code quality fixes: line length = 80

playground/agents/mm_agent_example.py

@@ -9,6 +9,9 @@ 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/agents/revgpt_agent.py

@@ -10,13 +10,19 @@ config = {
     "plugin_ids": [os.getenv("REVGPT_PLUGIN_IDS")],
     "disable_history": os.getenv("REVGPT_DISABLE_HISTORY") == "True",
     "PUID": os.getenv("REVGPT_PUID"),
-    "unverified_plugin_domains": [os.getenv("REVGPT_UNVERIFIED_PLUGIN_DOMAINS")],
+    "unverified_plugin_domains": [
+        os.getenv("REVGPT_UNVERIFIED_PLUGIN_DOMAINS")
+    ],
 }
 
 llm = RevChatGPTModel(access_token=os.getenv("ACCESS_TOKEN"), **config)
 
 worker = Worker(ai_name="Optimus Prime", llm=llm)
 
-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."
+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."
+)
 response = worker.run(task)
 print(response)

playground/demos/accountant_team/accountant_team.py

@@ -103,7 +103,8 @@ class AccountantSwarms:
 
         # Provide decision making support to the accountant
         decision_making_support_agent_output = decision_making_support_agent.run(
-            f"{self.decision_making_support_agent_instructions}: {summary_agent_output}"
+            f"{self.decision_making_support_agent_instructions}:"
+            f" {summary_agent_output}"
         )
 
         return decision_making_support_agent_output
@@ -113,5 +114,7 @@ swarm = AccountantSwarms(
     pdf_path="tesla.pdf",
     fraud_detection_instructions="Detect fraud in 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:",
+    decision_making_support_agent_instructions=(
+        "Provide decision making support to the business owner:"
+    ),
 )

playground/demos/ai_research_team/main.py

@@ -48,6 +48,7 @@ paper_implementor_agent = Flow(
 
 paper = pdf_to_text(PDF_PATH)
 algorithmic_psuedocode_agent = paper_summarizer_agent.run(
-    f"Focus on creating the algorithmic pseudocode for the novel method in this paper: {paper}"
+    "Focus on creating the algorithmic pseudocode for the novel method in this"
+    f" paper: {paper}"
 )
 pytorch_code = paper_implementor_agent.run(algorithmic_psuedocode_agent)

playground/demos/autotemp/autotemp.py

@@ -9,11 +9,18 @@ class AutoTemp:
     """
 
     def __init__(
-        self, api_key, default_temp=0.0, alt_temps=None, auto_select=True, max_workers=6
+        self,
+        api_key,
+        default_temp=0.0,
+        alt_temps=None,
+        auto_select=True,
+        max_workers=6,
     ):
         self.api_key = api_key
         self.default_temp = default_temp
-        self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
+        self.alt_temps = (
+            alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
+        )
         self.auto_select = auto_select
         self.max_workers = max_workers
         self.llm = OpenAIChat(
@@ -62,12 +69,15 @@ class AutoTemp:
         if not scores:
             return "No valid outputs generated.", None
 
-        sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
+        sorted_scores = sorted(
+            scores.items(), key=lambda item: item[1], reverse=True
+        )
         best_temp, best_score = sorted_scores[0]
         best_output = outputs[best_temp]
 
         return (
-            f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}"
+            f"Best AutoTemp Output (Temp {best_temp} | Score:"
+            f" {best_score}):\n{best_output}"
             if self.auto_select
             else "\n".join(
                 f"Temp {temp} | Score: {score}:\n{outputs[temp]}"

playground/demos/blog_gen/blog_gen.py

@@ -7,7 +7,10 @@ from swarms.structs import SequentialWorkflow
 
 class BlogGen:
     def __init__(
-        self, api_key, blog_topic, temperature_range: str = "0.4,0.6,0.8,1.0,1.2"
+        self,
+        api_key,
+        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.auto_temp = AutoTemp(api_key)
@@ -40,7 +43,10 @@ class BlogGen:
             topic_output = topic_result.generations[0][0].text
             print(
                 colored(
-                    f"\nTopic Selection Task Output:\n----------------------------\n{topic_output}\n",
+                    (
+                        "\nTopic Selection Task"
+                        f" Output:\n----------------------------\n{topic_output}\n"
+                    ),
                     "white",
                 )
             )
@@ -58,7 +64,10 @@ class BlogGen:
             initial_draft_output = auto_temp_output  # Assuming AutoTemp.run returns the best output directly
             print(
                 colored(
-                    f"\nInitial Draft Output:\n----------------------------\n{initial_draft_output}\n",
+                    (
+                        "\nInitial Draft"
+                        f" Output:\n----------------------------\n{initial_draft_output}\n"
+                    ),
                     "white",
                 )
             )
@@ -71,7 +80,10 @@ class BlogGen:
             review_output = review_result.generations[0][0].text
             print(
                 colored(
-                    f"\nReview Output:\n----------------------------\n{review_output}\n",
+                    (
+                        "\nReview"
+                        f" Output:\n----------------------------\n{review_output}\n"
+                    ),
                     "white",
                 )
             )
@@ -80,22 +92,28 @@ class BlogGen:
             distribution_prompt = self.DISTRIBUTION_AGENT_SYSTEM_PROMPT.replace(
                 "{{ARTICLE_TOPIC}}", chosen_topic
             )
-            distribution_result = self.openai_chat.generate([distribution_prompt])
+            distribution_result = self.openai_chat.generate(
+                [distribution_prompt]
+            )
             distribution_output = distribution_result.generations[0][0].text
             print(
                 colored(
-                    f"\nDistribution Output:\n----------------------------\n{distribution_output}\n",
+                    (
+                        "\nDistribution"
+                        f" Output:\n----------------------------\n{distribution_output}\n"
+                    ),
                     "white",
                 )
             )
 
             # Final compilation of the blog
-            final_blog_content = (
+            final_blog_content = f"{initial_draft_output}\n\n{review_output}\n\n{distribution_output}"
-                f"{initial_draft_output}\n\n{review_output}\n\n{distribution_output}"
-            )
             print(
                 colored(
-                    f"\nFinal Blog Content:\n----------------------------\n{final_blog_content}\n",
+                    (
+                        "\nFinal Blog"
+                        f" Content:\n----------------------------\n{final_blog_content}\n"
+                    ),
                     "green",
                 )
             )

playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent.py

@@ -4,7 +4,10 @@ from swarms.models import Idefics
 # Multi Modality Auto Agent
 llm = Idefics(max_length=2000)
 
-task = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+task = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
 
 ## Initialize the workflow
 flow = Flow(

playground/demos/nutrition/nutrition.py

@@ -10,9 +10,16 @@ load_dotenv()
 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."
+MEAL_PLAN_PROMPT = (
+    "Based on the following user preferences: dietary restrictions as"
+    " vegetarian, preferred cuisines as Italian and Indian, a total caloric"
+    " intake of around 2000 calories per day, and an exclusion of legumes,"
+    " create a detailed weekly meal plan. Include a variety of meals for"
+    " breakfast, lunch, dinner, and optional snacks."
+)
 IMAGE_ANALYSIS_PROMPT = (
-    "Identify the items in this fridge, including their quantities and condition."
+    "Identify the items in this fridge, including their quantities and"
+    " condition."
 )
 
 
@@ -45,7 +52,9 @@ def create_vision_agent(image_path):
                     {"type": "text", "text": IMAGE_ANALYSIS_PROMPT},
                     {
                         "type": "image_url",
-                        "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}"
+                        },
                     },
                 ],
             }
@@ -53,7 +62,9 @@ def create_vision_agent(image_path):
         "max_tokens": 300,
     }
     response = requests.post(
-        "https://api.openai.com/v1/chat/completions", headers=headers, json=payload
+        "https://api.openai.com/v1/chat/completions",
+        headers=headers,
+        json=payload,
     )
     return response.json()
 
@@ -65,10 +76,11 @@ def generate_integrated_shopping_list(
     # Prepare the prompt for the LLM
     fridge_contents = image_analysis["choices"][0]["message"]["content"]
     prompt = (
-        f"Based on this meal plan: {meal_plan_output}, "
+        f"Based on this meal plan: {meal_plan_output}, and the following items"
-        f"and the following items in the fridge: {fridge_contents}, "
+        f" in the fridge: {fridge_contents}, considering dietary preferences as"
-        f"considering dietary preferences as vegetarian with a preference for Italian and Indian cuisines, "
+        " vegetarian with a preference for Italian and Indian cuisines,"
-        f"generate a comprehensive shopping list that includes only the items needed."
+        " generate a comprehensive shopping list that includes only the items"
+        " needed."
     )
 
     # Send the prompt to the LLM and return the response
@@ -94,7 +106,9 @@ user_preferences = {
 }
 
 # Generate Meal Plan
-meal_plan_output = meal_plan_agent.run(f"Generate a meal plan: {user_preferences}")
+meal_plan_output = meal_plan_agent.run(
+    f"Generate a meal plan: {user_preferences}"
+)
 
 # Vision Agent - Analyze an Image
 image_analysis_output = create_vision_agent("full_fridge.jpg")

playground/demos/positive_med/positive_med.py

@@ -39,9 +39,9 @@ def get_review_prompt(article):
 
 
 def social_media_prompt(article: str, goal: str = "Clicks and engagement"):
-    prompt = SOCIAL_MEDIA_SYSTEM_PROMPT_AGENT.replace("{{ARTICLE}}", article).replace(
+    prompt = SOCIAL_MEDIA_SYSTEM_PROMPT_AGENT.replace(
-        "{{GOAL}}", goal
+        "{{ARTICLE}}", article
-    )
+    ).replace("{{GOAL}}", goal)
     return prompt
 
 
@@ -50,7 +50,8 @@ topic_selection_task = (
     "Generate 10 topics on gaining mental clarity using ancient practices"
 )
 topics = llm(
-    f"Your System Instructions: {TOPIC_GENERATOR}, Your current task: {topic_selection_task}"
+    f"Your System Instructions: {TOPIC_GENERATOR}, Your current task:"
+    f" {topic_selection_task}"
 )
 
 dashboard = print(
@@ -109,7 +110,9 @@ reviewed_draft = print(
 
 
 # Agent that publishes on social media
-distribution_agent = llm(social_media_prompt(draft_blog, goal="Clicks and engagement"))
+distribution_agent = llm(
+    social_media_prompt(draft_blog, goal="Clicks and engagement")
+)
 distribution_agent_out = print(
     colored(
         f"""

playground/models/bioclip.py

@@ -1,6 +1,8 @@
 from swarms.models.bioclip import BioClip
 
-clip = BioClip("hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224")
+clip = BioClip(
+    "hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224"
+)
 
 labels = [
     "adenocarcinoma histopathology",

playground/models/idefics.py

@@ -2,11 +2,17 @@ from swarms.models import idefics
 
 model = idefics()
 
-user_input = "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+user_input = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
 response = model.chat(user_input)
 print(response)
 
-user_input = "User: And who is that? https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+user_input = (
+    "User: And who is that?"
+    " https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+)
 response = model.chat(user_input)
 print(response)
 

playground/models/llama_function_caller.py

@@ -28,7 +28,9 @@ llama_caller.add_func(
 )
 
 # Call the function
-result = llama_caller.call_function("get_weather", location="Paris", format="Celsius")
+result = llama_caller.call_function(
+    "get_weather", location="Paris", format="Celsius"
+)
 print(result)
 
 # Stream a user prompt

playground/models/vilt.py

@@ -3,5 +3,6 @@ from swarms.models.vilt import Vilt
 model = Vilt()
 
 output = model(
-    "What is this image", "http://images.cocodataset.org/val2017/000000039769.jpg"
+    "What is this image",
+    "http://images.cocodataset.org/val2017/000000039769.jpg",
 )

playground/structs/flow_tools.py

@@ -30,7 +30,9 @@ 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("  "))
+            chunks = (
+                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:
             results = f"Error: {e}"
@@ -58,5 +60,6 @@ flow = Flow(
 )
 
 out = flow.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/swarms/debate.py

@@ -36,7 +36,9 @@ class DialogueAgent:
         message = self.model(
             [
                 self.system_message,
-                HumanMessage(content="\n".join(self.message_history + [self.prefix])),
+                HumanMessage(
+                    content="\n".join(self.message_history + [self.prefix])
+                ),
             ]
         )
         return message.content
@@ -124,19 +126,19 @@ game_description = f"""Here is the topic for the presidential debate: {topic}.
 The presidential candidates are: {', '.join(character_names)}."""
 
 player_descriptor_system_message = SystemMessage(
-    content="You can add detail to the description of each presidential candidate."
+    content=(
+        "You can add detail to the description of each presidential candidate."
+    )
 )
 
 
 def generate_character_description(character_name):
     character_specifier_prompt = [
         player_descriptor_system_message,
-        HumanMessage(
+        HumanMessage(content=f"""{game_description}
-            content=f"""{game_description}
             Please reply with a creative description of the presidential candidate, {character_name}, in {word_limit} words or less, that emphasizes their personalities.
             Speak directly to {character_name}.
-            Do not add anything else."""
+            Do not add anything else."""),
-        ),
     ]
     character_description = ChatOpenAI(temperature=1.0)(
         character_specifier_prompt
@@ -155,9 +157,7 @@ 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):
-    return SystemMessage(
+    return SystemMessage(content=f"""{character_header}
-        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}.
 Do not say the same things over and over again.
@@ -169,13 +169,12 @@ Speak only from the perspective of {character_name}.
 Stop speaking the moment you finish speaking from your perspective.
 Never forget to keep your response to {word_limit} words!
 Do not add anything else.
-    """
+    """)
-        )
-    )
 
 
 character_descriptions = [
-    generate_character_description(character_name) for character_name in character_names
+    generate_character_description(character_name)
+    for character_name in character_names
 ]
 character_headers = [
     generate_character_header(character_name, character_description)
@@ -185,7 +184,9 @@ character_headers = [
 ]
 character_system_messages = [
     generate_character_system_message(character_name, character_headers)
-    for character_name, character_headers in zip(character_names, character_headers)
+    for character_name, character_headers in zip(
+        character_names, character_headers
+    )
 ]
 
 for (
@@ -207,7 +208,10 @@ for (
 
 class BidOutputParser(RegexParser):
     def get_format_instructions(self) -> str:
-        return "Your response should be an integer delimited by angled brackets, like this: <int>."
+        return (
+            "Your response should be an integer delimited by angled brackets,"
+            " like this: <int>."
+        )
 
 
 bid_parser = BidOutputParser(
@@ -248,8 +252,7 @@ for character_name, bidding_template in zip(
 
 topic_specifier_prompt = [
     SystemMessage(content="You can make a task more specific."),
-    HumanMessage(
+    HumanMessage(content=f"""{game_description}
-        content=f"""{game_description}
 
         You are the debate moderator.
         Please make the debate topic more specific.
@@ -257,8 +260,7 @@ topic_specifier_prompt = [
         Be creative and imaginative.
         Please reply with the specified topic in {word_limit} words or less.
         Speak directly to the presidential candidates: {*character_names,}.
-        Do not add anything else."""
+        Do not add anything else."""),
-    ),
 ]
 specified_topic = ChatOpenAI(temperature=1.0)(topic_specifier_prompt).content
 
@@ -321,7 +323,9 @@ for character_name, character_system_message, bidding_template in zip(
 max_iters = 10
 n = 0
 
-simulator = DialogueSimulator(agents=characters, selection_function=select_next_speaker)
+simulator = DialogueSimulator(
+    agents=characters, selection_function=select_next_speaker
+)
 simulator.reset()
 simulator.inject("Debate Moderator", specified_topic)
 print(f"(Debate Moderator): {specified_topic}")

playground/swarms/multi_agent_debate.py

@@ -36,7 +36,11 @@ agents = [worker1, worker2, worker3]
 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."
+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."
+)
 results = debate.run(task, max_iters=4)
 
 # Print results

playground/swarms/orchestrate.py

@@ -10,4 +10,6 @@ node = Worker(
 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?")
+orchestrator.chat(
+    sender_id=7, receiver_id=9, message="Can you help me with this task?"
+)

playground/swarms/orchestrator.py

@@ -10,4 +10,6 @@ node = Worker(
 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?")
+orchestrator.chat(
+    sender_id=7, receiver_id=9, message="Can you help me with this task?"
+)

playground/swarms/swarms_example.py

@@ -7,7 +7,10 @@ api_key = ""
 swarm = HierarchicalSwarm(api_key)
 
 # Define an objective
-objective = "Find 20 potential customers for a HierarchicalSwarm based AI Agent automation infrastructure"
+objective = (
+    "Find 20 potential customers for a HierarchicalSwarm based AI Agent"
+    " automation infrastructure"
+)
 
 # Run HierarchicalSwarm
 swarm.run(objective)

pyproject.toml

@@ -85,3 +85,10 @@ aggressive = 3
 
 [tool.ruff]
 line-length = 80
+
+[tool.black]
+line-length = 80
+target-version = ['py38']
+preview = true
+
+

swarms/agents/omni_modal_agent.py

@@ -18,7 +18,12 @@ from swarms.agents.message import Message
 
 class Step:
     def __init__(
-        self, task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool
+        self,
+        task: str,
+        id: int,
+        dep: List[int],
+        args: Dict[str, str],
+        tool: BaseTool,
     ):
         self.task = task
         self.id = id

swarms/memory/base.py

@@ -37,7 +37,7 @@ class BaseVectorStore(ABC):
         self,
         artifacts: dict[str, list[TextArtifact]],
         meta: Optional[dict] = None,
-        **kwargs
+        **kwargs,
     ) -> None:
         execute_futures_dict(
             {
@@ -54,7 +54,7 @@ class BaseVectorStore(ABC):
         artifact: TextArtifact,
         namespace: Optional[str] = None,
         meta: Optional[dict] = None,
-        **kwargs
+        **kwargs,
     ) -> str:
         if not meta:
             meta = {}
@@ -67,7 +67,11 @@ class BaseVectorStore(ABC):
             vector = artifact.generate_embedding(self.embedding_driver)
 
         return self.upsert_vector(
-            vector, vector_id=artifact.id, namespace=namespace, meta=meta, **kwargs
+            vector,
+            vector_id=artifact.id,
+            namespace=namespace,
+            meta=meta,
+            **kwargs,
         )
 
     def upsert_text(
@@ -76,14 +80,14 @@ class BaseVectorStore(ABC):
         vector_id: Optional[str] = None,
         namespace: Optional[str] = None,
         meta: Optional[dict] = None,
-        **kwargs
+        **kwargs,
     ) -> str:
         return self.upsert_vector(
             self.embedding_driver.embed_string(string),
             vector_id=vector_id,
             namespace=namespace,
             meta=meta if meta else {},
-            **kwargs
+            **kwargs,
         )
 
     @abstractmethod
@@ -93,12 +97,14 @@ class BaseVectorStore(ABC):
         vector_id: Optional[str] = None,
         namespace: Optional[str] = None,
         meta: Optional[dict] = None,
-        **kwargs
+        **kwargs,
     ) -> str:
         ...
 
