code quality

.env.example

@@ -1,44 +0,0 @@
-OPENAI_API_KEY="your_openai_api_key_here"
-GOOGLE_API_KEY=""
-ANTHROPIC_API_KEY=""
-AI21_API_KEY="your_api_key_here"
-COHERE_API_KEY="your_api_key_here"
-ALEPHALPHA_API_KEY="your_api_key_here"
-HUGGINFACEHUB_API_KEY="your_api_key_here"
-STABILITY_API_KEY="your_api_key_here"
-
-WOLFRAM_ALPHA_APPID="your_wolfram_alpha_appid_here"
-ZAPIER_NLA_API_KEY="your_zapier_nla_api_key_here"
-
-EVAL_PORT=8000
-MODEL_NAME="gpt-4"
-CELERY_BROKER_URL="redis://localhost:6379"
-
-SERVER="http://localhost:8000"
-USE_GPU=True
-PLAYGROUND_DIR="playground"
-
-LOG_LEVEL="INFO"
-BOT_NAME="Orca"
-
-WINEDB_HOST="your_winedb_host_here"
-WINEDB_PASSWORD="your_winedb_password_here"
-BING_SEARCH_URL="your_bing_search_url_here"
-
-BING_SUBSCRIPTION_KEY="your_bing_subscription_key_here"
-SERPAPI_API_KEY="your_serpapi_api_key_here"
-IFTTTKey="your_iftttkey_here"
-
-BRAVE_API_KEY="your_brave_api_key_here"
-SPOONACULAR_KEY="your_spoonacular_key_here"
-HF_API_KEY="your_huggingface_api_key_here"
-
-
-REDIS_HOST=
-REDIS_PORT=
-
-#dbs
-PINECONE_API_KEY=""
-BING_COOKIE=""
-
-PSG_CONNECTION_STRING=""

multi_modal_auto_agent.py

@@ -13,8 +13,8 @@ img = "images/swarms.jpeg"
 ## Initialize the workflow
 agent = Agent(
     llm=llm,
-    sop=MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
     max_loops="auto",
+    sop=MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
 )
 
 agent.run(task=task, img=img)

playground/demos/ad_gen/ad_gen.py

@@ -10,11 +10,16 @@ openai_api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABILITY_API_KEY")
 
 # Initialize the language model and image generation model
-llm = OpenAIChat(openai_api_key=openai_api_key, temperature=0.5, max_tokens=3000)
+llm = OpenAIChat(
+    openai_api_key=openai_api_key, temperature=0.5, max_tokens=3000
+)
 sd_api = StableDiffusion(api_key=stability_api_key)
 
+
 def run_task(description, product_name, agent, **kwargs):
-    full_description = f"{description} about {product_name}"  # Incorporate product name into the task
+    full_description = (  # Incorporate product name into the task
+        f"{description} about {product_name}"
+    )
     result = agent.run(task=full_description, **kwargs)
     return result
 
@@ -23,8 +28,24 @@ def run_task(description, product_name, agent, **kwargs):
 class ProductPromptGenerator:
     def __init__(self, product_name):
         self.product_name = product_name
-        self.themes = ["lightning", "sunset", "ice cave", "space", "forest", "ocean", "mountains", "cityscape"]
-        self.styles = ["translucent", "floating in mid-air", "expanded into pieces", "glowing", "mirrored", "futuristic"]
+        self.themes = [
+            "lightning",
+            "sunset",
+            "ice cave",
+            "space",
+            "forest",
+            "ocean",
+            "mountains",
+            "cityscape",
+        ]
+        self.styles = [
+            "translucent",
+            "floating in mid-air",
+            "expanded into pieces",
+            "glowing",
+            "mirrored",
+            "futuristic",
+        ]
         self.contexts = ["high realism product ad (extremely creative)"]
 
     def generate_prompt(self):
@@ -33,8 +54,12 @@ class ProductPromptGenerator:
         context = random.choice(self.contexts)
         return f"{theme} inside a {style} {self.product_name}, {context}"
 
+
 # User input
-product_name = input("Enter a product name for ad creation (e.g., 'PS5', 'AirPods', 'Kirkland Vodka'): ")
+product_name = input(
+    "Enter a product name for ad creation (e.g., 'PS5', 'AirPods', 'Kirkland"
+    " Vodka'): "
+)
 
