[CLEANUP]

example.py

@@ -1,4 +1,4 @@
-from swarms import Agent, AnthropicChat
+from swarms import Agent, Anthropic
 from langchain.tools import tool
 
 
@@ -13,7 +13,7 @@ def search_api(query: str, max_results: int = 10):
 
 ## Initialize the workflow
 agent = Agent(
-    llm=AnthropicChat(),
+    llm=Anthropic(),
     max_loops="auto",
     autosave=True,
     dashboard=False,

playground/swarms/hierarchical_swarm.py

@@ -0,0 +1,23 @@
+"""
+Boss selects what agent to use
+B -> W1, W2, W3
+"""
+from typing import List, Optional
+from pydantic import BaseModel, Field
+from swarms.utils.json_utils import str_to_json
+
+class HierarchicalSwarm(BaseModel):
+    class Config:
+        arbitrary_types_allowed = True
+
+    agents: Optional[List[str]] = Field(
+        None, title="List of agents in the hierarchical swarm"
+    )
+    task: Optional[str] = Field(None, title="Task to be done by the agents")
+
+
+all_agents = HierarchicalSwarm()
+
+agents_schema = HierarchicalSwarm.model_json_schema()
+agents_schema = str_to_json(agents_schema)
+print(agents_schema)

pyproject.toml

@@ -47,7 +47,7 @@ tiktoken = "0.4.0"
 ratelimit = "2.2.1"
 loguru = "0.7.2"
 huggingface-hub = "*"
-pydantic = "*"
+pydantic = "2.6.4"
 tenacity = "8.2.2"
 Pillow = "9.4.0"
 chromadb = "*"

swarms/models/__init__.py

@@ -1,12 +1,3 @@
-from swarms.models.popular_llms import (
-    AnthropicChat,
-    CohereChat,
-    MosaicMLChat,
-    OpenAILLM,
-    ReplicateLLM,
-    AzureOpenAILLM,
-    OpenAIChatLLM,
-)
 from swarms.models.base_embedding_model import BaseEmbeddingModel
 from swarms.models.base_llm import AbstractLLM  # noqa: E402
 from swarms.models.base_multimodal_model import BaseMultiModalModel
@@ -15,7 +6,6 @@ from swarms.models.clipq import CLIPQ  # noqa: E402
 from swarms.models.fire_function import FireFunctionCaller
 from swarms.models.fuyu import Fuyu  # noqa: E402
 from swarms.models.gemini import Gemini  # noqa: E402
-from swarms.models.gigabind import Gigabind  # noqa: E402
 from swarms.models.gpt4_vision_api import GPT4VisionAPI  # noqa: E402
 from swarms.models.huggingface import HuggingfaceLLM  # noqa: E402
 from swarms.models.idefics import Idefics  # noqa: E402
@@ -28,8 +18,30 @@ from swarms.models.mpt import MPT7B  # noqa: E402
 from swarms.models.nougat import Nougat  # noqa: E402
 from swarms.models.openai_tts import OpenAITTS  # noqa: E402
 from swarms.models.petals import Petals  # noqa: E402
+from swarms.models.popular_llms import (
+    AnthropicChat as Anthropic,
+)
+from swarms.models.popular_llms import (
+    AzureOpenAILLM as AzureOpenAI,
+)
+from swarms.models.popular_llms import (
+    CohereChat as Cohere,
+)
+from swarms.models.popular_llms import (
+    MosaicMLChat as MosaicML,
+)
+from swarms.models.popular_llms import (
+    OpenAIChatLLM as OpenAIChat,
+)
+from swarms.models.popular_llms import (
+    OpenAILLM as OpenAI,
+)
+from swarms.models.popular_llms import (
+    ReplicateLLM as Replicate,
+)
 from swarms.models.qwen import QwenVLMultiModal  # noqa: E402
-from swarms.models.roboflow_model import RoboflowMultiModal
+
+# from swarms.models.roboflow_model import RoboflowMultiModal
 from swarms.models.sam_supervision import SegmentAnythingMarkGenerator
 from swarms.models.sampling_params import SamplingParams, SamplingType
 from swarms.models.timm import TimmModel  # noqa: E402
@@ -41,57 +53,54 @@ from swarms.models.types import (  # noqa: E402
     TextModality,
     VideoModality,
 )
-from swarms.models.ultralytics_model import UltralyticsModel
+
+# from swarms.models.ultralytics_model import UltralyticsModel
 from swarms.models.vilt import Vilt  # noqa: E402
 from swarms.models.wizard_storytelling import WizardLLMStoryTeller
 from swarms.models.zephyr import Zephyr  # noqa: E402
 from swarms.models.zeroscope import ZeroscopeTTV  # noqa: E402
 