     @abstractmethod
-    def load_entry(self, vector_id: str, namespace: Optional[str] = None) -> Entry:
+    def load_entry(
+        self, vector_id: str, namespace: Optional[str] = None
+    ) -> Entry:
         ...
 
     @abstractmethod
@@ -112,6 +118,6 @@ class BaseVectorStore(ABC):
         count: Optional[int] = None,
         namespace: Optional[str] = None,
         include_vectors: bool = False,
-        **kwargs
+        **kwargs,
     ) -> list[QueryResult]:
         ...

swarms/memory/chroma.py

@@ -111,7 +111,9 @@ class Chroma(VectorStore):
                         chroma_db_impl="duckdb+parquet",
                     )
                 else:
-                    _client_settings = chromadb.config.Settings(is_persistent=True)
+                    _client_settings = chromadb.config.Settings(
+                        is_persistent=True
+                    )
                 _client_settings.persist_directory = persist_directory
             else:
                 _client_settings = chromadb.config.Settings()
@@ -124,9 +126,11 @@ class Chroma(VectorStore):
         self._embedding_function = embedding_function
         self._collection = self._client.get_or_create_collection(
             name=collection_name,
-            embedding_function=self._embedding_function.embed_documents
+            embedding_function=(
-            if self._embedding_function is not None
+                self._embedding_function.embed_documents
-            else None,
+                if self._embedding_function is not None
+                else None
+            ),
             metadata=collection_metadata,
         )
         self.override_relevance_score_fn = relevance_score_fn
@@ -203,7 +207,9 @@ class Chroma(VectorStore):
                 metadatas = [metadatas[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
+                    [embeddings[idx] for idx in non_empty_ids]
+                    if embeddings
+                    else None
                 )
                 ids_with_metadata = [ids[idx] for idx in non_empty_ids]
                 try:
@@ -216,7 +222,8 @@ class Chroma(VectorStore):
                 except ValueError as e:
                     if "Expected metadata value to be" in str(e):
                         msg = (
-                            "Try filtering complex metadata from the document using "
+                            "Try filtering complex metadata from the document"
+                            " using "
                             "langchain.vectorstores.utils.filter_complex_metadata."
                         )
                         raise ValueError(e.args[0] + "\n\n" + msg)
@@ -258,7 +265,9 @@ class Chroma(VectorStore):
         Returns:
             List[Document]: List of documents most similar to the query text.
         """
-        docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
+        docs_and_scores = self.similarity_search_with_score(
+            query, k, filter=filter
+        )
         return [doc for doc, _ in docs_and_scores]
 
     def similarity_search_by_vector(
@@ -428,7 +437,9 @@ class Chroma(VectorStore):
 
         candidates = _results_to_docs(results)
 
-        selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
+        selected_results = [
+            r for i, r in enumerate(candidates) if i in mmr_selected
+        ]
         return selected_results
 
     def max_marginal_relevance_search(
@@ -460,7 +471,8 @@ 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)
@@ -543,7 +555,9 @@ class Chroma(VectorStore):
         """
         return self.update_documents([document_id], [document])
 
-    def update_documents(self, ids: List[str], documents: List[Document]) -> None:
+    def update_documents(
+        self, ids: List[str], documents: List[Document]
+    ) -> None:
         """Update a document in the collection.
 
         Args:
@@ -554,7 +568,8 @@ 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)
 
@@ -645,7 +660,9 @@ class Chroma(VectorStore):
                     ids=batch[0],
                 )
         else:
-            chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
+            chroma_collection.add_texts(
+                texts=texts, metadatas=metadatas, ids=ids
+            )
         return chroma_collection
 
     @classmethod

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(
-            f"Number of columns in X and Y must be the same. X has shape {X.shape} "
+            "Number of columns in X and Y must be the same. X has shape"
-            f"and Y has shape {Y.shape}."
+            f" {X.shape} and Y has shape {Y.shape}."
         )
     try:
         import simsimd as simd
@@ -32,8 +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 "
+            "Unable to import simsimd, defaulting to NumPy implementation. If"
-            "to use simsimd please install with `pip install simsimd`."
+            " 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)

swarms/memory/db.py

@@ -151,7 +151,9 @@ class InMemoryTaskDB(TaskDB):
     ) -> Artifact:
         artifact_id = str(uuid.uuid4())
         artifact = Artifact(
-            artifact_id=artifact_id, file_name=file_name, relative_path=relative_path
+            artifact_id=artifact_id,
+            file_name=file_name,
+            relative_path=relative_path,
         )
         task = await self.get_task(task_id)
         task.artifacts.append(artifact)

swarms/memory/ocean.py

@@ -91,7 +91,9 @@ class OceanDB:
         try:
             return collection.add(documents=[document], ids=[id])
         except Exception as e:
-            logging.error(f"Failed to append document to the collection. Error {e}")
+            logging.error(
+                f"Failed to append document to the collection. Error {e}"
+            )
             raise
 
     def add_documents(self, collection, documents: List[str], ids: List[str]):
@@ -137,7 +139,9 @@ class OceanDB:
                 the results of the query
         """
         try:
-            results = collection.query(query_texts=query_texts, n_results=n_results)
+            results = collection.query(
+                query_texts=query_texts, n_results=n_results
+            )
             return results
         except Exception as e:
             logging.error(f"Failed to query the collection. Error {e}")

swarms/memory/pg.py

@@ -89,11 +89,15 @@ class PgVectorVectorStore(BaseVectorStore):
     engine: Optional[Engine] = field(default=None, kw_only=True)
     table_name: str = field(kw_only=True)
     _model: any = field(
-        default=Factory(lambda self: self.default_vector_model(), takes_self=True)
+        default=Factory(
+            lambda self: self.default_vector_model(), takes_self=True
+        )
     )
 
     @connection_string.validator
-    def validate_connection_string(self, _, connection_string: Optional[str]) -> None:
+    def validate_connection_string(
+        self, _, connection_string: Optional[str]
+    ) -> None:
         # If an engine is provided, the connection string is not used.
         if self.engine is not None:
             return
@@ -104,7 +108,8 @@ class PgVectorVectorStore(BaseVectorStore):
 
         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
@@ -148,7 +153,7 @@ class PgVectorVectorStore(BaseVectorStore):
         vector_id: Optional[str] = None,
         namespace: Optional[str] = None,
         meta: Optional[dict] = None,
-        **kwargs
+        **kwargs,
     ) -> str:
         """Inserts or updates a vector in the collection."""
         with Session(self.engine) as session:
@@ -208,7 +213,7 @@ class PgVectorVectorStore(BaseVectorStore):
         namespace: Optional[str] = None,
         include_vectors: bool = False,
         distance_metric: str = "cosine_distance",
-        **kwargs
+        **kwargs,
     ) -> list[BaseVectorStore.QueryResult]:
         """Performs a search on the collection to find vectors similar to the provided input vector,
         optionally filtering to only those that match the provided namespace.

swarms/memory/pinecone.py

@@ -108,7 +108,7 @@ class PineconeVectorStoreStore(BaseVector):
         vector_id: Optional[str] = None,
         namespace: Optional[str] = None,
         meta: Optional[dict] = None,
-        **kwargs
+        **kwargs,
     ) -> str:
         """Upsert vector"""
         vector_id = vector_id if vector_id else str_to_hash(str(vector))
@@ -123,7 +123,9 @@ class PineconeVectorStoreStore(BaseVector):
         self, vector_id: str, namespace: Optional[str] = None
     ) -> Optional[BaseVector.Entry]:
         """Load entry"""
-        result = self.index.fetch(ids=[vector_id], namespace=namespace).to_dict()
+        result = self.index.fetch(
+            ids=[vector_id], namespace=namespace
+        ).to_dict()
         vectors = list(result["vectors"].values())
 
         if len(vectors) > 0:
@@ -138,7 +140,9 @@ class PineconeVectorStoreStore(BaseVector):
         else:
             return None
 
-    def load_entries(self, namespace: Optional[str] = None) -> list[BaseVector.Entry]:
+    def load_entries(
+        self, namespace: Optional[str] = None
+    ) -> list[BaseVector.Entry]:
         """Load entries"""
         # This is a hacky way to query up to 10,000 values from Pinecone. Waiting on an official API for fetching
         # all values from a namespace:
@@ -169,7 +173,7 @@ class PineconeVectorStoreStore(BaseVector):
         include_vectors: bool = False,
         # PineconeVectorStoreStorageDriver-specific params:
         include_metadata=True,
-        **kwargs
+        **kwargs,
     ) -> list[BaseVector.QueryResult]:
         """Query vectors"""
         vector = self.embedding_driver.embed_string(query)
@@ -196,6 +200,9 @@ class PineconeVectorStoreStore(BaseVector):
 
     def create_index(self, name: str, **kwargs) -> None:
         """Create index"""
-        params = {"name": name, "dimension": self.embedding_driver.dimensions} | kwargs
+        params = {
+            "name": name,
+            "dimension": self.embedding_driver.dimensions,
+        } | kwargs
 
         pinecone.create_index(**params)

swarms/memory/schemas.py

@@ -50,7 +50,9 @@ class StepInput(BaseModel):
 class StepOutput(BaseModel):
     __root__: Any = Field(
         ...,
-        description="Output that the task step has produced. Any value is allowed.",
+        description=(
+            "Output that the task step has produced. Any value is allowed."
+        ),
         example='{\n"tokens": 7894,\n"estimated_cost": "0,24$"\n}',
     )
 
@@ -112,8 +114,9 @@ class Step(StepRequestBody):
         None,
         description="Output of the task step.",
         example=(
-            "I am going to use the write_to_file command and write Washington to a file"
+            "I am going to use the write_to_file command and write Washington"
-            " called output.txt <write_to_file('output.txt', 'Washington')"
+            " to a file called output.txt <write_to_file('output.txt',"
+            " 'Washington')"
         ),
     )
     additional_output: Optional[StepOutput] = None

swarms/memory/utils.py

@@ -57,7 +57,7 @@ def maximal_marginal_relevance(
 def filter_complex_metadata(
     documents: List[Document],
     *,
-    allowed_types: Tuple[Type, ...] = (str, bool, int, float)
+    allowed_types: Tuple[Type, ...] = (str, bool, int, float),
 ) -> List[Document]:
     """Filter out metadata types that are not supported for a vector store."""
     updated_documents = []

swarms/models/__init__.py

@@ -7,7 +7,11 @@ sys.stderr = log_file
 from swarms.models.anthropic import Anthropic  # noqa: E402
 from swarms.models.petals import Petals  # noqa: E402
 from swarms.models.mistral import Mistral  # noqa: E402
-from swarms.models.openai_models import OpenAI, AzureOpenAI, OpenAIChat  # noqa: E402
+from swarms.models.openai_models import (
+    OpenAI,
+    AzureOpenAI,
+    OpenAIChat,
+)  # noqa: E402
 from swarms.models.zephyr import Zephyr  # noqa: E402
 from swarms.models.biogpt import BioGPT  # noqa: E402
 from swarms.models.huggingface import HuggingfaceLLM  # noqa: E402

swarms/models/anthropic.py

@@ -50,7 +50,9 @@ def xor_args(*arg_groups: Tuple[str, ...]) -> Callable:
             ]
             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]
+                invalid_group_names = [
+                    ", ".join(arg_groups[i]) for i in invalid_groups
+                ]
                 raise ValueError(
                     "Exactly one argument in each of the following"
                     " groups must be defined:"
@@ -106,7 +108,10 @@ def mock_now(dt_value):  # type: ignore
 
 
 def guard_import(
-    module_name: str, *, pip_name: Optional[str] = None, package: Optional[str] = None
+    module_name: str,
+    *,
+    pip_name: Optional[str] = None,
+    package: Optional[str] = None,
 ) -> Any:
     """Dynamically imports a module and raises a helpful exception if the module is not
     installed."""
@@ -180,18 +185,18 @@ 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(
+            warnings.warn(f"""WARNING! {field_name} is not default parameter.
-                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(extra_kwargs.keys())
+    invalid_model_kwargs = all_required_field_names.intersection(
+        extra_kwargs.keys()
+    )
     if invalid_model_kwargs:
         raise ValueError(
-            f"Parameters {invalid_model_kwargs} should be specified explicitly. "
+            f"Parameters {invalid_model_kwargs} should be specified explicitly."
-            "Instead they were passed in as part of `model_kwargs` parameter."
+            " Instead they were passed in as part of `model_kwargs` parameter."
         )
 
     return extra_kwargs
@@ -250,7 +255,9 @@ class _AnthropicCommon(BaseLanguageModel):
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
         values["anthropic_api_key"] = convert_to_secret_str(
-            get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
+            get_from_dict_or_env(
+                values, "anthropic_api_key", "ANTHROPIC_API_KEY"
+            )
         )
         # Get custom api url from environment.
         values["anthropic_api_url"] = get_from_dict_or_env(
@@ -305,7 +312,9 @@ class _AnthropicCommon(BaseLanguageModel):
         """Get the identifying parameters."""
         return {**{}, **self._default_params}
 
-    def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
+    def _get_anthropic_stop(
+        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")
 
@@ -354,8 +363,8 @@ class Anthropic(LLM, _AnthropicCommon):
     def raise_warning(cls, values: Dict) -> Dict:
         """Raise warning that this class is deprecated."""
         warnings.warn(
-            "This Anthropic LLM is deprecated. "
+            "This Anthropic LLM is deprecated. Please use `from"
-            "Please use `from langchain.chat_models import ChatAnthropic` instead"
+            " langchain.chat_models import ChatAnthropic` instead"
         )
         return values
 
@@ -372,12 +381,16 @@ class Anthropic(LLM, _AnthropicCommon):
             return prompt  # Already wrapped.
 
         # Guard against common errors in specifying wrong number of newlines.
-        corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt)
+        corrected_prompt, n_subs = re.subn(
+            r"^\n*Human:", self.HUMAN_PROMPT, prompt
+        )
         if n_subs == 1:
             return corrected_prompt
 
         # 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"
+        return (
+            f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
+        )
 
     def _call(
         self,
@@ -476,7 +489,10 @@ class Anthropic(LLM, _AnthropicCommon):
         params = {**self._default_params, **kwargs}
 
         for token in self.client.completions.create(
-            prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params
+            prompt=self._wrap_prompt(prompt),
+            stop_sequences=stop,
+            stream=True,
+            **params,
         ):
             chunk = GenerationChunk(text=token.completion)
             yield chunk

swarms/models/bioclip.py

@@ -98,7 +98,9 @@ class BioClip:
         ) = open_clip.create_model_and_transforms(model_path)
         self.tokenizer = open_clip.get_tokenizer(model_path)
         self.device = (
-            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+            torch.device("cuda")
+            if torch.cuda.is_available()
+            else torch.device("cpu")
         )
         self.model.to(self.device)
         self.model.eval()
@@ -110,13 +112,17 @@ 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
         ).to(self.device)
 
         with torch.no_grad():
-            image_features, text_features, logit_scale = self.model(image, texts)
+            image_features, text_features, logit_scale = self.model(
+                image, texts
+            )
             logits = (
                 (logit_scale * image_features @ text_features.t())
                 .detach()
@@ -142,7 +148,9 @@ class BioClip:
         title = (
             metadata["filename"]
             + "\n"
-            + "\n".join([f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()])
+            + "\n".join(
+                [f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()]
+            )
         )
         ax.set_title(title, fontsize=14)
         plt.tight_layout()

swarms/models/biogpt.py

@@ -154,7 +154,7 @@ class BioGPT:
                 min_length=self.min_length,
                 max_length=self.max_length,
                 num_beams=num_beams,
-                early_stopping=early_stopping
+                early_stopping=early_stopping,
             )
         return self.tokenizer.decode(beam_output[0], skip_special_tokens=True)
 

swarms/models/cohere_chat.py

@@ -96,7 +96,9 @@ class BaseCohere(Serializable):
                 values, "cohere_api_key", "COHERE_API_KEY"
             )
             client_name = values["user_agent"]
-            values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
+            values["client"] = cohere.Client(
+                cohere_api_key, client_name=client_name
+            )
             values["async_client"] = cohere.AsyncClient(
                 cohere_api_key, client_name=client_name
             )
@@ -172,17 +174,23 @@ class Cohere(LLM, BaseCohere):
         """Return type of llm."""
         return "cohere"
 
-    def _invocation_params(self, stop: Optional[List[str]], **kwargs: Any) -> dict:
+    def _invocation_params(
+        self, stop: Optional[List[str]], **kwargs: Any
+    ) -> dict:
         params = self._default_params
         if self.stop is not None and stop is not None:
-            raise ValueError("`stop` found in both the input and default params.")
+            raise ValueError(
+                "`stop` found in both the input and default params."
+            )
         elif self.stop is not None:
             params["stop_sequences"] = self.stop
         else:
             params["stop_sequences"] = stop
         return {**params, **kwargs}
 
-    def _process_response(self, response: Any, stop: Optional[List[str]]) -> str:
+    def _process_response(
+        self, response: Any, stop: Optional[List[str]]
+    ) -> str:
         text = response.generations[0].text
         # If stop tokens are provided, Cohere's endpoint returns them.
         # In order to make this consistent with other endpoints, we strip them.

