KEY environment variable not set.")
+        raise ValueError(
+            "OPENAI_API_KEY environment variable not set."
+        )
 
     blog_topic = input("Enter the topic for the blog generation: ")
 

playground/demos/developer_swarm/main.py

@@ -37,12 +37,18 @@ llm = OpenAIChat(openai_api_key=api_key, max_tokens=5000)
 
 # Documentation agent
 documentation_agent = Agent(
-    llm=llm, sop=DOCUMENTATION_SOP, max_loops=1,
+    llm=llm,
+    sop=DOCUMENTATION_SOP,
+    max_loops=1,
 )
 
 
 # Tests agent
-tests_agent = Agent(llm=llm, sop=TEST_SOP, max_loops=2,)
+tests_agent = Agent(
+    llm=llm,
+    sop=TEST_SOP,
+    max_loops=2,
+)
 
 
 # Run the documentation agent
@@ -52,6 +58,6 @@ documentation = documentation_agent.run(
 
 # Run the tests agent
 tests = tests_agent.run(
-    f"Write tests for the following code:{TASK} here is the documentation:"
-    f" {documentation}"
+    f"Write tests for the following code:{TASK} here is the"
+    f" documentation: {documentation}"
 )

playground/demos/nutrition/nutrition.py

@@ -12,14 +12,15 @@ 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."
+    " 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."
+    "Identify the items in this fridge, including their quantities"
+    " and condition."
 )
 
 
@@ -74,12 +75,15 @@ 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"]
+    fridge_contents = image_analysis["choices"][0]["message"][
+        "content"
+    ]
     prompt = (
-        f"Based on this meal plan: {meal_plan_output}, and the following items"
-        f" in the fridge: {fridge_contents}, considering dietary preferences as"
-        " vegetarian with a preference for Italian and Indian cuisines,"
-        " generate a comprehensive shopping list that includes only the items"
+        f"Based on this meal plan: {meal_plan_output}, and the"
+        f" following items in the fridge: {fridge_contents},"
+        " considering dietary preferences as vegetarian with a"
+        " preference for Italian and Indian cuisines, generate a"
+        " comprehensive shopping list that includes only the items"
         " needed."
     )
 
@@ -124,6 +128,10 @@ print("Integrated Shopping List:", integrated_shopping_list)
 
 with open("nutrition_output.txt", "w") as file:
     file.write("Meal Plan:\n" + meal_plan_output + "\n\n")
-    file.write("Integrated Shopping List:\n" + integrated_shopping_list + "\n")
+    file.write(
+        "Integrated Shopping List:\n"
+        + integrated_shopping_list
+        + "\n"
+    )
 
 print("Outputs have been saved to nutrition_output.txt")

playground/demos/positive_med/positive_med.py

@@ -41,7 +41,9 @@ def get_review_prompt(article):
     return prompt
 
 
-def social_media_prompt(article: str, goal: str = "Clicks and engagement"):
+def social_media_prompt(
+    article: str, goal: str = "Clicks and engagement"
+):
     prompt = SOCIAL_MEDIA_SYSTEM_PROMPT_AGENT.replace(
         "{{ARTICLE}}", article
     ).replace("{{GOAL}}", goal)
@@ -50,11 +52,12 @@ def social_media_prompt(article: str, goal: str = "Clicks and engagement"):
 
 # Agent that generates topics
 topic_selection_task = (
-    "Generate 10 topics on gaining mental clarity using ancient practices"
+    "Generate 10 topics on gaining mental clarity using ancient"
+    " practices"
 )
 topics = llm(
-    f"Your System Instructions: {TOPIC_GENERATOR_SYSTEM_PROMPT}, Your current"
-    f" task: {topic_selection_task}"
+    f"Your System Instructions: {TOPIC_GENERATOR_SYSTEM_PROMPT}, Your"
+    f" current task: {topic_selection_task}"
 )
 
 dashboard = print(

playground/demos/swarm_of_mma_manufacturing/main.py

@@ -24,8 +24,12 @@ api_key = os.getenv("OPENAI_API_KEY")
 
 llm = GPT4VisionAPI(openai_api_key=api_key)
 
-assembly_line = "playground/demos/swarm_of_mma_manufacturing/assembly_line.jpg"
-red_robots = "playground/demos/swarm_of_mma_manufacturing/red_robots.jpg"
+assembly_line = (
+    "playground/demos/swarm_of_mma_manufacturing/assembly_line.jpg"
+)
+red_robots = (
+    "playground/demos/swarm_of_mma_manufacturing/red_robots.jpg"
+)
 robots = "playground/demos/swarm_of_mma_manufacturing/robots.jpg"
 tesla_assembly_line = (
     "playground/demos/swarm_of_mma_manufacturing/tesla_assembly.jpg"
@@ -35,29 +39,31 @@ tesla_assembly_line = (
 # Define detailed prompts for each agent
 tasks = {
     "health_safety": (
-        "Analyze the factory's working environment for health safety. Focus on"
-        " cleanliness, ventilation, spacing between workstations, and personal"
-        " protective equipment availability."
+        "Analyze the factory's working environment for health safety."
+        " Focus on cleanliness, ventilation, spacing between"
+        " workstations, and personal protective equipment"
+        " availability."
     ),
     "productivity": (
-        "Review the factory's workflow efficiency, machine utilization, and"
-        " employee engagement. Identify operational delays or bottlenecks."
+        "Review the factory's workflow efficiency, machine"
+        " utilization, and employee engagement. Identify operational"
+        " delays or bottlenecks."
     ),
     "safety": (
-        "Analyze the factory's safety measures, including fire exits, safety"
-        " signage, and emergency response equipment."
+        "Analyze the factory's safety measures, including fire exits,"
+        " safety signage, and emergency response equipment."
     ),
     "security": (
-        "Evaluate the factory's security systems, entry/exit controls, and"
-        " potential vulnerabilities."
+        "Evaluate the factory's security systems, entry/exit"
+        " controls, and potential vulnerabilities."
     ),
     "sustainability": (
-        "Inspect the factory's sustainability practices, including waste"
-        " management, energy usage, and eco-friendly processes."
+        "Inspect the factory's sustainability practices, including"
+        " waste management, energy usage, and eco-friendly processes."
     ),
     "efficiency": (
-        "Assess the manufacturing process's efficiency, considering the layout,"
-        " logistics, and automation level."
+        "Assess the manufacturing process's efficiency, considering"
+        " the layout, logistics, and automation level."
     ),
 }
 
@@ -73,7 +79,10 @@ efficiency_prompt = tasks["efficiency"]
 
 # Health security agent
 health_security_agent = Agent(
-    llm=llm, sop_list=health_safety_prompt, max_loops=2, multi_modal=True
+    llm=llm,
+    sop_list=health_safety_prompt,
+    max_loops=2,
+    multi_modal=True,
 )
 
 # Quality control agent
@@ -98,10 +107,14 @@ health_check = health_security_agent.run(
 )
 
 # Add the third task to the productivity_check_agent
-productivity_check = productivity_check_agent.run(health_check, assembly_line)
+productivity_check = productivity_check_agent.run(
+    health_check, assembly_line
+)
 
 # Add the fourth task to the security_check_agent
-security_check = security_check_agent.add(productivity_check, red_robots)
+security_check = security_check_agent.add(
+    productivity_check, red_robots
+)
 
 # Add the fifth task to the efficiency_check_agent
 efficiency_check = efficiency_check_agent.run(

playground/memory/qdrant/usage.py

@@ -2,7 +2,8 @@ from langchain.document_loaders import CSVLoader
 from swarms.memory import qdrant
 
 loader = CSVLoader(
-    file_path="../document_parsing/aipg/aipg.csv", encoding="utf-8-sig"
+    file_path="../document_parsing/aipg/aipg.csv",
+    encoding="utf-8-sig",
 )
 docs = loader.load()
 

playground/models/bingchat.py

@@ -24,7 +24,9 @@ llm = OpenAIChat(
 )
 
 # Initialize the Worker with the custom tool
-worker = Worker(llm=llm, ai_name="EdgeGPT Worker", external_tools=[edgegpt])
+worker = Worker(
+    llm=llm, ai_name="EdgeGPT Worker", external_tools=[edgegpt]
+)
 
 # Use the worker to process a task
 task = "Hello, my name is ChatGPT"

playground/models/bioclip.py

@@ -17,5 +17,8 @@ labels = [
 ]
 
 result = clip("swarms.jpeg", labels)
-metadata = {"filename": "images/.jpg".split("/")[-1], "top_probs": result}
+metadata = {
+    "filename": "images/.jpg".split("/")[-1],
+    "top_probs": result,
+}
 clip.plot_image_with_metadata("swarms.jpeg", metadata)

playground/models/distilled_whiserpx.py

@@ -7,4 +7,6 @@ model_wrapper = DistilWhisperModel()
 transcription = model_wrapper("path/to/audio.mp3")
 
 # For async usage
-transcription = asyncio.run(model_wrapper.async_transcribe("path/to/audio.mp3"))
+transcription = asyncio.run(
+    model_wrapper.async_transcribe("path/to/audio.mp3")
+)

playground/models/mpt.py

@@ -1,7 +1,9 @@
 from swarms.models.mpt import MPT
 
 mpt_instance = MPT(
-    "mosaicml/mpt-7b-storywriter", "EleutherAI/gpt-neox-20b", max_tokens=150
+    "mosaicml/mpt-7b-storywriter",
+    "EleutherAI/gpt-neox-20b",
+    max_tokens=150,
 )
 
 mpt_instance.generate("Once upon a time in a land far, far away...")

playground/structs/flow_tools.py

@@ -31,7 +31,9 @@ async def async_load_playwright(url: str) -> str:
             text = soup.get_text()
             lines = (line.strip() for line in text.splitlines())
             chunks = (
-                phrase.strip() for line in lines for phrase in line.split("  ")
+                phrase.strip()
+                for line in lines
+                for phrase in line.split("  ")
             )
             results = "\n".join(chunk for chunk in chunks if chunk)
         except Exception as e:
@@ -60,6 +62,6 @@ agent = Agent(
 )
 
 out = agent.run(
-    "Generate a 10,000 word blog on mental clarity and the benefits of"
-    " meditation."
+    "Generate a 10,000 word blog on mental clarity and the benefits"
+    " of meditation."
 )

playground/structs/sequential_workflow.py

@@ -18,7 +18,9 @@ flow2 = Agent(llm=llm, max_loops=1, dashboard=False)
 workflow = SequentialWorkflow(max_loops=1)
 
 # Add tasks to the workflow
-workflow.add("Generate a 10,000 word blog on health and wellness.", flow1)
+workflow.add(
+    "Generate a 10,000 word blog on health and wellness.", flow1
+)
 
 # Suppose the next task takes the output of the first task as input
 workflow.add("Summarize the generated blog", flow2)

playground/swarms/chat.py

@@ -1,7 +1,11 @@
 from swarms import Orchestrator, Worker
 
 # Instantiate the Orchestrator with 10 agents
-orchestrator = Orchestrator(Worker, agent_list=[Worker] * 10, task_queue=[])
+orchestrator = Orchestrator(
+    Worker, agent_list=[Worker] * 10, task_queue=[]
+)
 
 # Agent 1 sends a message to Agent 2
-orchestrator.chat(sender_id=1, receiver_id=2, message="Hello, Agent 2!")
+orchestrator.chat(
+    sender_id=1, receiver_id=2, message="Hello, Agent 2!"
+)

playground/swarms/debate.py

@@ -37,7 +37,9 @@ class DialogueAgent:
             [
                 self.system_message,
                 HumanMessage(
-                    content="\n".join(self.message_history + [self.prefix])
+                    content="\n".join(
+                        self.message_history + [self.prefix]
+                    )
                 ),
             ]
         )
@@ -76,7 +78,9 @@ class DialogueSimulator:
 
     def step(self) -> tuple[str, str]:
         # 1. choose the next speaker
-        speaker_idx = self.select_next_speaker(self._step, self.agents)
+        speaker_idx = self.select_next_speaker(
+            self._step, self.agents
+        )
         speaker = self.agents[speaker_idx]
 
         # 2. next speaker sends message
@@ -114,7 +118,9 @@ class BiddingDialogueAgent(DialogueAgent):
             message_history="\n".join(self.message_history),
             recent_message=self.message_history[-1],
         )
-        bid_string = self.model([SystemMessage(content=prompt)]).content
+        bid_string = self.model(
+            [SystemMessage(content=prompt)]
+        ).content
         return bid_string
 
 
@@ -127,7 +133,8 @@ The presidential candidates are: {', '.join(character_names)}."""
 
 player_descriptor_system_message = SystemMessage(
     content=(
-        "You can add detail to the description of each presidential candidate."
+        "You can add detail to the description of each presidential"
+        " candidate."
     )
 )
 
@@ -156,7 +163,9 @@ Your goal is to be as creative as possible and make the voters think you are the
 """
 
 
-def generate_character_system_message(character_name, character_header):
+def generate_character_system_message(
+    character_name, character_header
+):
     return SystemMessage(content=f"""{character_header}
 You will speak in the style of {character_name}, and exaggerate their personality.
 You will come up with creative ideas related to {topic}.
@@ -183,7 +192,9 @@ character_headers = [
     )
 ]
 character_system_messages = [
-    generate_character_system_message(character_name, character_headers)
+    generate_character_system_message(
+        character_name, character_headers
+    )
     for character_name, character_headers in zip(
         character_names, character_headers
     )
@@ -209,8 +220,8 @@ for (
 class BidOutputParser(RegexParser):
     def get_format_instructions(self) -> str:
         return (
-            "Your response should be an integer delimited by angled brackets,"
-            " like this: <int>."
+            "Your response should be an integer delimited by angled"
+            " brackets, like this: <int>."
         )
 
 
@@ -262,7 +273,9 @@ topic_specifier_prompt = [
         Speak directly to the presidential candidates: {*character_names,}.
         Do not add anything else."""),
 ]
-specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
+specified_topic = ChatOpenAI(temperature=1.0)(
+    topic_specifier_prompt
+).content
 
 print(f"Original topic:\n{topic}\n")
 print(f"Detailed topic:\n{specified_topic}\n")
@@ -273,7 +286,8 @@ print(f"Detailed topic:\n{specified_topic}\n")
     wait=tenacity.wait_none(),  # No waiting time between retries
     retry=tenacity.retry_if_exception_type(ValueError),
     before_sleep=lambda retry_state: print(
-        f"ValueError occurred: {retry_state.outcome.exception()}, retrying..."
+        f"ValueError occurred: {retry_state.outcome.exception()},"
+        " retrying..."
     ),
     retry_error_callback=lambda retry_state: 0,
 )  # Default value when all retries are exhausted
@@ -286,7 +300,9 @@ def ask_for_bid(agent) -> str:
     return bid
 
 
-def select_next_speaker(step: int, agents: List[DialogueAgent]) -> int:
+def select_next_speaker(
+    step: int, agents: List[DialogueAgent]
+) -> int:
     bids = []
     for agent in agents:
         bid = ask_for_bid(agent)
@@ -309,7 +325,9 @@ def select_next_speaker(step: int, agents: List[DialogueAgent]) -> int:
 
 characters = []
 for character_name, character_system_message, bidding_template in zip(
-    character_names, character_system_messages, character_bidding_templates
+    character_names,
+    character_system_messages,
+    character_bidding_templates,
 ):
     characters.append(
         BiddingDialogueAgent(

playground/swarms/dialogue_simulator.py

@@ -2,7 +2,9 @@ from swarms.swarms import DialogueSimulator
 from swarms.workers.worker import Worker
 from swarms.models import OpenAIChat
 
-llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5)
+llm = OpenAIChat(
+    model_name="gpt-4", openai_api_key="api-key", temperature=0.5
+)
 
 worker1 = Worker(
     llm=llm,

playground/swarms/groupchat.py

@@ -45,5 +45,7 @@ manager = Agent(
 agents = [flow1, flow2, flow3]
 
 group_chat = GroupChat(agents=agents, messages=[], max_round=10)
-chat_manager = GroupChatManager(groupchat=group_chat, selector=manager)
+chat_manager = GroupChatManager(
+    groupchat=group_chat, selector=manager
+)
 chat_history = chat_manager("Write me a riddle")

playground/swarms/multi_agent_debate.py

@@ -1,4 +1,7 @@
-from swarms.swarms.multi_agent_debate import MultiAgentDebate, select_speaker
+from swarms.swarms.multi_agent_debate import (
+    MultiAgentDebate,
+    select_speaker,
+)
 from swarms.workers.worker import Worker
 from swarms.models import OpenAIChat
 
@@ -37,9 +40,9 @@ debate = MultiAgentDebate(agents, select_speaker)
 
 # Run task
 task = (
-    "What were the winning boston marathon times for the past 5 years (ending"
-    " in 2022)? Generate a table of the year, name, country of origin, and"
-    " times."
+    "What were the winning boston marathon times for the past 5 years"
+    " (ending in 2022)? Generate a table of the year, name, country"
+    " of origin, and times."
 )
 results = debate.run(task, max_iters=4)
 

playground/swarms/orchestrate.py

@@ -7,9 +7,13 @@ node = Worker(
 
 
 # Instantiate the Orchestrator with 10 agents
-orchestrator = Orchestrator(node, agent_list=[node] * 10, task_queue=[])
+orchestrator = Orchestrator(
+    node, agent_list=[node] * 10, task_queue=[]
+)
 
 # Agent 7 sends a message to Agent 9
 orchestrator.chat(
-    sender_id=7, receiver_id=9, message="Can you help me with this task?"
+    sender_id=7,
+    receiver_id=9,
+    message="Can you help me with this task?",
 )

playground/swarms/orchestrator.py

@@ -7,9 +7,13 @@ node = Worker(
 
 
 # Instantiate the Orchestrator with 10 agents
-orchestrator = Orchestrator(node, agent_list=[node] * 10, task_queue=[])
+orchestrator = Orchestrator(
+    node, agent_list=[node] * 10, task_queue=[]
+)
 
 # Agent 7 sends a message to Agent 9
 orchestrator.chat(
-    sender_id=7, receiver_id=9, message="Can you help me with this task?"
+    sender_id=7,
+    receiver_id=9,
+    message="Can you help me with this task?",
 )

playground/swarms/swarms_example.py

@@ -8,8 +8,8 @@ swarm = HierarchicalSwarm(api_key)
 
 # Define an objective
 objective = (
-    "Find 20 potential customers for a HierarchicalSwarm based AI Agent"
-    " automation infrastructure"
+    "Find 20 potential customers for a HierarchicalSwarm based AI"
+    " Agent automation infrastructure"
 )
 
 # Run HierarchicalSwarm

pyproject.toml

@@ -77,17 +77,17 @@ mypy-protobuf = "^3.0.0"
 
 
 [tool.autopep8]
-max_line_length = 80
+max_line_length = 70
 ignore = "E501,W6"  # or ["E501", "W6"]
 in-place = true
 recursive = true
 aggressive = 3
 
 [tool.ruff]
-line-length = 80
+line-length = 70
 
 [tool.black]
-line-length = 80
+line-length = 70
 target-version = ['py38']
 preview = true
 

sequential_workflow_example.py

@@ -33,12 +33,16 @@ agent3 = Agent(llm=biochat, max_loops=1, dashboard=False)
 workflow = SequentialWorkflow(max_loops=1)
 
 # Add tasks to the workflow
-workflow.add("Generate a 10,000 word blog on health and wellness.", agent1)
+workflow.add(
+    "Generate a 10,000 word blog on health and wellness.", agent1
+)
 
 # Suppose the next task takes the output of the first task as input
 workflow.add("Summarize the generated blog", agent2)
 
-workflow.add("Create a references sheet of materials for the curriculm", agent3)
+workflow.add(
+    "Create a references sheet of materials for the curriculm", agent3
+)
 
 # Run the workflow
 workflow.run()

swarms/memory/base.py

@@ -28,7 +28,8 @@ class BaseVectorStore(ABC):
 
     embedding_driver: Any
     futures_executor: futures.Executor = field(
-        default=Factory(lambda: futures.ThreadPoolExecutor()), kw_only=True
+        default=Factory(lambda: futures.ThreadPoolExecutor()),
+        kw_only=True,
     )
 
     def upsert_text_artifacts(
@@ -40,7 +41,11 @@ class BaseVectorStore(ABC):
         execute_futures_dict(
             {
                 namespace: self.futures_executor.submit(
-                    self.upsert_text_artifact, a, namespace, meta, **kwargs
+                    self.upsert_text_artifact,
+                    a,
+                    namespace,
+                    meta,
+                    **kwargs,
                 )
                 for namespace, artifact_list in artifacts.items()
                 for a in artifact_list
@@ -62,7 +67,9 @@ class BaseVectorStore(ABC):
         if artifact.embedding:
             vector = artifact.embedding
         else:
-            vector = artifact.generate_embedding(self.embedding_driver)
+            vector = artifact.generate_embedding(
+                self.embedding_driver
+            )
 
         return self.upsert_vector(
             vector,
@@ -106,7 +113,9 @@ class BaseVectorStore(ABC):
         ...
 
     @abstractmethod
-    def load_entries(self, namespace: Optional[str] = None) -> list[Entry]:
+    def load_entries(
+        self, namespace: Optional[str] = None
+    ) -> list[Entry]:
         ...
 
