dockerfile running

Dockerfile

@@ -0,0 +1,31 @@
+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set environment variables to make Python output unbuffered and disable the PIP cache
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+ENV PIP_NO_CACHE_DIR off
+ENV PIP_DISABLE_PIP_VERSION_CHECK on
+ENV PIP_DEFAULT_TIMEOUT 100
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install Poetry
+RUN pip install poetry
+
+# Disable virtualenv creation by poetry and install dependencies
+RUN poetry config virtualenvs.create false
+RUN poetry install --no-interaction --no-ansi
+
+# Install the 'swarms' package if it's not included in the poetry.lock
+RUN pip install swarms
+
+# Assuming tests require pytest to run
+RUN pip install pytest
+
+# Run pytest on all tests in the tests directory
+CMD find ./tests -name '*.py' -exec pytest {} +

demos/ui_software_demo.py

@@ -2,4 +2,4 @@
 Autonomous swarm that optimizes UI autonomously
 
 GPT4Vision ->> GPT4 ->> UI
-"""
+"""

swarms/__init__.py

@@ -9,6 +9,6 @@
 from swarms.agents import *
 from swarms.swarms import *
 from swarms.structs import *
-from swarms.models import *  
+from swarms.models import *
 from swarms.chunkers import *
 from swarms.workers import *

swarms/agents/__init__.py

@@ -8,6 +8,7 @@
 
 # from swarms.agents.idea_to_image_agent import Idea2Image
 from swarms.agents.simple_agent import SimpleAgent
+
 """Agent Infrastructure, models, memory, utils, tools"""
 
 __all__ = [

swarms/agents/agent.py

@@ -8,7 +8,8 @@
 from langchain.chat_models.base import BaseChatModel
 from langchain.memory import ChatMessageHistory
 from langchain.prompts.chat import (
-    BaseChatPromptTemplate,)
+    BaseChatPromptTemplate,
+)
 from langchain.schema import (
     BaseChatMessageHistory,
     Document,
@@ -70,12 +71,14 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):  # type: ignore[misc]
     send_token_limit: int = 4196
 
     def construct_full_prompt(self, goals: List[str]) -> str:
-        prompt_start = ("Your decisions must always be made independently "
-                        "without seeking user assistance.\n"
-                        "Play to your strengths as an LLM and pursue simple "
-                        "strategies with no legal complications.\n"
-                        "If you have completed all your tasks, make sure to "
-                        'use the "finish" command.')
+        prompt_start = (
+            "Your decisions must always be made independently "
+            "without seeking user assistance.\n"
+            "Play to your strengths as an LLM and pursue simple "
+            "strategies with no legal complications.\n"
+            "If you have completed all your tasks, make sure to "
+            'use the "finish" command.'
+        )
         # Construct full prompt
         full_prompt = (
             f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
@@ -87,23 +90,25 @@ def construct_full_prompt(self, goals: List[str]) -> str:
         return full_prompt
 
     def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
-        base_prompt = SystemMessage(
-            content=self.construct_full_prompt(kwargs["goals"]))
+        base_prompt = SystemMessage(content=self.construct_full_prompt(kwargs["goals"]))
         time_prompt = SystemMessage(
-            content=f"The current time and date is {time.strftime('%c')}")
-        used_tokens = self.token_counter(
-            base_prompt.content) + self.token_counter(time_prompt.content)
+            content=f"The current time and date is {time.strftime('%c')}"
+        )
+        used_tokens = self.token_counter(base_prompt.content) + self.token_counter(
+            time_prompt.content
+        )
         memory: VectorStoreRetriever = kwargs["memory"]
         previous_messages = kwargs["messages"]
-        relevant_docs = memory.get_relevant_documents(
-            str(previous_messages[-10:]))
+        relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:]))
         relevant_memory = [d.page_content for d in relevant_docs]
         relevant_memory_tokens = sum(
-            [self.token_counter(doc) for doc in relevant_memory])
+            [self.token_counter(doc) for doc in relevant_memory]
+        )
         while used_tokens + relevant_memory_tokens > 2500:
             relevant_memory = relevant_memory[:-1]
             relevant_memory_tokens = sum(
-                [self.token_counter(doc) for doc in relevant_memory])
+                [self.token_counter(doc) for doc in relevant_memory]
+            )
         content_format = (
             f"This reminds you of these events from your past:\n{relevant_memory}\n\n"
         )
@@ -141,23 +146,13 @@ def __init__(self) -> None:
         self.performance_evaluation: List[str] = []
         self.response_format = {
             "thoughts": {
-                "text":
-                    "thought",
-                "reasoning":
-                    "reasoning",
-                "plan":
-                    "- short bulleted\n- list that conveys\n- long-term plan",
-                "criticism":
-                    "constructive self-criticism",
-                "speak":
-                    "thoughts summary to say to user",
-            },
-            "command": {
-                "name": "command name",
-                "args": {
-                    "arg name": "value"
-                }
+                "text": "thought",
+                "reasoning": "reasoning",
+                "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+                "criticism": "constructive self-criticism",
+                "speak": "thoughts summary to say to user",
             },
+            "command": {"name": "command name", "args": {"arg name": "value"}},
         }
 
     def add_constraint(self, constraint: str) -> None:
@@ -195,9 +190,7 @@ def add_performance_evaluation(self, evaluation: str) -> None:
         """
         self.performance_evaluation.append(evaluation)
 
-    def _generate_numbered_list(self,
-                                items: list,
-                                item_type: str = "list") -> str:
+    def _generate_numbered_list(self, items: list, item_type: str = "list") -> str:
         """
         Generate a numbered list from given items based on the item_type.
 
