[CLEANUP]

README.md

@@ -587,50 +587,6 @@ workflow.run()
 
 
 
-### `ModelParallelizer`
-The ModelParallelizer allows you to run multiple models concurrently, comparing their outputs. This feature enables you to easily compare the performance and results of different models, helping you make informed decisions about which model to use for your specific task.
-
-Plug-and-Play Integration: The structure provides a seamless integration with various models, including OpenAIChat, Anthropic, Mixtral, and Gemini. You can easily plug in any of these models and start using them without the need for extensive modifications or setup.
-
-
-```python
-import os
-
-from dotenv import load_dotenv
-
-from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat
-
-load_dotenv()
-
-# API Keys
-anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
-openai_api_key = os.getenv("OPENAI_API_KEY")
-gemini_api_key = os.getenv("GEMINI_API_KEY")
-
-# Initialize the models
-llm = OpenAIChat(openai_api_key=openai_api_key)
-anthropic = Anthropic(anthropic_api_key=anthropic_api_key)
-mixtral = Mixtral()
-gemini = Gemini(gemini_api_key=gemini_api_key)
-
-# Initialize the parallelizer
-llms = [llm, anthropic, mixtral, gemini]
-parallelizer = ModelParallelizer(llms)
-
-# Set the task
-task = "Generate a 10,000 word blog on health and wellness."
-
-# Run the task
-out = parallelizer.run(task)
-
-# Print the responses 1 by 1
-for i in range(len(out)):
-    print(f"Response from LLM {i}: {out[i]}")
-```
-
-
-
-
 ### `SwarmNetwork`
 `SwarmNetwork` provides the infrasturcture for building extremely dense and complex multi-agent applications that span across various types of agents.
 
@@ -757,106 +713,6 @@ print(f"Task result: {task.result}")
 
 
 
-### `BlockList`
-- Modularity and Flexibility: BlocksList allows users to create custom swarms by adding or removing different classes or functions as blocks. This means users can easily tailor the functionality of their swarm to suit their specific needs.
-
-- Ease of Management: With methods to add, remove, update, and retrieve blocks, BlocksList provides a straightforward way to manage the components of a swarm. This makes it easier to maintain and update the swarm over time.
-
-- Enhanced Searchability: BlocksList offers methods to get blocks by various attributes such as name, type, ID, and parent-related properties. This makes it easier for users to find and work with specific blocks in a large and complex swarm.
-
-```python
-import os
-
-from dotenv import load_dotenv
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from pydantic import BaseModel
-from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
-
-# Load the environment variables
-load_dotenv()
-
-# Get the environment variables
-openai_api_key = os.getenv("OPENAI_API_KEY")
-gemini_api_key = os.getenv("GEMINI_API_KEY")
-
-# Tool Agent
-model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b")
-tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")
-
-# Initialize the schema for the person's information
-class Schema(BaseModel):
-    name: str = Field(..., title="Name of the person")
-    agent: int = Field(..., title="Age of the person")
-    is_student: bool = Field(
-        ..., title="Whether the person is a student"
-    )
-    courses: list[str] = Field(
-        ..., title="List of courses the person is taking"
-    )
-
-# Convert the schema to a JSON string
-json_schema = base_model_to_json(Schema)
-
-
-toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema)
-
-# Blocks List which enables you to build custom swarms by adding classes or functions
-swarm = BlocksList(
-    "SocialMediaSwarm",
-    "A swarm of social media agents",
-    [
-        OpenAI(openai_api_key=openai_api_key),
-        Mixtral(),
-        GPT4VisionAPI(openai_api_key=openai_api_key),
-        Gemini(gemini_api_key=gemini_api_key),
-    ],
-)
-
-
-# Add the new block to the swarm
-swarm.add(toolagent)
-
-# Remove a block from the swarm
-swarm.remove(toolagent)
-
-# Update a block in the swarm
-swarm.update(toolagent)
-
-# Get a block at a specific index
-block_at_index = swarm.get(0)
-
-# Get all blocks in the swarm
-all_blocks = swarm.get_all()
-
-# Get blocks by name
-openai_blocks = swarm.get_by_name("OpenAI")
-
-# Get blocks by type
-gpt4_blocks = swarm.get_by_type("GPT4VisionAPI")
-
-# Get blocks by ID
-block_by_id = swarm.get_by_id(toolagent.id)
-
-# Get blocks by parent
-blocks_by_parent = swarm.get_by_parent(swarm)
-
-# Get blocks by parent ID
-blocks_by_parent_id = swarm.get_by_parent_id(swarm.id)
-
-# Get blocks by parent name
-blocks_by_parent_name = swarm.get_by_parent_name(swarm.name)
-
-# Get blocks by parent type
-blocks_by_parent_type = swarm.get_by_parent_type(type(swarm).__name__)
-
-# Get blocks by parent description
-blocks_by_parent_description = swarm.get_by_parent_description(swarm.description)
-
-# Run the block in the swarm
-inference = swarm.run_block(toolagent, "Hello World")
-print(inference)
-```
-
 