     @abstractmethod

swarms/memory/chroma.py

@@ -35,11 +35,18 @@ def _results_to_docs(results: Any) -> List[Document]:
     return [doc for doc, _ in _results_to_docs_and_scores(results)]
 
 
-def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
+def _results_to_docs_and_scores(
+    results: Any,
+) -> List[Tuple[Document, float]]:
     return [
         # TODO: Chroma can do batch querying,
         # we shouldn't hard code to the 1st result
-        (Document(page_content=result[0], metadata=result[1] or {}), result[2])
+        (
+            Document(
+                page_content=result[0], metadata=result[1] or {}
+            ),
+            result[2],
+        )
         for result in zip(
             results["documents"][0],
             results["metadatas"][0],
@@ -94,13 +101,16 @@ class Chroma(VectorStore):
                 # If client_settings is provided with persist_directory specified,
                 # then it is "in-memory and persisting to disk" mode.
                 client_settings.persist_directory = (
-                    persist_directory or client_settings.persist_directory
+                    persist_directory
+                    or client_settings.persist_directory
                 )
                 if client_settings.persist_directory is not None:
                     # Maintain backwards compatibility with chromadb < 0.4.0
                     major, minor, _ = chromadb.__version__.split(".")
                     if int(major) == 0 and int(minor) < 4:
-                        client_settings.chroma_db_impl = "duckdb+parquet"
+                        client_settings.chroma_db_impl = (
+                            "duckdb+parquet"
+                        )
 
                 _client_settings = client_settings
             elif persist_directory:
@@ -120,7 +130,8 @@ class Chroma(VectorStore):
             self._client_settings = _client_settings
             self._client = chromadb.Client(_client_settings)
             self._persist_directory = (
-                _client_settings.persist_directory or persist_directory
+                _client_settings.persist_directory
+                or persist_directory
             )
 
         self._embedding_function = embedding_function
@@ -189,7 +200,9 @@ class Chroma(VectorStore):
         embeddings = None
         texts = list(texts)
         if self._embedding_function is not None:
-            embeddings = self._embedding_function.embed_documents(texts)
+            embeddings = self._embedding_function.embed_documents(
+                texts
+            )
         if metadatas:
             # fill metadatas with empty dicts if somebody
             # did not specify metadata for all texts
@@ -205,13 +218,17 @@ class Chroma(VectorStore):
                     empty_ids.append(idx)
             if non_empty_ids:
                 metadatas = [metadatas[idx] for idx in non_empty_ids]
-                texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
+                texts_with_metadatas = [
+                    texts[idx] for idx in non_empty_ids
+                ]
                 embeddings_with_metadatas = (
                     [embeddings[idx] for idx in non_empty_ids]
                     if embeddings
                     else None
                 )
-                ids_with_metadata = [ids[idx] for idx in non_empty_ids]
+                ids_with_metadata = [
+                    ids[idx] for idx in non_empty_ids
+                ]
                 try:
                     self._collection.upsert(
                         metadatas=metadatas,
@@ -222,7 +239,8 @@ class Chroma(VectorStore):
                 except ValueError as e:
                     if "Expected metadata value to be" in str(e):
                         msg = (
-                            "Try filtering complex metadata from the document"
+                            "Try filtering complex metadata from the"
+                            " document"
                             " using "
                             "langchain.vectorstores.utils.filter_complex_metadata."
                         )
@@ -230,9 +248,13 @@ class Chroma(VectorStore):
                     else:
                         raise e
             if empty_ids:
-                texts_without_metadatas = [texts[j] for j in empty_ids]
+                texts_without_metadatas = [
+                    texts[j] for j in empty_ids
+                ]
                 embeddings_without_metadatas = (
-                    [embeddings[j] for j in empty_ids] if embeddings else None
+                    [embeddings[j] for j in empty_ids]
+                    if embeddings
+                    else None
                 )
                 ids_without_metadatas = [ids[j] for j in empty_ids]
                 self._collection.upsert(
@@ -351,7 +373,9 @@ class Chroma(VectorStore):
                 where_document=where_document,
             )
         else:
-            query_embedding = self._embedding_function.embed_query(query)
+            query_embedding = self._embedding_function.embed_query(
+                query
+            )
             results = self.__query_collection(
                 query_embeddings=[query_embedding],
                 n_results=k,
@@ -388,9 +412,9 @@ class Chroma(VectorStore):
             return self._max_inner_product_relevance_score_fn
         else:
             raise ValueError(
-                "No supported normalization function"
-                f" for distance metric of type: {distance}."
-                "Consider providing relevance_score_fn to Chroma constructor."
+                "No supported normalization function for distance"
+                f" metric of type: {distance}.Consider providing"
+                " relevance_score_fn to Chroma constructor."
             )
 
     def max_marginal_relevance_search_by_vector(
@@ -426,7 +450,12 @@ class Chroma(VectorStore):
             n_results=fetch_k,
             where=filter,
             where_document=where_document,
-            include=["metadatas", "documents", "distances", "embeddings"],
+            include=[
+                "metadatas",
+                "documents",
+                "distances",
+                "embeddings",
+            ],
         )
         mmr_selected = maximal_marginal_relevance(
             np.array(embedding, dtype=np.float32),
@@ -471,8 +500,8 @@ class Chroma(VectorStore):
         """
         if self._embedding_function is None:
             raise ValueError(
-                "For MMR search, you must specify an embedding function"
-                " oncreation."
+                "For MMR search, you must specify an embedding"
+                " function oncreation."
             )
 
         embedding = self._embedding_function.embed_query(query)
@@ -546,7 +575,9 @@ class Chroma(VectorStore):
         if int(major) == 0 and int(minor) < 4:
             self._client.persist()
 
-    def update_document(self, document_id: str, document: Document) -> None:
+    def update_document(
+        self, document_id: str, document: Document
+    ) -> None:
         """Update a document in the collection.
 
         Args:
@@ -568,8 +599,8 @@ class Chroma(VectorStore):
         metadata = [document.metadata for document in documents]
         if self._embedding_function is None:
             raise ValueError(
-                "For update, you must specify an embedding function on"
-                " creation."
+                "For update, you must specify an embedding function"
+                " on creation."
             )
         embeddings = self._embedding_function.embed_documents(text)
 
@@ -711,7 +742,9 @@ class Chroma(VectorStore):
             **kwargs,
         )
 
-    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
+    def delete(
+        self, ids: Optional[List[str]] = None, **kwargs: Any
+    ) -> None:
         """Delete by vector IDs.
 
         Args:

swarms/memory/cosine_similarity.py

@@ -18,8 +18,8 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
     Y = np.array(Y)
     if X.shape[1] != Y.shape[1]:
         raise ValueError(
-            "Number of columns in X and Y must be the same. X has shape"
-            f" {X.shape} and Y has shape {Y.shape}."
+            "Number of columns in X and Y must be the same. X has"
+            f" shape {X.shape} and Y has shape {Y.shape}."
         )
     try:
         import simsimd as simd
@@ -32,9 +32,9 @@ def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
         return Z
     except ImportError:
         logger.info(
-            "Unable to import simsimd, defaulting to NumPy implementation. If"
-            " you want to use simsimd please install with `pip install"
-            " simsimd`."
+            "Unable to import simsimd, defaulting to NumPy"
+            " implementation. If you want to use simsimd please"
+            " install with `pip install simsimd`."
         )
         X_norm = np.linalg.norm(X, axis=1)
         Y_norm = np.linalg.norm(Y, axis=1)
@@ -68,9 +68,15 @@ def cosine_similarity_top_k(
     score_array = cosine_similarity(X, Y)
     score_threshold = score_threshold or -1.0
     score_array[score_array < score_threshold] = 0
-    top_k = min(top_k or len(score_array), np.count_nonzero(score_array))
-    top_k_idxs = np.argpartition(score_array, -top_k, axis=None)[-top_k:]
-    top_k_idxs = top_k_idxs[np.argsort(score_array.ravel()[top_k_idxs])][::-1]
+    top_k = min(
+        top_k or len(score_array), np.count_nonzero(score_array)
+    )
+    top_k_idxs = np.argpartition(score_array, -top_k, axis=None)[
+        -top_k:
+    ]
+    top_k_idxs = top_k_idxs[
+        np.argsort(score_array.ravel()[top_k_idxs])
+    ][::-1]
     ret_idxs = np.unravel_index(top_k_idxs, score_array.shape)
     scores = score_array.ravel()[top_k_idxs].tolist()
     return list(zip(*ret_idxs)), scores  # type: ignore

swarms/memory/pg.py

@@ -84,7 +84,9 @@ class PgVectorVectorStore(BaseVectorStore):
 
     """
 
-    connection_string: Optional[str] = field(default=None, kw_only=True)
+    connection_string: Optional[str] = field(
+        default=None, kw_only=True
+    )
     create_engine_params: dict = field(factory=dict, kw_only=True)
     engine: Optional[Engine] = field(default=None, kw_only=True)
     table_name: str = field(kw_only=True)
@@ -104,12 +106,14 @@ class PgVectorVectorStore(BaseVectorStore):
 
         # If an engine is not provided, a connection string is required.
         if connection_string is None:
-            raise ValueError("An engine or connection string is required")
+            raise ValueError(
+                "An engine or connection string is required"
+            )
 
         if not connection_string.startswith("postgresql://"):
             raise ValueError(
-                "The connection string must describe a Postgres database"
-                " connection"
+                "The connection string must describe a Postgres"
+                " database connection"
             )
 
     @engine.validator
@@ -120,7 +124,9 @@ class PgVectorVectorStore(BaseVectorStore):
 
         # If a connection string is not provided, an engine is required.
         if engine is None:
-            raise ValueError("An engine or connection string is required")
+            raise ValueError(
+                "An engine or connection string is required"
+            )
 
     def __attrs_post_init__(self) -> None:
         """If a an engine is provided, it will be used to connect to the database.
@@ -139,10 +145,14 @@ class PgVectorVectorStore(BaseVectorStore):
     ) -> None:
         """Provides a mechanism to initialize the database schema and extensions."""
         if install_uuid_extension:
-            self.engine.execute('CREATE EXTENSION IF NOT EXISTS "uuid-ossp";')
+            self.engine.execute(
+                'CREATE EXTENSION IF NOT EXISTS "uuid-ossp";'
+            )
 
         if install_vector_extension:
-            self.engine.execute('CREATE EXTENSION IF NOT EXISTS "vector";')
+            self.engine.execute(
+                'CREATE EXTENSION IF NOT EXISTS "vector";'
+            )
 
         if create_schema:
             self._model.metadata.create_all(self.engine)
@@ -246,7 +256,9 @@ class PgVectorVectorStore(BaseVectorStore):
             return [
                 BaseVectorStore.QueryResult(
                     id=str(result[0].id),
-                    vector=result[0].vector if include_vectors else None,
+                    vector=(
+                        result[0].vector if include_vectors else None
+                    ),
                     score=result[1],
                     meta=result[0].meta,
                     namespace=result[0].namespace,

swarms/memory/pinecone.py

@@ -111,7 +111,9 @@ class PineconeVectorStoreStore(BaseVectorStore):
         **kwargs,
     ) -> str:
         """Upsert vector"""
-        vector_id = vector_id if vector_id else str_to_hash(str(vector))
+        vector_id = (
+            vector_id if vector_id else str_to_hash(str(vector))
+        )
 
         params = {"namespace": namespace} | kwargs
 
@@ -179,7 +181,11 @@ class PineconeVectorStoreStore(BaseVectorStore):
         vector = self.embedding_driver.embed_string(query)
 
         params = {
-            "top_k": count if count else BaseVectorStore.DEFAULT_QUERY_COUNT,
+            "top_k": (
+                count
+                if count
+                else BaseVectorStore.DEFAULT_QUERY_COUNT
+            ),
             "namespace": namespace,
             "include_values": include_vectors,
             "include_metadata": include_metadata,

swarms/memory/qdrant.py

@@ -2,7 +2,11 @@ from typing import List
 from sentence_transformers import SentenceTransformer
 from httpx import RequestError
 from qdrant_client import QdrantClient
-from qdrant_client.http.models import Distance, VectorParams, PointStruct
+from qdrant_client.http.models import (
+    Distance,
+    VectorParams,
+    PointStruct,
+)
 
 
 class Qdrant:
@@ -33,7 +37,9 @@ class Qdrant:
         https: bool = True,
     ):
         try:
-            self.client = QdrantClient(url=host, port=port, api_key=api_key)
+            self.client = QdrantClient(
+                url=host, port=port, api_key=api_key
+            )
             self.collection_name = collection_name
             self._load_embedding_model(model_name)
             self._setup_collection()
@@ -56,7 +62,10 @@ class Qdrant:
         try:
             exists = self.client.get_collection(self.collection_name)
             if exists:
-                print(f"Collection '{self.collection_name}' already exists.")
+                print(
+                    f"Collection '{self.collection_name}' already"
+                    " exists."
+                )
         except Exception as e:
             self.client.create_collection(
                 collection_name=self.collection_name,
@@ -93,7 +102,8 @@ class Qdrant:
                     )
                 else:
                     print(
-                        f"Document at index {i} is missing 'page_content' key"
+                        f"Document at index {i} is missing"
+                        " 'page_content' key"
                     )
             except Exception as e:
                 print(f"Error processing document at index {i}: {e}")
@@ -121,7 +131,9 @@ class Qdrant:
             SearchResult or None: Returns the search results if successful, otherwise None.
         """
         try:
-            query_vector = self.model.encode(query, normalize_embeddings=True)
+            query_vector = self.model.encode(
+                query, normalize_embeddings=True
+            )
             search_result = self.client.search(
                 collection_name=self.collection_name,
                 query_vector=query_vector,

swarms/memory/schemas.py

@@ -9,7 +9,9 @@ from pydantic import BaseModel, Field
 class TaskInput(BaseModel):
     __root__: Any = Field(
         ...,
-        description="The input parameters for the task. Any value is allowed.",
+        description=(
+            "The input parameters for the task. Any value is allowed."
+        ),
         example='{\n"debug": false,\n"mode": "benchmarks"\n}',
     )
 
@@ -25,7 +27,9 @@ class Artifact(BaseModel):
     )
     relative_path: Optional[str] = Field(
         None,
-        description="Relative path of the artifact in the agent's workspace",
+        description=(
+            "Relative path of the artifact in the agent's workspace"
+        ),
         example="python/code/",
     )
 
@@ -34,7 +38,9 @@ class ArtifactUpload(BaseModel):
     file: bytes = Field(..., description="File to upload")
     relative_path: Optional[str] = Field(
         None,
-        description="Relative path of the artifact in the agent's workspace",
+        description=(
+            "Relative path of the artifact in the agent's workspace"
+        ),
         example="python/code/",
     )
 
@@ -42,7 +48,10 @@ class ArtifactUpload(BaseModel):
 class StepInput(BaseModel):
     __root__: Any = Field(
         ...,
-        description="Input parameters for the task step. Any value is allowed.",
+        description=(
+            "Input parameters for the task step. Any value is"
+            " allowed."
+        ),
         example='{\n"file_to_refactor": "models.py"\n}',
     )
 
@@ -51,7 +60,8 @@ class StepOutput(BaseModel):
     __root__: Any = Field(
         ...,
         description=(
-            "Output that the task step has produced. Any value is allowed."
+            "Output that the task step has produced. Any value is"
+            " allowed."
         ),
         example='{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}',
     )
@@ -61,7 +71,9 @@ class TaskRequestBody(BaseModel):
     input: Optional[str] = Field(
         None,
         description="Input prompt for the task.",
-        example="Write the words you receive to the file 'output.txt'.",
+        example=(
+            "Write the words you receive to the file 'output.txt'."
+        ),
     )
     additional_input: Optional[TaskInput] = None
 
@@ -84,7 +96,9 @@ class Task(TaskRequestBody):
 
 class StepRequestBody(BaseModel):
     input: Optional[str] = Field(
-        None, description="Input prompt for the step.", example="Washington"
+        None,
+        description="Input prompt for the step.",
+        example="Washington",
     )
     additional_input: Optional[StepInput] = None
 
@@ -107,22 +121,28 @@ class Step(StepRequestBody):
         example="6bb1801a-fd80-45e8-899a-4dd723cc602e",
     )
     name: Optional[str] = Field(
-        None, description="The name of the task step.", example="Write to file"
+        None,
+        description="The name of the task step.",
+        example="Write to file",
+    )
+    status: Status = Field(
+        ..., description="The status of the task step."
     )
-    status: Status = Field(..., description="The status of the task step.")
     output: Optional[str] = Field(
         None,
         description="Output of the task step.",
         example=(
-            "I am going to use the write_to_file command and write Washington"
-            " to a file called output.txt <write_to_file('output.txt',"
-            " 'Washington')"
+            "I am going to use the write_to_file command and write"
+            " Washington to a file called output.txt"
+            " <write_to_file('output.txt', 'Washington')"
         ),
     )
     additional_output: Optional[StepOutput] = None
     artifacts: List[Artifact] = Field(
-        [], description="A list of artifacts that the step has produced."
+        [],
+        description="A list of artifacts that the step has produced.",
     )
     is_last: Optional[bool] = Field(
-        False, description="Whether this is the last step in the task."
+        False,
+        description="Whether this is the last step in the task.",
     )

swarms/memory/utils.py

@@ -31,26 +31,33 @@ def maximal_marginal_relevance(
         return []
     if query_embedding.ndim == 1:
         query_embedding = np.expand_dims(query_embedding, axis=0)
-    similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
+    similarity_to_query = cosine_similarity(
+        query_embedding, embedding_list
+    )[0]
     most_similar = int(np.argmax(similarity_to_query))
     idxs = [most_similar]
     selected = np.array([embedding_list[most_similar]])
     while len(idxs) < min(k, len(embedding_list)):
         best_score = -np.inf
         idx_to_add = -1
-        similarity_to_selected = cosine_similarity(embedding_list, selected)
+        similarity_to_selected = cosine_similarity(
+            embedding_list, selected
+        )
         for i, query_score in enumerate(similarity_to_query):
             if i in idxs:
                 continue
             redundant_score = max(similarity_to_selected[i])
             equation_score = (
-                lambda_mult * query_score - (1 - lambda_mult) * redundant_score
+                lambda_mult * query_score
+                - (1 - lambda_mult) * redundant_score
             )
             if equation_score > best_score:
                 best_score = equation_score
                 idx_to_add = i
         idxs.append(idx_to_add)
-        selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
+        selected = np.append(
+            selected, [embedding_list[idx_to_add]], axis=0
+        )
     return idxs
 
 

swarms/models/__init__.py

@@ -10,14 +10,18 @@ from swarms.models.openai_models import (
 from swarms.models.zephyr import Zephyr  # noqa: E402
 from swarms.models.biogpt import BioGPT  # noqa: E402
 from swarms.models.huggingface import HuggingfaceLLM  # noqa: E402
-from swarms.models.wizard_storytelling import WizardLLMStoryTeller  # noqa: E402
+from swarms.models.wizard_storytelling import (
+    WizardLLMStoryTeller,
+)  # noqa: E402
 from swarms.models.mpt import MPT7B  # noqa: E402
 
 # MultiModal Models
 from swarms.models.idefics import Idefics  # noqa: E402
 from swarms.models.vilt import Vilt  # noqa: E402
 from swarms.models.nougat import Nougat  # noqa: E402
-from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA  # noqa: E402
+from swarms.models.layoutlm_document_qa import (
+    LayoutLMDocumentQA,
+)  # noqa: E402
 from swarms.models.gpt4_vision_api import GPT4VisionAPI  # noqa: E402
 
 # from swarms.models.gpt4v import GPT4Vision

swarms/models/anthropic.py

@@ -45,10 +45,16 @@ def xor_args(*arg_groups: Tuple[str, ...]) -> Callable:
         def wrapper(*args: Any, **kwargs: Any) -> Any:
             """Validate exactly one arg in each group is not None."""
             counts = [
-                sum(1 for arg in arg_group if kwargs.get(arg) is not None)
+                sum(
+                    1
+                    for arg in arg_group
+                    if kwargs.get(arg) is not None
+                )
                 for arg_group in arg_groups
             ]
-            invalid_groups = [i for i, count in enumerate(counts) if count != 1]
+            invalid_groups = [
+                i for i, count in enumerate(counts) if count != 1
+            ]
             if invalid_groups:
                 invalid_group_names = [
                     ", ".join(arg_groups[i]) for i in invalid_groups
@@ -119,8 +125,9 @@ def guard_import(
         module = importlib.import_module(module_name, package)
     except ImportError:
         raise ImportError(
-            f"Could not import {module_name} python package. "
-            f"Please install it with `pip install {pip_name or module_name}`."
+            f"Could not import {module_name} python package. Please"
+            " install it with `pip install"
+            f" {pip_name or module_name}`."
         )
     return module
 
@@ -134,25 +141,33 @@ def check_package_version(
 ) -> None:
     """Check the version of a package."""
     imported_version = parse(version(package))
-    if lt_version is not None and imported_version >= parse(lt_version):
+    if lt_version is not None and imported_version >= parse(
+        lt_version
+    ):
         raise ValueError(
-            f"Expected {package} version to be < {lt_version}. Received "
-            f"{imported_version}."
+            f"Expected {package} version to be < {lt_version}."
+            f" Received {imported_version}."
         )
-    if lte_version is not None and imported_version > parse(lte_version):
+    if lte_version is not None and imported_version > parse(
+        lte_version
+    ):
         raise ValueError(
-            f"Expected {package} version to be <= {lte_version}. Received "
-            f"{imported_version}."
+            f"Expected {package} version to be <= {lte_version}."
+            f" Received {imported_version}."
         )
-    if gt_version is not None and imported_version <= parse(gt_version):
+    if gt_version is not None and imported_version <= parse(
+        gt_version
+    ):
         raise ValueError(
-            f"Expected {package} version to be > {gt_version}. Received "
-            f"{imported_version}."
+            f"Expected {package} version to be > {gt_version}."
+            f" Received {imported_version}."
         )
-    if gte_version is not None and imported_version < parse(gte_version):
+    if gte_version is not None and imported_version < parse(
+        gte_version
+    ):
         raise ValueError(
-            f"Expected {package} version to be >= {gte_version}. Received "
-            f"{imported_version}."
+            f"Expected {package} version to be >= {gte_version}."
+            f" Received {imported_version}."
         )
 
 
@@ -185,9 +200,11 @@ def build_extra_kwargs(
         if field_name in extra_kwargs:
             raise ValueError(f"Found {field_name} supplied twice.")
         if field_name not in all_required_field_names:
-            warnings.warn(f"""WARNING! {field_name} is not default parameter.
+            warnings.warn(
+                f"""WARNING! {field_name} is not default parameter.
                 {field_name} was transferred to model_kwargs.
-                Please confirm that {field_name} is what you intended.""")
+                Please confirm that {field_name} is what you intended."""
+            )
             extra_kwargs[field_name] = values.pop(field_name)
 
     invalid_model_kwargs = all_required_field_names.intersection(
@@ -195,8 +212,9 @@ def build_extra_kwargs(
     )
     if invalid_model_kwargs:
         raise ValueError(
-            f"Parameters {invalid_model_kwargs} should be specified explicitly."
-            " Instead they were passed in as part of `model_kwargs` parameter."
+            f"Parameters {invalid_model_kwargs} should be specified"
+            " explicitly. Instead they were passed in as part of"
+            " `model_kwargs` parameter."
         )
 
     return extra_kwargs
@@ -273,12 +291,16 @@ class _AnthropicCommon(BaseLanguageModel):
             check_package_version("anthropic", gte_version="0.3")
             values["client"] = anthropic.Anthropic(
                 base_url=values["anthropic_api_url"],
-                api_key=values["anthropic_api_key"].get_secret_value(),
+                api_key=values[
+                    "anthropic_api_key"
+                ].get_secret_value(),
                 timeout=values["default_request_timeout"],
             )
             values["async_client"] = anthropic.AsyncAnthropic(
                 base_url=values["anthropic_api_url"],
-                api_key=values["anthropic_api_key"].get_secret_value(),
+                api_key=values[
+                    "anthropic_api_key"
+                ].get_secret_value(),
                 timeout=values["default_request_timeout"],
             )
             values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
@@ -316,7 +338,9 @@ class _AnthropicCommon(BaseLanguageModel):
         self, stop: Optional[List[str]] = None
     ) -> List[str]:
         if not self.HUMAN_PROMPT or not self.AI_PROMPT:
-            raise NameError("Please ensure the anthropic package is loaded")
+            raise NameError(
+                "Please ensure the anthropic package is loaded"
+            )
 
         if stop is None:
             stop = []
@@ -375,7 +399,9 @@ class Anthropic(LLM, _AnthropicCommon):
 
     def _wrap_prompt(self, prompt: str) -> str:
         if not self.HUMAN_PROMPT or not self.AI_PROMPT:
-            raise NameError("Please ensure the anthropic package is loaded")
+            raise NameError(
+                "Please ensure the anthropic package is loaded"
+            )
 
         if prompt.startswith(self.HUMAN_PROMPT):
             return prompt  # Already wrapped.
@@ -389,7 +415,8 @@ class Anthropic(LLM, _AnthropicCommon):
 