@@ -215,11 +208,16 @@ def _generate_numbered_list(self,
                 for i, item in enumerate(items)
             ]
             finish_description = (
-                "use this to signal that you have finished all your objectives")
-            finish_args = ('"response": "final response to let '
-                           'people know you have finished your objectives"')
-            finish_string = (f"{len(items) + 1}. {FINISH_NAME}: "
-                             f"{finish_description}, args: {finish_args}")
+                "use this to signal that you have finished all your objectives"
+            )
+            finish_args = (
+                '"response": "final response to let '
+                'people know you have finished your objectives"'
+            )
+            finish_string = (
+                f"{len(items) + 1}. {FINISH_NAME}: "
+                f"{finish_description}, args: {finish_args}"
+            )
             return "\n".join(command_strings + [finish_string])
         else:
             return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
@@ -240,7 +238,8 @@ def generate_prompt_string(self) -> str:
             f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
             "You should only respond in JSON format as described below "
             f"\nResponse Format: \n{formatted_response_format} "
-            "\nEnsure the response can be parsed by Python json.loads")
+            "\nEnsure the response can be parsed by Python json.loads"
+        )
 
         return prompt_string
 
@@ -261,11 +260,13 @@ def get_prompt(tools: List[BaseTool]) -> str:
     prompt_generator.add_constraint(
         "~16000 word limit for short term memory. "
         "Your short term memory is short, "
-        "so immediately save important information to files.")
+        "so immediately save important information to files."
+    )
     prompt_generator.add_constraint(
         "If you are unsure how you previously did something "
         "or want to recall past events, "
-        "thinking about similar events will help you remember.")
+        "thinking about similar events will help you remember."
+    )
     prompt_generator.add_constraint("No user assistance")
     prompt_generator.add_constraint(
         'Exclusively use the commands listed in double quotes e.g. "command name"'
@@ -277,23 +278,29 @@ def get_prompt(tools: List[BaseTool]) -> str:
 
     # Add resources to the PromptGenerator object
     prompt_generator.add_resource(
-        "Internet access for searches and information gathering.")
+        "Internet access for searches and information gathering."
+    )
     prompt_generator.add_resource("Long Term memory management.")
     prompt_generator.add_resource(
-        "GPT-3.5 powered Agents for delegation of simple tasks.")
+        "GPT-3.5 powered Agents for delegation of simple tasks."
+    )
     prompt_generator.add_resource("File output.")
 
     # Add performance evaluations to the PromptGenerator object
     prompt_generator.add_performance_evaluation(
         "Continuously review and analyze your actions "
-        "to ensure you are performing to the best of your abilities.")
+        "to ensure you are performing to the best of your abilities."
+    )
     prompt_generator.add_performance_evaluation(
-        "Constructively self-criticize your big-picture behavior constantly.")
+        "Constructively self-criticize your big-picture behavior constantly."
+    )
     prompt_generator.add_performance_evaluation(
-        "Reflect on past decisions and strategies to refine your approach.")
+        "Reflect on past decisions and strategies to refine your approach."
+    )
     prompt_generator.add_performance_evaluation(
         "Every command has a cost, so be smart and efficient. "
-        "Aim to complete tasks in the least number of steps.")
+        "Aim to complete tasks in the least number of steps."
+    )
 
     # Generate the prompt string
     prompt_string = prompt_generator.generate_prompt_string()
@@ -364,8 +371,10 @@ def from_llm_and_tools(
         )
 
     def run(self, goals: List[str]) -> str:
-        user_input = ("Determine which next command to use, "
-                      "and respond using the format specified above:")
+        user_input = (
+            "Determine which next command to use, "
+            "and respond using the format specified above:"
+        )
         # Interaction Loop
         loop_count = 0
         while True:
@@ -382,10 +391,8 @@ def run(self, goals: List[str]) -> str:
 
             # Print Assistant thoughts
             print(assistant_reply)
-            self.chat_history_memory.add_message(
-                HumanMessage(content=user_input))
-            self.chat_history_memory.add_message(
-                AIMessage(content=assistant_reply))
+            self.chat_history_memory.add_message(HumanMessage(content=user_input))
+            self.chat_history_memory.add_message(AIMessage(content=assistant_reply))
 
             # Get command name and arguments
             action = self.output_parser.parse(assistant_reply)
@@ -411,7 +418,8 @@ def run(self, goals: List[str]) -> str:
                 result = (
                     f"Unknown command '{action.name}'. "
                     "Please refer to the 'COMMANDS' list for available "
-                    "commands and only respond in the specified JSON format.")
+                    "commands and only respond in the specified JSON format."
+                )
 
             memory_to_add = f"Assistant Reply: {assistant_reply} \nResult: {result} "
             if self.feedback_tool is not None: