[FEAT][BlockList]

README.md

@@ -456,6 +456,115 @@ 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
+
+# Import the models, structs, and telemetry modules
+from swarms import (
+    Gemini,
+    GPT4VisionAPI,
+    Mixtral,
+    OpenAI,
+    ToolAgent,
+    BlocksList,
+)
+
+# 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")
+json_schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "number"},
+        "is_student": {"type": "boolean"},
+        "courses": {"type": "array", "items": {"type": "string"}},
+    },
+}
+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)
+```
+
+
 ## Real-World Deployment
 
 ### Multi-Agent Swarm for Logistics

block.py

@@ -0,0 +1,97 @@
+import os
+
+from dotenv import load_dotenv
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Import the models, structs, and telemetry modules
+from swarms import (
+    Gemini,
+    GPT4VisionAPI,
+    Mixtral,
+    OpenAI,
+    ToolAgent,
+    BlocksList,
+)
+
+# 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")
+json_schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "number"},
+        "is_student": {"type": "boolean"},
+        "courses": {"type": "array", "items": {"type": "string"}},
+    },
+}
+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)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "3.3.4"
+version = "3.4.1"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -41,7 +41,7 @@ datasets = "*"
 optimum = "1.15.0"
 diffusers = "*"
 PyPDF2 = "3.0.1"
-accelerate = "0.22.0"
+accelerate = "*"
 sentencepiece = "0.1.98"
 wget = "3.2"
 tensorflow = "2.14.0"
@@ -66,6 +66,7 @@ soundfile = "0.12.1"
 torchvision = "0.16.1"
 rich = "13.5.2"
 sqlalchemy = "*"
+bitsandbytes = "*"
 pgvector = "*"
 qdrant-client = "*"
 sentence-transformers = "*"

requirements.txt

@@ -17,6 +17,7 @@ openai==0.28.0
 attrs==22.2.0
 datasets==2.10.1
 pydantic==1.10.12
+bitsandbytes
 soundfile==0.12.1
 arize-phoenix
 weaviate-client==3.25.3

swarms/__init__.py

@@ -1,6 +1,6 @@
-from swarms.utils.disable_logging import disable_logging
+from swarms.telemetry.bootup import bootup  # noqa: E402, F403
 
-disable_logging()
+bootup()
 
 from swarms.agents import *  # noqa: E402, F403
 from swarms.structs import *  # noqa: E402, F403

swarms/models/huggingface.py

@@ -131,6 +131,7 @@ class HuggingfaceLLM(AbstractLLM):
         temperature: float = 0.7,
         top_k: int = 40,
         top_p: float = 0.8,
+        dtype = torch.bfloat16,
         *args,
         **kwargs,
     ):
@@ -146,7 +147,6 @@ class HuggingfaceLLM(AbstractLLM):
         self.verbose = verbose
         self.distributed = distributed
         self.decoding = decoding
-        self.model, self.tokenizer = None, None
         self.quantize = quantize
         self.quantization_config = quantization_config
         self.max_workers = max_workers
@@ -155,6 +155,7 @@ class HuggingfaceLLM(AbstractLLM):
         self.temperature = temperature
         self.top_k = top_k
         self.top_p = top_p
+        self.dtype = dtype
 
         if self.distributed:
             assert (
@@ -168,39 +169,26 @@ class HuggingfaceLLM(AbstractLLM):
                     "load_in_4bit": True,
                     "bnb_4bit_use_double_quant": True,
                     "bnb_4bit_quant_type": "nf4",
-                    "bnb_4bit_compute_dtype": torch.bfloat16,
+                    "bnb_4bit_compute_dtype": dtype,
                 }
             bnb_config = BitsAndBytesConfig(**quantization_config)
 
-        try:
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.model_id
-            )
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_id
+        ).to(self.device)
 
-            if quantize:
-                self.model = AutoModelForCausalLM.from_pretrained(
-                    self.model_id,
-                    quantization_config=bnb_config,
-                    *args,
-                    **kwargs,
-                ).to(self.device)
-            else:
-                self.model = AutoModelForCausalLM.from_pretrained(
-                    self.model_id, *args, **kwargs
-                ).to(self.device)
+        if quantize:
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_id,
+                quantization_config=bnb_config,
+                *args,
+                **kwargs,
+            ).to(self.device)
+        else:
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_id, *args, **kwargs
+            ).to(self.device)
 