         # As a last resort, wrap the prompt ourselves to emulate instruct-style.
         return (
-            f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
+            f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here"
+            " you go:\n"
         )
 
     def _call(
@@ -419,7 +446,10 @@ class Anthropic(LLM, _AnthropicCommon):
         if self.streaming:
             completion = ""
             for chunk in self._stream(
-                prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+                prompt=prompt,
+                stop=stop,
+                run_manager=run_manager,
+                **kwargs,
             ):
                 completion += chunk.text
             return completion
@@ -447,7 +477,10 @@ class Anthropic(LLM, _AnthropicCommon):
         if self.streaming:
             completion = ""
             async for chunk in self._astream(
-                prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
+                prompt=prompt,
+                stop=stop,
+                run_manager=run_manager,
+                **kwargs,
             ):
                 completion += chunk.text
             return completion
@@ -533,10 +566,14 @@ class Anthropic(LLM, _AnthropicCommon):
             chunk = GenerationChunk(text=token.completion)
             yield chunk
             if run_manager:
-                await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
+                await run_manager.on_llm_new_token(
+                    chunk.text, chunk=chunk
+                )
 
     def get_num_tokens(self, text: str) -> int:
         """Calculate number of tokens."""
         if not self.count_tokens:
-            raise NameError("Please ensure the anthropic package is loaded")
+            raise NameError(
+                "Please ensure the anthropic package is loaded"
+            )
         return self.count_tokens(text)

swarms/models/base.py

@@ -27,7 +27,9 @@ class AbstractModel(ABC):
 
     def chat(self, task: str, history: str = "") -> str:
         """Chat with the model"""
-        complete_task = task + " | " + history  # Delimiter for clarity
+        complete_task = (
+            task + " | " + history
+        )  # Delimiter for clarity
         return self.run(complete_task)
 
     def __call__(self, task: str) -> str:

swarms/models/base_multimodal_model.py

@@ -107,7 +107,9 @@ class BaseMultiModalModel:
             image_pil = Image.open(BytesIO(response.content))
             return image_pil
         except requests.RequestException as error:
-            print(f"Error fetching image from {img} and error: {error}")
+            print(
+                f"Error fetching image from {img} and error: {error}"
+            )
             return None
 
     def encode_img(self, img: str):
@@ -142,14 +144,18 @@ class BaseMultiModalModel:
 
         """
         # Instantiate the thread pool executor
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+        with ThreadPoolExecutor(
+            max_workers=self.max_workers
+        ) as executor:
             results = executor.map(self.run, tasks, imgs)
 
         # Print the results for debugging
         for result in results:
             print(result)
 
-    def run_batch(self, tasks_images: List[Tuple[str, str]]) -> List[str]:
+    def run_batch(
+        self, tasks_images: List[Tuple[str, str]]
+    ) -> List[str]:
         """Process a batch of tasks and images"""
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
@@ -176,7 +182,9 @@ class BaseMultiModalModel:
         """Process a batch of tasks and images asynchronously with retries"""
         loop = asyncio.get_event_loop()
         futures = [
-            loop.run_in_executor(None, self.run_with_retries, task, img)
+            loop.run_in_executor(
+                None, self.run_with_retries, task, img
+            )
             for task, img in tasks_images
         ]
         return await asyncio.gather(*futures)
@@ -194,7 +202,9 @@ class BaseMultiModalModel:
                 print(f"Error with the request {error}")
                 continue
 
-    def run_batch_with_retries(self, tasks_images: List[Tuple[str, str]]):
+    def run_batch_with_retries(
+        self, tasks_images: List[Tuple[str, str]]
+    ):
         """Run the model with retries"""
         for i in range(self.retries):
             try:

swarms/models/bioclip.py

@@ -112,11 +112,12 @@ class BioClip:
         template: str = "this is a photo of ",
         context_length: int = 256,
     ):
-        image = torch.stack([self.preprocess_val(Image.open(img_path))]).to(
-            self.device
-        )
+        image = torch.stack(
+            [self.preprocess_val(Image.open(img_path))]
+        ).to(self.device)
         texts = self.tokenizer(
-            [template + l for l in labels], context_length=context_length
+            [template + l for l in labels],
+            context_length=context_length,
         ).to(self.device)
 
         with torch.no_grad():
@@ -128,7 +129,9 @@ class BioClip:
                 .detach()
                 .softmax(dim=-1)
             )
-            sorted_indices = torch.argsort(logits, dim=-1, descending=True)
+            sorted_indices = torch.argsort(
+                logits, dim=-1, descending=True
+            )
             logits = logits.cpu().numpy()
             sorted_indices = sorted_indices.cpu().numpy()
 
@@ -149,7 +152,10 @@ class BioClip:
             metadata["filename"]
             + "\n"
             + "\n".join(
-                [f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()]
+                [
+                    f"{k}: {v*100:.1f}"
+                    for k, v in metadata["top_probs"].items()
+                ]
             )
         )
         ax.set_title(title, fontsize=14)

swarms/models/biogpt.py

@@ -34,7 +34,12 @@ advantage of BioGPT on biomedical literature to generate fluent descriptions for
 """
 
 import torch
-from transformers import pipeline, set_seed, BioGptTokenizer, BioGptForCausalLM
+from transformers import (
+    pipeline,
+    set_seed,
+    BioGptTokenizer,
+    BioGptForCausalLM,
+)
 
 
 class BioGPT:
@@ -85,8 +90,12 @@ class BioGPT:
         self.do_sample = do_sample
         self.min_length = min_length
 
-        self.model = BioGptForCausalLM.from_pretrained(self.model_name)
-        self.tokenizer = BioGptTokenizer.from_pretrained(self.model_name)
+        self.model = BioGptForCausalLM.from_pretrained(
+            self.model_name
+        )
+        self.tokenizer = BioGptTokenizer.from_pretrained(
+            self.model_name
+        )
 
     def __call__(self, text: str):
         """
@@ -103,7 +112,9 @@ class BioGPT:
         """
         set_seed(42)
         generator = pipeline(
-            "text-generation", model=self.model, tokenizer=self.tokenizer
+            "text-generation",
+            model=self.model,
+            tokenizer=self.tokenizer,
         )
         out = generator(
             text,
@@ -156,7 +167,9 @@ class BioGPT:
                 num_beams=num_beams,
                 early_stopping=early_stopping,
             )
-        return self.tokenizer.decode(beam_output[0], skip_special_tokens=True)
+        return self.tokenizer.decode(
+            beam_output[0], skip_special_tokens=True
+        )
 
     # Feature 1: Set a new tokenizer and model
     def set_pretrained_model(self, model_name):
@@ -167,8 +180,12 @@ class BioGPT:
             model_name (str): Name of the pretrained model.
         """
         self.model_name = model_name
-        self.model = BioGptForCausalLM.from_pretrained(self.model_name)
-        self.tokenizer = BioGptTokenizer.from_pretrained(self.model_name)
+        self.model = BioGptForCausalLM.from_pretrained(
+            self.model_name
+        )
+        self.tokenizer = BioGptTokenizer.from_pretrained(
+            self.model_name
+        )
 
     # Feature 2: Get the model's config details
     def get_config(self):

swarms/models/cohere_chat.py

@@ -32,7 +32,9 @@ def _create_retry_decorator(llm) -> Callable[[Any], Any]:
     return retry(
         reraise=True,
         stop=stop_after_attempt(llm.max_retries),
-        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+        wait=wait_exponential(
+            multiplier=1, min=min_seconds, max=max_seconds
+        ),
         retry=(retry_if_exception_type(cohere.error.CohereError)),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -65,7 +67,9 @@ class BaseCohere(Serializable):
 
     client: Any  #: :meta private:
     async_client: Any  #: :meta private:
-    model: Optional[str] = Field(default=None, description="Model name to use.")
+    model: Optional[str] = Field(
+        default=None, description="Model name to use."
+    )
     """Model name to use."""
 
     temperature: float = 0.75

swarms/models/dalle3.py

@@ -116,7 +116,9 @@ class Dalle3:
         byte_array = byte_stream.getvalue()
         return byte_array
 
-    @backoff.on_exception(backoff.expo, Exception, max_time=max_time_seconds)
+    @backoff.on_exception(
+        backoff.expo, Exception, max_time=max_time_seconds
+    )
     def __call__(self, task: str):
         """
         Text to image conversion using the Dalle3 API
@@ -169,8 +171,8 @@ class Dalle3:
             print(
                 colored(
                     (
-                        f"Error running Dalle3: {error} try optimizing your api"
-                        " key and or try again"
+                        f"Error running Dalle3: {error} try"
+                        " optimizing your api key and or try again"
                     ),
                     "red",
                 )
@@ -198,7 +200,9 @@ class Dalle3:
             with open(full_path, "wb") as file:
                 file.write(response.content)
         else:
-            raise ValueError(f"Failed to download image from {img_url}")
+            raise ValueError(
+                f"Failed to download image from {img_url}"
+            )
 
     def create_variations(self, img: str):
         """
@@ -234,14 +238,21 @@ class Dalle3:
             print(
                 colored(
                     (
-                        f"Error running Dalle3: {error} try optimizing your api"
-                        " key and or try again"
+                        f"Error running Dalle3: {error} try"
+                        " optimizing your api key and or try again"
                     ),
                     "red",
                 )
             )
-            print(colored(f"Error running Dalle3: {error.http_status}", "red"))
-            print(colored(f"Error running Dalle3: {error.error}", "red"))
+            print(
+                colored(
+                    f"Error running Dalle3: {error.http_status}",
+                    "red",
+                )
+            )
+            print(
+                colored(f"Error running Dalle3: {error.error}", "red")
+            )
             raise error
 
     def print_dashboard(self):
@@ -300,7 +311,9 @@ class Dalle3:
                 executor.submit(self, task): task for task in tasks
             }
             results = []
-            for future in concurrent.futures.as_completed(future_to_task):
+            for future in concurrent.futures.as_completed(
+                future_to_task
+            ):
                 task = future_to_task[future]
                 try:
                     img = future.result()
@@ -311,19 +324,27 @@ class Dalle3:
                     print(
                         colored(
                             (
-                                f"Error running Dalle3: {error} try optimizing"
-                                " your api key and or try again"
+                                f"Error running Dalle3: {error} try"
+                                " optimizing your api key and or try"
+                                " again"
                             ),
                             "red",
                         )
                     )
                     print(
                         colored(
-                            f"Error running Dalle3: {error.http_status}", "red"
+                            (
+                                "Error running Dalle3:"
+                                f" {error.http_status}"
+                            ),
+                            "red",
                         )
                     )
                     print(
-                        colored(f"Error running Dalle3: {error.error}", "red")
+                        colored(
+                            f"Error running Dalle3: {error.error}",
+                            "red",
+                        )
                     )
                     raise error
 
@@ -339,7 +360,9 @@ class Dalle3:
         """Str method for the Dalle3 class"""
         return f"Dalle3(image_url={self.image_url})"
 
-    @backoff.on_exception(backoff.expo, Exception, max_tries=max_retries)
+    @backoff.on_exception(
+        backoff.expo, Exception, max_tries=max_retries
+    )
     def rate_limited_call(self, task: str):
         """Rate limited call to the Dalle3 API"""
         return self.__call__(task)

swarms/models/distilled_whisperx.py

@@ -6,7 +6,11 @@ from typing import Union
 
 import torch
 from termcolor import colored
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from transformers import (
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    pipeline,
+)
 
 
 def async_retry(max_retries=3, exceptions=(Exception,), delay=1):
@@ -29,8 +33,8 @@ def async_retry(max_retries=3, exceptions=(Exception,), delay=1):
                     if retries <= 0:
                         raise
                     print(
-                        f"Retry after exception: {e}, Attempts remaining:"
-                        f" {retries}"
+                        f"Retry after exception: {e}, Attempts"
+                        f" remaining: {retries}"
                     )
                     await asyncio.sleep(delay)
 
@@ -66,7 +70,9 @@ class DistilWhisperModel:
     def __init__(self, model_id="distil-whisper/distil-large-v2"):
         self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
         self.torch_dtype = (
-            torch.float16 if torch.cuda.is_available() else torch.float32
+            torch.float16
+            if torch.cuda.is_available()
+            else torch.float32
         )
         self.model_id = model_id
         self.model = AutoModelForSpeechSeq2Seq.from_pretrained(
@@ -106,7 +112,9 @@ class DistilWhisperModel:
         :return: The transcribed text.
         """
         loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(None, self.transcribe, inputs)
+        return await loop.run_in_executor(
+            None, self.transcribe, inputs
+        )
 
     def real_time_transcribe(self, audio_file_path, chunk_duration=5):
         """
@@ -130,13 +138,21 @@ class DistilWhisperModel:
                 sample_rate = audio_input.sampling_rate
                 len(audio_input.array) / sample_rate
                 chunks = [
-                    audio_input.array[i : i + sample_rate * chunk_duration]
+                    audio_input.array[
+                        i : i + sample_rate * chunk_duration
+                    ]
                     for i in range(
-                        0, len(audio_input.array), sample_rate * chunk_duration
+                        0,
+                        len(audio_input.array),
+                        sample_rate * chunk_duration,
                     )
                 ]
 
-                print(colored("Starting real-time transcription...", "green"))
+                print(
+                    colored(
+                        "Starting real-time transcription...", "green"
+                    )
+                )
 
                 for i, chunk in enumerate(chunks):
                     # Process the current chunk
@@ -146,8 +162,8 @@ class DistilWhisperModel:
                         return_tensors="pt",
                         padding=True,
                     )
-                    processed_inputs = processed_inputs.input_values.to(
-                        self.device
+                    processed_inputs = (
+                        processed_inputs.input_values.to(self.device)
                     )
 
                     # Generate transcription for the chunk
@@ -158,7 +174,9 @@ class DistilWhisperModel:
 
                     # Print the chunk's transcription
                     print(
-                        colored(f"Chunk {i+1}/{len(chunks)}: ", "yellow")
+                        colored(
+                            f"Chunk {i+1}/{len(chunks)}: ", "yellow"
+                        )
                         + transcription
                     )
 
@@ -167,5 +185,8 @@ class DistilWhisperModel:
 
         except Exception as e:
             print(
-                colored(f"An error occurred during transcription: {e}", "red")
+                colored(
+                    f"An error occurred during transcription: {e}",
+                    "red",
+                )
             )

swarms/models/eleven_labs.py

@@ -13,7 +13,8 @@ def _import_elevenlabs() -> Any:
         import elevenlabs
     except ImportError as e:
         raise ImportError(
-            "Cannot import elevenlabs, please install `pip install elevenlabs`."
+            "Cannot import elevenlabs, please install `pip install"
+            " elevenlabs`."
         ) from e
     return elevenlabs
 
@@ -52,16 +53,18 @@ class ElevenLabsText2SpeechTool(BaseTool):
 
     name: str = "eleven_labs_text2speech"
     description: str = (
-        "A wrapper around Eleven Labs Text2Speech. "
-        "Useful for when you need to convert text to speech. "
-        "It supports multiple languages, including English, German, Polish, "
-        "Spanish, Italian, French, Portuguese, and Hindi. "
+        "A wrapper around Eleven Labs Text2Speech. Useful for when"
+        " you need to convert text to speech. It supports multiple"
+        " languages, including English, German, Polish, Spanish,"
+        " Italian, French, Portuguese, and Hindi. "
     )
 
     @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key exists in environment."""
-        _ = get_from_dict_or_env(values, "eleven_api_key", "ELEVEN_API_KEY")
+        _ = get_from_dict_or_env(
+            values, "eleven_api_key", "ELEVEN_API_KEY"
+        )
 
         return values
 
@@ -102,7 +105,9 @@ class ElevenLabsText2SpeechTool(BaseTool):
 
     def save(self, speech_file: str, path: str) -> None:
         """Save the speech file to a path."""
-        raise NotImplementedError("Saving not implemented for this tool.")
+        raise NotImplementedError(
+            "Saving not implemented for this tool."
+        )
 
     def __str__(self):
         return "ElevenLabsText2SpeechTool"

swarms/models/embeddings_base.py

@@ -14,7 +14,9 @@ class Embeddings(ABC):
     def embed_query(self, text: str) -> List[float]:
         """Embed query text."""
 
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+    async def aembed_documents(
+        self, texts: List[str]
+    ) -> List[List[float]]:
         """Embed search docs."""
         raise NotImplementedError
 

swarms/models/fastvit.py

@@ -83,4 +83,6 @@ class FastViT:
         top_classes = top_classes.cpu().numpy().tolist()
         # top_class_labels = [FASTVIT_IMAGENET_1K_CLASSES[i] for i in top_classes] # Uncomment if class labels are needed
 
-        return ClassificationResult(class_id=top_classes, confidence=top_probs)
+        return ClassificationResult(
+            class_id=top_classes, confidence=top_probs
+        )

swarms/models/fuyu.py

@@ -43,7 +43,9 @@ class Fuyu:
         self.device_map = device_map
         self.max_new_tokens = max_new_tokens
 
-        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            pretrained_path
+        )
         self.image_processor = FuyuImageProcessor()
         self.processor = FuyuProcessor(
             image_processor=self.image_processor,
@@ -87,5 +89,7 @@ class Fuyu:
             image_pil = Image.open(BytesIO(response.content))
             return image_pil
         except requests.RequestException as error:
-            print(f"Error fetching image from {img} and error: {error}")
+            print(
+                f"Error fetching image from {img} and error: {error}"
+            )
             return None

swarms/models/gpt4_vision_api.py

@@ -15,7 +15,10 @@ from termcolor import colored
 try:
     import cv2
 except ImportError:
-    print("OpenCV not installed. Please install OpenCV to use this model.")
+    print(
+        "OpenCV not installed. Please install OpenCV to use this"
+        " model."
+    )
     raise ImportError
 
 # Load environment variables
@@ -127,7 +130,10 @@ class GPT4VisionAPI:
             payload = {
                 "model": self.model_name,
                 "messages": [
-                    {"role": "system", "content": [self.system_prompt]},
+                    {
+                        "role": "system",
+                        "content": [self.system_prompt],
+                    },
                     {
                         "role": "user",
                         "content": [
@@ -135,9 +141,7 @@ class GPT4VisionAPI:
                             {
                                 "type": "image_url",
                                 "image_url": {
-                                    "url": (
-                                        f"data:image/jpeg;base64,{base64_image}"
-                                    )
+                                    "url": f"data:image/jpeg;base64,{base64_image}"
                                 },
                             },
                         ],
@@ -241,7 +245,9 @@ class GPT4VisionAPI:
             if not success:
                 break
             _, buffer = cv2.imencode(".jpg", frame)
-            base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
+            base64_frames.append(
+                base64.b64encode(buffer).decode("utf-8")
+            )
 
         video.release()
         print(len(base64_frames), "frames read.")
@@ -266,7 +272,10 @@ class GPT4VisionAPI:
             payload = {
                 "model": self.model_name,
                 "messages": [
-                    {"role": "system", "content": [self.system_prompt]},
+                    {
+                        "role": "system",
+                        "content": [self.system_prompt],
+                    },
                     {
                         "role": "user",
                         "content": [
@@ -274,9 +283,7 @@ class GPT4VisionAPI:
                             {
                                 "type": "image_url",
                                 "image_url": {
-                                    "url": (
-                                        f"data:image/jpeg;base64,{base64_image}"
-                                    )
+                                    "url": f"data:image/jpeg;base64,{base64_image}"
                                 },
                             },
                         ],
@@ -326,7 +333,9 @@ class GPT4VisionAPI:
 
         """
         # Instantiate the thread pool executor
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
+        with ThreadPoolExecutor(
+            max_workers=self.max_workers
+        ) as executor:
             results = executor.map(self.run, tasks, imgs)
 
         # Print the results for debugging
@@ -372,9 +381,7 @@ class GPT4VisionAPI:
                             {
                                 "type": "image_url",
                                 "image_url": {
-                                    "url": (
-                                        f"data:image/jpeg;base64,{base64_image}"
-                                    )
+                                    "url": f"data:image/jpeg;base64,{base64_image}"
                                 },
                             },
                         ],
@@ -384,7 +391,9 @@ class GPT4VisionAPI:
             }
             async with aiohttp.ClientSession() as session:
                 async with session.post(
-                    self.openai_proxy, headers=headers, data=json.dumps(payload)
+                    self.openai_proxy,
+                    headers=headers,
+                    data=json.dumps(payload),
                 ) as response:
                     out = await response.json()
                     content = out["choices"][0]["message"]["content"]
@@ -393,7 +402,9 @@ class GPT4VisionAPI:
             print(f"Error with the request {error}")
             raise error
 
-    def run_batch(self, tasks_images: List[Tuple[str, str]]) -> List[str]:
+    def run_batch(
+        self, tasks_images: List[Tuple[str, str]]
+    ) -> List[str]:
         """Process a batch of tasks and images"""
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
@@ -420,7 +431,9 @@ class GPT4VisionAPI:
         """Process a batch of tasks and images asynchronously with retries"""
         loop = asyncio.get_event_loop()
         futures = [
-            loop.run_in_executor(None, self.run_with_retries, task, img)
+            loop.run_in_executor(
+                None, self.run_with_retries, task, img
+            )
             for task, img in tasks_images
         ]
         return await asyncio.gather(*futures)
@@ -428,7 +441,9 @@ class GPT4VisionAPI:
     def health_check(self):
         """Health check for the GPT4Vision model"""
         try:
-            response = requests.get("https://api.openai.com/v1/engines")
+            response = requests.get(
+                "https://api.openai.com/v1/engines"
+            )
             return response.status_code == 200
         except requests.RequestException as error:
             print(f"Health check failed: {error}")

swarms/models/gpt4v.py

@@ -65,7 +65,9 @@ class GPT4Vision:
     model: str = "gpt-4-vision-preview"
     backoff_factor: float = 2.0
     timeout_seconds: int = 10
-    openai_api_key: Optional[str] = None or os.getenv("OPENAI_API_KEY")
+    openai_api_key: Optional[str] = None or os.getenv(
+        "OPENAI_API_KEY"
+    )
     # 'Low' or 'High' for respesctively fast or high quality, but high more token usage
     quality: str = "low"
     # Max tokens to use for the API request, the maximum might be 3,000 but we don't know
@@ -131,9 +133,14 @@ class GPT4Vision:
             return out
         except openai.OpenAIError as e:
             # logger.error(f"OpenAI API error: {e}")
-            return f"OpenAI API error: Could not process the image. {e}"
+            return (
+                f"OpenAI API error: Could not process the image. {e}"
+            )
         except Exception as e:
-            return f"Unexpected error occurred while processing the image. {e}"
+            return (
+                "Unexpected error occurred while processing the"
+                f" image. {e}"
+            )
 
     def clean_output(self, output: str):
         # Regex pattern to find the Choice object representation in the output
@@ -182,11 +189,18 @@ class GPT4Vision:
             return print(response.choices[0])
         except openai.OpenAIError as e:
             # logger.error(f"OpenAI API error: {e}")
-            return f"OpenAI API error: Could not process the image. {e}"
+            return (
+                f"OpenAI API error: Could not process the image. {e}"
+            )
         except Exception as e:
-            return f"Unexpected error occurred while processing the image. {e}"
+            return (
+                "Unexpected error occurred while processing the"
+                f" image. {e}"
+            )
 
-    def run_batch(self, tasks_images: List[Tuple[str, str]]) -> List[str]:
+    def run_batch(
+        self, tasks_images: List[Tuple[str, str]]
+    ) -> List[str]:
         """Process a batch of tasks and images"""
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
@@ -213,7 +227,9 @@ class GPT4Vision:
         """Process a batch of tasks and images asynchronously with retries"""
         loop = asyncio.get_event_loop()
         futures = [
-            loop.run_in_executor(None, self.run_with_retries, task, img)
+            loop.run_in_executor(
+                None, self.run_with_retries, task, img
+            )
             for task, img in tasks_images
         ]
         return await asyncio.gather(*futures)
@@ -240,7 +256,9 @@ class GPT4Vision:
     def health_check(self):
         """Health check for the GPT4Vision model"""
         try:
-            response = requests.get("https://api.openai.com/v1/engines")
+            response = requests.get(
+                "https://api.openai.com/v1/engines"
+            )
             return response.status_code == 200
         except requests.RequestException as error:
             print(f"Health check failed: {error}")

swarms/models/huggingface.py

@@ -7,7 +7,11 @@ from typing import List, Tuple
 import torch
 from termcolor import colored
 from torch.nn.parallel import DistributedDataParallel as DDP
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
 
 
 class HuggingfaceLLM:
@@ -173,7 +177,10 @@ class HuggingfaceLLM:
                 self.model_id, *args, **kwargs
             )
             self.model = AutoModelForCausalLM.from_pretrained(
-                self.model_id, quantization_config=bnb_config, *args, **kwargs
+                self.model_id,
+                quantization_config=bnb_config,
+                *args,
+                **kwargs,
             )
 
             self.model  # .to(self.device)
@@ -182,7 +189,11 @@ class HuggingfaceLLM:
             # raise
             print(
                 colored(
-                    f"Failed to load the model and or the tokenizer: {e}", "red"
+                    (
+                        "Failed to load the model and or the"
+                        f" tokenizer: {e}"
+                    ),
+                    "red",
                 )
             )
 