swarms/models/dalle3.py

@@ -169,8 +169,8 @@ class Dalle3:
             print(
                 colored(
                     (
-                        f"Error running Dalle3: {error} try optimizing your api key and"
+                        f"Error running Dalle3: {error} try optimizing your api"
-                        " or try again"
+                        " key and or try again"
                     ),
                     "red",
                 )
@@ -234,8 +234,8 @@ class Dalle3:
             print(
                 colored(
                     (
-                        f"Error running Dalle3: {error} try optimizing your api key and"
+                        f"Error running Dalle3: {error} try optimizing your api"
-                        " or try again"
+                        " key and or try again"
                     ),
                     "red",
                 )
@@ -248,8 +248,7 @@ class Dalle3:
         """Print the Dalle3 dashboard"""
         print(
             colored(
-                (
+                f"""Dalle3 Dashboard: 
-                    f"""Dalle3 Dashboard: 
                     --------------------
 
                     Model: {self.model}
@@ -265,13 +264,14 @@ class Dalle3:
                     --------------------
                     
                     
-                    """
+                    """,
-                ),
                 "green",
             )
         )
 
-    def process_batch_concurrently(self, tasks: List[str], max_workers: int = 5):
+    def process_batch_concurrently(
+        self, tasks: List[str], max_workers: int = 5
+    ):
         """
 
         Process a batch of tasks concurrently
@@ -293,8 +293,12 @@ class Dalle3:
         ['https://cdn.openai.com/dall-e/encoded/feats/feats_01J9J5ZKJZJY9.png',
 
         """
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        with concurrent.futures.ThreadPoolExecutor(
-            future_to_task = {executor.submit(self, task): task for task in tasks}
+            max_workers=max_workers
+        ) as executor:
+            future_to_task = {
+                executor.submit(self, task): task for task in tasks
+            }
             results = []
             for future in concurrent.futures.as_completed(future_to_task):
                 task = future_to_task[future]
@@ -307,14 +311,20 @@ class Dalle3:
                     print(
                         colored(
                             (
-                                f"Error running Dalle3: {error} try optimizing your api key and"
+                                f"Error running Dalle3: {error} try optimizing"
-                                " or try again"
+                                " your api key and or try again"
                             ),
                             "red",
                         )
                     )
-                    print(colored(f"Error running Dalle3: {error.http_status}", "red"))
+                    print(
-                    print(colored(f"Error running Dalle3: {error.error}", "red"))
+                        colored(
+                            f"Error running Dalle3: {error.http_status}", "red"
+                        )
+                    )
+                    print(
+                        colored(f"Error running Dalle3: {error.error}", "red")
+                    )
                     raise error
 
     def _generate_uuid(self):

swarms/models/distilled_whisperx.py

@@ -28,7 +28,10 @@ def async_retry(max_retries=3, exceptions=(Exception,), delay=1):
                     retries -= 1
                     if retries <= 0:
                         raise
-                    print(f"Retry after exception: {e}, Attempts remaining: {retries}")
+                    print(
+                        f"Retry after exception: {e}, Attempts remaining:"
+                        f" {retries}"
+                    )
                     await asyncio.sleep(delay)
 
         return wrapper
@@ -62,7 +65,9 @@ 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
+        self.torch_dtype = (
+            torch.float16 if torch.cuda.is_available() else torch.float32
+        )
         self.model_id = model_id
         self.model = AutoModelForSpeechSeq2Seq.from_pretrained(
             model_id,
@@ -119,7 +124,9 @@ class DistilWhisperModel:
         try:
             with torch.no_grad():
                 # Load the whole audio file, but process and transcribe it in chunks
-                audio_input = self.processor.audio_file_to_array(audio_file_path)
+                audio_input = self.processor.audio_file_to_array(
+                    audio_file_path
+                )
                 sample_rate = audio_input.sampling_rate
                 len(audio_input.array) / sample_rate
                 chunks = [
@@ -139,7 +146,9 @@ 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
                     logits = self.model.generate(processed_inputs)
@@ -157,4 +166,6 @@ class DistilWhisperModel:
                     time.sleep(chunk_duration)
 
         except Exception as e:
-            print(colored(f"An error occurred during transcription: {e}", "red"))
+            print(
+                colored(f"An error occurred during transcription: {e}", "red")
+            )

swarms/models/eleven_labs.py

@@ -79,7 +79,9 @@ class ElevenLabsText2SpeechTool(BaseTool):
                 f.write(speech)
             return f.name
         except Exception as e:
-            raise RuntimeError(f"Error while running ElevenLabsText2SpeechTool: {e}")
+            raise RuntimeError(
+                f"Error while running ElevenLabsText2SpeechTool: {e}"
+            )
 
     def play(self, speech_file: str) -> None:
         """Play the text as speech."""
@@ -93,7 +95,9 @@ class ElevenLabsText2SpeechTool(BaseTool):
         """Stream the text as speech as it is generated.
         Play the text in your speakers."""
         elevenlabs = _import_elevenlabs()
-        speech_stream = elevenlabs.generate(text=query, model=self.model, stream=True)
+        speech_stream = elevenlabs.generate(
+            text=query, model=self.model, stream=True
+        )
         elevenlabs.stream(speech_stream)
 
     def save(self, speech_file: str, path: str) -> None:

swarms/models/fastvit.py

@@ -10,7 +10,9 @@ from pydantic import BaseModel, StrictFloat, StrictInt, validator
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Load the classes for image classification
-with open(os.path.join(os.path.dirname(__file__), "fast_vit_classes.json")) as f:
+with open(
+    os.path.join(os.path.dirname(__file__), "fast_vit_classes.json")
+) as f:
     FASTVIT_IMAGENET_1K_CLASSES = json.load(f)
 
 
@@ -20,7 +22,9 @@ class ClassificationResult(BaseModel):
 
     @validator("class_id", "confidence", pre=True, each_item=True)
     def check_list_contents(cls, v):
-        assert isinstance(v, int) or isinstance(v, float), "must be integer or float"
+        assert isinstance(v, int) or isinstance(
+            v, float
+        ), "must be integer or float"
         return v
 
 
@@ -50,7 +54,9 @@ class FastViT:
             "hf_hub:timm/fastvit_s12.apple_in1k", pretrained=True
         ).to(DEVICE)
         data_config = timm.data.resolve_model_data_config(self.model)
-        self.transforms = timm.data.create_transform(**data_config, is_training=False)
+        self.transforms = timm.data.create_transform(
+            **data_config, is_training=False
+        )
         self.model.eval()
 
     def __call__(

swarms/models/fuyu.py

@@ -46,7 +46,9 @@ class Fuyu:
         self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path)
         self.image_processor = FuyuImageProcessor()
         self.processor = FuyuProcessor(
-            image_processor=self.image_processor, tokenizer=self.tokenizer, **kwargs
+            image_processor=self.image_processor,
+            tokenizer=self.tokenizer,
+            **kwargs,
         )
         self.model = FuyuForCausalLM.from_pretrained(
             pretrained_path,
@@ -69,8 +71,12 @@ class Fuyu:
         for k, v in model_inputs.items():
             model_inputs[k] = v.to(self.device_map)
 
-        output = self.model.generate(**model_inputs, max_new_tokens=self.max_new_tokens)
+        output = self.model.generate(
-        text = self.processor.batch_decode(output[:, -7:], skip_special_tokens=True)
+            **model_inputs, max_new_tokens=self.max_new_tokens
+        )
+        text = self.processor.batch_decode(
+            output[:, -7:], skip_special_tokens=True
+        )
         return print(str(text))
 
     def get_img_from_web(self, img_url: str):

swarms/models/gpt4v.py

@@ -190,12 +190,15 @@ class GPT4Vision:
         """Process a batch of tasks and images"""
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
-                executor.submit(self.run, task, img) for task, img in tasks_images
+                executor.submit(self.run, task, img)
+                for task, img in tasks_images
             ]
             results = [future.result() for future in futures]
         return results
 
-    async def run_batch_async(self, tasks_images: List[Tuple[str, str]]) -> List[str]:
+    async def run_batch_async(
+        self, tasks_images: List[Tuple[str, str]]
+    ) -> List[str]:
         """Process a batch of tasks and images asynchronously"""
         loop = asyncio.get_event_loop()
         futures = [

swarms/models/huggingface.py

@@ -133,7 +133,9 @@ class HuggingfaceLLM:
     ):
         self.logger = logging.getLogger(__name__)
         self.device = (
-            device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+            device
+            if device
+            else ("cuda" if torch.cuda.is_available() else "cpu")
         )
         self.model_id = model_id
         self.max_length = max_length
@@ -178,7 +180,11 @@ class HuggingfaceLLM:
         except Exception as e:
             # self.logger.error(f"Failed to load the model or the tokenizer: {e}")
             # raise
-            print(colored(f"Failed to load the model and or the tokenizer: {e}", "red"))
+            print(
+                colored(
+                    f"Failed to load the model and or the tokenizer: {e}", "red"
+                )
+            )
 
     def print_error(self, error: str):
         """Print error"""
@@ -207,12 +213,16 @@ class HuggingfaceLLM:
                 if self.distributed:
                     self.model = DDP(self.model)
             except Exception as error:
-                self.logger.error(f"Failed to load the model or the tokenizer: {error}")
+                self.logger.error(
+                    f"Failed to load the model or the tokenizer: {error}"
+                )
                 raise
 
     def concurrent_run(self, tasks: List[str], max_workers: int = 5):
         """Concurrently generate text for a list of prompts."""
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=max_workers
+        ) as executor:
             results = list(executor.map(self.run, tasks))
         return results
 
@@ -220,7 +230,8 @@ class HuggingfaceLLM:
         """Process a batch of tasks and images"""
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
-                executor.submit(self.run, task, img) for task, img in tasks_images
+                executor.submit(self.run, task, img)
+                for task, img in tasks_images
             ]
             results = [future.result() for future in futures]
         return results
@@ -243,7 +254,9 @@ 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()
 
@@ -279,8 +292,8 @@ class HuggingfaceLLM:
             print(
                 colored(
                     (
-                        f"HuggingfaceLLM could not generate text because of error: {e},"
+                        "HuggingfaceLLM could not generate text because of"
-                        " try optimizing your arguments"
+                        f" error: {e}, try optimizing your arguments"
                     ),
                     "red",
                 )
@@ -305,7 +318,9 @@ 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()
 

swarms/models/idefics.py

@@ -66,7 +66,9 @@ class Idefics:
         max_length=100,
     ):
         self.device = (
-            device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+            device
+            if device
+            else ("cuda" if torch.cuda.is_available() else "cpu")
         )
         self.model = IdeficsForVisionText2Text.from_pretrained(
             checkpoint,

swarms/models/jina_embeds.py

@@ -54,7 +54,9 @@ class JinaEmbeddings:
     ):
         self.logger = logging.getLogger(__name__)
         self.device = (
-            device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+            device
+            if device
+            else ("cuda" if torch.cuda.is_available() else "cpu")
         )
         self.model_id = model_id
         self.max_length = max_length
@@ -83,7 +85,9 @@ class JinaEmbeddings:
 
         try:
             self.model = AutoModelForCausalLM.from_pretrained(
-                self.model_id, quantization_config=bnb_config, trust_remote_code=True
+                self.model_id,
+                quantization_config=bnb_config,
+                trust_remote_code=True,
             )
 
             self.model  # .to(self.device)
@@ -112,7 +116,9 @@ class JinaEmbeddings:
                 if self.distributed:
                     self.model = DDP(self.model)
             except Exception as error:
-                self.logger.error(f"Failed to load the model or the tokenizer: {error}")
+                self.logger.error(
+                    f"Failed to load the model or the tokenizer: {error}"
+                )
                 raise
 
     def run(self, task: str):

swarms/models/kosmos2.py

@@ -70,11 +70,13 @@ class Kosmos2(BaseModel):
         prompt = "<grounding>An image of"
 
         inputs = self.processor(text=prompt, images=image, return_tensors="pt")
-        outputs = self.model.generate(**inputs, use_cache=True, max_new_tokens=64)
+        outputs = self.model.generate(
+            **inputs, use_cache=True, max_new_tokens=64
+        )
 
-        generated_text = self.processor.batch_decode(outputs, skip_special_tokens=True)[
+        generated_text = self.processor.batch_decode(
-            0
+            outputs, skip_special_tokens=True
-        ]
+        )[0]
 
         # The actual processing of generated_text to entities would go here
         # For the purpose of this example, assume a mock function 'extract_entities' exists:
@@ -99,7 +101,9 @@ class Kosmos2(BaseModel):
         if not entities:
             return Detections.empty()
 
-        class_ids = [0] * len(entities)  # Replace with actual class ID extraction logic
+        class_ids = [0] * len(
+            entities
+        )  # Replace with actual class ID extraction logic
         xyxys = [
             (
                 e[1][0] * image.width,
@@ -111,7 +115,9 @@ class Kosmos2(BaseModel):
         ]
         confidences = [1.0] * len(entities)  # Placeholder confidence
 
-        return Detections(xyxy=xyxys, class_id=class_ids, confidence=confidences)
+        return Detections(
+            xyxy=xyxys, class_id=class_ids, confidence=confidences
+        )
 
 
 # Usage:

swarms/models/kosmos_two.py

@@ -145,12 +145,12 @@ class Kosmos:
         elif isinstance(image, torch.Tensor):
             # pdb.set_trace()
             image_tensor = image.cpu()
-            reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[
+            reverse_norm_mean = torch.tensor(
-                :, None, None
+                [0.48145466, 0.4578275, 0.40821073]
-            ]
+            )[:, None, None]
-            reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[
+            reverse_norm_std = torch.tensor(
-                :, None, None
+                [0.26862954, 0.26130258, 0.27577711]
-            ]
+            )[:, None, None]
             image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
             pil_img = T.ToPILImage()(image_tensor)
             image_h = pil_img.height
@@ -188,7 +188,11 @@ class Kosmos:
                 # random color
                 color = tuple(np.random.randint(0, 255, size=3).tolist())
                 new_image = cv2.rectangle(
-                    new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line
+                    new_image,
+                    (orig_x1, orig_y1),
+                    (orig_x2, orig_y2),
+                    color,
+                    box_line,
                 )
 
                 l_o, r_o = (
@@ -211,7 +215,10 @@ class Kosmos:
 
                 # add text background
                 (text_width, text_height), _ = cv2.getTextSize(
-                    f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line
+                    f"  {entity_name}",
+                    cv2.FONT_HERSHEY_COMPLEX,
+                    text_size,
+                    text_line,
                 )
                 text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = (
                     x1,
@@ -222,7 +229,8 @@ class Kosmos:
 
                 for prev_bbox in previous_bboxes:
                     while is_overlapping(
-                        (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox
+                        (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
@@ -230,14 +238,18 @@ class Kosmos:
                         text_bg_y2 += (
                             text_height + text_offset_original + 2 * text_spaces
                         )
-                        y1 += text_height + text_offset_original + 2 * text_spaces
+                        y1 += (
+                            text_height + text_offset_original + 2 * text_spaces
+                        )
 
                         if text_bg_y2 >= image_h:
                             text_bg_y1 = max(
                                 0,
                                 image_h
                                 - (
-                                    text_height + text_offset_original + 2 * text_spaces
+                                    text_height
+                                    + text_offset_original
+                                    + 2 * text_spaces
                                 ),
                             )
                             text_bg_y2 = image_h
@@ -270,7 +282,9 @@ class Kosmos:
                     cv2.LINE_AA,
                 )
                 # previous_locations.append((x1, y1))
-                previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
+                previous_bboxes.append(
+                    (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)
+                )
 
         pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]])
         if save_path:

swarms/models/llama_function_caller.py

@@ -121,7 +121,11 @@ class LlamaFunctionCaller:
         )
 
     def add_func(
-        self, name: str, function: Callable, description: str, arguments: List[Dict]
+        self,
+        name: str,
+        function: Callable,
+        description: str,
+        arguments: List[Dict],
     ):
         """
         Adds a new function to the LlamaFunctionCaller.
@@ -172,12 +176,17 @@ class LlamaFunctionCaller:
 
         if self.streaming:
             out = self.model.generate(
-                **inputs, streamer=streamer, max_new_tokens=self.max_tokens, **kwargs
+                **inputs,
+                streamer=streamer,
+                max_new_tokens=self.max_tokens,
+                **kwargs,
             )
 
             return out
         else:
-            out = self.model.generate(**inputs, max_length=self.max_tokens, **kwargs)
+            out = self.model.generate(
+                **inputs, max_length=self.max_tokens, **kwargs
+            )
             # return self.tokenizer.decode(out[0], skip_special_tokens=True)
             return out
 

swarms/models/mistral.py

@@ -49,7 +49,9 @@ 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.")
+            raise ValueError(
+                "CUDA is not available. Please choose a different device."
+            )
 
         # Load the model and tokenizer
         self.model = None
@@ -70,7 +72,9 @@ 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,
@@ -87,7 +91,9 @@ 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,

swarms/models/mpt.py

@@ -29,7 +29,9 @@ class MPT7B:
 
     """
 
-    def __init__(self, model_name: str, tokenizer_name: str, max_tokens: int = 100):
+    def __init__(
+        self, model_name: str, tokenizer_name: str, max_tokens: int = 100
+    ):
         # Loading model and tokenizer details
         self.model_name = model_name
         self.tokenizer_name = tokenizer_name
@@ -118,7 +120,10 @@ class MPT7B:
         """
         with torch.autocast("cuda", dtype=torch.bfloat16):
             return self.pipe(
-                prompt, max_new_tokens=self.max_tokens, do_sample=True, use_cache=True
+                prompt,
+                max_new_tokens=self.max_tokens,
+                do_sample=True,
+                use_cache=True,
             )[0]["generated_text"]
 
     async def generate_async(self, prompt: str) -> str:

swarms/models/nougat.py

@@ -41,8 +41,12 @@ class Nougat:
         self.min_length = min_length
         self.max_new_tokens = max_new_tokens
 
-        self.processor = NougatProcessor.from_pretrained(self.model_name_or_path)
+        self.processor = NougatProcessor.from_pretrained(
-        self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name_or_path)
+            self.model_name_or_path
+        )
+        self.model = VisionEncoderDecoderModel.from_pretrained(
+            self.model_name_or_path
+        )
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         self.model.to(self.device)
 
@@ -63,8 +67,12 @@ class Nougat:
             max_new_tokens=self.max_new_tokens,
         )
 
-        sequence = self.processor.batch_decode(outputs, skip_special_tokens=True)[0]
+        sequence = self.processor.batch_decode(
-        sequence = self.processor.post_process_generation(sequence, fix_markdown=False)
+            outputs, skip_special_tokens=True
+        )[0]
+        sequence = self.processor.post_process_generation(
+            sequence, fix_markdown=False
+        )
 
         out = print(sequence)
         return out

swarms/models/openai_embeddings.py

@@ -43,7 +43,9 @@ def get_pydantic_field_names(cls: Any) -> Set[str]:
 logger = logging.getLogger(__name__)
 
 
-def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any]:
+def _create_retry_decorator(
+    embeddings: OpenAIEmbeddings,
+) -> Callable[[Any], Any]:
     import llm
 
     min_seconds = 4
@@ -118,7 +120,9 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
     return _embed_with_retry(**kwargs)
 
 
-async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
+async def async_embed_with_retry(
+    embeddings: OpenAIEmbeddings, **kwargs: Any
+) -> Any:
     """Use tenacity to retry the embedding call."""
 