 ## Majority Voting
 Multiple-agents will evaluate an idea based off of an parsing or evaluation function. From papers like "[More agents is all you need](https://arxiv.org/pdf/2402.05120.pdf)
@@ -1249,6 +1105,9 @@ print(output)
 
 ```
 
+## `HierarhicalSwarm`
+Coming soon...
+
 
 ---
 
@@ -1265,6 +1124,7 @@ The swarms package has been meticlously crafted for extreme use-ability and unde
 ├── agents
 ├── artifacts
 ├── memory
+├── schemas
 ├── models
 ├── prompts
 ├── structs
@@ -1313,13 +1173,12 @@ Accelerate Bugs, Features, and Demos to implement by supporting us here:
 
 
 ## Docker Instructions
-- [Learn More Here About Deployments In Docker]()
+- [Learn More Here About Deployments In Docker](https://swarms.apac.ai/en/latest/docker_setup/)
 
 
 ## Swarm Newsletter 🤖 🤖 🤖 📧 
 Sign up to the Swarm newsletter to receive  updates on the latest Autonomous agent research papers, step by step guides on creating multi-agent app, and much more Swarmie goodiness 😊
 
-
 [CLICK HERE TO SIGNUP](https://docs.google.com/forms/d/e/1FAIpQLSfqxI2ktPR9jkcIwzvHL0VY6tEIuVPd-P2fOWKnd6skT9j1EQ/viewform?usp=sf_link)
 
 # License

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.0.4"
+version = "5.0.5"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]

scripts/code_quality.sh

@@ -5,15 +5,16 @@
 
 # Run autopep8 with max aggressiveness (-aaa) and in-place modification (-i)
 # on all Python files (*.py) under the 'tests' directory.
-autopep8 --in-place --aggressive --aggressive --recursive --experimental --list-fixes zeta/
+autopep8 --in-place --aggressive --aggressive --recursive --experimental --list-fixes swarms/
 
 # Run black with default settings, since black does not have an aggressiveness level.
 # Black will format all Python files it finds in the 'tests' directory.
-black --experimental-string-processing zeta/
+black .
 
 # Run ruff on the 'tests' directory.
 # Add any additional flags if needed according to your version of ruff.
-ruff zeta/ --fix
+ruff . --fix
+ruff clean
 
 # YAPF
 yapf --recursive --in-place --verbose --style=google --parallel tests

swarms/models/__init__.py

@@ -8,8 +8,7 @@
 from swarms.models.kosmos_two import Kosmos  # noqa: E402
 from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA
 from swarms.models.llava import LavaMultiModal  # noqa: E402
-from swarms.models.mistral import Mistral  # noqa: E402
-from swarms.models.mixtral import Mixtral  # noqa: E402
+
 from swarms.models.nougat import Nougat  # noqa: E402
 from swarms.models.palm import GooglePalm as Palm  # noqa: E402
 from swarms.models.openai_tts import OpenAITTS  # noqa: E402
@@ -53,8 +52,6 @@
     "Kosmos",
     "LayoutLMDocumentQA",
     "LavaMultiModal",
-    "Mistral",
-    "Mixtral",
     "Nougat",
     "Palm",
     "OpenAITTS",

swarms/models/llama3_hosted.py

@@ -32,6 +32,7 @@ def __init__(
         temperature: float = 0.8,
         max_tokens: int = 4000,
         system_prompt: str = "You are a helpful assistant.",
+        base_url: str = "http://34.204.8.31:30001/v1/chat/completions",
         *args,
         **kwargs,
     ):
@@ -40,6 +41,7 @@ def __init__(
         self.temperature = temperature
         self.max_tokens = max_tokens
         self.system_prompt = system_prompt
+        self.base_url = base_url
 
     def run(self, task: str, *args, **kwargs) -> str:
         """