@@ -198,7 +209,9 @@ class HuggingfaceLLM:
         """Load the model"""
         if not self.model or not self.tokenizer:
             try:
-                self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    self.model_id
+                )
 
                 bnb_config = (
                     BitsAndBytesConfig(**self.quantization_config)
@@ -214,7 +227,8 @@ class HuggingfaceLLM:
                     self.model = DDP(self.model)
             except Exception as error:
                 self.logger.error(
-                    f"Failed to load the model or the tokenizer: {error}"
+                    "Failed to load the model or the tokenizer:"
+                    f" {error}"
                 )
                 raise
 
@@ -226,7 +240,9 @@ class HuggingfaceLLM:
             results = list(executor.map(self.run, tasks))
         return results
 
-    def run_batch(self, tasks_images: List[Tuple[str, str]]) -> List[str]:
+    def run_batch(
+        self, tasks_images: List[Tuple[str, str]]
+    ) -> List[str]:
         """Process a batch of tasks and images"""
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
@@ -254,9 +270,9 @@ class HuggingfaceLLM:
         self.print_dashboard(task)
 
         try:
-            inputs = self.tokenizer.encode(task, return_tensors="pt").to(
-                self.device
-            )
+            inputs = self.tokenizer.encode(
+                task, return_tensors="pt"
+            ).to(self.device)
 
             # self.log.start()
 
@@ -266,7 +282,9 @@ class HuggingfaceLLM:
                         output_sequence = []
 
                         outputs = self.model.generate(
-                            inputs, max_length=len(inputs) + 1, do_sample=True
+                            inputs,
+                            max_length=len(inputs) + 1,
+                            do_sample=True,
                         )
                         output_tokens = outputs[0][-1]
                         output_sequence.append(output_tokens.item())
@@ -274,7 +292,8 @@ class HuggingfaceLLM:
                         # print token in real-time
                         print(
                             self.tokenizer.decode(
-                                [output_tokens], skip_special_tokens=True
+                                [output_tokens],
+                                skip_special_tokens=True,
                             ),
                             end="",
                             flush=True,
@@ -287,13 +306,16 @@ class HuggingfaceLLM:
                     )
 
             del inputs
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return self.tokenizer.decode(
+                outputs[0], skip_special_tokens=True
+            )
         except Exception as e:
             print(
                 colored(
                     (
-                        "HuggingfaceLLM could not generate text because of"
-                        f" error: {e}, try optimizing your arguments"
+                        "HuggingfaceLLM could not generate text"
+                        f" because of error: {e}, try optimizing your"
+                        " arguments"
                     ),
                     "red",
                 )
@@ -318,9 +340,9 @@ class HuggingfaceLLM:
         self.print_dashboard(task)
 
         try:
-            inputs = self.tokenizer.encode(task, return_tensors="pt").to(
-                self.device
-            )
+            inputs = self.tokenizer.encode(
+                task, return_tensors="pt"
+            ).to(self.device)
 
             # self.log.start()
 
@@ -330,7 +352,9 @@ class HuggingfaceLLM:
                         output_sequence = []
 
                         outputs = self.model.generate(
-                            inputs, max_length=len(inputs) + 1, do_sample=True
+                            inputs,
+                            max_length=len(inputs) + 1,
+                            do_sample=True,
                         )
                         output_tokens = outputs[0][-1]
                         output_sequence.append(output_tokens.item())
@@ -338,7 +362,8 @@ class HuggingfaceLLM:
                         # print token in real-time
                         print(
                             self.tokenizer.decode(
-                                [output_tokens], skip_special_tokens=True
+                                [output_tokens],
+                                skip_special_tokens=True,
                             ),
                             end="",
                             flush=True,
@@ -352,7 +377,9 @@ class HuggingfaceLLM:
 
             del inputs
 
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return self.tokenizer.decode(
+                outputs[0], skip_special_tokens=True
+            )
         except Exception as e:
             self.logger.error(f"Failed to generate the text: {e}")
             raise

swarms/models/idefics.py

@@ -100,10 +100,14 @@ class Idefics:
         """
         inputs = (
             self.processor(
-                prompts, add_end_of_utterance_token=False, return_tensors="pt"
+                prompts,
+                add_end_of_utterance_token=False,
+                return_tensors="pt",
             ).to(self.device)
             if batched_mode
-            else self.processor(prompts[0], return_tensors="pt").to(self.device)
+            else self.processor(prompts[0], return_tensors="pt").to(
+                self.device
+            )
         )
 
         exit_condition = self.processor.tokenizer(
@@ -111,7 +115,8 @@ class Idefics:
         ).input_ids
 
         bad_words_ids = self.processor.tokenizer(
-            ["<image>", "<fake_token_around_image"], add_special_tokens=False
+            ["<image>", "<fake_token_around_image"],
+            add_special_tokens=False,
         ).input_ids
 
         generated_ids = self.model.generate(
@@ -145,10 +150,14 @@ class Idefics:
         """
         inputs = (
             self.processor(
-                prompts, add_end_of_utterance_token=False, return_tensors="pt"
+                prompts,
+                add_end_of_utterance_token=False,
+                return_tensors="pt",
             ).to(self.device)
             if batched_mode
-            else self.processor(prompts[0], return_tensors="pt").to(self.device)
+            else self.processor(prompts[0], return_tensors="pt").to(
+                self.device
+            )
         )
 
         exit_condition = self.processor.tokenizer(
@@ -156,7 +165,8 @@ class Idefics:
         ).input_ids
 
         bad_words_ids = self.processor.tokenizer(
-            ["<image>", "<fake_token_around_image"], add_special_tokens=False
+            ["<image>", "<fake_token_around_image"],
+            add_special_tokens=False,
         ).input_ids
 
         generated_ids = self.model.generate(

swarms/models/jina_embeds.py

@@ -3,7 +3,11 @@ import logging
 import torch
 from numpy.linalg import norm
 from torch.nn.parallel import DistributedDataParallel as DDP
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
 
 
 def cos_sim(a, b):
@@ -92,14 +96,18 @@ class JinaEmbeddings:
 
             self.model  # .to(self.device)
         except Exception as e:
-            self.logger.error(f"Failed to load the model or the tokenizer: {e}")
+            self.logger.error(
+                f"Failed to load the model or the tokenizer: {e}"
+            )
             raise
 
     def load_model(self):
         """Load the model"""
         if not self.model or not self.tokenizer:
             try:
-                self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    self.model_id
+                )
 
                 bnb_config = (
                     BitsAndBytesConfig(**self.quantization_config)
@@ -117,7 +125,8 @@ class JinaEmbeddings:
                     self.model = DDP(self.model)
             except Exception as error:
                 self.logger.error(
-                    f"Failed to load the model or the tokenizer: {error}"
+                    "Failed to load the model or the tokenizer:"
+                    f" {error}"
                 )
                 raise
 
@@ -137,7 +146,9 @@ class JinaEmbeddings:
         max_length = self.max_length
 
         try:
-            embeddings = self.model.encode([task], max_length=max_length)
+            embeddings = self.model.encode(
+                [task], max_length=max_length
+            )
 
             if self.cos_sim:
                 print(cos_sim(embeddings[0], embeddings[1]))
@@ -186,7 +197,9 @@ class JinaEmbeddings:
         max_length = self.max_length
 
         try:
-            embeddings = self.model.encode([task], max_length=max_length)
+            embeddings = self.model.encode(
+                [task], max_length=max_length
+            )
 
             if self.cos_sim:
                 print(cos_sim(embeddings[0], embeddings[1]))

swarms/models/kosmos2.py

@@ -20,7 +20,9 @@ class Detections(BaseModel):
         ), "All fields must have the same length."
         return values
 
-    @validator("xyxy", "class_id", "confidence", pre=True, each_item=True)
+    @validator(
+        "xyxy", "class_id", "confidence", pre=True, each_item=True
+    )
     def check_not_empty(cls, v):
         if isinstance(v, list) and len(v) == 0:
             raise ValueError("List must not be empty")
@@ -69,7 +71,9 @@ class Kosmos2(BaseModel):
         image = Image.open(img)
         prompt = "<grounding>An image of"
 
-        inputs = self.processor(text=prompt, images=image, return_tensors="pt")
+        inputs = self.processor(
+            text=prompt, images=image, return_tensors="pt"
+        )
         outputs = self.model.generate(
             **inputs, use_cache=True, max_new_tokens=64
         )
@@ -83,7 +87,9 @@ class Kosmos2(BaseModel):
         entities = self.extract_entities(generated_text)
 
         # Convert entities to detections format
-        detections = self.process_entities_to_detections(entities, image)
+        detections = self.process_entities_to_detections(
+            entities, image
+        )
         return detections
 
     def extract_entities(

swarms/models/kosmos_two.py

@@ -51,7 +51,9 @@ class Kosmos:
 
     def run(self, prompt, image):
         """Run Kosmos"""
-        inputs = self.processor(text=prompt, images=image, return_tensors="pt")
+        inputs = self.processor(
+            text=prompt, images=image, return_tensors="pt"
+        )
         generated_ids = self.model.generate(
             pixel_values=inputs["pixel_values"],
             input_ids=inputs["input_ids"][:, :-1],
@@ -65,13 +67,15 @@ class Kosmos:
             generated_ids,
             skip_special_tokens=True,
         )[0]
-        processed_text, entities = self.processor.post_process_generation(
-            generated_texts
+        processed_text, entities = (
+            self.processor.post_process_generation(generated_texts)
         )
 
     def __call__(self, prompt, image):
         """Run call"""
-        inputs = self.processor(text=prompt, images=image, return_tensors="pt")
+        inputs = self.processor(
+            text=prompt, images=image, return_tensors="pt"
+        )
         generated_ids = self.model.generate(
             pixel_values=inputs["pixel_values"],
             input_ids=inputs["input_ids"][:, :-1],
@@ -85,8 +89,8 @@ class Kosmos:
             generated_ids,
             skip_special_tokens=True,
         )[0]
-        processed_text, entities = self.processor.post_process_generation(
-            generated_texts
+        processed_text, entities = (
+            self.processor.post_process_generation(generated_texts)
         )
 
     # tasks
@@ -117,7 +121,9 @@ class Kosmos:
         prompt = "<grounding> Describe this image in detail"
         self.run(prompt, image_url)
 
-    def draw_entity_boxes_on_image(image, entities, show=False, save_path=None):
+    def draw_entity_boxes_on_image(
+        image, entities, show=False, save_path=None
+    ):
         """_summary_
         Args:
             image (_type_): image or image path
@@ -144,13 +150,17 @@ class Kosmos:
             reverse_norm_std = torch.tensor(
                 [0.26862954, 0.26130258, 0.27577711]
             )[:, None, None]
-            image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
+            image_tensor = (
+                image_tensor * reverse_norm_std + reverse_norm_mean
+            )
             pil_img = T.ToPILImage()(image_tensor)
             image_h = pil_img.height
             image_w = pil_img.width
             image = np.array(pil_img)[:, :, [2, 1, 0]]
         else:
-            raise ValueError(f"invaild image format, {type(image)} for {image}")
+            raise ValueError(
+                f"invaild image format, {type(image)} for {image}"
+            )
 
         if len(entities) == 0:
             return image
@@ -179,7 +189,9 @@ class Kosmos:
                 )
                 # draw bbox
                 # random color
-                color = tuple(np.random.randint(0, 255, size=3).tolist())
+                color = tuple(
+                    np.random.randint(0, 255, size=3).tolist()
+                )
                 new_image = cv2.rectangle(
                     new_image,
                     (orig_x1, orig_y1),
@@ -196,7 +208,12 @@ class Kosmos:
                 x1 = orig_x1 - l_o
                 y1 = orig_y1 - l_o
 
-                if y1 < text_height + text_offset_original + 2 * text_spaces:
+                if (
+                    y1
+                    < text_height
+                    + text_offset_original
+                    + 2 * text_spaces
+                ):
                     y1 = (
                         orig_y1
                         + r_o
@@ -215,24 +232,40 @@ class Kosmos:
                 )
                 text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = (
                     x1,
-                    y1 - (text_height + text_offset_original + 2 * text_spaces),
+                    y1
+                    - (
+                        text_height
+                        + text_offset_original
+                        + 2 * text_spaces
+                    ),
                     x1 + text_width,
                     y1,
                 )
 
                 for prev_bbox in previous_bboxes:
                     while is_overlapping(
-                        (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2),
+                        (
+                            text_bg_x1,
+                            text_bg_y1,
+                            text_bg_x2,
+                            text_bg_y2,
+                        ),
                         prev_bbox,
                     ):
                         text_bg_y1 += (
-                            text_height + text_offset_original + 2 * text_spaces
+                            text_height
+                            + text_offset_original
+                            + 2 * text_spaces
                         )
                         text_bg_y2 += (
-                            text_height + text_offset_original + 2 * text_spaces
+                            text_height
+                            + text_offset_original
+                            + 2 * text_spaces
                         )
                         y1 += (
-                            text_height + text_offset_original + 2 * text_spaces
+                            text_height
+                            + text_offset_original
+                            + 2 * text_spaces
                         )
 
                         if text_bg_y2 >= image_h:

swarms/models/llama_function_caller.py

@@ -170,7 +170,9 @@ class LlamaFunctionCaller:
         prompt = f"{task}\n\n"
 
         # Encode and send to the model
-        inputs = self.tokenizer([prompt], return_tensors="pt").to(self.runtime)
+        inputs = self.tokenizer([prompt], return_tensors="pt").to(
+            self.runtime
+        )
 
         streamer = TextStreamer(self.tokenizer)
 

swarms/models/llava.py

@@ -70,7 +70,10 @@ class MultiModalLlava:
 
     def chat(self):
         """Interactive chat in terminal"""
-        print("Starting chat with LlavaModel. Type 'exit' to end the session.")
+        print(
+            "Starting chat with LlavaModel. Type 'exit' to end the"
+            " session."
+        )
         while True:
             user_input = input("You: ")
             if user_input.lower() == "exit":

swarms/models/mistral.py

@@ -50,7 +50,8 @@ class Mistral:
         # Check if the specified device is available
         if not torch.cuda.is_available() and device == "cuda":
             raise ValueError(
-                "CUDA is not available. Please choose a different device."
+                "CUDA is not available. Please choose a different"
+                " device."
             )
 
         # Load the model and tokenizer
@@ -62,19 +63,25 @@ class Mistral:
 
     def load_model(self):
         try:
-            self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_name
+            )
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.model_name
+            )
             self.model.to(self.device)
         except Exception as e:
-            raise ValueError(f"Error loading the Mistral model: {str(e)}")
+            raise ValueError(
+                f"Error loading the Mistral model: {str(e)}"
+            )
 
     def run(self, task: str):
         """Run the model on a given task."""
 
         try:
-            model_inputs = self.tokenizer([task], return_tensors="pt").to(
-                self.device
-            )
+            model_inputs = self.tokenizer(
+                [task], return_tensors="pt"
+            ).to(self.device)
             generated_ids = self.model.generate(
                 **model_inputs,
                 max_length=self.max_length,
@@ -82,7 +89,9 @@ class Mistral:
                 temperature=self.temperature,
                 max_new_tokens=self.max_length,
             )
-            output_text = self.tokenizer.batch_decode(generated_ids)[0]
+            output_text = self.tokenizer.batch_decode(generated_ids)[
+                0
+            ]
             return output_text
         except Exception as e:
             raise ValueError(f"Error running the model: {str(e)}")
@@ -91,9 +100,9 @@ class Mistral:
         """Run the model on a given task."""
 
         try:
-            model_inputs = self.tokenizer([task], return_tensors="pt").to(
-                self.device
-            )
+            model_inputs = self.tokenizer(
+                [task], return_tensors="pt"
+            ).to(self.device)
             generated_ids = self.model.generate(
                 **model_inputs,
                 max_length=self.max_length,
@@ -101,7 +110,9 @@ class Mistral:
                 temperature=self.temperature,
                 max_new_tokens=self.max_length,
             )
-            output_text = self.tokenizer.batch_decode(generated_ids)[0]
+            output_text = self.tokenizer.batch_decode(generated_ids)[
+                0
+            ]
             return output_text
         except Exception as e:
             raise ValueError(f"Error running the model: {str(e)}")

swarms/models/mpt.py

@@ -30,7 +30,10 @@ class MPT7B:
     """
 
     def __init__(
-        self, model_name: str, tokenizer_name: str, max_tokens: int = 100
+        self,
+        model_name: str,
+        tokenizer_name: str,
+        max_tokens: int = 100,
     ):
         # Loading model and tokenizer details
         self.model_name = model_name
@@ -138,9 +141,13 @@ class MPT7B:
         """Call the model asynchronously""" ""
         return await self.run_async(task, *args, **kwargs)
 
-    def batch_generate(self, prompts: list, temperature: float = 1.0) -> list:
+    def batch_generate(
+        self, prompts: list, temperature: float = 1.0
+    ) -> list:
         """Batch generate text"""
-        self.logger.info(f"Generating text for {len(prompts)} prompts...")
+        self.logger.info(
+            f"Generating text for {len(prompts)} prompts..."
+        )
         results = []
         with torch.autocast("cuda", dtype=torch.bfloat16):
             for prompt in prompts:

swarms/models/nougat.py

@@ -61,7 +61,9 @@ class Nougat:
     def __call__(self, img: str):
         """Call the model with an image_path str as an input"""
         image = Image.open(img)
-        pixel_values = self.processor(image, return_tensors="pt").pixel_values
+        pixel_values = self.processor(
+            image, return_tensors="pt"
+        ).pixel_values
 
         # Generate transcriptions, here we only generate 30 tokens
         outputs = self.model.generate(
@@ -92,7 +94,9 @@ class Nougat:
 
         # Convert the matches to a readable format
         cleaned_data = [
-            "Date: {}, Amount: {}".format(date, amount.replace(",", ""))
+            "Date: {}, Amount: {}".format(
+                date, amount.replace(",", "")
+            )
             for date, amount in matches
         ]
 

swarms/models/openai_embeddings.py

@@ -55,13 +55,17 @@ def _create_retry_decorator(
     return retry(
         reraise=True,
         stop=stop_after_attempt(embeddings.max_retries),
-        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+        wait=wait_exponential(
+            multiplier=1, min=min_seconds, max=max_seconds
+        ),
         retry=(
             retry_if_exception_type(llm.error.Timeout)
             | retry_if_exception_type(llm.error.APIError)
             | retry_if_exception_type(llm.error.APIConnectionError)
             | retry_if_exception_type(llm.error.RateLimitError)
-            | retry_if_exception_type(llm.error.ServiceUnavailableError)
+            | retry_if_exception_type(
+                llm.error.ServiceUnavailableError
+            )
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -77,13 +81,17 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
     async_retrying = AsyncRetrying(
         reraise=True,
         stop=stop_after_attempt(embeddings.max_retries),
-        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+        wait=wait_exponential(
+            multiplier=1, min=min_seconds, max=max_seconds
+        ),
         retry=(
             retry_if_exception_type(llm.error.Timeout)
             | retry_if_exception_type(llm.error.APIError)
             | retry_if_exception_type(llm.error.APIConnectionError)
             | retry_if_exception_type(llm.error.RateLimitError)
-            | retry_if_exception_type(llm.error.ServiceUnavailableError)
+            | retry_if_exception_type(
+                llm.error.ServiceUnavailableError
+            )
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -104,11 +112,15 @@ def _check_response(response: dict) -> dict:
     if any(len(d["embedding"]) == 1 for d in response["data"]):
         import llm
 
-        raise llm.error.APIError("OpenAI API returned an empty embedding")
+        raise llm.error.APIError(
+            "OpenAI API returned an empty embedding"
+        )
     return response
 
 
-def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
+def embed_with_retry(
+    embeddings: OpenAIEmbeddings, **kwargs: Any
+) -> Any:
     """Use tenacity to retry the embedding call."""
     retry_decorator = _create_retry_decorator(embeddings)
 
@@ -176,9 +188,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
     client: Any  #: :meta private:
     model: str = "text-embedding-ada-002"
-    deployment: str = (
-        model  # to support Azure OpenAI Service custom deployment names
-    )
+    deployment: str = model  # to support Azure OpenAI Service custom deployment names
     openai_api_version: Optional[str] = None
     # to support Azure OpenAI Service custom endpoints
     openai_api_base: Optional[str] = None
@@ -191,12 +201,16 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     openai_api_key: Optional[str] = None
     openai_organization: Optional[str] = None
     allowed_special: Union[Literal["all"], Set[str]] = set()
-    disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
+    disallowed_special: Union[
+        Literal["all"], Set[str], Sequence[str]
+    ] = "all"
     chunk_size: int = 1000
     """Maximum number of texts to embed in each batch"""
     max_retries: int = 6
     """Maximum number of retries to make when generating."""
-    request_timeout: Optional[Union[float, Tuple[float, float]]] = None
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = (
+        None
+    )
     """Timeout in seconds for the OpenAPI request."""
     headers: Any = None
     tiktoken_model_name: Optional[str] = None
@@ -226,7 +240,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         extra = values.get("model_kwargs", {})
         for field_name in list(values):
             if field_name in extra:
-                raise ValueError(f"Found {field_name} supplied twice.")
+                raise ValueError(
+                    f"Found {field_name} supplied twice."
+                )
             if field_name not in all_required_field_names:
                 warnings.warn(
                     f"""WARNING! {field_name} is not default parameter.
@@ -240,9 +256,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         )
         if invalid_model_kwargs:
             raise ValueError(
-                f"Parameters {invalid_model_kwargs} should be specified"
-                " explicitly. Instead they were passed in as part of"
-                " `model_kwargs` parameter."
+                f"Parameters {invalid_model_kwargs} should be"
+                " specified explicitly. Instead they were passed in"
+                " as part of `model_kwargs` parameter."
             )
 
         values["model_kwargs"] = extra
@@ -272,7 +288,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             "OPENAI_PROXY",
             default="",
         )
-        if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
+        if values["openai_api_type"] in (
+            "azure",
+            "azure_ad",
+            "azuread",
+        ):
             default_api_version = "2022-12-01"
         else:
             default_api_version = ""
@@ -324,9 +344,15 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         return openai_args
 
     def _get_len_safe_embeddings(
-        self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
+        self,
+        texts: List[str],
+        *,
+        engine: str,
+        chunk_size: Optional[int] = None,
     ) -> List[List[float]]:
-        embeddings: List[List[float]] = [[] for _ in range(len(texts))]
+        embeddings: List[List[float]] = [
+            [] for _ in range(len(texts))
+        ]
         try:
             import tiktoken
         except ImportError:
@@ -343,7 +369,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             encoding = tiktoken.encoding_for_model(model_name)
         except KeyError:
             logger.warning(
-                "Warning: model not found. Using cl100k_base encoding."
+                "Warning: model not found. Using cl100k_base"
+                " encoding."
             )
             model = "cl100k_base"
             encoding = tiktoken.get_encoding(model)
@@ -358,7 +385,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j : j + self.embedding_ctx_length])
+                tokens.append(
+                    token[j : j + self.embedding_ctx_length]
+                )
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -380,10 +409,16 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
-            batched_embeddings.extend(r["embedding"] for r in response["data"])
+            batched_embeddings.extend(
+                r["embedding"] for r in response["data"]
+            )
 