     @_async_retry_decorator(embeddings)
@@ -172,7 +176,9 @@ 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
@@ -229,11 +235,14 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 )
                 extra[field_name] = values.pop(field_name)
 
-        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
+        invalid_model_kwargs = all_required_field_names.intersection(
+            extra.keys()
+        )
         if invalid_model_kwargs:
             raise ValueError(
-                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
+                f"Parameters {invalid_model_kwargs} should be specified"
-                "Instead they were passed in as part of `model_kwargs` parameter."
+                " explicitly. Instead they were passed in as part of"
+                " `model_kwargs` parameter."
             )
 
         values["model_kwargs"] = extra
@@ -333,7 +342,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         try:
             encoding = tiktoken.encoding_for_model(model_name)
         except KeyError:
-            logger.warning("Warning: model not found. Using cl100k_base encoding.")
+            logger.warning(
+                "Warning: model not found. Using cl100k_base encoding."
+            )
             model = "cl100k_base"
             encoding = tiktoken.get_encoding(model)
         for i, text in enumerate(texts):
@@ -384,11 +395,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                     self,
                     input="",
                     **self._invocation_params,
-                )[
+                )["data"][0]["embedding"]
-                    "data"
-                ][0]["embedding"]
             else:
-                average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
+                average = np.average(
+                    _result, axis=0, weights=num_tokens_in_batch[i]
+                )
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
 
         return embeddings
@@ -414,7 +425,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         try:
             encoding = tiktoken.encoding_for_model(model_name)
         except KeyError:
-            logger.warning("Warning: model not found. Using cl100k_base encoding.")
+            logger.warning(
+                "Warning: model not found. Using cl100k_base encoding."
+            )
             model = "cl100k_base"
             encoding = tiktoken.get_encoding(model)
         for i, text in enumerate(texts):
@@ -458,7 +471,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                     )
                 )["data"][0]["embedding"]
             else:
-                average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
+                average = np.average(
+                    _result, axis=0, weights=num_tokens_in_batch[i]
+                )
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
 
         return embeddings
@@ -495,7 +510,9 @@ 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 await self._aget_len_safe_embeddings(texts, engine=self.deployment)
+        return await self._aget_len_safe_embeddings(
+            texts, engine=self.deployment
+        )
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint for embedding query text.

swarms/models/openai_function_caller.py

@@ -146,7 +146,8 @@ class OpenAIFunctionCaller:
         self.messages.append({"role": role, "content": content})
 
     @retry(
-        wait=wait_random_exponential(multiplier=1, max=40), stop=stop_after_attempt(3)
+        wait=wait_random_exponential(multiplier=1, max=40),
+        stop=stop_after_attempt(3),
     )
     def chat_completion_request(
         self,
@@ -194,17 +195,22 @@ class OpenAIFunctionCaller:
             elif message["role"] == "user":
                 print(
                     colored(
-                        f"user: {message['content']}\n", role_to_color[message["role"]]
+                        f"user: {message['content']}\n",
+                        role_to_color[message["role"]],
                     )
                 )
-            elif message["role"] == "assistant" and message.get("function_call"):
+            elif message["role"] == "assistant" and message.get(
+                "function_call"
+            ):
                 print(
                     colored(
                         f"assistant: {message['function_call']}\n",
                         role_to_color[message["role"]],
                     )
                 )
-            elif message["role"] == "assistant" and not message.get("function_call"):
+            elif message["role"] == "assistant" and not message.get(
+                "function_call"
+            ):
                 print(
                     colored(
                         f"assistant: {message['content']}\n",

swarms/models/openai_models.py

@@ -62,19 +62,25 @@ def _stream_response_to_generation_chunk(
     return GenerationChunk(
         text=stream_response["choices"][0]["text"],
         generation_info=dict(
-            finish_reason=stream_response["choices"][0].get("finish_reason", None),
+            finish_reason=stream_response["choices"][0].get(
+                "finish_reason", None
+            ),
             logprobs=stream_response["choices"][0].get("logprobs", None),
         ),
     )
 
 
-def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
+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]["logprobs"] = stream_response["choices"][0][
+        "logprobs"
+    ]
 
 
 def _streaming_response_template() -> Dict[str, Any]:
@@ -315,9 +321,11 @@ class BaseOpenAI(BaseLLM):
                     chunk.text,
                     chunk=chunk,
                     verbose=self.verbose,
-                    logprobs=chunk.generation_info["logprobs"]
+                    logprobs=(
-                    if chunk.generation_info
+                        chunk.generation_info["logprobs"]
-                    else None,
+                        if chunk.generation_info
+                        else None
+                    ),
                 )
 
     async def _astream(
@@ -339,9 +347,11 @@ class BaseOpenAI(BaseLLM):
                     chunk.text,
                     chunk=chunk,
                     verbose=self.verbose,
-                    logprobs=chunk.generation_info["logprobs"]
+                    logprobs=(
-                    if chunk.generation_info
+                        chunk.generation_info["logprobs"]
-                    else None,
+                        if chunk.generation_info
+                        else None
+                    ),
                 )
 
     def _generate(
@@ -377,10 +387,14 @@ class BaseOpenAI(BaseLLM):
         for _prompts in sub_prompts:
             if self.streaming:
                 if len(_prompts) > 1:
-                    raise ValueError("Cannot stream results with multiple prompts.")
+                    raise ValueError(
+                        "Cannot stream results with multiple prompts."
+                    )
 
                 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:
@@ -389,12 +403,16 @@ class BaseOpenAI(BaseLLM):
                 choices.append(
                     {
                         "text": generation.text,
-                        "finish_reason": generation.generation_info.get("finish_reason")
+                        "finish_reason": (
-                        if generation.generation_info
+                            generation.generation_info.get("finish_reason")
-                        else None,
+                            if generation.generation_info
-                        "logprobs": generation.generation_info.get("logprobs")
+                            else None
-                        if generation.generation_info
+                        ),
-                        else None,
+                        "logprobs": (
+                            generation.generation_info.get("logprobs")
+                            if generation.generation_info
+                            else None
+                        ),
                     }
                 )
             else:
@@ -424,7 +442,9 @@ class BaseOpenAI(BaseLLM):
         for _prompts in sub_prompts:
             if self.streaming:
                 if len(_prompts) > 1:
-                    raise ValueError("Cannot stream results with multiple prompts.")
+                    raise ValueError(
+                        "Cannot stream results with multiple prompts."
+                    )
 
                 generation: Optional[GenerationChunk] = None
                 async for chunk in self._astream(
@@ -438,12 +458,16 @@ class BaseOpenAI(BaseLLM):
                 choices.append(
                     {
                         "text": generation.text,
-                        "finish_reason": generation.generation_info.get("finish_reason")
+                        "finish_reason": (
-                        if generation.generation_info
+                            generation.generation_info.get("finish_reason")
-                        else None,
+                            if generation.generation_info
-                        "logprobs": generation.generation_info.get("logprobs")
+                            else None
-                        if generation.generation_info
+                        ),
-                        else None,
+                        "logprobs": (
+                            generation.generation_info.get("logprobs")
+                            if generation.generation_info
+                            else None
+                        ),
                     }
                 )
             else:
@@ -463,7 +487,9 @@ class BaseOpenAI(BaseLLM):
         """Get the sub prompts for llm call."""
         if stop is not None:
             if "stop" in params:
-                raise ValueError("`stop` found in both the input and default params.")
+                raise ValueError(
+                    "`stop` found in both the input and default params."
+                )
             params["stop"] = stop
         if params["max_tokens"] == -1:
             if len(prompts) != 1:
@@ -541,7 +567,9 @@ class BaseOpenAI(BaseLLM):
         try:
             enc = tiktoken.encoding_for_model(model_name)
         except KeyError:
-            logger.warning("Warning: model not found. Using cl100k_base encoding.")
+            logger.warning(
+                "Warning: model not found. Using cl100k_base encoding."
+            )
             model = "cl100k_base"
             enc = tiktoken.get_encoding(model)
 
@@ -602,8 +630,9 @@ class BaseOpenAI(BaseLLM):
 
         if context_size is None:
             raise ValueError(
-                f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
+                f"Unknown model: {modelname}. Please provide a valid OpenAI"
-                "Known models are: " + ", ".join(model_token_mapping.keys())
+                " model name.Known models are: "
+                + ", ".join(model_token_mapping.keys())
             )
 
         return context_size
@@ -753,7 +782,9 @@ class OpenAIChat(BaseLLM):
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
-        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+        all_required_field_names = {
+            field.alias for field in cls.__fields__.values()
+        }
 
         extra = values.get("model_kwargs", {})
         for field_name in list(values):
@@ -820,13 +851,21 @@ class OpenAIChat(BaseLLM):
     ) -> Tuple:
         if len(prompts) > 1:
             raise ValueError(
-                f"OpenAIChat currently only supports single prompt, got {prompts}"
+                "OpenAIChat currently only supports single prompt, got"
+                f" {prompts}"
             )
-        messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
+        messages = self.prefix_messages + [
-        params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
+            {"role": "user", "content": prompts[0]}
+        ]
+        params: Dict[str, Any] = {
+            **{"model": self.model_name},
+            **self._default_params,
+        }
         if stop is not None:
             if "stop" in params:
-                raise ValueError("`stop` found in both the input and default params.")
+                raise ValueError(
+                    "`stop` found in both the input and default params."
+                )
             params["stop"] = stop
         if params.get("max_tokens") == -1:
             # for ChatGPT api, omitting max_tokens is equivalent to having no limit
@@ -897,7 +936,11 @@ class OpenAIChat(BaseLLM):
         }
         return LLMResult(
             generations=[
-                [Generation(text=full_response["choices"][0]["message"]["content"])]
+                [
+                    Generation(
+                        text=full_response["choices"][0]["message"]["content"]
+                    )
+                ]
             ],
             llm_output=llm_output,
         )
@@ -911,7 +954,9 @@ class OpenAIChat(BaseLLM):
     ) -> LLMResult:
         if self.streaming:
             generation: Optional[GenerationChunk] = None
-            async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
+            async for chunk in self._astream(
+                prompts[0], stop, run_manager, **kwargs
+            ):
                 if generation is None:
                     generation = chunk
                 else:
@@ -930,7 +975,11 @@ class OpenAIChat(BaseLLM):
         }
         return LLMResult(
             generations=[
-                [Generation(text=full_response["choices"][0]["message"]["content"])]
+                [
+                    Generation(
+                        text=full_response["choices"][0]["message"]["content"]
+                    )
+                ]
             ],
             llm_output=llm_output,
         )

swarms/models/palm.py

@@ -37,10 +37,16 @@ def _create_retry_decorator() -> Callable[[Any], Any]:
     return retry(
         reraise=True,
         stop=stop_after_attempt(max_retries),
-        wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
+        wait=wait_exponential(
+            multiplier=multiplier, min=min_seconds, max=max_seconds
+        ),
         retry=(
-            retry_if_exception_type(google.api_core.exceptions.ResourceExhausted)
+            retry_if_exception_type(
-            | retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable)
+                google.api_core.exceptions.ResourceExhausted
+            )
+            | retry_if_exception_type(
+                google.api_core.exceptions.ServiceUnavailable
+            )
             | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError)
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
@@ -64,7 +70,9 @@ def _strip_erroneous_leading_spaces(text: str) -> str:
     The PaLM API will sometimes erroneously return a single leading space in all
     lines > 1. This function strips that space.
     """
-    has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:])
+    has_leading_space = all(
+        not line or line[0] == " " for line in text.split("\n")[1:]
+    )
     if has_leading_space:
         return text.replace("\n ", "\n")
     else:
@@ -112,7 +120,10 @@ class GooglePalm(BaseLLM, BaseModel):
 
         values["client"] = genai
 
-        if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
+        if (
+            values["temperature"] is not None
+            and not 0 <= values["temperature"] <= 1
+        ):
             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:
@@ -121,7 +132,10 @@ class GooglePalm(BaseLLM, BaseModel):
         if values["top_k"] is not None and values["top_k"] <= 0:
             raise ValueError("top_k must be positive")
 
-        if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0:
+        if (
+            values["max_output_tokens"] is not None
+            and values["max_output_tokens"] <= 0
+        ):
             raise ValueError("max_output_tokens must be greater than zero")
 
         return values

swarms/models/simple_ada.py

@@ -16,4 +16,6 @@ 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

@@ -90,7 +90,9 @@ class SpeechT5:
         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")
+        self.embeddings_dataset = load_dataset(
+            self.dataset_name, split="validation"
+        )
 
     def __call__(self, text: str, speaker_id: float = 7306):
         """Call the model on some text and return the speech."""
@@ -121,7 +123,9 @@ class SpeechT5:
     def set_embeddings_dataset(self, dataset_name):
         """Set the embeddings dataset to a new dataset."""
         self.dataset_name = dataset_name
-        self.embeddings_dataset = load_dataset(self.dataset_name, split="validation")
+        self.embeddings_dataset = load_dataset(
+            self.dataset_name, split="validation"
+        )
 
     # Feature 1: Get sampling rate
     def get_sampling_rate(self):

swarms/models/ssd_1b.py

@@ -141,8 +141,8 @@ class SSD1B:
             print(
                 colored(
                     (
-                        f"Error running SSD1B: {error} try optimizing your api key and"
+                        f"Error running SSD1B: {error} try optimizing your api"
-                        " or try again"
+                        " key and or try again"
                     ),
                     "red",
                 )
@@ -167,8 +167,7 @@ class SSD1B:
         """Print the SSD1B dashboard"""
         print(
             colored(
-                (
+                f"""SSD1B Dashboard: 
-                    f"""SSD1B Dashboard: 
                     --------------------
 
                     Model: {self.model}
@@ -184,13 +183,14 @@ class SSD1B:
                     --------------------
                     
                     
-                    """
+                    """,
-                ),
                 "green",
             )
         )
 
-    def process_batch_concurrently(self, tasks: List[str], max_workers: int = 5):
+    def process_batch_concurrently(
+        self, tasks: List[str], max_workers: int = 5
+    ):
         """
 
         Process a batch of tasks concurrently
@@ -211,8 +211,12 @@ class SSD1B:
         >>> print(results)
 
         """
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        with concurrent.futures.ThreadPoolExecutor(
-            future_to_task = {executor.submit(self, task): task for task in tasks}
+            max_workers=max_workers
+        ) as executor:
+            future_to_task = {
+                executor.submit(self, task): task for task in tasks
+            }
             results = []
             for future in concurrent.futures.as_completed(future_to_task):
                 task = future_to_task[future]
@@ -225,13 +229,17 @@ class SSD1B:
                     print(
                         colored(
                             (
-                                f"Error running SSD1B: {error} try optimizing your api key and"
+                                f"Error running SSD1B: {error} try optimizing"
-                                " or try again"
+                                " your api key and or try again"
                             ),
                             "red",
                         )
                     )
-                    print(colored(f"Error running SSD1B: {error.http_status}", "red"))
+                    print(
+                        colored(
+                            f"Error running SSD1B: {error.http_status}", "red"
+                        )
+                    )
                     print(colored(f"Error running SSD1B: {error.error}", "red"))
                     raise error
 

swarms/models/whisperx.py

@@ -66,7 +66,9 @@ class WhisperX:
         compute_type = "float16"
 