@@ -52,7 +54,6 @@ def run(self, task: str, *args, **kwargs) -> str:
             str: The generated response from the Llama3 model.
 
         """
-        url = "http://34.204.8.31:30001/v1/chat/completions"
 
         payload = json.dumps(
             {
@@ -70,7 +71,7 @@ def run(self, task: str, *args, **kwargs) -> str:
         headers = {"Content-Type": "application/json"}
 
         response = requests.request(
-            "POST", url, headers=headers, data=payload
+            "POST", self.base_url, headers=headers, data=payload
         )
 
         response_json = response.json()

swarms/schemas/plan.py

@@ -1,6 +1,6 @@
 from typing import List
 from pydantic import BaseModel
-from swarms.structs.step import Step
+from swarms.schemas.step import Step
 
 
 class Plan(BaseModel):

swarms/structs/__init__.py

@@ -8,7 +8,6 @@
 from swarms.structs.base_structure import BaseStructure
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.base_workflow import BaseWorkflow
-from swarms.structs.block_wrapper import block
 from swarms.structs.concurrent_workflow import ConcurrentWorkflow
 from swarms.structs.conversation import Conversation
 from swarms.structs.groupchat import GroupChat, GroupChatManager
@@ -21,19 +20,18 @@
 )
 from swarms.structs.message import Message
 from swarms.structs.message_pool import MessagePool
-from swarms.structs.model_parallizer import ModelParallelizer
 from swarms.structs.multi_agent_collab import MultiAgentCollaboration
 from swarms.structs.multi_process_workflow import (
     MultiProcessWorkflow,
 )
 from swarms.structs.multi_threaded_workflow import (
     MultiThreadedWorkflow,
 )
-from swarms.structs.plan import Plan
+from swarms.schemas.plan import Plan
 from swarms.structs.rearrange import AgentRearrange, rearrange
 from swarms.structs.recursive_workflow import RecursiveWorkflow
 from swarms.structs.round_robin import RoundRobinSwarm
-from swarms.structs.schemas import (
+from swarms.schemas.schemas import (
     Artifact,
     ArtifactUpload,
     StepInput,
@@ -43,7 +41,7 @@
     TaskRequestBody,
 )
 from swarms.structs.sequential_workflow import SequentialWorkflow
-from swarms.structs.step import Step
+from swarms.schemas.step import Step
 from swarms.structs.swarm_net import SwarmNetwork
 from swarms.structs.swarming_architectures import (
     broadcast,
@@ -96,7 +94,6 @@
     "BaseStructure",
     "BaseSwarm",
     "BaseWorkflow",
-    "block",
     "ConcurrentWorkflow",
     "Conversation",
     "GroupChat",
@@ -106,7 +103,6 @@
     "most_frequent",
     "parse_code_completion",
     "Message",
-    "ModelParallelizer",
     "MultiAgentCollaboration",
     "MultiProcessWorkflow",
     "MultiThreadedWorkflow",

swarms/structs/agent.py

@@ -8,6 +8,7 @@
 import time
 import uuid
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
 import yaml
 from loguru import logger
 from pydantic import BaseModel

swarms/structs/message.py

@@ -1,7 +1,6 @@
-import datetime
 from typing import Dict, Optional
-
-from pydantic import BaseModel
+from datetime import datetime
+from pydantic import BaseModel, Field
 
 
 class Message(BaseModel):
@@ -18,7 +17,7 @@ class Message(BaseModel):
     print(mes)
     """
 
-    timestamp: datetime = datetime.now()
+    timestamp: datetime = Field(default_factory=datetime.now)
     sender: str
     content: str
     metadata: Optional[Dict[str, str]] = {}