-        results: List[List[List[float]]] = [[] for _ in range(len(texts))]
-        num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
+        results: List[List[List[float]]] = [
+            [] for _ in range(len(texts))
+        ]
+        num_tokens_in_batch: List[List[int]] = [
+            [] for _ in range(len(texts))
+        ]
         for i in range(len(indices)):
             results[indices[i]].append(batched_embeddings[i])
             num_tokens_in_batch[indices[i]].append(len(tokens[i]))
@@ -400,16 +435,24 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 average = np.average(
                     _result, axis=0, weights=num_tokens_in_batch[i]
                 )
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embeddings[i] = (
+                average / np.linalg.norm(average)
+            ).tolist()
 
         return embeddings
 
     # please refer to
     # https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
     async def _aget_len_safe_embeddings(
-        self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
+        self,
+        texts: List[str],
+        *,
+        engine: str,
+        chunk_size: Optional[int] = None,
     ) -> List[List[float]]:
-        embeddings: List[List[float]] = [[] for _ in range(len(texts))]
+        embeddings: List[List[float]] = [
+            [] for _ in range(len(texts))
+        ]
         try:
             import tiktoken
         except ImportError:
@@ -426,7 +469,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             encoding = tiktoken.encoding_for_model(model_name)
         except KeyError:
             logger.warning(
-                "Warning: model not found. Using cl100k_base encoding."
+                "Warning: model not found. Using cl100k_base"
+                " encoding."
             )
             model = "cl100k_base"
             encoding = tiktoken.get_encoding(model)
@@ -441,7 +485,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j : j + self.embedding_ctx_length])
+                tokens.append(
+                    token[j : j + self.embedding_ctx_length]
+                )
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -452,10 +498,16 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
-            batched_embeddings.extend(r["embedding"] for r in response["data"])
+            batched_embeddings.extend(
+                r["embedding"] for r in response["data"]
+            )
 
-        results: List[List[List[float]]] = [[] for _ in range(len(texts))]
-        num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
+        results: List[List[List[float]]] = [
+            [] for _ in range(len(texts))
+        ]
+        num_tokens_in_batch: List[List[int]] = [
+            [] for _ in range(len(texts))
+        ]
         for i in range(len(indices)):
             results[indices[i]].append(batched_embeddings[i])
             num_tokens_in_batch[indices[i]].append(len(tokens[i]))
@@ -474,7 +526,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 average = np.average(
                     _result, axis=0, weights=num_tokens_in_batch[i]
                 )
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embeddings[i] = (
+                average / np.linalg.norm(average)
+            ).tolist()
 
         return embeddings
 
@@ -493,7 +547,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         """
         # NOTE: to keep things simple, we assume the list may contain texts longer
         #       than the maximum context and use length-safe embedding function.
-        return self._get_len_safe_embeddings(texts, engine=self.deployment)
+        return self._get_len_safe_embeddings(
+            texts, engine=self.deployment
+        )
 
     async def aembed_documents(
         self, texts: List[str], chunk_size: Optional[int] = 0

swarms/models/openai_function_caller.py

@@ -3,7 +3,11 @@ from typing import Any, Dict, List, Optional, Union
 import openai
 import requests
 from pydantic import BaseModel, validator
-from tenacity import retry, stop_after_attempt, wait_random_exponential
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_random_exponential,
+)
 from termcolor import colored
 
 
@@ -100,7 +104,9 @@ class FunctionSpecification(BaseModel):
 
         for req_param in self.required or []:
             if req_param not in params:
-                raise ValueError(f"Missing required parameter: {req_param}")
+                raise ValueError(
+                    f"Missing required parameter: {req_param}"
+                )
 
 
 class OpenAIFunctionCaller:
@@ -220,7 +226,10 @@ class OpenAIFunctionCaller:
             elif message["role"] == "tool":
                 print(
                     colored(
-                        f"function ({message['name']}): {message['content']}\n",
+                        (
+                            f"function ({message['name']}):"
+                            f" {message['content']}\n"
+                        ),
                         role_to_color[message["role"]],
                     )
                 )

swarms/models/openai_models.py

@@ -27,7 +27,10 @@ from langchain.llms.base import BaseLLM, create_base_retry_decorator
 from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import Generation, LLMResult
 from langchain.schema.output import GenerationChunk
-from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
+from langchain.utils import (
+    get_from_dict_or_env,
+    get_pydantic_field_names,
+)
 from langchain.utils.utils import build_extra_kwargs
 
 
@@ -44,7 +47,9 @@ def is_openai_v1() -> bool:
 
 
 def update_token_usage(
-    keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
+    keys: Set[str],
+    response: Dict[str, Any],
+    token_usage: Dict[str, Any],
 ) -> None:
     """Update token usage."""
     _keys_to_use = keys.intersection(response["usage"])
@@ -65,7 +70,9 @@ def _stream_response_to_generation_chunk(
             finish_reason=stream_response["choices"][0].get(
                 "finish_reason", None
             ),
-            logprobs=stream_response["choices"][0].get("logprobs", None),
+            logprobs=stream_response["choices"][0].get(
+                "logprobs", None
+            ),
         ),
     )
 
@@ -74,13 +81,15 @@ def _update_response(
     response: Dict[str, Any], stream_response: Dict[str, Any]
 ) -> None:
     """Update response from the stream response."""
-    response["choices"][0]["text"] += stream_response["choices"][0]["text"]
-    response["choices"][0]["finish_reason"] = stream_response["choices"][0].get(
-        "finish_reason", None
-    )
-    response["choices"][0]["logprobs"] = stream_response["choices"][0][
-        "logprobs"
+    response["choices"][0]["text"] += stream_response["choices"][0][
+        "text"
     ]
+    response["choices"][0]["finish_reason"] = stream_response[
+        "choices"
+    ][0].get("finish_reason", None)
+    response["choices"][0]["logprobs"] = stream_response["choices"][
+        0
+    ]["logprobs"]
 
 
 def _streaming_response_template() -> Dict[str, Any]:
@@ -111,7 +120,9 @@ def _create_retry_decorator(
         openai.error.ServiceUnavailableError,
     ]
     return create_base_retry_decorator(
-        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
+        error_types=errors,
+        max_retries=llm.max_retries,
+        run_manager=run_manager,
     )
 
 
@@ -121,7 +132,9 @@ def completion_with_retry(
     **kwargs: Any,
 ) -> Any:
     """Use tenacity to retry the completion call."""
-    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+    retry_decorator = _create_retry_decorator(
+        llm, run_manager=run_manager
+    )
 
     @retry_decorator
     def _completion_with_retry(**kwargs: Any) -> Any:
@@ -136,7 +149,9 @@ async def acompletion_with_retry(
     **kwargs: Any,
 ) -> Any:
     """Use tenacity to retry the async completion call."""
-    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+    retry_decorator = _create_retry_decorator(
+        llm, run_manager=run_manager
+    )
 
     @retry_decorator
     async def _completion_with_retry(**kwargs: Any) -> Any:
@@ -160,7 +175,9 @@ class BaseOpenAI(BaseLLM):
             attributes["openai_api_base"] = self.openai_api_base
 
         if self.openai_organization != "":
-            attributes["openai_organization"] = self.openai_organization
+            attributes["openai_organization"] = (
+                self.openai_organization
+            )
 
         if self.openai_proxy != "":
             attributes["openai_proxy"] = self.openai_proxy
@@ -199,9 +216,13 @@ class BaseOpenAI(BaseLLM):
     openai_proxy: Optional[str] = None
     batch_size: int = 20
     """Batch size to use when passing multiple documents to generate."""
-    request_timeout: Optional[Union[float, Tuple[float, float]]] = None
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = (
+        None
+    )
     """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
-    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
+    logit_bias: Optional[Dict[str, float]] = Field(
+        default_factory=dict
+    )
     """Adjust the probability of specific tokens being generated."""
     max_retries: int = 6
     """Maximum number of retries to make when generating."""
@@ -278,7 +299,9 @@ class BaseOpenAI(BaseLLM):
         if values["streaming"] and values["n"] > 1:
             raise ValueError("Cannot stream results when n > 1.")
         if values["streaming"] and values["best_of"] > 1:
-            raise ValueError("Cannot stream results when best_of > 1.")
+            raise ValueError(
+                "Cannot stream results when best_of > 1."
+            )
         return values
 
     @property
@@ -310,7 +333,9 @@ class BaseOpenAI(BaseLLM):
         **kwargs: Any,
     ) -> Iterator[GenerationChunk]:
         params = {**self._invocation_params, **kwargs, "stream": True}
-        self.get_sub_prompts(params, [prompt], stop)  # this mutates params
+        self.get_sub_prompts(
+            params, [prompt], stop
+        )  # this mutates params
         for stream_resp in completion_with_retry(
             self, prompt=prompt, run_manager=run_manager, **params
         ):
@@ -336,7 +361,9 @@ class BaseOpenAI(BaseLLM):
         **kwargs: Any,
     ) -> AsyncIterator[GenerationChunk]:
         params = {**self._invocation_params, **kwargs, "stream": True}
-        self.get_sub_prompts(params, [prompt], stop)  # this mutate params
+        self.get_sub_prompts(
+            params, [prompt], stop
+        )  # this mutate params
         async for stream_resp in await acompletion_with_retry(
             self, prompt=prompt, run_manager=run_manager, **params
         ):
@@ -404,7 +431,9 @@ class BaseOpenAI(BaseLLM):
                     {
                         "text": generation.text,
                         "finish_reason": (
-                            generation.generation_info.get("finish_reason")
+                            generation.generation_info.get(
+                                "finish_reason"
+                            )
                             if generation.generation_info
                             else None
                         ),
@@ -417,7 +446,10 @@ class BaseOpenAI(BaseLLM):
                 )
             else:
                 response = completion_with_retry(
-                    self, prompt=_prompts, run_manager=run_manager, **params
+                    self,
+                    prompt=_prompts,
+                    run_manager=run_manager,
+                    **params,
                 )
                 choices.extend(response["choices"])
                 update_token_usage(_keys, response, token_usage)
@@ -459,7 +491,9 @@ class BaseOpenAI(BaseLLM):
                     {
                         "text": generation.text,
                         "finish_reason": (
-                            generation.generation_info.get("finish_reason")
+                            generation.generation_info.get(
+                                "finish_reason"
+                            )
                             if generation.generation_info
                             else None
                         ),
@@ -472,7 +506,10 @@ class BaseOpenAI(BaseLLM):
                 )
             else:
                 response = await acompletion_with_retry(
-                    self, prompt=_prompts, run_manager=run_manager, **params
+                    self,
+                    prompt=_prompts,
+                    run_manager=run_manager,
+                    **params,
                 )
                 choices.extend(response["choices"])
                 update_token_usage(_keys, response, token_usage)
@@ -488,15 +525,19 @@ class BaseOpenAI(BaseLLM):
         if stop is not None:
             if "stop" in params:
                 raise ValueError(
-                    "`stop` found in both the input and default params."
+                    "`stop` found in both the input and default"
+                    " params."
                 )
             params["stop"] = stop
         if params["max_tokens"] == -1:
             if len(prompts) != 1:
                 raise ValueError(
-                    "max_tokens set to -1 not supported for multiple inputs."
+                    "max_tokens set to -1 not supported for multiple"
+                    " inputs."
                 )
-            params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
+            params["max_tokens"] = self.max_tokens_for_prompt(
+                prompts[0]
+            )
         sub_prompts = [
             prompts[i : i + self.batch_size]
             for i in range(0, len(prompts), self.batch_size)
@@ -504,7 +545,10 @@ class BaseOpenAI(BaseLLM):
         return sub_prompts
 
     def create_llm_result(
-        self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
+        self,
+        choices: Any,
+        prompts: List[str],
+        token_usage: Dict[str, int],
     ) -> LLMResult:
         """Create the LLMResult from the choices and prompts."""
         generations = []
@@ -522,8 +566,13 @@ class BaseOpenAI(BaseLLM):
                     for choice in sub_choices
                 ]
             )
-        llm_output = {"token_usage": token_usage, "model_name": self.model_name}
-        return LLMResult(generations=generations, llm_output=llm_output)
+        llm_output = {
+            "token_usage": token_usage,
+            "model_name": self.model_name,
+        }
+        return LLMResult(
+            generations=generations, llm_output=llm_output
+        )
 
     @property
     def _invocation_params(self) -> Dict[str, Any]:
@@ -542,7 +591,10 @@ class BaseOpenAI(BaseLLM):
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""
-        return {**{"model_name": self.model_name}, **self._default_params}
+        return {
+            **{"model_name": self.model_name},
+            **self._default_params,
+        }
 
     @property
     def _llm_type(self) -> str:
@@ -558,9 +610,9 @@ class BaseOpenAI(BaseLLM):
             import tiktoken
         except ImportError:
             raise ImportError(
-                "Could not import tiktoken python package. "
-                "This is needed in order to calculate get_num_tokens. "
-                "Please install it with `pip install tiktoken`."
+                "Could not import tiktoken python package. This is"
+                " needed in order to calculate get_num_tokens. Please"
+                " install it with `pip install tiktoken`."
             )
 
         model_name = self.tiktoken_model_name or self.model_name
@@ -568,7 +620,8 @@ class BaseOpenAI(BaseLLM):
             enc = tiktoken.encoding_for_model(model_name)
         except KeyError:
             logger.warning(
-                "Warning: model not found. Using cl100k_base encoding."
+                "Warning: model not found. Using cl100k_base"
+                " encoding."
             )
             model = "cl100k_base"
             enc = tiktoken.get_encoding(model)
@@ -630,8 +683,8 @@ class BaseOpenAI(BaseLLM):
 
         if context_size is None:
             raise ValueError(
-                f"Unknown model: {modelname}. Please provide a valid OpenAI"
-                " model name.Known models are: "
+                f"Unknown model: {modelname}. Please provide a valid"
+                " OpenAI model name.Known models are: "
                 + ", ".join(model_token_mapping.keys())
             )
 
@@ -678,7 +731,10 @@ class OpenAI(BaseOpenAI):
 
     @property
     def _invocation_params(self) -> Dict[str, Any]:
-        return {**{"model": self.model_name}, **super()._invocation_params}
+        return {
+            **{"model": self.model_name},
+            **super()._invocation_params,
+        }
 
 
 class AzureOpenAI(BaseOpenAI):
@@ -802,7 +858,9 @@ class OpenAIChat(BaseLLM):
         for field_name in list(values):
             if field_name not in all_required_field_names:
                 if field_name in extra:
-                    raise ValueError(f"Found {field_name} supplied twice.")
+                    raise ValueError(
+                        f"Found {field_name} supplied twice."
+                    )
                 extra[field_name] = values.pop(field_name)
         values["model_kwargs"] = extra
         return values
@@ -826,7 +884,10 @@ class OpenAIChat(BaseLLM):
             default="",
         )
         openai_organization = get_from_dict_or_env(
-            values, "openai_organization", "OPENAI_ORGANIZATION", default=""
+            values,
+            "openai_organization",
+            "OPENAI_ORGANIZATION",
+            default="",
         )
         try:
             import openai
@@ -847,9 +908,10 @@ class OpenAIChat(BaseLLM):
             values["client"] = openai.ChatCompletion
         except AttributeError:
             raise ValueError(
-                "`openai` has no `ChatCompletion` attribute, this is likely "
-                "due to an old version of the openai package. Try upgrading it "
-                "with `pip install --upgrade openai`."
+                "`openai` has no `ChatCompletion` attribute, this is"
+                " likely due to an old version of the openai package."
+                " Try upgrading it with `pip install --upgrade"
+                " openai`."
             )
         return values
 
@@ -863,8 +925,8 @@ class OpenAIChat(BaseLLM):
     ) -> Tuple:
         if len(prompts) > 1:
             raise ValueError(
-                "OpenAIChat currently only supports single prompt, got"
-                f" {prompts}"
+                "OpenAIChat currently only supports single prompt,"
+                f" got {prompts}"
             )
         messages = self.prefix_messages + [
             {"role": "user", "content": prompts[0]}
@@ -876,7 +938,8 @@ class OpenAIChat(BaseLLM):
         if stop is not None:
             if "stop" in params:
                 raise ValueError(
-                    "`stop` found in both the input and default params."
+                    "`stop` found in both the input and default"
+                    " params."
                 )
             params["stop"] = stop
         if params.get("max_tokens") == -1:
@@ -896,7 +959,9 @@ class OpenAIChat(BaseLLM):
         for stream_resp in completion_with_retry(
             self, messages=messages, run_manager=run_manager, **params
         ):
-            token = stream_resp["choices"][0]["delta"].get("content", "")
+            token = stream_resp["choices"][0]["delta"].get(
+                "content", ""
+            )
             chunk = GenerationChunk(text=token)
             yield chunk
             if run_manager:
@@ -914,7 +979,9 @@ class OpenAIChat(BaseLLM):
         async for stream_resp in await acompletion_with_retry(
             self, messages=messages, run_manager=run_manager, **params
         ):
-            token = stream_resp["choices"][0]["delta"].get("content", "")
+            token = stream_resp["choices"][0]["delta"].get(
+                "content", ""
+            )
             chunk = GenerationChunk(text=token)
             yield chunk
             if run_manager:
@@ -929,7 +996,9 @@ class OpenAIChat(BaseLLM):
     ) -> LLMResult:
         if self.streaming:
             generation: Optional[GenerationChunk] = None
-            for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
+            for chunk in self._stream(
+                prompts[0], stop, run_manager, **kwargs
+            ):
                 if generation is None:
                     generation = chunk
                 else:
@@ -950,7 +1019,9 @@ class OpenAIChat(BaseLLM):
             generations=[
                 [
                     Generation(
-                        text=full_response["choices"][0]["message"]["content"]
+                        text=full_response["choices"][0]["message"][
+                            "content"
+                        ]
                     )
                 ]
             ],
@@ -989,7 +1060,9 @@ class OpenAIChat(BaseLLM):
             generations=[
                 [
                     Generation(
-                        text=full_response["choices"][0]["message"]["content"]
+                        text=full_response["choices"][0]["message"][
+                            "content"
+                        ]
                     )
                 ]
             ],
@@ -999,7 +1072,10 @@ class OpenAIChat(BaseLLM):
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""
-        return {**{"model_name": self.model_name}, **self._default_params}
+        return {
+            **{"model_name": self.model_name},
+            **self._default_params,
+        }
 
     @property
     def _llm_type(self) -> str:
@@ -1015,9 +1091,9 @@ class OpenAIChat(BaseLLM):
             import tiktoken
         except ImportError:
             raise ImportError(
-                "Could not import tiktoken python package. "
-                "This is needed in order to calculate get_num_tokens. "
-                "Please install it with `pip install tiktoken`."
+                "Could not import tiktoken python package. This is"
+                " needed in order to calculate get_num_tokens. Please"
+                " install it with `pip install tiktoken`."
             )
 
         enc = tiktoken.encoding_for_model(self.model_name)

swarms/models/palm.py

@@ -47,7 +47,9 @@ def _create_retry_decorator() -> Callable[[Any], Any]:
             | retry_if_exception_type(
                 google.api_core.exceptions.ServiceUnavailable
             )
-            | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError)
+            | retry_if_exception_type(
+                google.api_core.exceptions.GoogleAPIError
+            )
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -114,8 +116,9 @@ class GooglePalm(BaseLLM, BaseModel):
             genai.configure(api_key=google_api_key)
         except ImportError:
             raise ImportError(
-                "Could not import google-generativeai python package. "
-                "Please install it with `pip install google-generativeai`."
+                "Could not import google-generativeai python package."
+                " Please install it with `pip install"
+                " google-generativeai`."
             )
 
         values["client"] = genai
@@ -124,9 +127,14 @@ class GooglePalm(BaseLLM, BaseModel):
             values["temperature"] is not None
             and not 0 <= values["temperature"] <= 1
         ):
-            raise ValueError("temperature must be in the range [0.0, 1.0]")
+            raise ValueError(
+                "temperature must be in the range [0.0, 1.0]"
+            )
 
-        if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
+        if (
+            values["top_p"] is not None
+            and not 0 <= values["top_p"] <= 1
+        ):
             raise ValueError("top_p must be in the range [0.0, 1.0]")
 
         if values["top_k"] is not None and values["top_k"] <= 0:
@@ -136,7 +144,9 @@ class GooglePalm(BaseLLM, BaseModel):
             values["max_output_tokens"] is not None
             and values["max_output_tokens"] <= 0
         ):
-            raise ValueError("max_output_tokens must be greater than zero")
+            raise ValueError(
+                "max_output_tokens must be greater than zero"
+            )
 
         return values
 
@@ -165,8 +175,12 @@ class GooglePalm(BaseLLM, BaseModel):
             prompt_generations = []
             for candidate in completion.candidates:
                 raw_text = candidate["output"]
-                stripped_text = _strip_erroneous_leading_spaces(raw_text)
-                prompt_generations.append(Generation(text=stripped_text))
+                stripped_text = _strip_erroneous_leading_spaces(
+                    raw_text
+                )
+                prompt_generations.append(
+                    Generation(text=stripped_text)
+                )
             generations.append(prompt_generations)
 
         return LLMResult(generations=generations)

swarms/models/pegasus.py

@@ -34,16 +34,22 @@ class PegasusEmbedding:
     """
 
     def __init__(
-        self, modality: str, multi_process: bool = False, n_processes: int = 4
+        self,
+        modality: str,
+        multi_process: bool = False,
+        n_processes: int = 4,
     ):
         self.modality = modality
         self.multi_process = multi_process
         self.n_processes = n_processes
         try:
-            self.pegasus = Pegasus(modality, multi_process, n_processes)
+            self.pegasus = Pegasus(
+                modality, multi_process, n_processes
+            )
         except Exception as e:
             logging.error(
-                f"Failed to initialize Pegasus with modality: {modality}: {e}"
+                "Failed to initialize Pegasus with modality:"
+                f" {modality}: {e}"
             )
             raise
 
@@ -52,5 +58,7 @@ class PegasusEmbedding:
         try:
             return self.pegasus.embed(data)
         except Exception as e:
-            logging.error(f"Failed to generate embeddings. Error: {e}")
+            logging.error(
+                f"Failed to generate embeddings. Error: {e}"
+            )
             raise

swarms/models/petals.py

@@ -38,6 +38,8 @@ class Petals:
     def __call__(self, prompt):
         """Generate text using the Petals API."""
         params = self._default_params()
-        inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
+        inputs = self.tokenizer(prompt, return_tensors="pt")[
+            "input_ids"
+        ]
         outputs = self.model.generate(inputs, **params)
         return self.tokenizer.decode(outputs[0])

swarms/models/sam.py

@@ -1,7 +1,12 @@
 import cv2
 import numpy as np
 from PIL import Image
-from transformers import SamImageProcessor, SamModel, SamProcessor, pipeline
+from transformers import (
+    SamImageProcessor,
+    SamModel,
+    SamProcessor,
+    pipeline,
+)
 
 try:
     import cv2
@@ -44,16 +49,18 @@ def compute_mask_iou_vectorized(masks: np.ndarray) -> np.ndarray:
     """
     if np.any(masks.sum(axis=(1, 2)) == 0):
         raise ValueError(
-            "One or more masks are empty. Please filter out empty masks before"
-            " using `compute_iou_vectorized` function."
+            "One or more masks are empty. Please filter out empty"
+            " masks before using `compute_iou_vectorized` function."
         )
 
     masks_bool = masks.astype(bool)
     masks_flat = masks_bool.reshape(masks.shape[0], -1)
-    intersection = np.logical_and(masks_flat[:, None], masks_flat[None, :]).sum(
-        axis=2
-    )
-    union = np.logical_or(masks_flat[:, None], masks_flat[None, :]).sum(axis=2)
+    intersection = np.logical_and(
+        masks_flat[:, None], masks_flat[None, :]
+    ).sum(axis=2)
+    union = np.logical_or(
+        masks_flat[:, None], masks_flat[None, :]
+    ).sum(axis=2)
     iou_matrix = intersection / union
     return iou_matrix
 