         # 1. Transcribe with original Whisper (batched) 🗣️
-        model = whisperx.load_model("large-v2", device, compute_type=compute_type)
+        model = whisperx.load_model(
+            "large-v2", device, compute_type=compute_type
+        )
         audio = whisperx.load_audio(audio_file)
         result = model.transcribe(audio, batch_size=batch_size)
 

swarms/models/wizard_storytelling.py

@@ -45,7 +45,9 @@ class WizardLLMStoryTeller:
     ):
         self.logger = logging.getLogger(__name__)
         self.device = (
-            device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+            device
+            if device
+            else ("cuda" if torch.cuda.is_available() else "cpu")
         )
         self.model_id = model_id
         self.max_length = max_length
@@ -101,7 +103,9 @@ class WizardLLMStoryTeller:
                 if self.distributed:
                     self.model = DDP(self.model)
             except Exception as error:
-                self.logger.error(f"Failed to load the model or the tokenizer: {error}")
+                self.logger.error(
+                    f"Failed to load the model or the tokenizer: {error}"
+                )
                 raise
 
     def run(self, prompt_text: str):

swarms/models/yarn_mistral.py

@@ -45,7 +45,9 @@ class YarnMistral128:
     ):
         self.logger = logging.getLogger(__name__)
         self.device = (
-            device if device else ("cuda" if torch.cuda.is_available() else "cpu")
+            device
+            if device
+            else ("cuda" if torch.cuda.is_available() else "cpu")
         )
         self.model_id = model_id
         self.max_length = max_length
@@ -106,7 +108,9 @@ class YarnMistral128:
                 if self.distributed:
                     self.model = DDP(self.model)
             except Exception as error:
-                self.logger.error(f"Failed to load the model or the tokenizer: {error}")
+                self.logger.error(
+                    f"Failed to load the model or the tokenizer: {error}"
+                )
                 raise
 
     def run(self, prompt_text: str):

swarms/prompts/agent_prompt.py

@@ -15,7 +15,9 @@ class PromptGenerator:
             "thoughts": {
                 "text": "thought",
                 "reasoning": "reasoning",
-                "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+                "plan": (
+                    "- short bulleted\n- list that conveys\n- long-term plan"
+                ),
                 "criticism": "constructive self-criticism",
                 "speak": "thoughts summary to say to user",
             },
@@ -66,13 +68,11 @@ class PromptGenerator:
         """
         formatted_response_format = json.dumps(self.response_format, indent=4)
         prompt_string = (
-            f"Constraints:\n{''.join(self.constraints)}\n\n"
+            f"Constraints:\n{''.join(self.constraints)}\n\nCommands:\n{''.join(self.commands)}\n\nResources:\n{''.join(self.resources)}\n\nPerformance"
-            f"Commands:\n{''.join(self.commands)}\n\n"
+            f" Evaluation:\n{''.join(self.performance_evaluation)}\n\nYou"
-            f"Resources:\n{''.join(self.resources)}\n\n"
+            " should only respond in JSON format as described below \nResponse"
-            f"Performance Evaluation:\n{''.join(self.performance_evaluation)}\n\n"
+            f" Format: \n{formatted_response_format} \nEnsure the response can"
-            "You should only respond in JSON format as described below "
+            " be parsed by Python json.loads"
-            f"\nResponse Format: \n{formatted_response_format} "
-            "\nEnsure the response can be parsed by Python json.loads"
         )
 
         return prompt_string

swarms/prompts/agent_prompts.py

@@ -5,26 +5,26 @@ def generate_agent_role_prompt(agent):
     """
     prompts = {
         "Finance Agent": (
-            "You are a seasoned finance analyst AI assistant. Your primary goal is to"
+            "You are a seasoned finance analyst AI assistant. Your primary goal"
-            " compose comprehensive, astute, impartial, and methodically arranged"
+            " is to compose comprehensive, astute, impartial, and methodically"
-            " financial reports based on provided data and trends."
+            " 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"
+            "You are a world-travelled AI tour guide assistant. Your main"
-            " draft engaging, insightful, unbiased, and well-structured travel reports"
+            " purpose is to draft engaging, insightful, unbiased, and"
-            " on given locations, including history, attractions, and cultural"
+            " well-structured travel reports on given locations, including"
-            " insights."
+            " history, attractions, and cultural insights."
         ),
         "Academic Research Agent": (
-            "You are an AI academic research assistant. Your primary responsibility is"
+            "You are an AI academic research assistant. Your primary"
-            " to create thorough, academically rigorous, unbiased, and systematically"
+            " responsibility is to create thorough, academically rigorous,"
-            " organized reports on a given research topic, following the standards of"
+            " unbiased, and systematically organized reports on a given"
-            " scholarly work."
+            " research topic, following the standards of scholarly work."
         ),
         "Default Agent": (
-            "You are an AI critical thinker research assistant. Your sole purpose is to"
+            "You are an AI critical thinker research assistant. Your sole"
-            " write well written, critically acclaimed, objective and structured"
+            " purpose is to write well written, critically acclaimed, objective"
-            " reports on given text."
+            " and structured reports on given text."
         ),
     }
 
@@ -39,12 +39,12 @@ def generate_report_prompt(question, research_summary):
     """
 
     return (
-        f'"""{research_summary}""" Using the above information, answer the following'
+        f'"""{research_summary}""" Using the above information, answer the'
-        f' question or topic: "{question}" in a detailed report -- The report should'
+        f' following question or topic: "{question}" in a detailed report --'
-        " focus on the answer to the question, should be well structured, informative,"
+        " The report should focus on the answer to the question, should be"
-        " in depth, with facts and numbers if available, a minimum of 1,200 words and"
+        " well structured, informative, in depth, with facts and numbers if"
-        " with markdown syntax and apa format. Write all source urls at the end of the"
+        " available, a minimum of 1,200 words and with markdown syntax and apa"
-        " report in apa format"
+        " format. Write all source urls at the end of the report in apa format"
     )
 
 
@@ -55,9 +55,10 @@ def generate_search_queries_prompt(question):
     """
 
     return (
-        "Write 4 google search queries to search online that form an objective opinion"
+        "Write 4 google search queries to search online that form an objective"
-        f' from the following: "{question}"You must respond with a list of strings in'
+        f' opinion from the following: "{question}"You must respond with a list'
-        ' the following format: ["query 1", "query 2", "query 3", "query 4"]'
+        ' of strings in the following format: ["query 1", "query 2", "query'
+        ' 3", "query 4"]'
     )
 
 
@@ -73,14 +74,15 @@ def generate_resource_report_prompt(question, research_summary):
     """
     return (
         f'"""{research_summary}""" Based on the above information, generate a'
-        " bibliography recommendation report for the following question or topic:"
+        " bibliography recommendation report for the following question or"
-        f' "{question}". The report should provide a detailed analysis of each'
+        f' topic: "{question}". The report should provide a detailed analysis'
-        " recommended resource, explaining how each source can contribute to finding"
+        " of each recommended resource, explaining how each source can"
-        " answers to the research question. Focus on the relevance, reliability, and"
+        " contribute to finding answers to the research question. Focus on the"
-        " significance of each source. Ensure that the report is well-structured,"
+        " relevance, reliability, and significance of each source. Ensure that"
-        " informative, in-depth, and follows Markdown syntax. Include relevant facts,"
+        " the report is well-structured, informative, in-depth, and follows"
-        " figures, and numbers whenever available. The report should have a minimum"
+        " Markdown syntax. Include relevant facts, figures, and numbers"
-        " length of 1,200 words."
+        " whenever available. The report should have a minimum length of 1,200"
+        " words."
     )
 
 
@@ -92,13 +94,14 @@ def generate_outline_report_prompt(question, research_summary):
     """
 
     return (
-        f'"""{research_summary}""" Using the above information, generate an outline for'
+        f'"""{research_summary}""" Using the above information, generate an'
-        " a research report in Markdown syntax for the following question or topic:"
+        " outline for a research report in Markdown syntax for the following"
-        f' "{question}". The outline should provide a well-structured framework for the'
+        f' question or topic: "{question}". The outline should provide a'
-        " research report, including the main sections, subsections, and key points to"
+        " well-structured framework for the research report, including the"
-        " be covered. The research report should be detailed, informative, in-depth,"
+        " main sections, subsections, and key points to be covered. The"
-        " and a minimum of 1,200 words. Use appropriate Markdown syntax to format the"
+        " research report should be detailed, informative, in-depth, and a"
-        " outline and ensure readability."
+        " minimum of 1,200 words. Use appropriate Markdown syntax to format"
+        " the outline and ensure readability."
     )
 
 
@@ -110,11 +113,12 @@ def generate_concepts_prompt(question, research_summary):
     """
 
     return (
-        f'"""{research_summary}""" Using the above information, generate a list of 5'
+        f'"""{research_summary}""" Using the above information, generate a list'
-        " main concepts to learn for a research report on the following question or"
+        " of 5 main concepts to learn for a research report on the following"
-        f' topic: "{question}". The outline should provide a well-structured'
+        f' question or topic: "{question}". The outline should provide a'
-        " frameworkYou must respond with a list of strings in the following format:"
+        " well-structured frameworkYou must respond with a list of strings in"
-        ' ["concepts 1", "concepts 2", "concepts 3", "concepts 4, concepts 5"]'
+        ' the following format: ["concepts 1", "concepts 2", "concepts 3",'
+        ' "concepts 4, concepts 5"]'
     )
 
 
@@ -128,10 +132,10 @@ def generate_lesson_prompt(concept):
     """
 
     prompt = (
-        f"generate a comprehensive lesson about {concept} in Markdown syntax. This"
+        f"generate a comprehensive lesson about {concept} in Markdown syntax."
-        f" should include the definitionof {concept}, its historical background and"
+        f" This should include the definitionof {concept}, its historical"
-        " development, its applications or uses in differentfields, and notable events"
+        " background and development, its applications or uses in"
-        f" or facts related to {concept}."
+        f" differentfields, and notable events or facts related to {concept}."
     )
 
     return prompt

swarms/prompts/base.py

@@ -12,7 +12,9 @@ if TYPE_CHECKING:
 
 
 def get_buffer_string(
-    messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
+    messages: Sequence[BaseMessage],
+    human_prefix: str = "Human",
+    ai_prefix: str = "AI",
 ) -> str:
     """Convert sequence of Messages to strings and concatenate them into one string.
 

swarms/prompts/chat_prompt.py

@@ -105,7 +105,9 @@ class ChatMessage(Message):
 
 
 def get_buffer_string(
-    messages: Sequence[Message], human_prefix: str = "Human", ai_prefix: str = "AI"
+    messages: Sequence[Message],
+    human_prefix: str = "Human",
+    ai_prefix: str = "AI",
 ) -> str:
     string_messages = []
     for m in messages:

swarms/prompts/multi_modal_prompts.py

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

swarms/prompts/python.py

@@ -1,16 +1,17 @@
 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"
+    " implementation, a series of unit tests, and a hint to change the"
-    " appropriately. Write your full implementation (restate the function"
+    " implementation appropriately. Write your full implementation (restate the"
-    " signature).\n\n-----"
+    " function signature).\n\n-----"
 )
 PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = (
-    "You are a Python writing assistant. You will be given a function implementation"
+    "You are a Python writing assistant. You will be given a function"
-    " and a series of unit tests. Your goal is to write a few sentences to explain why"
+    " implementation and a series of unit tests. Your goal is to write a few"
-    " your implementation is wrong as indicated by the tests. You will need this as a"
+    " sentences to explain why your implementation is wrong as indicated by the"
-    " hint when you try again later. Only provide the few sentence description in your"
+    " tests. You will need this as a hint when you try again later. Only"
-    " answer, not the implementation.\n\n-----"
+    " 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"
@@ -18,25 +19,26 @@ USE_PYTHON_CODEBLOCK_INSTRUCTION = (
 )
 
 PY_SIMPLE_CHAT_INSTRUCTION = (
-    "You are an AI that only responds with python code, NOT ENGLISH. You will be given"
+    "You are an AI that only responds with python code, NOT ENGLISH. You will"
-    " a function signature and its docstring by the user. Write your full"
+    " be given a function signature and its docstring by the user. Write your"
-    " implementation (restate the function signature)."
+    " 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"
+    "You are an AI that only responds with only python code. You will be given"
-    " function signature and its docstring by the user. Write your full implementation"
+    " a function signature and its docstring by the user. Write your full"
-    " (restate the function signature)."
+    " 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"
+    " implementation, a series of unit tests, and a hint to change the"
-    " appropriately. Write your full implementation (restate the function signature)."
+    " 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"
+    "You are an AI Python assistant. You will be given your previous"
-    " a function, a series of unit tests results, and your self-reflection on your"
+    " implementation of a function, a series of unit tests results, and your"
-    " previous implementation. Write your full implementation (restate the function"
+    " self-reflection on your previous implementation. Write your full"
-    " signature)."
+    " implementation (restate the function signature)."
 )
 PY_REFLEXION_FEW_SHOT_ADD = '''Example 1:
 [previous impl]:
@@ -172,18 +174,19 @@ 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"
+    " implementation and a series of unit tests. Your goal is to write a few"
-    " to explain why your implementation is wrong as indicated by the tests. You will"
+    " sentences to explain why your implementation is wrong as indicated by the"
-    " need this as a hint when you try again later. Only provide the few sentence"
+    " tests. You will need this as a hint when you try again later. Only"
-    " description in your answer, not the implementation."
+    " 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"
+    " implementation and a series of unit test results. Your goal is to write a"
-    " sentences to explain why your implementation is wrong as indicated by the tests."
+    " few sentences to explain why your implementation is wrong as indicated by"
-    " You will need this as guidance when you try again later. Only provide the few"
+    " the tests. You will need this as guidance when you try again later. Only"
-    " sentence description in your answer, not the implementation. You will be given a"
+    " provide the few sentence description in your answer, not the"
-    " few examples by the user."
+    " implementation. You will be given a few examples by the user."
 )
 PY_SELF_REFLECTION_FEW_SHOT = """Example 1:
 [function impl]:

swarms/prompts/sales.py

@@ -1,23 +1,26 @@
 conversation_stages = {
     "1": (
-        "Introduction: Start the conversation by introducing yourself and your company."
+        "Introduction: Start the conversation by introducing yourself and your"
-        " Be polite and respectful while keeping the tone of the conversation"
+        " company. Be polite and respectful while keeping the tone of the"
-        " professional. Your greeting should be welcoming. Always clarify in your"
+        " conversation professional. Your greeting should be welcoming. Always"
-        " greeting the reason why you are contacting the prospect."
+        " 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"
+        "Qualification: Qualify the prospect by confirming if they are the"
-        " to talk to regarding your product/service. Ensure that they have the"
+        " right person to talk to regarding your product/service. Ensure that"
-        " authority to make purchasing decisions."
+        " they have the authority to make purchasing decisions."
     ),
     "3": (
-        "Value proposition: Briefly explain how your product/service can benefit the"
+        "Value proposition: Briefly explain how your product/service can"
-        " prospect. Focus on the unique selling points and value proposition of your"
+        " benefit the prospect. Focus on the unique selling points and value"
-        " product/service that sets it apart from competitors."
+        " 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"
+        "Needs analysis: Ask open-ended questions to uncover the prospect's"
-        " pain points. Listen carefully to their responses and take notes."
+        " needs and pain points. Listen carefully to their responses and take"
+        " notes."
     ),
     "5": (
         "Solution presentation: Based on the prospect's needs, present your"
@@ -29,9 +32,9 @@ conversation_stages = {
         " testimonials to support your claims."
     ),
     "7": (
-        "Close: Ask for the sale by proposing a next step. This could be a demo, a"
+        "Close: Ask for the sale by proposing a next step. This could be a"
-        " trial or a meeting with decision-makers. Ensure to summarize what has been"
+        " demo, a trial or a meeting with decision-makers. Ensure to summarize"
-        " discussed and reiterate the benefits."
+        " what has been discussed and reiterate the benefits."
     ),
 }
 

swarms/prompts/sales_prompts.py

@@ -46,24 +46,27 @@ Conversation history:
 
 conversation_stages = {
     "1": (
-        "Introduction: Start the conversation by introducing yourself and your company."
+        "Introduction: Start the conversation by introducing yourself and your"
-        " Be polite and respectful while keeping the tone of the conversation"
+        " company. Be polite and respectful while keeping the tone of the"
-        " professional. Your greeting should be welcoming. Always clarify in your"
+        " conversation professional. Your greeting should be welcoming. Always"
-        " greeting the reason why you are contacting the prospect."
+        " 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"
+        "Qualification: Qualify the prospect by confirming if they are the"
-        " to talk to regarding your product/service. Ensure that they have the"
+        " right person to talk to regarding your product/service. Ensure that"
-        " authority to make purchasing decisions."
+        " they have the authority to make purchasing decisions."
     ),
     "3": (
-        "Value proposition: Briefly explain how your product/service can benefit the"
+        "Value proposition: Briefly explain how your product/service can"
-        " prospect. Focus on the unique selling points and value proposition of your"
+        " benefit the prospect. Focus on the unique selling points and value"
-        " product/service that sets it apart from competitors."
+        " 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"
+        "Needs analysis: Ask open-ended questions to uncover the prospect's"
-        " pain points. Listen carefully to their responses and take notes."
+        " needs and pain points. Listen carefully to their responses and take"
+        " notes."
     ),
     "5": (
         "Solution presentation: Based on the prospect's needs, present your"
@@ -75,8 +78,8 @@ conversation_stages = {
         " testimonials to support your claims."
     ),
     "7": (
-        "Close: Ask for the sale by proposing a next step. This could be a demo, a"
+        "Close: Ask for the sale by proposing a next step. This could be a"
-        " trial or a meeting with decision-makers. Ensure to summarize what has been"
+        " demo, a trial or a meeting with decision-makers. Ensure to summarize"
-        " discussed and reiterate the benefits."
+        " what has been discussed and reiterate the benefits."
     ),
 }

swarms/structs/autoscaler.py

@@ -7,7 +7,11 @@ from typing import Callable, Dict, List
 from termcolor import colored
 
 from swarms.structs.flow import Flow
-from swarms.utils.decorators import error_decorator, log_decorator, timing_decorator
+from swarms.utils.decorators import (
+    error_decorator,
+    log_decorator,
+    timing_decorator,
+)
 
 
 class AutoScaler:
@@ -69,7 +73,9 @@ class AutoScaler:
         try:
             self.tasks_queue.put(task)
         except Exception as error:
-            print(f"Error adding task to queue: {error} try again with a new task")
+            print(
+                f"Error adding task to queue: {error} try again with a new task"
+            )
 
     @log_decorator
     @error_decorator
@@ -108,10 +114,15 @@ class AutoScaler:
 
                 if pending_tasks / len(self.agents_pool) > self.busy_threshold:
                     self.scale_up()
-                elif active_agents / len(self.agents_pool) < self.idle_threshold:
+                elif (
+                    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")
+            print(
+                f"Error monitoring and scaling: {error} try again with a new"
+                " task"
+            )
 