 # Generate creative concept
 prompt_generator = ProductPromptGenerator(product_name)
@@ -46,9 +71,17 @@ design_flow = Agent(llm=llm, max_loops=1, dashboard=False)
 copywriting_flow = Agent(llm=llm, max_loops=1, dashboard=False)
 
 # Execute tasks
-concept_result = run_task("Generate a creative concept", product_name, concept_flow)
-design_result = run_task("Suggest visual design ideas", product_name, design_flow)
-copywriting_result = run_task("Create compelling ad copy for the product photo", product_name, copywriting_flow)
+concept_result = run_task(
+    "Generate a creative concept", product_name, concept_flow
+)
+design_result = run_task(
+    "Suggest visual design ideas", product_name, design_flow
+)
+copywriting_result = run_task(
+    "Create compelling ad copy for the product photo",
+    product_name,
+    copywriting_flow,
+)
 
 # Generate product image
 image_paths = sd_api.run(creative_prompt)

playground/demos/developer_swarm/main.py

@@ -33,28 +33,17 @@ code
 """
 
 # Initialize the language model
-llm = OpenAIChat(
-    openai_api_key=api_key,
-    max_tokens=5000
-)
+llm = OpenAIChat(openai_api_key=api_key, max_tokens=5000)
 
 
 # Documentation agent
 documentation_agent = Agent(
-    llm=llm,
-    sop=DOCUMENTATION_SOP,
-    max_loops=1,
-    multi_modal=True
+    llm=llm, sop=DOCUMENTATION_SOP, max_loops=1, multi_modal=True
 )
 
 
 # Tests agent
-tests_agent = Agent(
-    llm=llm,
-    sop=TEST_SOP,
-    max_loops=2,
-    multi_modal=True
-)
+tests_agent = Agent(llm=llm, sop=TEST_SOP, max_loops=2, multi_modal=True)
 
 
 # Run the documentation agent
@@ -64,5 +53,6 @@ documentation = documentation_agent.run(
 
 # Run the tests agent
 tests = tests_agent.run(
-    f"Write tests for the following code:{TASK} here is the documentation: {documentation}"
+    f"Write tests for the following code:{TASK} here is the documentation:"
+    f" {documentation}"
 )

playground/demos/swarm_of_mma_manufacturing/main.py

@@ -22,14 +22,14 @@ load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 
-llm = GPT4VisionAPI(
-    openai_api_key=api_key
-)
+llm = GPT4VisionAPI(openai_api_key=api_key)
 
 assembly_line = "playground/demos/swarm_of_mma_manufacturing/assembly_line.jpg"
 red_robots = "playground/demos/swarm_of_mma_manufacturing/red_robots.jpg"
 robots = "playground/demos/swarm_of_mma_manufacturing/robots.jpg"
-tesla_assembly_line = "playground/demos/swarm_of_mma_manufacturing/tesla_assembly.jpg"
+tesla_assembly_line = (
+    "playground/demos/swarm_of_mma_manufacturing/tesla_assembly.jpg"
+)
 
 
 # Define detailed prompts for each agent
@@ -73,55 +73,37 @@ efficiency_prompt = tasks["efficiency"]
 
 # Health security agent
 health_security_agent = Agent(
-    llm=llm,
-    sop_list=health_safety_prompt,
-    max_loops=2,
-    multi_modal=True
+    llm=llm, sop_list=health_safety_prompt, max_loops=2, multi_modal=True
 )
 
 # Quality control agent
 productivity_check_agent = Agent(
-    llm=llm,
-    sop=productivity_prompt,
-    max_loops=2,
-    multi_modal=True
+    llm=llm, sop=productivity_prompt, max_loops=2, multi_modal=True
 )
 
 # Security agent
 security_check_agent = Agent(
-    llm=llm,
-    sop=security_prompt,
-    max_loops=2,
-    multi_modal=True
+    llm=llm, sop=security_prompt, max_loops=2, multi_modal=True
 )
 
 # Efficiency agent
 efficiency_check_agent = Agent(
-    llm=llm,
-    sop=efficiency_prompt,
-    max_loops=2,
-    multi_modal=True
+    llm=llm, sop=efficiency_prompt, max_loops=2, multi_modal=True
 )
 
