Faster install

docs/corporate/monthly_formula.py

@@ -0,0 +1,66 @@
+def calculate_monthly_charge(
+    development_time_hours: float,
+    hourly_rate: float,
+    amortization_months: int,
+    api_calls_per_month: int,
+    cost_per_api_call: float,
+    monthly_maintenance: float,
+    additional_monthly_costs: float,
+    profit_margin_percentage: float,
+) -> float:
+    """
+    Calculate the monthly charge for a service based on various cost factors.
+
+    Parameters:
+    - development_time_hours (float): The total number of hours spent on development and setup.
+    - hourly_rate (float): The rate per hour for development and setup.
+    - amortization_months (int): The number of months over which to amortize the development and setup costs.
+    - api_calls_per_month (int): The number of API calls made per month.
+    - cost_per_api_call (float): The cost per API call.
+    - monthly_maintenance (float): The monthly maintenance cost.
+    - additional_monthly_costs (float): Any additional monthly costs.
+    - profit_margin_percentage (float): The desired profit margin as a percentage.
+
+    Returns:
+    - monthly_charge (float): The calculated monthly charge for the service.
+    """
+
+    # Calculate Development and Setup Costs (amortized monthly)
+    development_and_setup_costs_monthly = (
+        development_time_hours * hourly_rate
+    ) / amortization_months
+
+    # Calculate Operational Costs per Month
+    operational_costs_monthly = (
+        (api_calls_per_month * cost_per_api_call)
+        + monthly_maintenance
+        + additional_monthly_costs
+    )
+
+    # Calculate Total Monthly Costs
+    total_monthly_costs = (
+        development_and_setup_costs_monthly
+        + operational_costs_monthly
+    )
+
+    # Calculate Pricing with Profit Margin
+    monthly_charge = total_monthly_costs * (
+        1 + profit_margin_percentage / 100
+    )
+
+    return monthly_charge
+
+
+# Example usage:
+monthly_charge = calculate_monthly_charge(
+    development_time_hours=100,
+    hourly_rate=500,
+    amortization_months=12,
+    api_calls_per_month=500000,
+    cost_per_api_call=0.002,
+    monthly_maintenance=1000,
+    additional_monthly_costs=300,
+    profit_margin_percentage=10000,
+)
+
+print(f"Monthly Charge: ${monthly_charge:.2f}")

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "4.6.0"
+version = "4.6.1"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -26,49 +26,28 @@ classifiers = [
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 torch = ">=2.1.1,<3.0"
-transformers = "4.39.0"
+transformers = ">= 4.39.0, <5.0.0"
 asyncio = ">=3.4.3,<4.0"
 einops = "0.7.0"
-google-generativeai = "0.3.1"
-langchain = "0.1.13"
 langchain-core = "0.1.33"
 langchain-community = "0.0.29"
 langchain-experimental = "0.0.55"
-faiss-cpu = "1.7.4"
 backoff = "2.2.1"
-datasets = "*"
-optimum = "1.15.0"
-supervision = "0.19.0"
-opencv-python = "4.9.0.80"
-diffusers = "*"
-anthropic = "0.21.3"
 toml = "*"
 pypdf = "4.1.0"
-accelerate = "*"
-sentencepiece = "0.1.98"
 httpx = "0.24.1"
-tiktoken = "0.5.2"
 ratelimit = "2.2.1"
 loguru = "0.7.2"
-huggingface-hub = "*"
 pydantic = "2.6.4"
 tenacity = "8.2.3"
 Pillow = "10.2.0"
-chromadb = "0.4.24"
-termcolor = "2.2.0"
-torchvision = "0.16.1"
 rich = "13.5.2"
-bitsandbytes = "*"
-sentence-transformers = "*"
-peft = "*"
 psutil = "*"
-timm = "*"
 sentry-sdk = "*"
 
 [tool.poetry.dev-dependencies]
 black = "23.3.0"
 