     @log_decorator
     @error_decorator
@@ -125,7 +136,9 @@ class AutoScaler:
             while True:
                 task = self.task_queue.get()
                 if task:
-                    available_agent = next((agent for agent in self.agents_pool))
+                    available_agent = next(
+                        (agent for agent in self.agents_pool)
+                    )
                     if available_agent:
                         available_agent.run(task)
         except Exception as error:

swarms/structs/flow.py

@@ -348,7 +348,8 @@ class Flow:
                 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"
@@ -479,8 +480,12 @@ class Flow:
             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"]))
+            print(
-            print(colored("All systems operational. Executing task...", "green"))
+                colored("Autonomous Agent Activated.", "cyan", attrs=["bold"])
+            )
+            print(
+                colored("All systems operational. Executing task...", "green")
+            )
         except Exception as error:
             print(
                 colored(
@@ -525,14 +530,16 @@ class Flow:
             loop_count = 0
             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")
 
                 # Check to see if stopping token is in the output to stop the loop
                 if self.stopping_token:
-                    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
@@ -629,7 +636,9 @@ class Flow:
             print(colored(f"\nLoop {loop_count} of {self.max_loops}", "blue"))
             print("\n")
 
-            if self._check_stopping_condition(response) or parse_done_token(response):
+            if self._check_stopping_condition(response) or parse_done_token(
+                response
+            ):
                 break
 
             # Adjust temperature, comment if no work
@@ -949,7 +958,8 @@ class Flow:
             if hasattr(self.llm, name):
                 value = getattr(self.llm, name)
                 if isinstance(
-                    value, (str, int, float, bool, list, dict, tuple, type(None))
+                    value,
+                    (str, int, float, bool, list, dict, tuple, type(None)),
                 ):
                     llm_params[name] = value
                 else:
@@ -1010,7 +1020,9 @@ class Flow:
 
         print(f"Flow state loaded from {file_path}")
 
-    def retry_on_failure(self, function, retries: int = 3, retry_delay: int = 1):
+    def retry_on_failure(
+        self, function, retries: int = 3, retry_delay: int = 1
+    ):
         """Retry wrapper for LLM calls."""
         attempt = 0
         while attempt < retries:

swarms/structs/non_linear_workflow.py

@@ -7,7 +7,11 @@ from typing import Callable, List, Dict, Any, Sequence
 
 class Task:
     def __init__(
-        self, id: str, task: str, flows: Sequence[Flow], dependencies: List[str] = []
+        self,
+        id: str,
+        task: str,
+        flows: Sequence[Flow],
+        dependencies: List[str] = [],
     ):
         self.id = id
         self.task = task
@@ -20,7 +24,9 @@ class Task:
         for flow in self.flows:
             result = flow.run(self.task, *args)
             self.results.append(result)
-            args = [result]  # The output of one flow becomes the input to the next
+            args = [
+                result
+            ]  # The output of one flow becomes the input to the next
 
 
 class Workflow:
@@ -41,7 +47,10 @@ class Workflow:
                 ):
                     future = self.executor.submit(
                         task.execute,
-                        {dep: self.tasks[dep].results for dep in task.dependencies},
+                        {
+                            dep: self.tasks[dep].results
+                            for dep in task.dependencies
+                        },
                     )
                     futures.append((future, task.id))
 

swarms/structs/sequential_workflow.py

@@ -113,7 +113,9 @@ class SequentialWorkflow:
     restore_state_filepath: Optional[str] = None
     dashboard: bool = False
 
-    def add(self, task: str, flow: Union[Callable, Flow], *args, **kwargs) -> None:
+    def add(
+        self, task: str, flow: Union[Callable, Flow], *args, **kwargs
+    ) -> None:
         """
         Add a task to the workflow.
 
@@ -182,7 +184,9 @@ class SequentialWorkflow:
             raise ValueError(f"Task {task_description} not found in workflow.")
 
     def save_workflow_state(
-        self, filepath: Optional[str] = "sequential_workflow_state.json", **kwargs
+        self,
+        filepath: Optional[str] = "sequential_workflow_state.json",
+        **kwargs,
     ) -> None:
         """
         Saves the workflow state to a json file.
@@ -348,8 +352,9 @@ 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 Flow flow"
+                                    "The 'task' argument is required for the"
-                                    f" execution in '{task.description}'"
+                                    " Flow flow execution in"
+                                    f" '{task.description}'"
                                 )
                             # Separate the 'task' argument from other kwargs
                             flow_task_arg = task.kwargs.pop("task")
@@ -373,7 +378,9 @@ class SequentialWorkflow:
 
                         # Autosave the workflow state
                         if self.autosave:
-                            self.save_workflow_state("sequential_workflow_state.json")
+                            self.save_workflow_state(
+                                "sequential_workflow_state.json"
+                            )
         except Exception as e:
             print(
                 colored(
@@ -404,8 +411,8 @@ 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 Flow flow"
+                                "The 'task' argument is required for the Flow"
-                                f" execution in '{task.description}'"
+                                f" flow execution in '{task.description}'"
                             )
                         # Separate the 'task' argument from other kwargs
                         flow_task_arg = task.kwargs.pop("task")
@@ -429,4 +436,6 @@ class SequentialWorkflow:
 
                     # Autosave the workflow state
                     if self.autosave:
-                        self.save_workflow_state("sequential_workflow_state.json")
+                        self.save_workflow_state(
+                            "sequential_workflow_state.json"
+                        )

swarms/swarms/autobloggen.py

@@ -103,7 +103,9 @@ class AutoBlogGenSwarm:
         review_agent = self.print_beautifully("Review Agent", review_agent)
 
         # Agent that publishes on social media
-        distribution_agent = self.llm(self.social_media_prompt(article=review_agent))
+        distribution_agent = self.llm(
+            self.social_media_prompt(article=review_agent)
+        )
         distribution_agent = self.print_beautifully(
             "Distribution Agent", distribution_agent
         )
@@ -115,7 +117,11 @@ class AutoBlogGenSwarm:
                 for i in range(self.iterations):
                     self.step()
             except Exception as error:
-                print(colored(f"Error while running AutoBlogGenSwarm {error}", "red"))
+                print(
+                    colored(
+                        f"Error while running AutoBlogGenSwarm {error}", "red"
+                    )
+                )
                 if attempt == self.retry_attempts - 1:
                     raise
 

swarms/swarms/base.py

@@ -117,7 +117,9 @@ class AbstractSwarm(ABC):
         pass
 
     @abstractmethod
-    def broadcast(self, message: str, sender: Optional["AbstractWorker"] = None):
+    def broadcast(
+        self, message: str, sender: Optional["AbstractWorker"] = None
+    ):
         """Broadcast a message to all workers"""
         pass
 

swarms/swarms/dialogue_simulator.py

@@ -23,7 +23,9 @@ class DialogueSimulator:
     >>> model.run("test")
     """
 
-    def __init__(self, agents: List[Callable], max_iters: int = 10, name: str = None):
+    def __init__(
+        self, agents: List[Callable], max_iters: int = 10, name: str = None
+    ):
         self.agents = agents
         self.max_iters = max_iters
         self.name = name
@@ -45,7 +47,8 @@ class DialogueSimulator:
 
                 for receiver in self.agents:
                     message_history = (
-                        f"Speaker Name: {speaker.name} and message: {speaker_message}"
+                        f"Speaker Name: {speaker.name} and message:"
+                        f" {speaker_message}"
                     )
                     receiver.run(message_history)
 
@@ -56,7 +59,9 @@ class DialogueSimulator:
             print(f"Error running dialogue simulator: {error}")
 
     def __repr__(self):
-        return f"DialogueSimulator({self.agents}, {self.max_iters}, {self.name})"
+        return (
+            f"DialogueSimulator({self.agents}, {self.max_iters}, {self.name})"
+        )
 
     def save_state(self):
         """Save the state of the dialogue simulator"""

swarms/swarms/god_mode.py

@@ -64,7 +64,8 @@ class GodMode:
             table.append([f"LLM {i+1}", response])
         print(
             colored(
-                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan"
+                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"),
+                "cyan",
             )
         )
 
@@ -83,7 +84,8 @@ class GodMode:
             table.append([f"LLM {i+1}", response])
         print(
             colored(
-                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan"
+                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"),
+                "cyan",
             )
         )
 
@@ -115,11 +117,13 @@ class GodMode:
             print(f"{i + 1}. {task}")
         print("\nLast Responses:")
         table = [
-            [f"LLM {i+1}", response] for i, response in enumerate(self.last_responses)
+            [f"LLM {i+1}", response]
+            for i, response in enumerate(self.last_responses)
         ]
         print(
             colored(
-                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"), "cyan"
+                tabulate(table, headers=["LLM", "Response"], tablefmt="pretty"),
+                "cyan",
             )
         )
 
@@ -137,7 +141,8 @@ class GodMode:
         """Asynchronous run the task string"""
         loop = asyncio.get_event_loop()
         futures = [
-            loop.run_in_executor(None, lambda llm: llm(task), llm) for llm in self.llms
+            loop.run_in_executor(None, lambda llm: llm(task), llm)
+            for llm in self.llms
         ]
         for response in await asyncio.gather(*futures):
             print(response)
@@ -145,13 +150,18 @@ class GodMode:
     def concurrent_run(self, task: str) -> List[str]:
         """Synchronously run the task on all llms and collect responses"""
         with ThreadPoolExecutor() as executor:
-            future_to_llm = {executor.submit(llm, task): llm for llm in self.llms}
+            future_to_llm = {
+                executor.submit(llm, task): llm for llm in self.llms
+            }
             responses = []
             for future in as_completed(future_to_llm):
                 try:
                     responses.append(future.result())
                 except Exception as error:
-                    print(f"{future_to_llm[future]} generated an exception: {error}")
+                    print(
+                        f"{future_to_llm[future]} generated an exception:"
+                        f" {error}"
+                    )
         self.last_responses = responses
         self.task_history.append(task)
         return responses

swarms/swarms/groupchat.py

@@ -47,7 +47,9 @@ class GroupChat:
 
     def next_agent(self, agent: Flow) -> Flow:
         """Return the next agent in the list."""
-        return self.agents[(self.agent_names.index(agent.name) + 1) % len(self.agents)]
+        return self.agents[
+            (self.agent_names.index(agent.name) + 1) % len(self.agents)
+        ]
 
     def select_speaker_msg(self):
         """Return the message for selecting the next speaker."""
@@ -78,9 +80,9 @@ class GroupChat:
                     {
                         "role": "system",
                         "content": (
-                            "Read the above conversation. Then select the next most"
+                            "Read the above conversation. Then select the next"
-                            f" suitable role from {self.agent_names} to play. Only"
+                            f" most suitable role from {self.agent_names} to"
-                            " return the role."
+                            " play. Only return the role."
                         ),
                     }
                 ]
@@ -126,7 +128,9 @@ class GroupChatManager:
         self.selector = selector
 
     def __call__(self, task: str):
-        self.groupchat.messages.append({"role": self.selector.name, "content": task})
+        self.groupchat.messages.append(
+            {"role": self.selector.name, "content": task}
+        )
         for i in range(self.groupchat.max_round):
             speaker = self.groupchat.select_speaker(
                 last_speaker=self.selector, selector=self.selector

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"
+            "Your response should be an integrater delimited by angled brackets"
-            " this: <int>"
+            " like this: <int>"
         )
 
 
@@ -194,11 +194,15 @@ class MultiAgentCollaboration:
             print("\n")
             n += 1
 
-    def select_next_speaker_roundtable(self, step: int, agents: List[Flow]) -> int:
+    def select_next_speaker_roundtable(
+        self, step: int, agents: List[Flow]
+    ) -> int:
         """Selects the next speaker."""
         return step % len(agents)
 
-    def select_next_speaker_director(step: int, agents: List[Flow], director) -> int:
+    def select_next_speaker_director(
+        step: int, agents: List[Flow], director
+    ) -> int:
         # if the step if even => director
         # => director selects next speaker
         if step % 2 == 1:
@@ -265,7 +269,10 @@ class MultiAgentCollaboration:
     def format_results(self, results):
         """Formats the results of the run method"""
         formatted_results = "\n".join(
-            [f"{result['agent']} responded: {result['response']}" for result in results]
+            [
+                f"{result['agent']} responded: {result['response']}"
+                for result in results
+            ]
         )
         return formatted_results
 
@@ -291,7 +298,12 @@ class MultiAgentCollaboration:
         return state
 
     def __repr__(self):
-        return f"MultiAgentCollaboration(agents={self.agents}, selection_function={self.select_next_speaker}, max_iters={self.max_iters}, autosave={self.autosave}, saved_file_path_name={self.saved_file_path_name})"
+        return (
+            f"MultiAgentCollaboration(agents={self.agents},"
+            f" selection_function={self.select_next_speaker},"
+            f" max_iters={self.max_iters}, autosave={self.autosave},"
+            f" saved_file_path_name={self.saved_file_path_name})"
+        )
 
     def performance(self):
         """Tracks and reports the performance of each agent"""

swarms/swarms/orchestrate.py

@@ -111,7 +111,9 @@ class Orchestrator:
 
         self.chroma_client = chromadb.Client()
 
-        self.collection = self.chroma_client.create_collection(name=collection_name)
+        self.collection = self.chroma_client.create_collection(
+            name=collection_name
+        )
 
         self.current_tasks = {}
 
@@ -148,13 +150,14 @@ class Orchestrator:
                 )
 
                 logging.info(
-                    f"Task {id(str)} has been processed by agent {id(agent)} with"
+                    f"Task {id(str)} has been processed by agent"
+                    f" {id(agent)} with"
                 )
 
             except Exception as error:
                 logging.error(
-                    f"Failed to process task {id(task)} by agent {id(agent)}. Error:"
+                    f"Failed to process task {id(task)} by agent {id(agent)}."
-                    f" {error}"
+                    f" Error: {error}"
                 )
             finally:
                 with self.condition:
@@ -175,7 +178,9 @@ class Orchestrator:
 
         try:
             # Query the vector database for documents created by the agents
-            results = self.collection.query(query_texts=[str(agent_id)], n_results=10)
+            results = self.collection.query(
+                query_texts=[str(agent_id)], n_results=10
+            )
 
             return results
         except Exception as e:
@@ -212,7 +217,9 @@ class Orchestrator:
             self.collection.add(documents=[result], ids=[str(id(result))])
 
         except Exception as e:
-            logging.error(f"Failed to append the agent output to database. Error: {e}")
+            logging.error(
+                f"Failed to append the agent output to database. Error: {e}"
+            )
             raise
 
     def run(self, objective: str):
@@ -226,7 +233,9 @@ class Orchestrator:
 
             results = [
                 self.assign_task(agent_id, task)
-                for agent_id, task in zip(range(len(self.agents)), self.task_queue)
+                for agent_id, task in zip(
+                    range(len(self.agents)), self.task_queue
+                )
             ]
 
             for result in results:

swarms/tools/autogpt.py

@@ -6,7 +6,9 @@ from typing import Optional
 import pandas as pd
 import torch
 from langchain.agents import tool
-from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
+from langchain.agents.agent_toolkits.pandas.base import (
+    create_pandas_dataframe_agent,
+)
 from langchain.chains.qa_with_sources.loading import (
     BaseCombineDocumentsChain,
 )
@@ -38,7 +40,10 @@ def pushd(new_dir):
 
 @tool
 def process_csv(
-    llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None
+    llm,
+    csv_file_path: str,
+    instructions: str,
+    output_path: Optional[str] = None,
 ) -> str:
     """Process a CSV by with pandas in a limited REPL.\
  Only use this after writing data to disk as a csv file.\
@@ -49,7 +54,9 @@ def process_csv(
             df = pd.read_csv(csv_file_path)
         except Exception as e:
             return f"Error: {e}"
-        agent = create_pandas_dataframe_agent(llm, df, max_iterations=30, verbose=False)
+        agent = create_pandas_dataframe_agent(
+            llm, df, max_iterations=30, verbose=False
+        )
         if output_path is not None:
             instructions += f" Save output to disk at {output_path}"
         try:
@@ -79,7 +86,9 @@ 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("  "))
+            chunks = (
+                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:
             results = f"Error: {e}"
@@ -110,7 +119,8 @@ def _get_text_splitter():
 class WebpageQATool(BaseTool):
     name = "query_webpage"
     description = (
-        "Browse a webpage and retrieve the information relevant to the question."
+        "Browse a webpage and retrieve the information relevant to the"
+        " question."
     )
     text_splitter: RecursiveCharacterTextSplitter = Field(
         default_factory=_get_text_splitter
@@ -176,7 +186,9 @@ def VQAinference(self, inputs):
 
     image_path, question = inputs.split(",")
     raw_image = Image.open(image_path).convert("RGB")
-    inputs = processor(raw_image, question, return_tensors="pt").to(device, torch_dtype)
+    inputs = processor(raw_image, question, return_tensors="pt").to(
+        device, torch_dtype
+    )
     out = model.generate(**inputs)
     answer = processor.decode(out[0], skip_special_tokens=True)
 

swarms/tools/mm_models.py

@@ -28,7 +28,9 @@ class MaskFormer:
     def __init__(self, device):
         print("Initializing MaskFormer to %s" % device)
         self.device = device
-        self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+        self.processor = CLIPSegProcessor.from_pretrained(
+            "CIDAS/clipseg-rd64-refined"
+        )
         self.model = CLIPSegForImageSegmentation.from_pretrained(
             "CIDAS/clipseg-rd64-refined"
         ).to(device)
@@ -76,23 +78,26 @@ class ImageEditing:
     @tool(
         name="Remove Something From The Photo",
         description=(
-            "useful when you want to remove and object or something from the photo "
+            "useful when you want to remove and object or something from the"
-            "from its description or location. "
+            " photo from its description or location. The input to this tool"
-            "The input to this tool should be a comma separated string of two, "
+            " should be a comma separated string of two, representing the"
-            "representing the image_path and the object need to be removed. "
+            " image_path and the object need to be removed. "
         ),
     )
     def inference_remove(self, inputs):
         image_path, to_be_removed_txt = inputs.split(",")
-        return self.inference_replace(f"{image_path},{to_be_removed_txt},background")
+        return self.inference_replace(
+            f"{image_path},{to_be_removed_txt},background"
+        )
 