@@ -96,7 +103,9 @@ def mask_non_max_suppression(
 
 
 def filter_masks_by_relative_area(
-    masks: np.ndarray, minimum_area: float = 0.01, maximum_area: float = 1.0
+    masks: np.ndarray,
+    minimum_area: float = 0.01,
+    maximum_area: float = 1.0,
 ) -> np.ndarray:
     """
     Filters masks based on their relative area within the total area of each mask.
@@ -123,18 +132,21 @@ def filter_masks_by_relative_area(
 
     if not (0 <= minimum_area <= 1) or not (0 <= maximum_area <= 1):
         raise ValueError(
-            "`minimum_area` and `maximum_area` must be between 0 and 1."
+            "`minimum_area` and `maximum_area` must be between 0"
+            " and 1."
         )
 
     if minimum_area > maximum_area:
         raise ValueError(
-            "`minimum_area` must be less than or equal to `maximum_area`."
+            "`minimum_area` must be less than or equal to"
+            " `maximum_area`."
         )
 
     total_area = masks.shape[1] * masks.shape[2]
     relative_areas = masks.sum(axis=(1, 2)) / total_area
     return masks[
-        (relative_areas >= minimum_area) & (relative_areas <= maximum_area)
+        (relative_areas >= minimum_area)
+        & (relative_areas <= maximum_area)
     ]
 
 
@@ -170,7 +182,9 @@ def adjust_mask_features_by_relative_area(
         if feature_type == FeatureType.ISLAND
         else cv2.RETR_CCOMP
     )
-    contours, _ = cv2.findContours(mask, operation, cv2.CHAIN_APPROX_SIMPLE)
+    contours, _ = cv2.findContours(
+        mask, operation, cv2.CHAIN_APPROX_SIMPLE
+    )
 
     for contour in contours:
         area = cv2.contourArea(contour)
@@ -180,7 +194,9 @@ def adjust_mask_features_by_relative_area(
                 image=mask,
                 contours=[contour],
                 contourIdx=-1,
-                color=(0 if feature_type == FeatureType.ISLAND else 255),
+                color=(
+                    0 if feature_type == FeatureType.ISLAND else 255
+                ),
                 thickness=-1,
             )
     return np.where(mask > 0, 1, 0).astype(bool)
@@ -198,7 +214,9 @@ def masks_to_marks(masks: np.ndarray) -> sv.Detections:
         sv.Detections: An object containing the masks and their bounding box
             coordinates.
     """
-    return sv.Detections(mask=masks, xyxy=sv.mask_to_xyxy(masks=masks))
+    return sv.Detections(
+        mask=masks, xyxy=sv.mask_to_xyxy(masks=masks)
+    )
 
 
 def refine_marks(
@@ -262,11 +280,15 @@ class SegmentAnythingMarkGenerator:
     """
 
     def __init__(
-        self, device: str = "cpu", model_name: str = "facebook/sam-vit-huge"
+        self,
+        device: str = "cpu",
+        model_name: str = "facebook/sam-vit-huge",
     ):
         self.model = SamModel.from_pretrained(model_name).to(device)
         self.processor = SamProcessor.from_pretrained(model_name)
-        self.image_processor = SamImageProcessor.from_pretrained(model_name)
+        self.image_processor = SamImageProcessor.from_pretrained(
+            model_name
+        )
         self.pipeline = pipeline(
             task="mask-generation",
             model=self.model,
@@ -285,7 +307,9 @@ class SegmentAnythingMarkGenerator:
             sv.Detections: An object containing the segmentation masks and their
                 corresponding bounding box coordinates.
         """
-        image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+        image = Image.fromarray(
+            cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        )
         outputs = self.pipeline(image, points_per_batch=64)
         masks = np.array(outputs["masks"])
         return masks_to_marks(masks=masks)

swarms/models/simple_ada.py

@@ -4,7 +4,9 @@ from openai import OpenAI
 client = OpenAI()
 
 
-def get_ada_embeddings(text: str, model: str = "text-embedding-ada-002"):
+def get_ada_embeddings(
+    text: str, model: str = "text-embedding-ada-002"
+):
     """
     Simple function to get embeddings from ada
 
@@ -16,6 +18,6 @@ def get_ada_embeddings(text: str, model: str = "text-embedding-ada-002"):
 
     text = text.replace("\n", " ")
 
-    return client.embeddings.create(input=[text], model=model)["data"][0][
-        "embedding"
-    ]
+    return client.embeddings.create(input=[text], model=model)[
+        "data"
+    ][0]["embedding"]

swarms/models/speecht5.py

@@ -87,9 +87,15 @@ class SpeechT5:
         self.model_name = model_name
         self.vocoder_name = vocoder_name
         self.dataset_name = dataset_name
-        self.processor = SpeechT5Processor.from_pretrained(self.model_name)
-        self.model = SpeechT5ForTextToSpeech.from_pretrained(self.model_name)
-        self.vocoder = SpeechT5HifiGan.from_pretrained(self.vocoder_name)
+        self.processor = SpeechT5Processor.from_pretrained(
+            self.model_name
+        )
+        self.model = SpeechT5ForTextToSpeech.from_pretrained(
+            self.model_name
+        )
+        self.vocoder = SpeechT5HifiGan.from_pretrained(
+            self.vocoder_name
+        )
         self.embeddings_dataset = load_dataset(
             self.dataset_name, split="validation"
         )
@@ -101,7 +107,9 @@ class SpeechT5:
         ).unsqueeze(0)
         inputs = self.processor(text=text, return_tensors="pt")
         speech = self.model.generate_speech(
-            inputs["input_ids"], speaker_embedding, vocoder=self.vocoder
+            inputs["input_ids"],
+            speaker_embedding,
+            vocoder=self.vocoder,
         )
         return speech
 
@@ -112,13 +120,19 @@ class SpeechT5:
     def set_model(self, model_name: str):
         """Set the model to a new model."""
         self.model_name = model_name
-        self.processor = SpeechT5Processor.from_pretrained(self.model_name)
-        self.model = SpeechT5ForTextToSpeech.from_pretrained(self.model_name)
+        self.processor = SpeechT5Processor.from_pretrained(
+            self.model_name
+        )
+        self.model = SpeechT5ForTextToSpeech.from_pretrained(
+            self.model_name
+        )
 
     def set_vocoder(self, vocoder_name):
         """Set the vocoder to a new vocoder."""
         self.vocoder_name = vocoder_name
-        self.vocoder = SpeechT5HifiGan.from_pretrained(self.vocoder_name)
+        self.vocoder = SpeechT5HifiGan.from_pretrained(
+            self.vocoder_name
+        )
 
     def set_embeddings_dataset(self, dataset_name):
         """Set the embeddings dataset to a new dataset."""
@@ -148,7 +162,9 @@ class SpeechT5:
     # Feature 4: Change dataset split (train, validation, test)
     def change_dataset_split(self, split="train"):
         """Change dataset split (train, validation, test)."""
-        self.embeddings_dataset = load_dataset(self.dataset_name, split=split)
+        self.embeddings_dataset = load_dataset(
+            self.dataset_name, split=split
+        )
 
     # Feature 5: Load a custom speaker embedding (xvector) for the text
     def load_custom_embedding(self, xvector):

swarms/models/ssd_1b.py

@@ -96,7 +96,9 @@ class SSD1B:
         byte_array = byte_stream.getvalue()
         return byte_array
 
-    @backoff.on_exception(backoff.expo, Exception, max_time=max_time_seconds)
+    @backoff.on_exception(
+        backoff.expo, Exception, max_time=max_time_seconds
+    )
     def __call__(self, task: str, neg_prompt: str):
         """
         Text to image conversion using the SSD1B API
@@ -124,7 +126,9 @@ class SSD1B:
         if task in self.cache:
             return self.cache[task]
         try:
-            img = self.pipe(prompt=task, neg_prompt=neg_prompt).images[0]
+            img = self.pipe(
+                prompt=task, neg_prompt=neg_prompt
+            ).images[0]
 
             # Generate a unique filename for the image
             img_name = f"{uuid.uuid4()}.{self.image_format}"
@@ -141,8 +145,8 @@ class SSD1B:
             print(
                 colored(
                     (
-                        f"Error running SSD1B: {error} try optimizing your api"
-                        " key and or try again"
+                        f"Error running SSD1B: {error} try optimizing"
+                        " your api key and or try again"
                     ),
                     "red",
                 )
@@ -218,7 +222,9 @@ class SSD1B:
                 executor.submit(self, task): task for task in tasks
             }
             results = []
-            for future in concurrent.futures.as_completed(future_to_task):
+            for future in concurrent.futures.as_completed(
+                future_to_task
+            ):
                 task = future_to_task[future]
                 try:
                     img = future.result()
@@ -229,18 +235,28 @@ class SSD1B:
                     print(
                         colored(
                             (
-                                f"Error running SSD1B: {error} try optimizing"
-                                " your api key and or try again"
+                                f"Error running SSD1B: {error} try"
+                                " optimizing your api key and or try"
+                                " again"
                             ),
                             "red",
                         )
                     )
                     print(
                         colored(
-                            f"Error running SSD1B: {error.http_status}", "red"
+                            (
+                                "Error running SSD1B:"
+                                f" {error.http_status}"
+                            ),
+                            "red",
+                        )
+                    )
+                    print(
+                        colored(
+                            f"Error running SSD1B: {error.error}",
+                            "red",
                         )
                     )
-                    print(colored(f"Error running SSD1B: {error.error}", "red"))
                     raise error
 
     def _generate_uuid(self):
@@ -255,7 +271,9 @@ class SSD1B:
         """Str method for the SSD1B class"""
         return f"SSD1B(image_url={self.image_url})"
 
-    @backoff.on_exception(backoff.expo, Exception, max_tries=max_retries)
+    @backoff.on_exception(
+        backoff.expo, Exception, max_tries=max_retries
+    )
     def rate_limited_call(self, task: str):
         """Rate limited call to the SSD1B API"""
         return self.__call__(task)

swarms/models/timm.py

@@ -34,7 +34,9 @@ class TimmModel:
         """Retrieve the list of supported models from timm."""
         return timm.list_models()
 
-    def _create_model(self, model_info: TimmModelInfo) -> torch.nn.Module:
+    def _create_model(
+        self, model_info: TimmModelInfo
+    ) -> torch.nn.Module:
         """
         Create a model instance from timm with specified parameters.
 

swarms/models/whisperx_model.py

@@ -93,7 +93,9 @@ class WhisperX:
 
         try:
             segments = result["segments"]
-            transcription = " ".join(segment["text"] for segment in segments)
+            transcription = " ".join(
+                segment["text"] for segment in segments
+            )
             return transcription
         except KeyError:
             print("The key 'segments' is not found in the result.")
@@ -128,7 +130,9 @@ class WhisperX:
 
         try:
             segments = result["segments"]
-            transcription = " ".join(segment["text"] for segment in segments)
+            transcription = " ".join(
+                segment["text"] for segment in segments
+            )
             return transcription
         except KeyError:
             print("The key 'segments' is not found in the result.")

swarms/models/wizard_storytelling.py

@@ -2,7 +2,11 @@ import logging
 
 import torch
 from torch.nn.parallel import DistributedDataParallel as DDP
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
 
 
 class WizardLLMStoryTeller:
@@ -74,21 +78,27 @@ class WizardLLMStoryTeller:
             bnb_config = BitsAndBytesConfig(**quantization_config)
 
         try:
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.model_id
+            )
             self.model = AutoModelForCausalLM.from_pretrained(
                 self.model_id, quantization_config=bnb_config
             )
 
             self.model  # .to(self.device)
         except Exception as e:
-            self.logger.error(f"Failed to load the model or the tokenizer: {e}")
+            self.logger.error(
+                f"Failed to load the model or the tokenizer: {e}"
+            )
             raise
 
     def load_model(self):
         """Load the model"""
         if not self.model or not self.tokenizer:
             try:
-                self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    self.model_id
+                )
 
                 bnb_config = (
                     BitsAndBytesConfig(**self.quantization_config)
@@ -104,7 +114,8 @@ class WizardLLMStoryTeller:
                     self.model = DDP(self.model)
             except Exception as error:
                 self.logger.error(
-                    f"Failed to load the model or the tokenizer: {error}"
+                    "Failed to load the model or the tokenizer:"
+                    f" {error}"
                 )
                 raise
 
@@ -124,9 +135,9 @@ class WizardLLMStoryTeller:
         max_length = self.max_length
 
         try:
-            inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to(
-                self.device
-            )
+            inputs = self.tokenizer.encode(
+                prompt_text, return_tensors="pt"
+            ).to(self.device)
 
             # self.log.start()
 
@@ -136,7 +147,9 @@ class WizardLLMStoryTeller:
                         output_sequence = []
 
                         outputs = self.model.generate(
-                            inputs, max_length=len(inputs) + 1, do_sample=True
+                            inputs,
+                            max_length=len(inputs) + 1,
+                            do_sample=True,
                         )
                         output_tokens = outputs[0][-1]
                         output_sequence.append(output_tokens.item())
@@ -144,7 +157,8 @@ class WizardLLMStoryTeller:
                         # print token in real-time
                         print(
                             self.tokenizer.decode(
-                                [output_tokens], skip_special_tokens=True
+                                [output_tokens],
+                                skip_special_tokens=True,
                             ),
                             end="",
                             flush=True,
@@ -157,7 +171,9 @@ class WizardLLMStoryTeller:
                     )
 
             del inputs
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return self.tokenizer.decode(
+                outputs[0], skip_special_tokens=True
+            )
         except Exception as e:
             self.logger.error(f"Failed to generate the text: {e}")
             raise
@@ -178,9 +194,9 @@ class WizardLLMStoryTeller:
         max_length = self.max_
 
         try:
-            inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to(
-                self.device
-            )
+            inputs = self.tokenizer.encode(
+                prompt_text, return_tensors="pt"
+            ).to(self.device)
 
             # self.log.start()
 
@@ -190,7 +206,9 @@ class WizardLLMStoryTeller:
                         output_sequence = []
 
                         outputs = self.model.generate(
-                            inputs, max_length=len(inputs) + 1, do_sample=True
+                            inputs,
+                            max_length=len(inputs) + 1,
+                            do_sample=True,
                         )
                         output_tokens = outputs[0][-1]
                         output_sequence.append(output_tokens.item())
@@ -198,7 +216,8 @@ class WizardLLMStoryTeller:
                         # print token in real-time
                         print(
                             self.tokenizer.decode(
-                                [output_tokens], skip_special_tokens=True
+                                [output_tokens],
+                                skip_special_tokens=True,
                             ),
                             end="",
                             flush=True,
@@ -212,7 +231,9 @@ class WizardLLMStoryTeller:
 
             del inputs
 
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return self.tokenizer.decode(
+                outputs[0], skip_special_tokens=True
+            )
         except Exception as e:
             self.logger.error(f"Failed to generate the text: {e}")
             raise

swarms/models/yarn_mistral.py

@@ -2,7 +2,11 @@ import logging
 
 import torch
 from torch.nn.parallel import DistributedDataParallel as DDP
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
 
 
 class YarnMistral128:
@@ -74,7 +78,9 @@ class YarnMistral128:
             bnb_config = BitsAndBytesConfig(**quantization_config)
 
         try:
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.model_id
+            )
             self.model = AutoModelForCausalLM.from_pretrained(
                 self.model_id,
                 quantization_config=bnb_config,
@@ -86,14 +92,18 @@ class YarnMistral128:
 
             self.model  # .to(self.device)
         except Exception as e:
-            self.logger.error(f"Failed to load the model or the tokenizer: {e}")
+            self.logger.error(
+                f"Failed to load the model or the tokenizer: {e}"
+            )
             raise
 
     def load_model(self):
         """Load the model"""
         if not self.model or not self.tokenizer:
             try:
-                self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    self.model_id
+                )
 
                 bnb_config = (
                     BitsAndBytesConfig(**self.quantization_config)
@@ -109,7 +119,8 @@ class YarnMistral128:
                     self.model = DDP(self.model)
             except Exception as error:
                 self.logger.error(
-                    f"Failed to load the model or the tokenizer: {error}"
+                    "Failed to load the model or the tokenizer:"
+                    f" {error}"
                 )
                 raise
 
@@ -129,9 +140,9 @@ class YarnMistral128:
         max_length = self.max_length
 
         try:
-            inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to(
-                self.device
-            )
+            inputs = self.tokenizer.encode(
+                prompt_text, return_tensors="pt"
+            ).to(self.device)
 
             # self.log.start()
 
@@ -141,7 +152,9 @@ class YarnMistral128:
                         output_sequence = []
 
                         outputs = self.model.generate(
-                            inputs, max_length=len(inputs) + 1, do_sample=True
+                            inputs,
+                            max_length=len(inputs) + 1,
+                            do_sample=True,
                         )
                         output_tokens = outputs[0][-1]
                         output_sequence.append(output_tokens.item())
@@ -149,7 +162,8 @@ class YarnMistral128:
                         # print token in real-time
                         print(
                             self.tokenizer.decode(
-                                [output_tokens], skip_special_tokens=True
+                                [output_tokens],
+                                skip_special_tokens=True,
                             ),
                             end="",
                             flush=True,
@@ -162,7 +176,9 @@ class YarnMistral128:
                     )
 
             del inputs
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return self.tokenizer.decode(
+                outputs[0], skip_special_tokens=True
+            )
         except Exception as e:
             self.logger.error(f"Failed to generate the text: {e}")
             raise
@@ -206,9 +222,9 @@ class YarnMistral128:
         max_length = self.max_
 
         try:
-            inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to(
-                self.device
-            )
+            inputs = self.tokenizer.encode(
+                prompt_text, return_tensors="pt"
+            ).to(self.device)
 
             # self.log.start()
 
@@ -218,7 +234,9 @@ class YarnMistral128:
                         output_sequence = []
 
                         outputs = self.model.generate(
-                            inputs, max_length=len(inputs) + 1, do_sample=True
+                            inputs,
+                            max_length=len(inputs) + 1,
+                            do_sample=True,
                         )
                         output_tokens = outputs[0][-1]
                         output_sequence.append(output_tokens.item())
@@ -226,7 +244,8 @@ class YarnMistral128:
                         # print token in real-time
                         print(
                             self.tokenizer.decode(
-                                [output_tokens], skip_special_tokens=True
+                                [output_tokens],
+                                skip_special_tokens=True,
                             ),
                             end="",
                             flush=True,
@@ -240,7 +259,9 @@ class YarnMistral128:
 
             del inputs
 
-            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            return self.tokenizer.decode(
+                outputs[0], skip_special_tokens=True
+            )
         except Exception as e:
             self.logger.error(f"Failed to generate the text: {e}")
             raise

swarms/models/yi_200k.py

@@ -87,7 +87,9 @@ class Yi34B200k:
             top_k=self.top_k,
             top_p=self.top_p,
         )
-        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return self.tokenizer.decode(
+            outputs[0], skip_special_tokens=True
+        )
 
 
 # # Example usage

swarms/models/zephyr.py

@@ -68,7 +68,9 @@ class Zephyr:
             tokenize=self.tokenize,
             add_generation_prompt=self.add_generation_prompt,
         )
-        outputs = self.pipe(prompt)  # max_new_token=self.max_new_tokens)
+        outputs = self.pipe(
+            prompt
+        )  # max_new_token=self.max_new_tokens)
         print(outputs[0]["generated_text"])
 
     def chat(self, message: str):

swarms/prompts/__init__.py

@@ -2,7 +2,9 @@ from swarms.prompts.code_interpreter import CODE_INTERPRETER
 from swarms.prompts.finance_agent_prompt import FINANCE_AGENT_PROMPT
 from swarms.prompts.growth_agent_prompt import GROWTH_AGENT_PROMPT
 from swarms.prompts.legal_agent_prompt import LEGAL_AGENT_PROMPT
-from swarms.prompts.operations_agent_prompt import OPERATIONS_AGENT_PROMPT
+from swarms.prompts.operations_agent_prompt import (
+    OPERATIONS_AGENT_PROMPT,
+)
 from swarms.prompts.product_agent_prompt import PRODUCT_AGENT_PROMPT
 
 

swarms/prompts/agent_output_parser.py

@@ -25,7 +25,9 @@ class AgentOutputParser(BaseAgentOutputParser):
     @staticmethod
     def _preprocess_json_input(input_str: str) -> str:
         corrected_str = re.sub(
-            r'(?<!\\)\\(?!["\\/bfnrt]|u[0-9a-fA-F]{4})', r"\\\\", input_str
+            r'(?<!\\)\\(?!["\\/bfnrt]|u[0-9a-fA-F]{4})',
+            r"\\\\",
+            input_str,
         )
         return corrected_str
 

swarms/prompts/agent_prompt.py

@@ -16,12 +16,16 @@ class PromptGenerator:
                 "text": "thought",
                 "reasoning": "reasoning",
                 "plan": (
-                    "- short bulleted\n- list that conveys\n- long-term plan"
+                    "- short bulleted\n- list that conveys\n-"
+                    " long-term plan"
                 ),
                 "criticism": "constructive self-criticism",
                 "speak": "thoughts summary to say to user",
             },
-            "command": {"name": "command name", "args": {"arg name": "value"}},
+            "command": {
+                "name": "command name",
+                "args": {"arg name": "value"},
+            },
         }
 
     def add_constraint(self, constraint: str) -> None:
@@ -66,13 +70,16 @@ class PromptGenerator:
         Returns:
             str: The generated prompt string.
         """
-        formatted_response_format = json.dumps(self.response_format, indent=4)
+        formatted_response_format = json.dumps(
+            self.response_format, indent=4
+        )
         prompt_string = (
             f"Constraints:\n{''.join(self.constraints)}\n\nCommands:\n{''.join(self.commands)}\n\nResources:\n{''.join(self.resources)}\n\nPerformance"
             f" Evaluation:\n{''.join(self.performance_evaluation)}\n\nYou"
-            " should only respond in JSON format as described below \nResponse"
-            f" Format: \n{formatted_response_format} \nEnsure the response can"
-            " be parsed by Python json.loads"
+            " should only respond in JSON format as described below"
+            " \nResponse Format:"
+            f" \n{formatted_response_format} \nEnsure the response"
+            " can be parsed by Python json.loads"
         )
 
         return prompt_string

swarms/prompts/agent_prompts.py

@@ -5,26 +5,30 @@ def generate_agent_role_prompt(agent):
     """
     prompts = {
         "Finance Agent": (
-            "You are a seasoned finance analyst AI assistant. Your primary goal"
-            " is to compose comprehensive, astute, impartial, and methodically"
-            " arranged financial reports based on provided data and trends."
+            "You are a seasoned finance analyst AI assistant. Your"
+            " primary goal is to compose comprehensive, astute,"
+            " impartial, and methodically arranged financial reports"
+            " based on provided data and trends."
         ),
         "Travel Agent": (
-            "You are a world-travelled AI tour guide assistant. Your main"
-            " purpose is to draft engaging, insightful, unbiased, and"
-            " well-structured travel reports on given locations, including"
-            " history, attractions, and cultural insights."
+            "You are a world-travelled AI tour guide assistant. Your"
+            " main purpose is to draft engaging, insightful,"
+            " unbiased, and well-structured travel reports on given"
+            " locations, including history, attractions, and cultural"
+            " insights."
         ),
         "Academic Research Agent": (
             "You are an AI academic research assistant. Your primary"
-            " responsibility is to create thorough, academically rigorous,"
-            " unbiased, and systematically organized reports on a given"
-            " research topic, following the standards of scholarly work."
+            " responsibility is to create thorough, academically"
+            " rigorous, unbiased, and systematically organized"
+            " reports on a given research topic, following the"
+            " standards of scholarly work."
         ),
         "Default Agent": (
-            "You are an AI critical thinker research assistant. Your sole"
-            " purpose is to write well written, critically acclaimed, objective"
-            " and structured reports on given text."
+            "You are an AI critical thinker research assistant. Your"
+            " sole purpose is to write well written, critically"
+            " acclaimed, objective and structured reports on given"
+            " text."
         ),
     }
 