 
 # Add the first task to the health_security_agent
 health_check = health_security_agent.run(
-    "Analyze the safety of this factory",
-    robots
+    "Analyze the safety of this factory", robots
 )
 
 # Add the third task to the productivity_check_agent
-productivity_check = productivity_check_agent.run(
-    health_check, assembly_line
-)
+productivity_check = productivity_check_agent.run(health_check, assembly_line)
 
 # Add the fourth task to the security_check_agent
-security_check = security_check_agent.add(
-    productivity_check, red_robots
-)
+security_check = security_check_agent.add(productivity_check, red_robots)
 
 # Add the fifth task to the efficiency_check_agent
 efficiency_check = efficiency_check_agent.run(
     security_check, tesla_assembly_line
 )
-

playground/memory/qdrant/usage.py

@@ -1,13 +1,19 @@
 from langchain.document_loaders import CSVLoader
 from swarms.memory import qdrant
 
-loader = CSVLoader(file_path="../document_parsing/aipg/aipg.csv", encoding='utf-8-sig')
+loader = CSVLoader(
+    file_path="../document_parsing/aipg/aipg.csv", encoding="utf-8-sig"
+)
 docs = loader.load()
 
 
 # Initialize the Qdrant instance
 # See qdrant documentation on how to run locally
-qdrant_client = qdrant.Qdrant(host ="https://697ea26c-2881-4e17-8af4-817fcb5862e8.europe-west3-0.gcp.cloud.qdrant.io", collection_name="qdrant", api_key="BhG2_yINqNU-aKovSEBadn69Zszhbo5uaqdJ6G_qDkdySjAljvuPqQ")
+qdrant_client = qdrant.Qdrant(
+    host="https://697ea26c-2881-4e17-8af4-817fcb5862e8.europe-west3-0.gcp.cloud.qdrant.io",
+    collection_name="qdrant",
+    api_key="BhG2_yINqNU-aKovSEBadn69Zszhbo5uaqdJ6G_qDkdySjAljvuPqQ",
+)
 qdrant_client.add_vectors(docs)
 
 # Perform a search

swarms/memory/qdrant.py

@@ -4,24 +4,33 @@ from httpx import RequestError
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams, PointStruct
 
+
 class Qdrant:
-    def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: str = "qdrant", model_name: str = "BAAI/bge-small-en-v1.5", https: bool = True):
+    def __init__(
+        self,
+        api_key: str,
+        host: str,
+        port: int = 6333,
+        collection_name: str = "qdrant",
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        https: bool = True,
+    ):
         """
-            Qdrant class for managing collections and performing vector operations using QdrantClient.
+        Qdrant class for managing collections and performing vector operations using QdrantClient.
 
-            Attributes:
-                client (QdrantClient): The Qdrant client for interacting with the Qdrant server.
-                collection_name (str): Name of the collection to be managed in Qdrant.
-                model (SentenceTransformer): The model used for generating sentence embeddings.
+        Attributes:
+            client (QdrantClient): The Qdrant client for interacting with the Qdrant server.
+            collection_name (str): Name of the collection to be managed in Qdrant.
+            model (SentenceTransformer): The model used for generating sentence embeddings.
 
-            Args:
-                api_key (str): API key for authenticating with Qdrant.
-                host (str): Host address of the Qdrant server.
-                port (int): Port number of the Qdrant server. Defaults to 6333.
-                collection_name (str): Name of the collection to be used or created. Defaults to "qdrant".
-                model_name (str): Name of the model to be used for embeddings. Defaults to "BAAI/bge-small-en-v1.5".
-                https (bool): Flag to indicate if HTTPS should be used. Defaults to True.
-            """
+        Args:
+            api_key (str): API key for authenticating with Qdrant.
+            host (str): Host address of the Qdrant server.
+            port (int): Port number of the Qdrant server. Defaults to 6333.
+            collection_name (str): Name of the collection to be used or created. Defaults to "qdrant".
+            model_name (str): Name of the model to be used for embeddings. Defaults to "BAAI/bge-small-en-v1.5".
+            https (bool): Flag to indicate if HTTPS should be used. Defaults to True.
+        """
         try:
             self.client = QdrantClient(url=host, port=port, api_key=api_key)
             self.collection_name = collection_name
@@ -50,7 +59,10 @@ class Qdrant:
         except Exception as e:
             self.client.create_collection(
                 collection_name=self.collection_name,
-                vectors_config=VectorParams(size=self.model.get_sentence_embedding_dimension(), distance=Distance.DOT),
+                vectors_config=VectorParams(
+                    size=self.model.get_sentence_embedding_dimension(),
+                    distance=Distance.DOT,
+                ),
             )
             print(f"Collection '{self.collection_name}' created.")
 