     @tool(
         name="Replace Something From The Photo",
         description=(
-            "useful when you want to replace an object from the object description or"
+            "useful when you want to replace an object from the object"
-            " location with another object from its description. The input to this tool"
+            " description or location with another object from its description."
-            " should be a comma separated string of three, representing the image_path,"
+            " The input to this tool should be a comma separated string of"
-            " the object to be replaced, the object to be replaced with "
+            " three, representing the image_path, the object to be replaced,"
+            " the object to be replaced with "
         ),
     )
     def inference_replace(self, inputs):
@@ -137,10 +142,10 @@ class InstructPix2Pix:
     @tool(
         name="Instruct Image Using Text",
         description=(
-            "useful when you want to the style of the image to be like the text. "
+            "useful when you want to the style of the image to be like the"
-            "like: make it look like a painting. or make it like a robot. "
+            " text. like: make it look like a painting. or make it like a"
-            "The input to this tool should be a comma separated string of two, "
+            " robot. The input to this tool should be a comma separated string"
-            "representing the image_path and the text. "
+            " of two, representing the image_path and the text. "
         ),
     )
     def inference(self, inputs):
@@ -149,14 +154,17 @@ class InstructPix2Pix:
         image_path, text = inputs.split(",")[0], ",".join(inputs.split(",")[1:])
         original_image = Image.open(image_path)
         image = self.pipe(
-            text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2
+            text,
+            image=original_image,
+            num_inference_steps=40,
+            image_guidance_scale=1.2,
         ).images[0]
         updated_image_path = get_new_image_name(image_path, func_name="pix2pix")
         image.save(updated_image_path)
 
         logger.debug(
-            f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text:"
+            f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct"
-            f" {text}, Output Image: {updated_image_path}"
+            f" Text: {text}, Output Image: {updated_image_path}"
         )
 
         return updated_image_path
@@ -173,17 +181,18 @@ class Text2Image:
         self.pipe.to(device)
         self.a_prompt = "best quality, extremely detailed"
         self.n_prompt = (
-            "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, "
+            "longbody, lowres, bad anatomy, bad hands, missing fingers, extra"
-            "fewer digits, cropped, worst quality, low quality"
+            " digit, fewer digits, cropped, worst quality, low quality"
         )
 
     @tool(
         name="Generate Image From User Input Text",
         description=(
-            "useful when you want to generate an image from a user input text and save"
+            "useful when you want to generate an image from a user input text"
-            " it to a file. like: generate an image of an object or something, or"
+            " and save it to a file. like: generate an image of an object or"
-            " generate an image that includes some objects. The input to this tool"
+            " something, or generate an image that includes some objects. The"
-            " should be a string, representing the text used to generate image. "
+            " input to this tool should be a string, representing the text used"
+            " to generate image. "
         ),
     )
     def inference(self, text):
@@ -205,7 +214,9 @@ class VisualQuestionAnswering:
         print("Initializing VisualQuestionAnswering to %s" % device)
         self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
         self.device = device
-        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+        self.processor = BlipProcessor.from_pretrained(
+            "Salesforce/blip-vqa-base"
+        )
         self.model = BlipForQuestionAnswering.from_pretrained(
             "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype
         ).to(self.device)
@@ -213,10 +224,11 @@ class VisualQuestionAnswering:
     @tool(
         name="Answer Question About The Image",
         description=(
-            "useful when you need an answer for a question based on an image. like:"
+            "useful when you need an answer for a question based on an image."
-            " what is the background color of the last image, how many cats in this"
+            " like: what is the background color of the last image, how many"
-            " figure, what is in this figure. The input to this tool should be a comma"
+            " cats in this figure, what is in this figure. The input to this"
-            " separated string of two, representing the image_path and the question"
+            " tool should be a comma separated string of two, representing the"
+            " image_path and the question"
         ),
     )
     def inference(self, inputs):
@@ -229,8 +241,8 @@ class VisualQuestionAnswering:
         answer = self.processor.decode(out[0], skip_special_tokens=True)
 
         logger.debug(
-            f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input"
+            f"\nProcessed VisualQuestionAnswering, Input Image: {image_path},"
-            f" Question: {question}, Output Answer: {answer}"
+            f" Input Question: {question}, Output Answer: {answer}"
         )
 
         return answer
@@ -245,7 +257,8 @@ class ImageCaptioning(BaseHandler):
             "Salesforce/blip-image-captioning-base"
         )
         self.model = BlipForConditionalGeneration.from_pretrained(
-            "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype
+            "Salesforce/blip-image-captioning-base",
+            torch_dtype=self.torch_dtype,
         ).to(self.device)
 
     def handle(self, filename: str):
@@ -264,8 +277,8 @@ class ImageCaptioning(BaseHandler):
         out = self.model.generate(**inputs)
         description = self.processor.decode(out[0], skip_special_tokens=True)
         print(
-            f"\nProcessed ImageCaptioning, Input Image: {filename}, Output Text:"
+            f"\nProcessed ImageCaptioning, Input Image: {filename}, Output"
-            f" {description}"
+            f" Text: {description}"
         )
 
         return IMAGE_PROMPT.format(filename=filename, description=description)

swarms/tools/tool.py

@@ -7,7 +7,17 @@ import warnings
 from abc import abstractmethod
 from functools import partial
 from inspect import signature
-from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
+from typing import (
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+)
 
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import (
@@ -27,7 +37,11 @@ from pydantic import (
     root_validator,
     validate_arguments,
 )
-from langchain.schema.runnable import Runnable, RunnableConfig, RunnableSerializable
+from langchain.schema.runnable import (
+    Runnable,
+    RunnableConfig,
+    RunnableSerializable,
+)
 
 
 class SchemaAnnotationError(TypeError):
@@ -52,7 +66,11 @@ def _get_filtered_args(
     """Get the arguments from a function's signature."""
     schema = inferred_model.schema()["properties"]
     valid_keys = signature(func).parameters
-    return {k: schema[k] for k in valid_keys if k not in ("run_manager", "callbacks")}
+    return {
+        k: schema[k]
+        for k in valid_keys
+        if k not in ("run_manager", "callbacks")
+    }
 
 
 class _SchemaConfig:
@@ -120,12 +138,11 @@ class ChildTool(BaseTool):
     ..."""
                 name = cls.__name__
                 raise SchemaAnnotationError(
-                    f"Tool definition for {name} must include valid type annotations"
+                    f"Tool definition for {name} must include valid type"
-                    " for argument 'args_schema' to behave as expected.\n"
+                    " annotations for argument 'args_schema' to behave as"
-                    "Expected annotation of 'Type[BaseModel]'"
+                    " expected.\nExpected annotation of 'Type[BaseModel]' but"
-                    f" but got '{args_schema_type}'.\n"
+                    f" got '{args_schema_type}'.\nExpected class looks"
-                    "Expected class looks like:\n"
+                    f" like:\n{typehint_mandate}"
-                    f"{typehint_mandate}"
                 )
 
     name: str
@@ -147,7 +164,9 @@ class ChildTool(BaseTool):
 
     callbacks: Callbacks = Field(default=None, exclude=True)
     """Callbacks to be called during tool execution."""
-    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
+    callback_manager: Optional[BaseCallbackManager] = Field(
+        default=None, exclude=True
+    )
     """Deprecated. Please use callbacks instead."""
     tags: Optional[List[str]] = None
     """Optional list of tags associated with the tool. Defaults to None
@@ -244,7 +263,9 @@ class ChildTool(BaseTool):
         else:
             if input_args is not None:
                 result = input_args.parse_obj(tool_input)
-                return {k: v for k, v in result.dict().items() if k in tool_input}
+                return {
+                    k: v for k, v in result.dict().items() if k in tool_input
+                }
         return tool_input
 
     @root_validator()
@@ -286,7 +307,9 @@ class ChildTool(BaseTool):
             *args,
         )
 
-    def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
+    def _to_args_and_kwargs(
+        self, tool_input: Union[str, Dict]
+    ) -> Tuple[Tuple, Dict]:
         # For backwards compatibility, if run_input is a string,
         # pass as a positional argument.
         if isinstance(tool_input, str):
@@ -353,8 +376,9 @@ class ChildTool(BaseTool):
                 observation = self.handle_tool_error(e)
             else:
                 raise ValueError(
-                    "Got unexpected type of `handle_tool_error`. Expected bool, str "
+                    "Got unexpected type of `handle_tool_error`. Expected"
-                    f"or callable. Received: {self.handle_tool_error}"
+                    " bool, str or callable. Received:"
+                    f" {self.handle_tool_error}"
                 )
             run_manager.on_tool_end(
                 str(observation), color="red", name=self.name, **kwargs
@@ -409,7 +433,9 @@ class ChildTool(BaseTool):
             # We then call the tool on the tool input to get an observation
             tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
             observation = (
-                await self._arun(*tool_args, run_manager=run_manager, **tool_kwargs)
+                await self._arun(
+                    *tool_args, run_manager=run_manager, **tool_kwargs
+                )
                 if new_arg_supported
                 else await self._arun(*tool_args, **tool_kwargs)
             )
@@ -428,8 +454,9 @@ class ChildTool(BaseTool):
                 observation = self.handle_tool_error(e)
             else:
                 raise ValueError(
-                    "Got unexpected type of `handle_tool_error`. Expected bool, str "
+                    "Got unexpected type of `handle_tool_error`. Expected"
-                    f"or callable. Received: {self.handle_tool_error}"
+                    " bool, str or callable. Received:"
+                    f" {self.handle_tool_error}"
                 )
             await run_manager.on_tool_end(
                 str(observation), color="red", name=self.name, **kwargs
@@ -484,14 +511,17 @@ class Tool(BaseTool):
         # assume it takes a single string input.
         return {"tool_input": {"type": "string"}}
 
-    def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
+    def _to_args_and_kwargs(
+        self, tool_input: Union[str, Dict]
+    ) -> Tuple[Tuple, Dict]:
         """Convert tool input to pydantic model."""
         args, kwargs = super()._to_args_and_kwargs(tool_input)
         # For backwards compatibility. The tool must be run with a single input
         all_args = list(args) + list(kwargs.values())
         if len(all_args) != 1:
             raise ToolException(
-                f"Too many arguments to single-input tool {self.name}. Args: {all_args}"
+                f"Too many arguments to single-input tool {self.name}. Args:"
+                f" {all_args}"
             )
         return tuple(all_args), {}
 
@@ -503,7 +533,9 @@ class Tool(BaseTool):
     ) -> Any:
         """Use the tool."""
         if self.func:
-            new_argument_supported = signature(self.func).parameters.get("callbacks")
+            new_argument_supported = signature(self.func).parameters.get(
+                "callbacks"
+            )
             return (
                 self.func(
                     *args,
@@ -537,12 +569,18 @@ class Tool(BaseTool):
             )
         else:
             return await asyncio.get_running_loop().run_in_executor(
-                None, partial(self._run, run_manager=run_manager, **kwargs), *args
+                None,
+                partial(self._run, run_manager=run_manager, **kwargs),
+                *args,
             )
 
     # TODO: this is for backwards compatibility, remove in future
     def __init__(
-        self, name: str, func: Optional[Callable], description: str, **kwargs: Any
+        self,
+        name: str,
+        func: Optional[Callable],
+        description: str,
+        **kwargs: Any,
     ) -> None:
         """Initialize tool."""
         super(Tool, self).__init__(
@@ -617,7 +655,9 @@ class StructuredTool(BaseTool):
     ) -> Any:
         """Use the tool."""
         if self.func:
-            new_argument_supported = signature(self.func).parameters.get("callbacks")
+            new_argument_supported = signature(self.func).parameters.get(
+                "callbacks"
+            )
             return (
                 self.func(
                     *args,
@@ -714,7 +754,9 @@ class StructuredTool(BaseTool):
         description = f"{name}{sig} - {description.strip()}"
         _args_schema = args_schema
         if _args_schema is None and infer_schema:
-            _args_schema = create_schema_from_function(f"{name}Schema", source_function)
+            _args_schema = create_schema_from_function(
+                f"{name}Schema", source_function
+            )
         return cls(
             name=name,
             func=func,
@@ -772,7 +814,9 @@ def tool(
                 async def ainvoke_wrapper(
                     callbacks: Optional[Callbacks] = None, **kwargs: Any
                 ) -> Any:
-                    return await runnable.ainvoke(kwargs, {"callbacks": callbacks})
+                    return await runnable.ainvoke(
+                        kwargs, {"callbacks": callbacks}
+                    )
 
                 def invoke_wrapper(
                     callbacks: Optional[Callbacks] = None, **kwargs: Any
@@ -821,7 +865,11 @@ def tool(
 
         return _make_tool
 
-    if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable):
+    if (
+        len(args) == 2
+        and isinstance(args[0], str)
+        and isinstance(args[1], Runnable)
+    ):
         return _make_with_name(args[0])(args[1])
     elif len(args) == 1 and isinstance(args[0], str):
         # if the argument is a string, then we use the string as the tool name

swarms/utils/apa.py

@@ -144,7 +144,9 @@ class Singleton(abc.ABCMeta, type):
     def __call__(cls, *args, **kwargs):
         """Call method for the singleton metaclass."""
         if cls not in cls._instances:
-            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
+            cls._instances[cls] = super(Singleton, cls).__call__(
+                *args, **kwargs
+            )
         return cls._instances[cls]
 
 

swarms/utils/code_interpreter.py

@@ -116,14 +116,20 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter):
                     # Most of the time it doesn't matter, but we should figure out why it happens frequently with:
                     # applescript
                     yield {"output": traceback.format_exc()}
-                    yield {"output": f"Retrying... ({retry_count}/{max_retries})"}
+                    yield {
+                        "output": f"Retrying... ({retry_count}/{max_retries})"
+                    }
                     yield {"output": "Restarting process."}
 
                 self.start_process()
 
                 retry_count += 1
                 if retry_count > max_retries:
-                    yield {"output": "Maximum retries reached. Could not execute code."}
+                    yield {
+                        "output": (
+                            "Maximum retries reached. Could not execute code."
+                        )
+                    }
                     return
 
         while True:
@@ -132,7 +138,9 @@ class SubprocessCodeInterpreter(BaseCodeInterpreter):
             else:
                 time.sleep(0.1)
             try:
-                output = self.output_queue.get(timeout=0.3)  # Waits for 0.3 seconds
+                output = self.output_queue.get(
+                    timeout=0.3
+                )  # Waits for 0.3 seconds
                 yield output
             except queue.Empty:
                 if self.done.is_set():

swarms/utils/decorators.py

@@ -31,7 +31,9 @@ def timing_decorator(func):
         start_time = time.time()
         result = func(*args, **kwargs)
         end_time = time.time()
-        logging.info(f"{func.__name__} executed in {end_time - start_time} seconds")
+        logging.info(
+            f"{func.__name__} executed in {end_time - start_time} seconds"
+        )
         return result
 
     return wrapper
@@ -79,7 +81,9 @@ def synchronized_decorator(func):
 def deprecated_decorator(func):
     @functools.wraps(func)
     def wrapper(*args, **kwargs):
-        warnings.warn(f"{func.__name__} is deprecated", category=DeprecationWarning)
+        warnings.warn(
+            f"{func.__name__} is deprecated", category=DeprecationWarning
+        )
         return func(*args, **kwargs)
 
     return wrapper

swarms/utils/futures.py

@@ -5,6 +5,8 @@ T = TypeVar("T")
 
 
 def execute_futures_dict(fs_dict: dict[str, futures.Future[T]]) -> dict[str, T]:
-    futures.wait(fs_dict.values(), timeout=None, return_when=futures.ALL_COMPLETED)
+    futures.wait(
+        fs_dict.values(), timeout=None, return_when=futures.ALL_COMPLETED
+    )
 
     return {key: future.result() for key, future in fs_dict.items()}

swarms/utils/loggers.py

@@ -113,8 +113,8 @@ class Logger:
         )
         error_handler.setLevel(logging.ERROR)
         error_formatter = AutoGptFormatter(
-            "%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
+            "%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d"
-            " %(message_no_color)s"
+            " %(title)s %(message_no_color)s"
         )
         error_handler.setFormatter(error_formatter)
 
@@ -140,7 +140,12 @@ class Logger:
         self.chat_plugins = []
 
     def typewriter_log(
-        self, title="", title_color="", content="", speak_text=False, level=logging.INFO
+        self,
+        title="",
+        title_color="",
+        content="",
+        speak_text=False,
+        level=logging.INFO,
     ):
         """
         Logs a message to the typewriter.
@@ -255,7 +260,9 @@ class Logger:
             if isinstance(message, list):
                 message = " ".join(message)
         self.logger.log(
-            level, message, extra={"title": str(title), "color": str(title_color)}
+            level,
+            message,
+            extra={"title": str(title), "color": str(title_color)},
         )
 
     def set_level(self, level):
@@ -284,12 +291,15 @@ class Logger:
         if not additionalText:
             additionalText = (
                 "Please ensure you've setup and configured everything"
-                " correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
+                " correctly. Read"
-                "double check. You can also create a github issue or join the discord"
+                " https://github.com/Torantulino/Auto-GPT#readme to double"
+                " check. You can also create a github issue or join the discord"
                 " and ask there!"
             )
 
-        self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
+        self.typewriter_log(
+            "DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText
+        )
 
     def log_json(self, data: Any, file_name: str) -> None:
         """
@@ -367,7 +377,9 @@ class TypingConsoleHandler(logging.StreamHandler):
                 print(word, end="", flush=True)
                 if i < len(words) - 1:
                     print(" ", end="", flush=True)
-                typing_speed = random.uniform(min_typing_speed, max_typing_speed)
+                typing_speed = random.uniform(
+                    min_typing_speed, max_typing_speed
+                )
                 time.sleep(typing_speed)
                 # type faster after each word
                 min_typing_speed = min_typing_speed * 0.95

swarms/utils/main.py

@@ -201,7 +201,9 @@ def dim_multiline(message: str) -> str:
     lines = message.split("\n")
     if len(lines) <= 1:
         return lines[0]
-    return lines[0] + ANSI("\n... ".join([""] + lines[1:])).to(Color.black().bright())
+    return lines[0] + ANSI("\n... ".join([""] + lines[1:])).to(
+        Color.black().bright()
+    )
 