-
 [tool.poetry.group.lint.dependencies]
 ruff = ">=0.0.249,<0.3.5"
 types-toml = "^0.10.8.1"

requirements.txt

@@ -1,40 +1,22 @@
-torch==2.1.1
-transformers
-pandas
-langchain==0.1.13
+mkdocs
+mkdocs-material
+mkdocs-glightbox
+torch>=2.1.1,<3.0
+transformers==4.39.0
+asyncio>=3.4.3,<4.0
+einops==0.7.0
 langchain-core==0.1.33
 langchain-community==0.0.29
-langsmith==0.1.17
-langchain-openai==0.0.5
-httpx==0.24.1
-Pillow==9.4.0
-datasets==2.14.5
-pydantic==2.6.4
-huggingface-hub
-requests_mock
-pypdf==4.0.1
-accelerate==0.22.0
-loguru==0.7.2
-optimum
-diffusers
-toml
-tiktoken==0.5.2
-colored
-addict
+langchain-experimental==0.0.55
 backoff==2.2.1
+toml
+pypdf==4.1.0
+httpx==0.24.1
 ratelimit==2.2.1
+loguru==0.7.2
+pydantic==2.6.4
+tenacity==8.2.3
+Pillow==10.2.0
 termcolor==2.2.0
-opencv-python==4.9.0.80
-timm
-torchvision==0.16.1
-rich==13.5.2
-mkdocs
-mkdocs-material
-anthropic==0.2.5
-mkdocs-glightbox
-pre-commit==3.6.2
 psutil
-black
-tenacity
-supervision
 sentry-sdk

swarms/__init__.py

@@ -17,6 +17,6 @@
 from swarms.prompts import *  # noqa: E402, F403
 from swarms.structs import *  # noqa: E402, F403
 from swarms.telemetry import *  # noqa: E402, F403
-from swarms.tokenizers import *  # noqa: E402, F403
 from swarms.tools import *  # noqa: E402, F403
 from swarms.utils import *  # noqa: E402, F403
+from swarms.schedulers import *  # noqa: E402, F403

swarms/agents/omni_modal_agent.py

@@ -9,8 +9,9 @@
     load_chat_planner,
 )
 from transformers import load_tool
-from swarms.utils.loguru_logger import logger
+
 from swarms.structs.agent import Agent
+from swarms.utils.loguru_logger import logger
 
 
 class OmniModalAgent(Agent):

swarms/agents/worker_agent.py

@@ -1,17 +1,13 @@
-import os
 from typing import List
 
-import faiss
-from langchain.docstore import InMemoryDocstore
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
 from langchain_experimental.autonomous_agents import AutoGPT
 
+from swarms.structs.agent import Agent
 from swarms.tools.tool import BaseTool
 from swarms.utils.decorators import error_decorator, timing_decorator
 
 
-class Worker:
+class Worker(Agent):
     """
     The Worker class represents an autonomous agent that can perform tassks through
     function calls or by running a chat.
@@ -53,6 +49,7 @@ def __init__(
         embedding_size: int = 1536,
         search_kwargs: dict = {"k": 8},
         verbose: bool = False,
+        memory: callable = None,
         *args,
         **kwargs,
     ):
@@ -67,6 +64,7 @@ def __init__(
         self.embedding_size = embedding_size
         self.search_kwargs = search_kwargs
         self.verbose = verbose
+        self.memory = memory
 
         self.setup_tools(external_tools)
         self.setup_memory()
@@ -121,22 +119,8 @@ def setup_memory(self):
         """
         Set up memory for the worker.
         """
-        openai_api_key = (
-            os.getenv("OPENAI_API_KEY") or self.openai_api_key
-        )
         try:
-            embeddings_model = OpenAIEmbeddings(
-                openai_api_key=openai_api_key
-            )
-            embedding_size = self.embedding_size
-            index = faiss.IndexFlatL2(embedding_size)
-
-            self.vectorstore = FAISS(
-                embeddings_model.embed_query,
-                index,
-                InMemoryDocstore({}),
-                {},
-            )
+            self.vectorstore = self.memory
 
         except Exception as error:
             raise RuntimeError(