@@ -39,12 +43,14 @@ def generate_report_prompt(question, research_summary):
     """
 
     return (
-        f'"""{research_summary}""" Using the above information, answer the'
-        f' following question or topic: "{question}" in a detailed report --'
-        " The report should focus on the answer to the question, should be"
-        " well structured, informative, in depth, with facts and numbers if"
-        " available, a minimum of 1,200 words and with markdown syntax and apa"
-        " format. Write all source urls at the end of the report in apa format"
+        f'"""{research_summary}""" Using the above information,'
+        f' answer the following question or topic: "{question}" in a'
+        " detailed report -- The report should focus on the answer"
+        " to the question, should be well structured, informative,"
+        " in depth, with facts and numbers if available, a minimum"
+        " of 1,200 words and with markdown syntax and apa format."
+        " Write all source urls at the end of the report in apa"
+        " format"
     )
 
 
@@ -55,10 +61,10 @@ def generate_search_queries_prompt(question):
     """
 
     return (
-        "Write 4 google search queries to search online that form an objective"
-        f' opinion from the following: "{question}"You must respond with a list'
-        ' of strings in the following format: ["query 1", "query 2", "query'
-        ' 3", "query 4"]'
+        "Write 4 google search queries to search online that form an"
+        f' objective opinion from the following: "{question}"You must'
+        " respond with a list of strings in the following format:"
+        ' ["query 1", "query 2", "query 3", "query 4"]'
     )
 
 
@@ -73,16 +79,17 @@ def generate_resource_report_prompt(question, research_summary):
         str: The resource report prompt for the given question and research summary.
     """
     return (
-        f'"""{research_summary}""" Based on the above information, generate a'
-        " bibliography recommendation report for the following question or"
-        f' topic: "{question}". The report should provide a detailed analysis'
-        " of each recommended resource, explaining how each source can"
-        " contribute to finding answers to the research question. Focus on the"
-        " relevance, reliability, and significance of each source. Ensure that"
-        " the report is well-structured, informative, in-depth, and follows"
-        " Markdown syntax. Include relevant facts, figures, and numbers"
-        " whenever available. The report should have a minimum length of 1,200"
-        " words."
+        f'"""{research_summary}""" Based on the above information,'
+        " generate a bibliography recommendation report for the"
+        f' following question or topic: "{question}". The report'
+        " should provide a detailed analysis of each recommended"
+        " resource, explaining how each source can contribute to"
+        " finding answers to the research question. Focus on the"
+        " relevance, reliability, and significance of each source."
+        " Ensure that the report is well-structured, informative,"
+        " in-depth, and follows Markdown syntax. Include relevant"
+        " facts, figures, and numbers whenever available. The report"
+        " should have a minimum length of 1,200 words."
     )
 
 
@@ -94,14 +101,15 @@ def generate_outline_report_prompt(question, research_summary):
     """
 
     return (
-        f'"""{research_summary}""" Using the above information, generate an'
-        " outline for a research report in Markdown syntax for the following"
-        f' question or topic: "{question}". The outline should provide a'
-        " well-structured framework for the research report, including the"
-        " main sections, subsections, and key points to be covered. The"
-        " research report should be detailed, informative, in-depth, and a"
-        " minimum of 1,200 words. Use appropriate Markdown syntax to format"
-        " the outline and ensure readability."
+        f'"""{research_summary}""" Using the above information,'
+        " generate an outline for a research report in Markdown"
+        f' syntax for the following question or topic: "{question}".'
+        " The outline should provide a well-structured framework for"
+        " the research report, including the main sections,"
+        " subsections, and key points to be covered. The research"
+        " report should be detailed, informative, in-depth, and a"
+        " minimum of 1,200 words. Use appropriate Markdown syntax to"
+        " format the outline and ensure readability."
     )
 
 
@@ -113,11 +121,12 @@ def generate_concepts_prompt(question, research_summary):
     """
 
     return (
-        f'"""{research_summary}""" Using the above information, generate a list'
-        " of 5 main concepts to learn for a research report on the following"
-        f' question or topic: "{question}". The outline should provide a'
-        " well-structured frameworkYou must respond with a list of strings in"
-        ' the following format: ["concepts 1", "concepts 2", "concepts 3",'
+        f'"""{research_summary}""" Using the above information,'
+        " generate a list of 5 main concepts to learn for a research"
+        f' report on the following question or topic: "{question}".'
+        " The outline should provide a well-structured frameworkYou"
+        " must respond with a list of strings in the following"
+        ' format: ["concepts 1", "concepts 2", "concepts 3",'
         ' "concepts 4, concepts 5"]'
     )
 
@@ -132,10 +141,11 @@ def generate_lesson_prompt(concept):
     """
 
     prompt = (
-        f"generate a comprehensive lesson about {concept} in Markdown syntax."
-        f" This should include the definitionof {concept}, its historical"
-        " background and development, its applications or uses in"
-        f" differentfields, and notable events or facts related to {concept}."
+        f"generate a comprehensive lesson about {concept} in Markdown"
+        f" syntax. This should include the definitionof {concept},"
+        " its historical background and development, its"
+        " applications or uses in differentfields, and notable"
+        f" events or facts related to {concept}."
     )
 
     return prompt

swarms/prompts/base.py

@@ -53,7 +53,10 @@ def get_buffer_string(
         else:
             raise ValueError(f"Got unsupported message type: {m}")
         message = f"{role}: {m.content}"
-        if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
+        if (
+            isinstance(m, AIMessage)
+            and "function_call" in m.additional_kwargs
+        ):
             message += f"{m.additional_kwargs['function_call']}"
         string_messages.append(message)
 
@@ -100,8 +103,8 @@ class BaseMessageChunk(BaseMessage):
                 merged[k] = v
             elif not isinstance(merged[k], type(v)):
                 raise ValueError(
-                    f'additional_kwargs["{k}"] already exists in this message,'
-                    " but with a different type."
+                    f'additional_kwargs["{k}"] already exists in this'
+                    " message, but with a different type."
                 )
             elif isinstance(merged[k], str):
                 merged[k] += v
@@ -109,7 +112,8 @@ class BaseMessageChunk(BaseMessage):
                 merged[k] = self._merge_kwargs_dict(merged[k], v)
             else:
                 raise ValueError(
-                    f"Additional kwargs key {k} already exists in this message."
+                    f"Additional kwargs key {k} already exists in"
+                    " this message."
                 )
         return merged
 

swarms/prompts/chat_prompt.py

@@ -10,10 +10,14 @@ class Message:
     Messages are the inputs and outputs of ChatModels.
     """
 
-    def __init__(self, content: str, role: str, additional_kwargs: Dict = None):
+    def __init__(
+        self, content: str, role: str, additional_kwargs: Dict = None
+    ):
         self.content = content
         self.role = role
-        self.additional_kwargs = additional_kwargs if additional_kwargs else {}
+        self.additional_kwargs = (
+            additional_kwargs if additional_kwargs else {}
+        )
 
     @abstractmethod
     def get_type(self) -> str:
@@ -65,7 +69,10 @@ class SystemMessage(Message):
     """
 
     def __init__(
-        self, content: str, role: str = "System", additional_kwargs: Dict = None
+        self,
+        content: str,
+        role: str = "System",
+        additional_kwargs: Dict = None,
     ):
         super().__init__(content, role, additional_kwargs)
 
@@ -97,7 +104,9 @@ class ChatMessage(Message):
     A Message that can be assigned an arbitrary speaker (i.e. role).
     """
 
-    def __init__(self, content: str, role: str, additional_kwargs: Dict = None):
+    def __init__(
+        self, content: str, role: str, additional_kwargs: Dict = None
+    ):
         super().__init__(content, role, additional_kwargs)
 
     def get_type(self) -> str:
@@ -112,7 +121,10 @@ def get_buffer_string(
     string_messages = []
     for m in messages:
         message = f"{m.role}: {m.content}"
-        if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
+        if (
+            isinstance(m, AIMessage)
+            and "function_call" in m.additional_kwargs
+        ):
             message += f"{m.additional_kwargs['function_call']}"
         string_messages.append(message)
 

swarms/prompts/multi_modal_prompts.py

@@ -1,6 +1,6 @@
 ERROR_PROMPT = (
-    "An error has occurred for the following text: \n{promptedQuery} Please"
-    " explain this error.\n {e}"
+    "An error has occurred for the following text: \n{promptedQuery}"
+    " Please explain this error.\n {e}"
 )
 
 IMAGE_PROMPT = """

swarms/prompts/python.py

@@ -1,17 +1,19 @@
-PY_SIMPLE_COMPLETION_INSTRUCTION = "# Write the body of this function only."
+PY_SIMPLE_COMPLETION_INSTRUCTION = (
+    "# Write the body of this function only."
+)
 PY_REFLEXION_COMPLETION_INSTRUCTION = (
-    "You are a Python writing assistant. You will be given your past function"
-    " implementation, a series of unit tests, and a hint to change the"
-    " implementation appropriately. Write your full implementation (restate the"
-    " function signature).\n\n-----"
+    "You are a Python writing assistant. You will be given your past"
+    " function implementation, a series of unit tests, and a hint to"
+    " change the implementation appropriately. Write your full"
+    " implementation (restate the function signature).\n\n-----"
 )
 PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = (
     "You are a Python writing assistant. You will be given a function"
-    " implementation and a series of unit tests. Your goal is to write a few"
-    " sentences to explain why your implementation is wrong as indicated by the"
-    " tests. You will need this as a hint when you try again later. Only"
-    " provide the few sentence description in your answer, not the"
-    " implementation.\n\n-----"
+    " implementation and a series of unit tests. Your goal is to"
+    " write a few sentences to explain why your implementation is"
+    " wrong as indicated by the tests. You will need this as a hint"
+    " when you try again later. Only provide the few sentence"
+    " description in your answer, not the implementation.\n\n-----"
 )
 USE_PYTHON_CODEBLOCK_INSTRUCTION = (
     "Use a Python code block to write your response. For"
@@ -19,26 +21,28 @@ USE_PYTHON_CODEBLOCK_INSTRUCTION = (
 )
 
 PY_SIMPLE_CHAT_INSTRUCTION = (
-    "You are an AI that only responds with python code, NOT ENGLISH. You will"
-    " be given a function signature and its docstring by the user. Write your"
-    " full implementation (restate the function signature)."
+    "You are an AI that only responds with python code, NOT ENGLISH."
+    " You will be given a function signature and its docstring by the"
+    " user. Write your full implementation (restate the function"
+    " signature)."
 )
 PY_SIMPLE_CHAT_INSTRUCTION_V2 = (
-    "You are an AI that only responds with only python code. You will be given"
-    " a function signature and its docstring by the user. Write your full"
-    " implementation (restate the function signature)."
+    "You are an AI that only responds with only python code. You will"
+    " be given a function signature and its docstring by the user."
+    " Write your full implementation (restate the function"
+    " signature)."
 )
 PY_REFLEXION_CHAT_INSTRUCTION = (
-    "You are an AI Python assistant. You will be given your past function"
-    " implementation, a series of unit tests, and a hint to change the"
-    " implementation appropriately. Write your full implementation (restate the"
-    " function signature)."
+    "You are an AI Python assistant. You will be given your past"
+    " function implementation, a series of unit tests, and a hint to"
+    " change the implementation appropriately. Write your full"
+    " implementation (restate the function signature)."
 )
 PY_REFLEXION_CHAT_INSTRUCTION_V2 = (
     "You are an AI Python assistant. You will be given your previous"
-    " implementation of a function, a series of unit tests results, and your"
-    " self-reflection on your previous implementation. Write your full"
-    " implementation (restate the function signature)."
+    " implementation of a function, a series of unit tests results,"
+    " and your self-reflection on your previous implementation. Write"
+    " your full implementation (restate the function signature)."
 )
 PY_REFLEXION_FEW_SHOT_ADD = '''Example 1:
 [previous impl]:
@@ -173,19 +177,20 @@ END EXAMPLES
 
 '''
 PY_SELF_REFLECTION_CHAT_INSTRUCTION = (
-    "You are a Python programming assistant. You will be given a function"
-    " implementation and a series of unit tests. Your goal is to write a few"
-    " sentences to explain why your implementation is wrong as indicated by the"
-    " tests. You will need this as a hint when you try again later. Only"
-    " provide the few sentence description in your answer, not the"
-    " implementation."
+    "You are a Python programming assistant. You will be given a"
+    " function implementation and a series of unit tests. Your goal"
+    " is to write a few sentences to explain why your implementation"
+    " is wrong as indicated by the tests. You will need this as a"
+    " hint when you try again later. Only provide the few sentence"
+    " description in your answer, not the implementation."
 )
 PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = (
-    "You are a Python programming assistant. You will be given a function"
-    " implementation and a series of unit test results. Your goal is to write a"
-    " few sentences to explain why your implementation is wrong as indicated by"
-    " the tests. You will need this as guidance when you try again later. Only"
-    " provide the few sentence description in your answer, not the"
+    "You are a Python programming assistant. You will be given a"
+    " function implementation and a series of unit test results. Your"
+    " goal is to write a few sentences to explain why your"
+    " implementation is wrong as indicated by the tests. You will"
+    " need this as guidance when you try again later. Only provide"
+    " the few sentence description in your answer, not the"
     " implementation. You will be given a few examples by the user."
 )
 PY_SELF_REFLECTION_FEW_SHOT = """Example 1:

swarms/prompts/sales.py

@@ -1,40 +1,43 @@
 conversation_stages = {
     "1": (
-        "Introduction: Start the conversation by introducing yourself and your"
-        " company. Be polite and respectful while keeping the tone of the"
-        " conversation professional. Your greeting should be welcoming. Always"
-        " clarify in your greeting the reason why you are contacting the"
-        " prospect."
+        "Introduction: Start the conversation by introducing yourself"
+        " and your company. Be polite and respectful while keeping"
+        " the tone of the conversation professional. Your greeting"
+        " should be welcoming. Always clarify in your greeting the"
+        " reason why you are contacting the prospect."
     ),
     "2": (
-        "Qualification: Qualify the prospect by confirming if they are the"
-        " right person to talk to regarding your product/service. Ensure that"
-        " they have the authority to make purchasing decisions."
+        "Qualification: Qualify the prospect by confirming if they"
+        " are the right person to talk to regarding your"
+        " product/service. Ensure that they have the authority to"
+        " make purchasing decisions."
     ),
     "3": (
-        "Value proposition: Briefly explain how your product/service can"
-        " benefit the prospect. Focus on the unique selling points and value"
-        " proposition of your product/service that sets it apart from"
-        " competitors."
+        "Value proposition: Briefly explain how your product/service"
+        " can benefit the prospect. Focus on the unique selling"
+        " points and value proposition of your product/service that"
+        " sets it apart from competitors."
     ),
     "4": (
-        "Needs analysis: Ask open-ended questions to uncover the prospect's"
-        " needs and pain points. Listen carefully to their responses and take"
-        " notes."
+        "Needs analysis: Ask open-ended questions to uncover the"
+        " prospect's needs and pain points. Listen carefully to their"
+        " responses and take notes."
     ),
     "5": (
-        "Solution presentation: Based on the prospect's needs, present your"
-        " product/service as the solution that can address their pain points."
+        "Solution presentation: Based on the prospect's needs,"
+        " present your product/service as the solution that can"
+        " address their pain points."
     ),
     "6": (
-        "Objection handling: Address any objections that the prospect may have"
-        " regarding your product/service. Be prepared to provide evidence or"
-        " testimonials to support your claims."
+        "Objection handling: Address any objections that the prospect"
+        " may have regarding your product/service. Be prepared to"
+        " provide evidence or testimonials to support your claims."
     ),
     "7": (
-        "Close: Ask for the sale by proposing a next step. This could be a"
-        " demo, a trial or a meeting with decision-makers. Ensure to summarize"
-        " what has been discussed and reiterate the benefits."
+        "Close: Ask for the sale by proposing a next step. This could"
+        " be a demo, a trial or a meeting with decision-makers."
+        " Ensure to summarize what has been discussed and reiterate"
+        " the benefits."
     ),
 }
 

swarms/prompts/sales_prompts.py

@@ -46,40 +46,43 @@ Conversation history:
 
 conversation_stages = {
     "1": (
-        "Introduction: Start the conversation by introducing yourself and your"
-        " company. Be polite and respectful while keeping the tone of the"
-        " conversation professional. Your greeting should be welcoming. Always"
-        " clarify in your greeting the reason why you are contacting the"
-        " prospect."
+        "Introduction: Start the conversation by introducing yourself"
+        " and your company. Be polite and respectful while keeping"
+        " the tone of the conversation professional. Your greeting"
+        " should be welcoming. Always clarify in your greeting the"
+        " reason why you are contacting the prospect."
     ),
     "2": (
-        "Qualification: Qualify the prospect by confirming if they are the"
-        " right person to talk to regarding your product/service. Ensure that"
-        " they have the authority to make purchasing decisions."
+        "Qualification: Qualify the prospect by confirming if they"
+        " are the right person to talk to regarding your"
+        " product/service. Ensure that they have the authority to"
+        " make purchasing decisions."
     ),
     "3": (
-        "Value proposition: Briefly explain how your product/service can"
-        " benefit the prospect. Focus on the unique selling points and value"
-        " proposition of your product/service that sets it apart from"
-        " competitors."
+        "Value proposition: Briefly explain how your product/service"
+        " can benefit the prospect. Focus on the unique selling"
+        " points and value proposition of your product/service that"
+        " sets it apart from competitors."
     ),
     "4": (
-        "Needs analysis: Ask open-ended questions to uncover the prospect's"
-        " needs and pain points. Listen carefully to their responses and take"
-        " notes."
+        "Needs analysis: Ask open-ended questions to uncover the"
+        " prospect's needs and pain points. Listen carefully to their"
+        " responses and take notes."
     ),
     "5": (
-        "Solution presentation: Based on the prospect's needs, present your"
-        " product/service as the solution that can address their pain points."
+        "Solution presentation: Based on the prospect's needs,"
+        " present your product/service as the solution that can"
+        " address their pain points."
     ),
     "6": (
-        "Objection handling: Address any objections that the prospect may have"
-        " regarding your product/service. Be prepared to provide evidence or"
-        " testimonials to support your claims."
+        "Objection handling: Address any objections that the prospect"
+        " may have regarding your product/service. Be prepared to"
+        " provide evidence or testimonials to support your claims."
     ),
     "7": (
-        "Close: Ask for the sale by proposing a next step. This could be a"
-        " demo, a trial or a meeting with decision-makers. Ensure to summarize"
-        " what has been discussed and reiterate the benefits."
+        "Close: Ask for the sale by proposing a next step. This could"
+        " be a demo, a trial or a meeting with decision-makers."
+        " Ensure to summarize what has been discussed and reiterate"
+        " the benefits."
     ),
 }

swarms/prompts/self_operating_prompt.py

@@ -54,7 +54,9 @@ IMPORTANT: Avoid repeating actions such as doing the same CLICK event twice in a
 Objective: {objective}
 """
 
-USER_QUESTION = "Hello, I can help you with anything. What would you like done?"
+USER_QUESTION = (
+    "Hello, I can help you with anything. What would you like done?"
+)
 
 SUMMARY_PROMPT = """
 You are a Self-Operating Computer. You just completed a request from a user by operating the computer. Now you need to share the results. 
@@ -89,7 +91,8 @@ def format_vision_prompt(objective, previous_action):
     """
     if previous_action:
         previous_action = (
-            f"Here was the previous action you took: {previous_action}"
+            "Here was the previous action you took:"
+            f" {previous_action}"
         )
     else:
         previous_action = ""

swarms/structs/agent.py

@@ -21,7 +21,9 @@ from swarms.prompts.tools import (
 )
 from swarms.tools.tool import BaseTool
 from swarms.utils.code_interpreter import SubprocessCodeInterpreter
-from swarms.utils.parse_code import extract_code_in_backticks_in_string
+from swarms.utils.parse_code import (
+    extract_code_in_backticks_in_string,
+)
 from swarms.utils.pdf_to_text import pdf_to_text
 
 
@@ -140,7 +142,7 @@ class Agent:
         get_llm_init_params(): Get LLM init params
         get_tool_description(): Get the tool description
         find_tool_by_name(name: str): Find a tool by name
-        
+
 
     Example:
     >>> from swarms.models import OpenAIChat
@@ -180,6 +182,7 @@ class Agent:
         dynamic_temperature_enabled: Optional[bool] = False,
         sop: Optional[str] = None,
         sop_list: Optional[List[str]] = None,
+        # memory: Optional[Vectorstore] = None,
         saved_state_path: Optional[str] = "flow_state.json",
         autosave: Optional[bool] = False,
         context_length: Optional[int] = 8192,
@@ -189,7 +192,7 @@ class Agent:
         multi_modal: Optional[bool] = None,
         pdf_path: Optional[str] = None,
         list_of_pdf: Optional[str] = None,
-        tokenizer: Optional[str] = None,
+        tokenizer: Optional[Any] = None,
         *args,
         **kwargs: Any,
     ):
@@ -305,8 +308,9 @@ class Agent:
                 return "\n".join(tool_descriptions)
             except Exception as error:
                 print(
-                    f"Error getting tool description: {error} try adding a"
-                    " description to the tool or removing the tool"
+                    f"Error getting tool description: {error} try"
+                    " adding a description to the tool or removing"
+                    " the tool"
                 )
         else:
             return "No tools available"
@@ -322,7 +326,9 @@ class Agent:
         """Construct the dynamic prompt"""
         tools_description = self.get_tool_description()
 
-        tool_prompt = self.tool_prompt_prep(tools_description, SCENARIOS)
+        tool_prompt = self.tool_prompt_prep(
+            tools_description, SCENARIOS
+        )
 
         return tool_prompt
 
@@ -435,27 +441,36 @@ class Agent:
     def activate_autonomous_agent(self):
         """Print the autonomous agent activation message"""
         try:
-            print(colored("Initializing Autonomous Agent...", "yellow"))
+            print(
+                colored("Initializing Autonomous Agent...", "yellow")
+            )
             # print(colored("Loading modules...", "yellow"))
             # print(colored("Modules loaded successfully.", "green"))
             print(
-                colored("Autonomous Agent Activated.", "cyan", attrs=["bold"])
+                colored(
+                    "Autonomous Agent Activated.",
+                    "cyan",
+                    attrs=["bold"],
+                )
             )
             print(
-                colored("All systems operational. Executing task...", "green")
+                colored(
+                    "All systems operational. Executing task...",
+                    "green",
+                )
             )
         except Exception as error:
             print(
                 colored(
                     (
-                        "Error activating autonomous agent. Try optimizing your"
-                        " parameters..."
+                        "Error activating autonomous agent. Try"
+                        " optimizing your parameters..."
                     ),
                     "red",
                 )
             )
             print(error)
-            
+
     def loop_count_print(self, loop_count, max_loops):
         """loop_count_print summary
 
@@ -463,11 +478,9 @@ class Agent:
             loop_count (_type_): _description_
             max_loops (_type_): _description_
         """
-        print(
-            colored(f"\nLoop {loop_count} of {max_loops}", "cyan")
-        )
+        print(colored(f"\nLoop {loop_count} of {max_loops}", "cyan"))
         print("\n")
-        
+
     def _history(self, user_name: str, task: str) -> str:
         """Generate the history for the history prompt
 
@@ -480,8 +493,10 @@ class Agent:
         """
         history = [f"{user_name}: {task}"]
         return history
-    
-    def _dynamic_prompt_setup(self, dynamic_prompt: str, task: str) -> str:
+
+    def _dynamic_prompt_setup(
+        self, dynamic_prompt: str, task: str
+    ) -> str:
         """_dynamic_prompt_setup summary
 
         Args:
@@ -491,11 +506,15 @@ class Agent:
         Returns:
             str: _description_
         """
-        dynamic_prompt = dynamic_prompt or self.construct_dynamic_prompt()
+        dynamic_prompt = (
+            dynamic_prompt or self.construct_dynamic_prompt()
+        )
         combined_prompt = f"{dynamic_prompt}\n{task}"
         return combined_prompt
 
-    def run(self, task: Optional[str], img: Optional[str] = None, **kwargs):
+    def run(
+        self, task: Optional[str], img: Optional[str] = None, **kwargs
+    ):
         """
         Run the autonomous agent loop
 
@@ -524,7 +543,10 @@ class Agent:
             loop_count = 0
 
             # While the max_loops is auto or the loop count is less than the max_loops
-            while self.max_loops == "auto" or loop_count < self.max_loops:
+            while (
+                self.max_loops == "auto"
+                or loop_count < self.max_loops
+            ):
                 # Loop count
                 loop_count += 1
                 self.loop_count_print(loop_count, self.max_loops)
@@ -542,7 +564,9 @@ class Agent:
                     self.dynamic_temperature()
 
                 # Preparing the prompt
-                task = self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response)
+                task = self.agent_history_prompt(
+                    FLOW_SYSTEM_PROMPT, response
+                )
 
                 attempt = 0
                 while attempt < self.retry_attempts:
@@ -581,7 +605,9 @@ class Agent:
                             # print(response)
                         break
                     except Exception as e:
-                        logging.error(f"Error generating response: {e}")
+                        logging.error(
+                            f"Error generating response: {e}"
+                        )
                         attempt += 1
                         time.sleep(self.retry_interval)
                 # Add the response to the history
@@ -595,7 +621,10 @@ class Agent:
             if self.autosave:
                 save_path = self.saved_state_path or "flow_state.json"
                 print(
-                    colored(f"Autosaving agent state to {save_path}", "green")
+                    colored(
+                        f"Autosaving agent state to {save_path}",
+                        "green",
+                    )
                 )
                 self.save_state(save_path)
 
@@ -637,12 +666,16 @@ class Agent:
         # for i in range(self.max_loops):
         while self.max_loops == "auto" or loop_count < self.max_loops:
             loop_count += 1
-            print(colored(f"\nLoop {loop_count} of {self.max_loops}", "blue"))
+            print(
+                colored(
+                    f"\nLoop {loop_count} of {self.max_loops}", "blue"
+                )
+            )
             print("\n")
 
-            if self._check_stopping_condition(response) or parse_done_token(
+            if self._check_stopping_condition(
                 response
-            ):
+            ) or parse_done_token(response):
                 break
 
             # Adjust temperature, comment if no work
@@ -650,7 +683,9 @@ class Agent:
                 self.dynamic_temperature()
 
             # Preparing the prompt
-            task = self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response)
+            task = self.agent_history_prompt(
+                FLOW_SYSTEM_PROMPT, response
+            )
 
             attempt = 0
             while attempt < self.retry_attempts:
@@ -678,7 +713,11 @@ class Agent:
 
         if self.autosave:
             save_path = self.saved_state_path or "flow_state.json"
-            print(colored(f"Autosaving agent state to {save_path}", "green"))
+            print(
+                colored(
+                    f"Autosaving agent state to {save_path}", "green"
+                )
+            )
             self.save_state(save_path)
 
         if self.return_history:
@@ -737,7 +776,9 @@ class Agent:
         Args:
             tasks (List[str]): A list of tasks to run.
         """
-        task_coroutines = [self.run_async(task, **kwargs) for task in tasks]
+        task_coroutines = [
+            self.run_async(task, **kwargs) for task in tasks
+        ]
         completed_tasks = await asyncio.gather(*task_coroutines)
         return completed_tasks
 
@@ -751,7 +792,9 @@ class Agent:
         return Agent(llm=llm, template=template)
 
     @staticmethod
-    def from_llm_and_template_file(llm: Any, template_file: str) -> "Agent":
+    def from_llm_and_template_file(
+        llm: Any, template_file: str
+    ) -> "Agent":
         """Create AgentStream from LLM and a template file."""
         with open(template_file, "r") as f:
             template = f.read()
@@ -785,16 +828,34 @@ class Agent:
         Prints the entire history and memory of the agent.
         Each message is colored and formatted for better readability.
         """
-        print(colored("Agent History and Memory", "cyan", attrs=["bold"]))
-        print(colored("========================", "cyan", attrs=["bold"]))
+        print(
+            colored(
+                "Agent History and Memory", "cyan", attrs=["bold"]
+            )
+        )
+        print(
+            colored(
+                "========================", "cyan", attrs=["bold"]
+            )
+        )
         for loop_index, history in enumerate(self.memory, start=1):
-            print(colored(f"\nLoop {loop_index}:", "yellow", attrs=["bold"]))
+            print(
+                colored(
+                    f"\nLoop {loop_index}:", "yellow", attrs=["bold"]
+                )
+            )
             for message in history:
                 speaker, _, message_text = message.partition(": ")
                 if "Human" in speaker:
-                    print(colored(f"{speaker}:", "green") + f" {message_text}")
+                    print(
+                        colored(f"{speaker}:", "green")
+                        + f" {message_text}"
+                    )
                 else:
-                    print(colored(f"{speaker}:", "blue") + f" {message_text}")
+                    print(
+                        colored(f"{speaker}:", "blue")
+                        + f" {message_text}"
+                    )
             print(colored("------------------------", "cyan"))
         print(colored("End of Agent History", "cyan", attrs=["bold"]))
 
@@ -963,7 +1024,16 @@ class Agent:
                 value = getattr(self.llm, name)
                 if isinstance(
                     value,
-                    (str, int, float, bool, list, dict, tuple, type(None)),
+                    (
+                        str,
+                        int,
+                        float,
+                        bool,
+                        list,
+                        dict,
+                        tuple,
+                        type(None),
+                    ),
                 ):
                     llm_params[name] = value
                 else:
@@ -1110,7 +1180,9 @@ class Agent:
         text = text or self.pdf_connector()
         pass
 
-    def tools_prompt_prep(self, docs: str = None, scenarios: str = None):
+    def tools_prompt_prep(
+        self, docs: str = None, scenarios: str = None
+    ):
         """
         Prepare the tool prompt
         """

swarms/structs/autoscaler.py

@@ -62,7 +62,9 @@ class AutoScaler:
         agent=None,
     ):
         self.agent = agent or Agent
-        self.agents_pool = [self.agent() for _ in range(initial_agents)]
+        self.agents_pool = [
+            self.agent() for _ in range(initial_agents)
+        ]
         self.task_queue = queue.Queue()
         self.scale_up_factor = scale_up_factor
         self.idle_threshold = idle_threshold
@@ -74,7 +76,8 @@ class AutoScaler:
             self.tasks_queue.put(task)
         except Exception as error:
             print(
-                f"Error adding task to queue: {error} try again with a new task"
+                f"Error adding task to queue: {error} try again with"
+                " a new task"
             )
 
     @log_decorator
@@ -84,20 +87,29 @@ class AutoScaler:
         """Add more agents"""
         try:
             with self.lock:
-                new_agents_counts = len(self.agents_pool) * self.scale_up_factor
+                new_agents_counts = (
+                    len(self.agents_pool) * self.scale_up_factor
+                )
                 for _ in range(new_agents_counts):
                     self.agents_pool.append(Agent())
         except Exception as error:
-            print(f"Error scaling up: {error} try again with a new task")
+            print(
+                f"Error scaling up: {error} try again with a new task"
+            )
 
     def scale_down(self):
         """scale down"""
         try:
             with self.lock:
-                if len(self.agents_pool) > 10:  # ensure minmum of 10 agents
+                if (
+                    len(self.agents_pool) > 10
+                ):  # ensure minmum of 10 agents
                     del self.agents_pool[-1]  # remove last agent
         except Exception as error:
-            print(f"Error scaling down: {error} try again with a new task")
+            print(
+                f"Error scaling down: {error} try again with a new"
+                " task"
+            )
 
     @log_decorator
     @error_decorator
@@ -109,19 +121,27 @@ class AutoScaler:
                 sleep(60)  # check minute
                 pending_tasks = self.task_queue.qsize()
                 active_agents = sum(
-                    [1 for agent in self.agents_pool if agent.is_busy()]
+                    [
+                        1
+                        for agent in self.agents_pool
+                        if agent.is_busy()
+                    ]
                 )
 
-                if pending_tasks / len(self.agents_pool) > self.busy_threshold:
+                if (
+                    pending_tasks / len(self.agents_pool)
+                    > self.busy_threshold
+                ):
                     self.scale_up()
                 elif (
-                    active_agents / len(self.agents_pool) < self.idle_threshold
+                    active_agents / len(self.agents_pool)
+                    < self.idle_threshold
                 ):
                     self.scale_down()
         except Exception as error:
             print(
-                f"Error monitoring and scaling: {error} try again with a new"
-                " task"
+                f"Error monitoring and scaling: {error} try again"
+                " with a new task"
             )
 
     @log_decorator
@@ -130,7 +150,9 @@ class AutoScaler:
     def start(self):
         """Start scaling"""
         try:
-            monitor_thread = threading.Thread(target=self.monitor_and_scale)
+            monitor_thread = threading.Thread(
+                target=self.monitor_and_scale
+            )
             monitor_thread.start()
 
             while True:
@@ -142,13 +164,17 @@ class AutoScaler:
                     if available_agent:
                         available_agent.run(task)
         except Exception as error:
-            print(f"Error starting: {error} try again with a new task")
+            print(
+                f"Error starting: {error} try again with a new task"
+            )
 
     def check_agent_health(self):
         """Checks the health of each agent and replaces unhealthy agents."""
         for i, agent in enumerate(self.agents_pool):
             if not agent.is_healthy():
-                logging.warning(f"Replacing unhealthy agent at index {i}")
+                logging.warning(
+                    f"Replacing unhealthy agent at index {i}"
+                )
                 self.agents_pool[i] = self.agent()
 
     def balance_load(self):
@@ -159,7 +185,9 @@ class AutoScaler:
                     task = self.task_queue.get()
                     agent.run(task)
 
-    def set_scaling_strategy(self, strategy: Callable[[int, int], int]):
+    def set_scaling_strategy(
+        self, strategy: Callable[[int, int], int]
+    ):
         """Set a custom scaling strategy."""
         self.custom_scale_strategy = strategy
 
@@ -179,7 +207,11 @@ class AutoScaler:
 
     def report_agent_metrics(self) -> Dict[str, List[float]]:
         """Collects and reports metrics from each agent."""
-        metrics = {"completion_time": [], "success_rate": [], "error_rate": []}
+        metrics = {
+            "completion_time": [],
+            "success_rate": [],
+            "error_rate": [],
+        }
         for agent in self.agents_pool:
             agent_metrics = agent.get_metrics()
             for key in metrics.keys():

swarms/structs/document.py

@@ -87,5 +87,7 @@ class BaseDocumentTransformer(ABC):
             A list of transformed Documents.
         """
         return await asyncio.get_running_loop().run_in_executor(
-            None, partial(self.transform_documents, **kwargs), documents
+            None,
+            partial(self.transform_documents, **kwargs),
+            documents,
         )

swarms/structs/sequential_workflow.py

@@ -69,11 +69,18 @@ class Task:
             # Add a prompt to notify the Agent of the sequential workflow
             if "prompt" in self.kwargs:
                 self.kwargs["prompt"] += (
-                    f"\n\nPrevious output: {self.result}" if self.result else ""
+                    f"\n\nPrevious output: {self.result}"
+                    if self.result
+                    else ""
                 )
             else:
-                self.kwargs["prompt"] = f"Main task: {self.description}" + (
-                    f"\n\nPrevious output: {self.result}" if self.result else ""
+                self.kwargs["prompt"] = (
+                    f"Main task: {self.description}"
+                    + (
+                        f"\n\nPrevious output: {self.result}"
+                        if self.result
+                        else ""
+                    )
                 )
             self.result = self.agent.run(*self.args, **self.kwargs)
         else:
@@ -116,7 +123,9 @@ class SequentialWorkflow:
     autosave: bool = False
     name: str = (None,)
     description: str = (None,)
-    saved_state_filepath: Optional[str] = "sequential_workflow_state.json"
+    saved_state_filepath: Optional[str] = (
+        "sequential_workflow_state.json"
+    )
     restore_state_filepath: Optional[str] = None
     dashboard: bool = False
 
@@ -181,7 +190,9 @@ class SequentialWorkflow:
 
     def remove_task(self, task: str) -> None:
         """Remove tasks from sequential workflow"""
-        self.tasks = [task for task in self.tasks if task.description != task]
+        self.tasks = [
+            task for task in self.tasks if task.description != task
+        ]
 
     def update_task(self, task: str, **updates) -> None:
         """
@@ -330,7 +341,9 @@ class SequentialWorkflow:
         )
         self.tasks.append(task)
 
-    def load_workflow_state(self, filepath: str = None, **kwargs) -> None:
+    def load_workflow_state(
+        self, filepath: str = None, **kwargs
+    ) -> None:
         """
         Loads the workflow state from a json file and restores the workflow state.
 
@@ -384,18 +397,22 @@ class SequentialWorkflow:
                             # Ensure that 'task' is provided in the kwargs
                             if "task" not in task.kwargs:
                                 raise ValueError(
-                                    "The 'task' argument is required for the"
-                                    " Agent agent execution in"
-                                    f" '{task.description}'"
+                                    "The 'task' argument is required"
+                                    " for the Agent agent execution"
+                                    f" in '{task.description}'"
                                 )
                             # Separate the 'task' argument from other kwargs
                             flow_task_arg = task.kwargs.pop("task")
                             task.result = task.agent.run(
-                                flow_task_arg, *task.args, **task.kwargs
+                                flow_task_arg,
+                                *task.args,
+                                **task.kwargs,
                             )
                         else:
                             # If it's not a Agent instance, call the agent directly
-                            task.result = task.agent(*task.args, **task.kwargs)
+                            task.result = task.agent(
+                                *task.args, **task.kwargs
+                            )
 
                         # Pass the result as an argument to the next task if it exists
                         next_task_index = self.tasks.index(task) + 1
@@ -417,9 +434,9 @@ class SequentialWorkflow:
             print(
                 colored(
                     (
-                        f"Error initializing the Sequential workflow: {e} try"
-                        " optimizing your inputs like the agent class and task"
-                        " description"
+                        "Error initializing the Sequential workflow:"
+                        f" {e} try optimizing your inputs like the"
+                        " agent class and task description"
                     ),
                     "red",
                     attrs=["bold", "underline"],
@@ -443,8 +460,9 @@ class SequentialWorkflow:
                         # Ensure that 'task' is provided in the kwargs
                         if "task" not in task.kwargs:
                             raise ValueError(
-                                "The 'task' argument is required for the Agent"
-                                f" agent execution in '{task.description}'"
+                                "The 'task' argument is required for"
+                                " the Agent agent execution in"
+                                f" '{task.description}'"
                             )
                         # Separate the 'task' argument from other kwargs
                         flow_task_arg = task.kwargs.pop("task")

swarms/swarms/base.py

@@ -144,7 +144,9 @@ class AbstractSwarm(ABC):
         pass
 
     @abstractmethod
-    def autoscaler(self, num_workers: int, worker: ["AbstractWorker"]):
+    def autoscaler(
+        self, num_workers: int, worker: ["AbstractWorker"]
+    ):
         """Autoscaler that acts like kubernetes for autonomous agents"""
         pass
 
@@ -159,7 +161,9 @@ class AbstractSwarm(ABC):
         pass
 
     @abstractmethod
-    def assign_task(self, worker: "AbstractWorker", task: Any) -> Dict:
+    def assign_task(
+        self, worker: "AbstractWorker", task: Any
+    ) -> Dict:
         """Assign a task to a worker"""
         pass
 

swarms/swarms/dialogue_simulator.py

@@ -24,7 +24,10 @@ class DialogueSimulator:
     """
 
     def __init__(
-        self, agents: List[Callable], max_iters: int = 10, name: str = None
+        self,
+        agents: List[Callable],
+        max_iters: int = 10,
+        name: str = None,
     ):
         self.agents = agents
         self.max_iters = max_iters
@@ -60,7 +63,8 @@ class DialogueSimulator:
 
     def __repr__(self):
         return (
-            f"DialogueSimulator({self.agents}, {self.max_iters}, {self.name})"
+            f"DialogueSimulator({self.agents}, {self.max_iters},"
+            f" {self.name})"
         )
 
     def save_state(self):

swarms/swarms/god_mode.py

@@ -64,7 +64,11 @@ class GodMode:
             table.append([f"LLM {i+1}", response])
         print(
             colored(
-                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"),
+                tabulate(
+                    table,
+                    headers=["LLM", "Response"],
+                    tablefmt="pretty",
+                ),
                 "cyan",
             )
         )
@@ -84,7 +88,11 @@ class GodMode:
             table.append([f"LLM {i+1}", response])
         print(
             colored(
-                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"),
+                tabulate(
+                    table,
+                    headers=["LLM", "Response"],
+                    tablefmt="pretty",
+                ),
                 "cyan",
             )
         )
@@ -122,7 +130,11 @@ class GodMode:
         ]
         print(
             colored(
-                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"),
+                tabulate(
+                    table,
+                    headers=["LLM", "Response"],
+                    tablefmt="pretty",
+                ),
                 "cyan",
             )
         )
@@ -159,8 +171,8 @@ class GodMode:
                     responses.append(future.result())
                 except Exception as error:
                     print(
-                        f"{future_to_llm[future]} generated an exception:"
-                        f" {error}"
+                        f"{future_to_llm[future]} generated an"
+                        f" exception: {error}"
                     )
         self.last_responses = responses
         self.task_history.append(task)

swarms/swarms/groupchat.py

@@ -43,12 +43,15 @@ class GroupChat:
         for agent in self.agents:
             if agent.name in name:
                 return agent
-        raise ValueError(f"No agent found with a name contained in '{name}'.")
+        raise ValueError(
+            f"No agent found with a name contained in '{name}'."
+        )
 
     def next_agent(self, agent: Agent) -> Agent:
         """Return the next agent in the list."""
         return self.agents[
-            (self.agent_names.index(agent.name) + 1) % len(self.agents)
+            (self.agent_names.index(agent.name) + 1)
+            % len(self.agents)
         ]
 
     def select_speaker_msg(self):
@@ -69,8 +72,8 @@ class GroupChat:
         n_agents = len(self.agent_names)
         if n_agents < 3:
             logger.warning(
-                f"GroupChat is underpopulated with {n_agents} agents. Direct"
-                " communication would be more efficient."
+                f"GroupChat is underpopulated with {n_agents} agents."
+                " Direct communication would be more efficient."
             )
 
         name = selector.generate_reply(
@@ -80,9 +83,10 @@ class GroupChat:
                     {
                         "role": "system",
                         "content": (
-                            "Read the above conversation. Then select the next"
-                            f" most suitable role from {self.agent_names} to"
-                            " play. Only return the role."
+                            "Read the above conversation. Then"
+                            " select the next most suitable role"
+                            f" from {self.agent_names} to play. Only"
+                            " return the role."
                         ),
                     }
                 ]
@@ -95,13 +99,18 @@ class GroupChat:
 
     def _participant_roles(self):
         return "\n".join(
-            [f"{agent.name}: {agent.system_message}" for agent in self.agents]
+            [
+                f"{agent.name}: {agent.system_message}"
+                for agent in self.agents
+            ]
         )
 
     def format_history(self, messages: List[Dict]) -> str:
         formatted_messages = []
         for message in messages:
-            formatted_message = f"'{message['role']}:{message['content']}"
+            formatted_message = (
+                f"'{message['role']}:{message['content']}"
+            )
             formatted_messages.append(formatted_message)
         return "\n".join(formatted_messages)
 

swarms/swarms/multi_agent_collab.py

@@ -13,8 +13,8 @@ from swarms.utils.logger import logger
 class BidOutputParser(RegexParser):
     def get_format_instructions(self) -> str:
         return (
-            "Your response should be an integrater delimited by angled brackets"
-            " like this: <int>"
+            "Your response should be an integrater delimited by"
+            " angled brackets like this: <int>"
         )
 
 
@@ -123,7 +123,9 @@ class MultiAgentCollaboration:
 
     def step(self) -> tuple[str, str]:
         """Steps through the multi-agent collaboration."""
-        speaker_idx = self.select_next_speaker(self._step, self.agents)
+        speaker_idx = self.select_next_speaker(
+            self._step, self.agents
+        )
         speaker = self.agents[speaker_idx]
         message = speaker.send()
         message = speaker.send()
@@ -146,7 +148,8 @@ class MultiAgentCollaboration:
         wait=tenacity.wait_none(),
         retry=tenacity.retry_if_exception_type(ValueError),
         before_sleep=lambda retry_state: print(
-            f"ValueError occured: {retry_state.outcome.exception()}, retying..."
+            f"ValueError occured: {retry_state.outcome.exception()},"
+            " retying..."
         ),
         retry_error_callback=lambda retry_state: 0,
     )
@@ -167,7 +170,9 @@ class MultiAgentCollaboration:
             bid = self.ask_for_bid(agent)
             bids.append(bid)
         max_value = max(bids)
-        max_indices = [i for i, x in enumerate(bids) if x == max_value]
+        max_indices = [
+            i for i, x in enumerate(bids) if x == max_value
+        ]
         idx = random.choice(max_indices)
         return idx
 
@@ -176,7 +181,8 @@ class MultiAgentCollaboration:
         wait=tenacity.wait_none(),
         retry=tenacity.retry_if_exception_type(ValueError),
         before_sleep=lambda retry_state: print(
-            f"ValueError occured: {retry_state.outcome.exception()}, retying..."
+            f"ValueError occured: {retry_state.outcome.exception()},"
+            " retying..."
         ),
         retry_error_callback=lambda retry_state: 0,
     )
@@ -256,7 +262,9 @@ 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:
@@ -309,7 +317,9 @@ class MultiAgentCollaboration:
         """Tracks and reports the performance of each agent"""
         performance_data = {}
         for agent in self.agents:
-            performance_data[agent.name] = agent.get_performance_metrics()
+            performance_data[agent.name] = (
+                agent.get_performance_metrics()
+            )
         return performance_data
 
     def set_interaction_rules(self, rules):