-        except Exception as e:
-            # self.logger.error(f"Failed to load the model or the tokenizer: {e}")
-            # raise
-            print(
-                colored(
-                    (
-                        "Failed to load the model and or the"
-                        f" tokenizer: {e}"
-                    ),
-                    "red",
-                )
-            )
 
     def print_error(self, error: str):
         """Print error"""
@@ -276,8 +264,7 @@ class HuggingfaceLLM(AbstractLLM):
                         *args,
                         **kwargs,
                     )
-
-            del inputs
+                    
             return self.tokenizer.decode(
                 outputs[0], skip_special_tokens=True
             )

swarms/structs/block_wrapper.py

@@ -1,5 +1,6 @@
-from typing import Callable, Any
 import logging
+from functools import wraps
+from typing import Any, Callable
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -20,6 +21,7 @@ def block(
         Callable[..., Any]: The transformed function.
     """
 
+    @wraps(function)
     def wrapper(*args, **kwargs):
         # Here you can add code to execute the function on various hardwares
         # For now, we'll just call the function normally

swarms/structs/blockslist.py

@@ -36,6 +36,15 @@ class BlocksList(BaseStructure):
         get_by_parent_name(parent_name: str): Get blocks by parent name.
         get_by_parent_type(parent_type: str): Get blocks by parent type.
         get_by_parent_description(parent_description: str): Get blocks by parent description.
+
+
+    Examples:
+    >>> from swarms.structs.block import Block
+    >>> from swarms.structs.blockslist import BlocksList
+    >>> block = Block("block", "A block")
+    >>> blockslist = BlocksList("blockslist", "A list of blocks", [block])
+    >>> blockslist
+
     """
 
     def __init__(
@@ -47,8 +56,10 @@ class BlocksList(BaseStructure):
         **kwargs,
     ):
         super().__init__(name=name, description=description, **kwargs)
-        self.parent = parent
+        self.name = name
+        self.description = description
         self.blocks = blocks
+        self.parent = parent
 
     def add(self, block: Any):
         self.blocks.append(block)
@@ -64,6 +75,26 @@ class BlocksList(BaseStructure):
 
     def get_all(self):
         return self.blocks
+    
+    def run_block(self, block: Any, task: str,  *args, **kwargs):
+        """Run the block for the specified task.
+
+        Args:
+            task (str): The task to be performed by the block.
+            *args: Variable length argument list.
+            **kwargs: Arbitrary keyword arguments.
+
+        Returns:
+            The output of the block.
+
+        Raises:
+            Exception: If an error occurs during the execution of the block.
+        """
+        try:
+            return block.run(task, *args, **kwargs)
+        except Exception as error:
+            print(f"[Error] [Block] {error}")
+            raise error
 
     def get_by_name(self, name: str):
         return [block for block in self.blocks if block.name == name]

swarms/structs/model_parallizer.py

@@ -41,14 +41,12 @@ class ModelParallelizer:
     def __init__(
         self,
         llms: List[Callable] = None,
-        load_balancing: bool = False,
         retry_attempts: int = 3,
         iters: int = None,
         *args,
         **kwargs,
     ):
         self.llms = llms
-        self.load_balancing = load_balancing
         self.retry_attempts = retry_attempts
         self.iters = iters
         self.last_responses = None
@@ -151,16 +149,6 @@ class ModelParallelizer:
             )
         )
 
-    def enable_load_balancing(self):
-        """Enable load balancing among LLMs."""
-        self.load_balancing = True
-        logger.info("Load balancing enabled.")
-
-    def disable_load_balancing(self):
-        """Disable load balancing."""
-        self.load_balancing = False
-        logger.info("Load balancing disabled.")
-
     async def arun(self, task: str):
         """Asynchronous run the task string"""
         loop = asyncio.get_event_loop()

swarms/telemetry/auto_upgrade_swarms.py

@@ -6,6 +6,10 @@ def auto_update():
     """auto update swarms"""
     try:
         if check_for_update():
+            print(
+                "There is a new version of swarms available!"
+                " Downloading..."
+            )
             subprocess.run(["pip", "install", "--upgrade", "swarms"])
     except Exception as e:
         print(e)

swarms/telemetry/bootup.py

@@ -0,0 +1,8 @@
+from swarms.utils.disable_logging import disable_logging
+from swarms.telemetry.auto_upgrade_swarms import auto_update
+
+
+def bootup():
+    """Bootup swarms"""
+    disable_logging()
+    auto_update()

swarms/telemetry/check_update.py

@@ -40,7 +40,3 @@ def check_for_update():
     return version.parse(latest_version) > version.parse(
         current_version
     )
-
-
-# out = check_for_update()
-# print(out)