-
 __all__ = [
     "AbstractLLM",
+    "Anthropic",
+    "AzureOpenAI",
     "BaseEmbeddingModel",
     "BaseMultiModalModel",
     "BioGPT",
     "CLIPQ",
+    "Cohere",
     "FireFunctionCaller",
     "Fuyu",
-    "Gigabind",
     "GPT4VisionAPI",
+    "Gemini",
     "HuggingfaceLLM",
     "Idefics",
     "Kosmos",
     "LayoutLMDocumentQA",
     "LavaMultiModal",
+    "Replicate",
+    "MPT7B",
     "Mistral",
     "Mixtral",
-    "MPT7B",
+    "MosaicML",
     "Nougat",
+    "OpenAI",
+    "OpenAIChat",
     "OpenAITTS",
     "Petals",
     "QwenVLMultiModal",
-    "RoboflowMultiModal",
     "SamplingParams",
     "SamplingType",
     "SegmentAnythingMarkGenerator",
+    "TextModality",
     "TimmModel",
     "TogetherLLM",
-    "UltralyticsModel",
     "Vilt",
+    "VideoModality",
     "WizardLLMStoryTeller",
     "Zephyr",
     "ZeroscopeTTV",
-    "AnthropicChat",
-    "CohereChat",
-    "MosaicMLChat",
-    "OpenAILLM",
-    "ReplicateLLM",
-    "AzureOpenAILLM",
-    "OpenAIChatLLM",
     "AudioModality",
     "ImageModality",
     "MultimodalData",
-    "TextModality",
-    "Gemini",
-    "VideoModality",
 ]

swarms/tools/tool.py

@@ -0,0 +1,6 @@
+from langchain.tools import (
+    BaseTool,
+    Tool,
+    tool,
+    StructuredTool,
+)  # noqa

swarms/tools/tool_type.py

@@ -45,7 +45,7 @@ class OmniTool(BaseModel):
             Tuple: A tuple containing the arguments and keyword arguments.
 
         """
-        try: 
+        try:
             self.transform_models_to_tools()
             logger.info(f"Number of tools: {len(self.tools)}")
             try:
@@ -53,9 +53,13 @@ class OmniTool(BaseModel):
                     logger.info(f"Running tool: {tool}")
                     tool(*args, **kwargs)
             except Exception as e:
-                logger.error(f"Error occurred while running tools: {e}")
+                logger.error(
+                    f"Error occurred while running tools: {e}"
+                )
             return args, kwargs
-        
+
         except Exception as error:
-            logger.error(f"Error occurred while running tools: {error}")
-            return args, kwargs
+            logger.error(
+                f"Error occurred while running tools: {error}"
+            )
+            return args, kwargs

swarms/utils/json_utils.py

@@ -3,7 +3,7 @@ import json
 from pydantic import BaseModel
 
 
-def base_model_schema_to_json(model: BaseModel):
+def base_model_schema_to_json(model: BaseModel, indent: int = 3):
     """
     Converts the JSON schema of a base model to a formatted JSON string.
 
@@ -13,7 +13,7 @@ def base_model_schema_to_json(model: BaseModel):
     Returns:
         str: The JSON schema of the base model as a formatted JSON string.
     """
-    return json.dumps(model.model_json_schema(), indent=2)
+    return json.dumps(model.model_json_schema(), indent=indent)
 
 
 def extract_json_from_str(response: str):
@@ -48,3 +48,19 @@ def base_model_to_json(base_model_instance: BaseModel) -> str:
     json_string = json.dumps(model_dict)
 
     return json_string
+
+
+
+def str_to_json(response: str, indent: int = 3):
+    """
+    Converts a string representation of JSON to a JSON object.
+
+    Args:
+        response (str): The string representation of JSON.
+        indent (int, optional): The number of spaces to use for indentation in the JSON output. Defaults to 3.
+
+    Returns:
+        str: The JSON object as a string.
+
+    """
+    return json.dumps(response, indent=indent)