 
 # +=============================> ANSI Ending
@@ -227,7 +229,9 @@ class AbstractUploader(ABC):
 
 
 class S3Uploader(AbstractUploader):
-    def __init__(self, accessKey: str, secretKey: str, region: str, bucket: str):
+    def __init__(
+        self, accessKey: str, secretKey: str, region: str, bucket: str
+    ):
         self.accessKey = accessKey
         self.secretKey = secretKey
         self.region = region
@@ -338,7 +342,9 @@ class FileHandler:
         self.handlers = handlers
         self.path = path
 
-    def register(self, filetype: FileType, handler: BaseHandler) -> "FileHandler":
+    def register(
+        self, filetype: FileType, handler: BaseHandler
+    ) -> "FileHandler":
         self.handlers[filetype] = handler
         return self
 
@@ -356,7 +362,9 @@ class FileHandler:
 
     def handle(self, url: str) -> str:
         try:
-            if url.startswith(os.environ.get("SERVER", "http://localhost:8000")):
+            if url.startswith(
+                os.environ.get("SERVER", "http://localhost:8000")
+            ):
                 local_filepath = url[
                     len(os.environ.get("SERVER", "http://localhost:8000")) + 1 :
                 ]

swarms/utils/parse_code.py

@@ -7,5 +7,7 @@ def extract_code_in_backticks_in_string(message: str) -> str:
 
     """
     pattern = r"`` ``(.*?)`` "  # Non-greedy match between six backticks
-    match = re.search(pattern, message, re.DOTALL)  # re.DOTALL to match newline chars
+    match = re.search(
+        pattern, message, re.DOTALL
+    )  # re.DOTALL to match newline chars
     return match.group(1).strip() if match else None

swarms/utils/serializable.py

@@ -109,9 +109,11 @@ class Serializable(BaseModel, ABC):
             "lc": 1,
             "type": "constructor",
             "id": [*self.lc_namespace, self.__class__.__name__],
-            "kwargs": lc_kwargs
+            "kwargs": (
-            if not secrets
+                lc_kwargs
-            else _replace_secrets(lc_kwargs, secrets),
+                if not secrets
+                else _replace_secrets(lc_kwargs, secrets)
+            ),
         }
 
     def to_json_not_implemented(self) -> SerializedNotImplemented:

tests/agents/omni_modal.py

@@ -35,4 +35,6 @@ def test_omnimodalagent_run(omni_agent):
 
 
 def test_task_executor_initialization(omni_agent):
-    assert omni_agent.task_executor is not None, "TaskExecutor initialization failed"
+    assert (
+        omni_agent.task_executor is not None
+    ), "TaskExecutor initialization failed"

tests/memory/oceandb.py

@@ -30,7 +30,9 @@ def test_create_collection():
 
 def test_create_collection_exception():
     with patch("oceandb.Client") as MockClient:
-        MockClient.create_collection.side_effect = Exception("Create collection error")
+        MockClient.create_collection.side_effect = Exception(
+            "Create collection error"
+        )
         db = OceanDB(MockClient)
         with pytest.raises(Exception) as e:
             db.create_collection("test", "modality")

tests/memory/pinecone.py

@@ -6,7 +6,9 @@ api_key = os.getenv("PINECONE_API_KEY") or ""
 
 
 def test_init():
-    with patch("pinecone.init") as MockInit, patch("pinecone.Index") as MockIndex:
+    with patch("pinecone.init") as MockInit, patch(
+        "pinecone.Index"
+    ) as MockIndex:
         store = PineconeVectorStore(
             api_key=api_key, index_name="test_index", environment="test_env"
         )

tests/models/LLM.py

@@ -11,7 +11,9 @@ class TestLLM(unittest.TestCase):
     @patch.object(ChatOpenAI, "__init__", return_value=None)
     def setUp(self, mock_hf_init, mock_openai_init):
         self.llm_openai = LLM(openai_api_key="mock_openai_key")
-        self.llm_hf = LLM(hf_repo_id="mock_repo_id", hf_api_token="mock_hf_token")
+        self.llm_hf = LLM(
+            hf_repo_id="mock_repo_id", hf_api_token="mock_hf_token"
+        )
         self.prompt = "Who won the FIFA World Cup in 1998?"
 
     def test_init(self):

tests/models/anthropic.py

@@ -74,7 +74,9 @@ def test_anthropic_default_params(anthropic_instance):
     }
 
 
-def test_anthropic_run(mock_anthropic_env, mock_requests_post, anthropic_instance):
+def test_anthropic_run(
+    mock_anthropic_env, mock_requests_post, anthropic_instance
+):
     mock_response = Mock()
     mock_response.json.return_value = {"completion": "Generated text"}
     mock_requests_post.return_value = mock_response
@@ -98,7 +100,9 @@ def test_anthropic_run(mock_anthropic_env, mock_requests_post, anthropic_instanc
     )
 
 
-def test_anthropic_call(mock_anthropic_env, mock_requests_post, anthropic_instance):
+def test_anthropic_call(
+    mock_anthropic_env, mock_requests_post, anthropic_instance
+):
     mock_response = Mock()
     mock_response.json.return_value = {"completion": "Generated text"}
     mock_requests_post.return_value = mock_response
@@ -193,18 +197,24 @@ def test_anthropic_convert_prompt(anthropic_instance):
 
 
 def test_anthropic_call_with_stop(anthropic_instance):
-    response = anthropic_instance("Translate to French.", stop=["stop1", "stop2"])
+    response = anthropic_instance(
+        "Translate to French.", stop=["stop1", "stop2"]
+    )
     assert response == "Mocked Response from Anthropic"
 
 
 def test_anthropic_stream_with_stop(anthropic_instance):
-    generator = anthropic_instance.stream("Write a story.", stop=["stop1", "stop2"])
+    generator = anthropic_instance.stream(
+        "Write a story.", stop=["stop1", "stop2"]
+    )
     for token in generator:
         assert isinstance(token, str)
 
 
 def test_anthropic_async_call_with_stop(anthropic_instance):
-    response = anthropic_instance.async_call("Tell me a joke.", stop=["stop1", "stop2"])
+    response = anthropic_instance.async_call(
+        "Tell me a joke.", stop=["stop1", "stop2"]
+    )
     assert response == "Mocked Response from Anthropic"
 
 

tests/models/auto_temp.py

@@ -47,7 +47,9 @@ def test_run_auto_select(auto_temp_agent):
 def test_run_no_scores(auto_temp_agent):
     task = "Invalid task."
     temperature_string = "0.4,0.6,0.8,1.0,1.2,1.4"
-    with ThreadPoolExecutor(max_workers=auto_temp_agent.max_workers) as executor:
+    with ThreadPoolExecutor(
+        max_workers=auto_temp_agent.max_workers
+    ) as executor:
         with patch.object(
             executor, "submit", side_effect=[None, None, None, None, None, None]
         ):

tests/models/bingchat.py

@@ -44,7 +44,9 @@ class TestBingChat(unittest.TestCase):
         original_image_gen = BingChat.ImageGen
         BingChat.ImageGen = MockImageGen
 
-        img_path = self.chat.create_img("Test prompt", auth_cookie="mock_auth_cookie")
+        img_path = self.chat.create_img(
+            "Test prompt", auth_cookie="mock_auth_cookie"
+        )
         self.assertEqual(img_path, "./output/mock_image.png")
 
         BingChat.ImageGen = original_image_gen

tests/models/bioclip.py

@@ -127,7 +127,9 @@ def test_clip_multiple_images(clip_instance, sample_image_path):
 
 
 # Test model inference performance
-def test_clip_inference_performance(clip_instance, sample_image_path, benchmark):
+def test_clip_inference_performance(
+    clip_instance, sample_image_path, benchmark
+):
     labels = [
         "adenocarcinoma histopathology",
         "brain MRI",

tests/models/biogpt.py

@@ -46,7 +46,10 @@ def test_cell_biology_response(biogpt_instance):
 
 # 40. Test for a question about protein structure
 def test_protein_structure_response(biogpt_instance):
-    question = "What's the difference between alpha helix and beta sheet structures in proteins?"
+    question = (
+        "What's the difference between alpha helix and beta sheet structures in"
+        " proteins?"
+    )
     response = biogpt_instance(question)
     assert response and isinstance(response, str)
 

tests/models/cohere.py

@@ -49,7 +49,9 @@ def test_cohere_stream_api_error_handling(cohere_instance):
     cohere_instance.model = "base"
     cohere_instance.cohere_api_key = "invalid-api-key"
     with pytest.raises(Exception):
-        generator = cohere_instance.stream("Error handling with invalid API key.")
+        generator = cohere_instance.stream(
+            "Error handling with invalid API key."
+        )
         for token in generator:
             pass
 
@@ -94,13 +96,17 @@ def test_cohere_call_with_stop(cohere_instance):
 
 
 def test_cohere_stream_with_stop(cohere_instance):
-    generator = cohere_instance.stream("Write a story.", stop=["stop1", "stop2"])
+    generator = cohere_instance.stream(
+        "Write a story.", stop=["stop1", "stop2"]
+    )
     for token in generator:
         assert isinstance(token, str)
 
 
 def test_cohere_async_call_with_stop(cohere_instance):
-    response = cohere_instance.async_call("Tell me a joke.", stop=["stop1", "stop2"])
+    response = cohere_instance.async_call(
+        "Tell me a joke.", stop=["stop1", "stop2"]
+    )
     assert response == "Mocked Response from Cohere"
 
 
@@ -187,14 +193,22 @@ def test_cohere_generate_with_embed_english_v2(cohere_instance):
 
 def test_cohere_generate_with_embed_english_light_v2(cohere_instance):
     cohere_instance.model = "embed-english-light-v2.0"
-    response = cohere_instance("Generate embeddings with English Light v2.0 model.")
+    response = cohere_instance(
-    assert response.startswith("Generated embeddings with English Light v2.0 model")
+        "Generate embeddings with English Light v2.0 model."
+    )
+    assert response.startswith(
+        "Generated embeddings with English Light v2.0 model"
+    )
 
 
 def test_cohere_generate_with_embed_multilingual_v2(cohere_instance):
     cohere_instance.model = "embed-multilingual-v2.0"
-    response = cohere_instance("Generate embeddings with Multilingual v2.0 model.")
+    response = cohere_instance(
-    assert response.startswith("Generated embeddings with Multilingual v2.0 model")
+        "Generate embeddings with Multilingual v2.0 model."
+    )
+    assert response.startswith(
+        "Generated embeddings with Multilingual v2.0 model"
+    )
 
 
 def test_cohere_generate_with_embed_english_v3(cohere_instance):
@@ -205,14 +219,22 @@ def test_cohere_generate_with_embed_english_v3(cohere_instance):
 
 def test_cohere_generate_with_embed_english_light_v3(cohere_instance):
     cohere_instance.model = "embed-english-light-v3.0"
-    response = cohere_instance("Generate embeddings with English Light v3.0 model.")
+    response = cohere_instance(
-    assert response.startswith("Generated embeddings with English Light v3.0 model")
+        "Generate embeddings with English Light v3.0 model."
+    )
+    assert response.startswith(
+        "Generated embeddings with English Light v3.0 model"
+    )
 
 
 def test_cohere_generate_with_embed_multilingual_v3(cohere_instance):
     cohere_instance.model = "embed-multilingual-v3.0"
-    response = cohere_instance("Generate embeddings with Multilingual v3.0 model.")
+    response = cohere_instance(
-    assert response.startswith("Generated embeddings with Multilingual v3.0 model")
+        "Generate embeddings with Multilingual v3.0 model."
+    )
+    assert response.startswith(
+        "Generated embeddings with Multilingual v3.0 model"
+    )
 
 
 def test_cohere_generate_with_embed_multilingual_light_v3(cohere_instance):
@@ -423,7 +445,9 @@ def test_cohere_representation_model_classification(cohere_instance):
 def test_cohere_representation_model_language_detection(cohere_instance):
     # Test using the Representation model for language detection
     cohere_instance.model = "embed-english-v3.0"
-    language = cohere_instance.detect_language("Detect the language of this text.")
+    language = cohere_instance.detect_language(
+        "Detect the language of this text."
+    )
     assert isinstance(language, str)
 
 
@@ -447,7 +471,9 @@ def test_cohere_representation_model_multilingual_embedding(cohere_instance):
     assert len(embedding) > 0
 
 
-def test_cohere_representation_model_multilingual_classification(cohere_instance):
+def test_cohere_representation_model_multilingual_classification(
+    cohere_instance,
+):
     # Test using the Representation model for multilingual text classification
     cohere_instance.model = "embed-multilingual-v3.0"
     classification = cohere_instance.classify("Classify multilingual text.")
@@ -456,7 +482,9 @@ def test_cohere_representation_model_multilingual_classification(cohere_instance
     assert "score" in classification
 
 
-def test_cohere_representation_model_multilingual_language_detection(cohere_instance):
+def test_cohere_representation_model_multilingual_language_detection(
+    cohere_instance,
+):
     # Test using the Representation model for multilingual language detection
     cohere_instance.model = "embed-multilingual-v3.0"
     language = cohere_instance.detect_language(
@@ -471,12 +499,17 @@ def test_cohere_representation_model_multilingual_max_tokens_limit_exceeded(
     # Test handling max tokens limit exceeded error for multilingual model
     cohere_instance.model = "embed-multilingual-v3.0"
     cohere_instance.max_tokens = 10
-    prompt = "This is a test prompt that will exceed the max tokens limit for multilingual model."
+    prompt = (
+        "This is a test prompt that will exceed the max tokens limit for"
+        " multilingual model."
+    )
     with pytest.raises(ValueError):
         cohere_instance.embed(prompt)
 
 
-def test_cohere_representation_model_multilingual_light_embedding(cohere_instance):
+def test_cohere_representation_model_multilingual_light_embedding(
+    cohere_instance,
+):
     # Test using the Representation model for multilingual light text embedding
     cohere_instance.model = "embed-multilingual-light-v3.0"
     embedding = cohere_instance.embed("Generate multilingual light embeddings.")
@@ -484,10 +517,14 @@ def test_cohere_representation_model_multilingual_light_embedding(cohere_instanc
     assert len(embedding) > 0
 
 
-def test_cohere_representation_model_multilingual_light_classification(cohere_instance):
+def test_cohere_representation_model_multilingual_light_classification(
+    cohere_instance,
+):
     # Test using the Representation model for multilingual light text classification
     cohere_instance.model = "embed-multilingual-light-v3.0"
-    classification = cohere_instance.classify("Classify multilingual light text.")
+    classification = cohere_instance.classify(
+        "Classify multilingual light text."
+    )
     assert isinstance(classification, dict)
     assert "class" in classification
     assert "score" in classification
@@ -510,7 +547,10 @@ def test_cohere_representation_model_multilingual_light_max_tokens_limit_exceede
     # Test handling max tokens limit exceeded error for multilingual light model
     cohere_instance.model = "embed-multilingual-light-v3.0"
     cohere_instance.max_tokens = 10
-    prompt = "This is a test prompt that will exceed the max tokens limit for multilingual light model."
+    prompt = (
+        "This is a test prompt that will exceed the max tokens limit for"
+        " multilingual light model."
+    )
     with pytest.raises(ValueError):
         cohere_instance.embed(prompt)
 
@@ -553,19 +593,26 @@ def test_cohere_representation_model_english_classification(cohere_instance):
     assert "score" in classification
 
 
-def test_cohere_representation_model_english_language_detection(cohere_instance):
+def test_cohere_representation_model_english_language_detection(
+    cohere_instance,
+):
     # Test using the Representation model for English language detection
     cohere_instance.model = "embed-english-v3.0"
-    language = cohere_instance.detect_language("Detect the language of English text.")
+    language = cohere_instance.detect_language(
+        "Detect the language of English text."
+    )
     assert isinstance(language, str)
 
 
-def test_cohere_representation_model_english_max_tokens_limit_exceeded(cohere_instance):
+def test_cohere_representation_model_english_max_tokens_limit_exceeded(
+    cohere_instance,
+):
     # Test handling max tokens limit exceeded error for English model
     cohere_instance.model = "embed-english-v3.0"
     cohere_instance.max_tokens = 10
     prompt = (
-        "This is a test prompt that will exceed the max tokens limit for English model."
+        "This is a test prompt that will exceed the max tokens limit for"
+        " English model."
     )
     with pytest.raises(ValueError):
         cohere_instance.embed(prompt)
@@ -579,7 +626,9 @@ def test_cohere_representation_model_english_light_embedding(cohere_instance):
     assert len(embedding) > 0
 
 
-def test_cohere_representation_model_english_light_classification(cohere_instance):
+def test_cohere_representation_model_english_light_classification(
+    cohere_instance,
+):
     # Test using the Representation model for English light text classification
     cohere_instance.model = "embed-english-light-v3.0"
     classification = cohere_instance.classify("Classify English light text.")
@@ -588,7 +637,9 @@ def test_cohere_representation_model_english_light_classification(cohere_instanc
     assert "score" in classification
 
 
-def test_cohere_representation_model_english_light_language_detection(cohere_instance):
+def test_cohere_representation_model_english_light_language_detection(
+    cohere_instance,
+):
     # Test using the Representation model for English light language detection
     cohere_instance.model = "embed-english-light-v3.0"
     language = cohere_instance.detect_language(
@@ -603,7 +654,10 @@ def test_cohere_representation_model_english_light_max_tokens_limit_exceeded(
     # Test handling max tokens limit exceeded error for English light model
     cohere_instance.model = "embed-english-light-v3.0"
     cohere_instance.max_tokens = 10
-    prompt = "This is a test prompt that will exceed the max tokens limit for English light model."
+    prompt = (
+        "This is a test prompt that will exceed the max tokens limit for"
+        " English light model."
+    )
     with pytest.raises(ValueError):
         cohere_instance.embed(prompt)