@@ -67,11 +79,21 @@ class Qdrant:
         points = []
         for i, doc in enumerate(docs):
             try:
-                if 'page_content' in doc:
-                    embedding = self.model.encode(doc['page_content'], normalize_embeddings=True)
-                    points.append(PointStruct(id=i + 1, vector=embedding, payload={"content": doc['page_content']}))
+                if "page_content" in doc:
+                    embedding = self.model.encode(
+                        doc["page_content"], normalize_embeddings=True
+                    )
+                    points.append(
+                        PointStruct(
+                            id=i + 1,
+                            vector=embedding,
+                            payload={"content": doc["page_content"]},
+                        )
+                    )
                 else:
-                    print(f"Document at index {i} is missing 'page_content' key")
+                    print(
+                        f"Document at index {i} is missing 'page_content' key"
+                    )
             except Exception as e:
                 print(f"Error processing document at index {i}: {e}")
 
@@ -102,7 +124,7 @@ class Qdrant:
             search_result = self.client.search(
                 collection_name=self.collection_name,
                 query_vector=query_vector,
-                limit=limit
+                limit=limit,
             )
             return search_result
         except Exception as e:

swarms/models/base_multimodal_model.py

@@ -56,6 +56,7 @@ class BaseMultiModalModel:
         >>> model.get_img_from_web("https://www.google.com/images/branding/googlelogo/")
 
     """
+
     def __init__(
         self,
         model_name: Optional[str],

swarms/models/gpt4_vision_api.py

@@ -95,7 +95,13 @@ class GPT4VisionAPI:
         pass
 
     # Function to handle vision tasks
-    def run(self, task: Optional[str] = None, img: Optional[str] = None, *args, **kwargs):
+    def run(
+        self,
+        task: Optional[str] = None,
+        img: Optional[str] = None,
+        *args,
+        **kwargs,
+    ):
         """Run the model."""
         try:
             base64_image = self.encode_image(img)

swarms/models/stable_diffusion.py

@@ -7,6 +7,7 @@ from typing import List
 
 load_dotenv()
 
+
 class StableDiffusion:
     """
     A class to interact with the Stable Diffusion API for generating images from text prompts.
@@ -41,7 +42,16 @@ class StableDiffusion:
         Generates an image based on the provided text prompt and returns the paths of the saved images.
     """
 
-    def __init__(self, api_key: str, api_host: str = "https://api.stability.ai", cfg_scale: int = 7, height: int = 1024, width: int = 1024, samples: int = 1, steps: int = 30):
+    def __init__(
+        self,
+        api_key: str,
+        api_host: str = "https://api.stability.ai",
+        cfg_scale: int = 7,
+        height: int = 1024,
+        width: int = 1024,
+        samples: int = 1,
+        steps: int = 30,
+    ):
         """
         Initialize the StableDiffusion class with API configurations.
 
@@ -73,7 +83,7 @@ class StableDiffusion:
         self.headers = {
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json",
-            "Accept": "application/json"
+            "Accept": "application/json",
         }
         self.output_dir = "images"
         os.makedirs(self.output_dir, exist_ok=True)
@@ -117,7 +127,9 @@ class StableDiffusion:
         image_paths = []
         for i, image in enumerate(data["artifacts"]):
             unique_id = uuid.uuid4()  # Generate a unique identifier
-            image_path = os.path.join(self.output_dir, f"{unique_id}_v1_txt2img_{i}.png")
+            image_path = os.path.join(
+                self.output_dir, f"{unique_id}_v1_txt2img_{i}.png"
+            )
             with open(image_path, "wb") as f:
                 f.write(base64.b64decode(image["base64"]))
             image_paths.append(image_path)

swarms/prompts/programming.py

@@ -1,4 +1,3 @@
-
 TEST_SOP = """
 Create 500 extensive and thorough tests for the code below using the guide, do not worry about your limits you do not have any
 just write the best tests possible and return the test code in markdown format. Create the tests for the code below and make it really high performance

swarms/structs/agent.py

@@ -620,7 +620,9 @@ class Agent:
             # If autosave is enabled then save the state
             if self.autosave:
                 save_path = self.saved_state_path or "flow_state.json"
-                print(colored(f"Autosaving agent state to {save_path}", "green"))
+                print(
+                    colored(f"Autosaving agent state to {save_path}", "green")
+                )
                 self.save_state(save_path)
 
             # If return history is enabled then return the response and history

swarms/structs/sequential_workflow.py

@@ -140,7 +140,9 @@ class SequentialWorkflow:
         """
         # If the agent is a Agent instance, we include the task in kwargs for Agent.run()
         if isinstance(agent, Agent):
-            kwargs["task"] = task  # Set the task as a keyword argument for Agent
+            kwargs["task"] = (
+                task  # Set the task as a keyword argument for Agent
+            )
 
         # Append the task to the tasks list
         if self.img:
@@ -156,7 +158,10 @@ class SequentialWorkflow:
         else:
             self.tasks.append(
                 Task(
-                    description=task, agent=agent, args=list(args), kwargs=kwargs
+                    description=task,
+                    agent=agent,
+                    args=list(args),
+                    kwargs=kwargs,
                 )
             )
 
@@ -448,7 +453,9 @@ class SequentialWorkflow:
                         )
                     else:
                         # If it's not a Agent instance, call the agent directly
-                        task.result = await task.agent(*task.args, **task.kwargs)
+                        task.result = await task.agent(
+                            *task.args, **task.kwargs
+                        )
 
                     # Pass the result as an argument to the next task if it exists
                     next_task_index = self.tasks.index(task) + 1

tests/memory/qdrant.py

@@ -6,35 +6,46 @@ from swarms.memory.qdrant import Qdrant
 
 @pytest.fixture
 def mock_qdrant_client():
-    with patch('your_module.QdrantClient') as MockQdrantClient:
+    with patch("your_module.QdrantClient") as MockQdrantClient:
         yield MockQdrantClient()
 
+
 @pytest.fixture
 def mock_sentence_transformer():
-    with patch('sentence_transformers.SentenceTransformer') as MockSentenceTransformer:
+    with patch(
+        "sentence_transformers.SentenceTransformer"
+    ) as MockSentenceTransformer:
         yield MockSentenceTransformer()
 
+
 @pytest.fixture
 def qdrant_client(mock_qdrant_client, mock_sentence_transformer):
     client = Qdrant(api_key="your_api_key", host="your_host")
     yield client
 
+
 def test_qdrant_init(qdrant_client, mock_qdrant_client):
     assert qdrant_client.client is not None
 
+
 def test_load_embedding_model(qdrant_client, mock_sentence_transformer):
     qdrant_client._load_embedding_model("model_name")
     mock_sentence_transformer.assert_called_once_with("model_name")
 
+
 def test_setup_collection(qdrant_client, mock_qdrant_client):
     qdrant_client._setup_collection()
-    mock_qdrant_client.get_collection.assert_called_once_with(qdrant_client.collection_name)
+    mock_qdrant_client.get_collection.assert_called_once_with(
+        qdrant_client.collection_name
+    )
+
 
 def test_add_vectors(qdrant_client, mock_qdrant_client):
     mock_doc = Mock(page_content="Sample text")
     qdrant_client.add_vectors([mock_doc])
     mock_qdrant_client.upsert.assert_called_once()
 
+
 def test_search_vectors(qdrant_client, mock_qdrant_client):
     qdrant_client.search_vectors("test query")
     mock_qdrant_client.search.assert_called_once()

tests/structs/test_flow.py

@@ -1168,7 +1168,9 @@ def test_flow_from_llm_and_template_file():
     llm_instance = mocked_llm  # Replace with your LLM class
     template_file = "template.txt"  # Create a template file for testing
 
-    flow_instance = Agent.from_llm_and_template_file(llm_instance, template_file)
+    flow_instance = Agent.from_llm_and_template_file(
+        llm_instance, template_file
+    )
 
     assert isinstance(flow_instance, Agent)