flow example, save and load state

flow.py

@@ -23,7 +23,12 @@ flow = Flow(
     # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
 )
 
-
+# out = flow.load_state("flow_state.json")
+# temp = flow.dynamic_temperature()
+# filter = flow.add_response_filter("Trump")
 out = flow.run("Generate a 10,000 word blog on health and wellness.")
-
+# out = flow.validate_response(out)
+# out = flow.analyze_feedback(out)
+# out = flow.print_history_and_memory()
+# out = flow.save_state("flow_state.json")
 print(out)

flow_state.json

@@ -0,0 +1,14 @@
+{
+    "memory": [
+        [
+            "Human: Generate a 10,000 word blog on health and wellness."
+        ]
+    ],
+    "llm_params": {},
+    "loop_interval": 1,
+    "retry_attempts": 3,
+    "retry_interval": 1,
+    "interactive": false,
+    "dashboard": true,
+    "dynamic_temperature": false
+}

swarms/agents/__init__.py

@@ -1,6 +1,7 @@
 from swarms.agents.omni_modal_agent import OmniModalAgent
 from swarms.agents.hf_agents import HFAgent
 from swarms.agents.message import Message
+
 # from swarms.agents.stream_response import stream
 from swarms.agents.base import AbstractAgent
 from swarms.agents.registry import Registry

swarms/models/distilled_whisperx.py

@@ -1,3 +1,3 @@
 """
 
-"""
+"""

swarms/models/huggingface.py

@@ -294,7 +294,7 @@ class HuggingfaceLLM:
         )
 
         print(dashboard)
-    
+
     def set_device(self, device):
         """
         Changes the device used for inference.

swarms/structs/flow.py

@@ -2,18 +2,16 @@
 TODO:
 - Add tools
 - Add open interpreter style conversation
-- Add configurable save and restore so the user can restore from previus flows
 - Add memory vector database retrieval
 """
 
 import json
 import logging
 import time
-from typing import Any, Callable, Dict, List, Optional, Tuple, Generator
+from typing import Any, Callable, Dict, List, Optional, Tuple
 from termcolor import colored
 import inspect
 import random
-# from swarms.tools.tool import BaseTool
 
 
 # Constants
@@ -36,7 +34,6 @@ When you have finished the task, and you feel as if you are done: output a speci
 This will enable you to leave the flow loop.
 """
 
-
 # Custome stopping condition
 def stop_when_repeats(response: str) -> bool:
     # Stop if the word stop appears in the response
@@ -209,7 +206,7 @@ class Flow:
 
         print(dashboard)
 
-    def run(self, task: str, **kwargs):
+    def run(self, task: str, save: bool = True, **kwargs):
         """
         Run the autonomous agent loop
 
@@ -223,7 +220,16 @@ class Flow:
         4. If stopping condition is not met, generate a response
         5. Repeat until stopping condition is met or max_loops is reached
 
+        Example:
+        >>> out = flow.run("Generate a 10,000 word blog on health and wellness.")
+
         """
+        # Start with a new history or continue from the last saved state
+        if not self.memory or not self.memory[-1]:
+            history = [f"Human: {task}"]
+        else:
+            history = self.memory[-1]
+
         response = task
         history = [f"Human: {task}"]
 
@@ -231,9 +237,12 @@ class Flow:
         if self.dashboard:
             self.print_dashboard(task)
 
-        for i in range(self.max_loops):
+        # Start or continue the loop process
+        for i in range(len(history), self.max_loops):
             print(colored(f"\nLoop {i+1} of {self.max_loops}", "blue"))
             print("\n")
+            response = history[-1].split(": ", 1)[-1]  # Get the last response
+
             if self._check_stopping_condition(response) or parse_done_token(response):
                 break
 
@@ -245,15 +254,8 @@ class Flow:
             while attempt < self.retry_attempts:
                 try:
                     response = self.llm(
-                        f"""
-                    SYSTEM_PROMPT: 
-                    {FLOW_SYSTEM_PROMPT}
-
-
-                    History: {response}
-                    
-                    """,
-                        **kwargs,
+                        self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response)
+                        ** kwargs,
                     )
                     # print(f"Next query: {response}")
                     # break
@@ -274,6 +276,10 @@ class Flow:
             history.append(response)
             time.sleep(self.loop_interval)
         self.memory.append(history)
+
+        if save:
+            self.save("flow_history.json")
+
         return response  # , history
 
     def _run(self, **kwargs: Any) -> str:
@@ -283,32 +289,31 @@ class Flow:
         logging.info(f"Message history: {history}")
         return response
 
-    def bulk_run(self, inputs: List[Dict[str, Any]]) -> List[str]:
-        """Generate responses for multiple input sets."""
-        return [self.run(**input_data) for input_data in inputs]
-
-    def run_dynamically(self, task: str, max_loops: Optional[int] = None):
+    def agent_history_prompt(
+        self,
+        system_prompt: str = FLOW_SYSTEM_PROMPT,
+        history=None,
+    ):
         """
-        Run the autonomous agent loop dynamically based on the <DONE>
-
-        # Usage Example
+        Generate the agent history prompt
 
-        # Initialize the Flow
-        flow = Flow(llm=lambda x: x, max_loops=5)
-
-        # Run dynamically based on <DONE> token and optional max loops
-        response = flow.run_dynamically("Generate a report <DONE>", max_loops=3)
-        print(response)
+        Args:
+            system_prompt (str): The system prompt
+            history (List[str]): The history of the conversation
 
-        response = flow.run_dynamically("Generate a report <DONE>")
-        print(response)
+        Returns:
+            str: The agent history prompt
+        """
+        agent_history_prompt = f"""
+            SYSTEM_PROMPT: {system_prompt}
 
+            History: {history}
         """
-        if "<DONE>" in task:
-            self.stopping_condition = parse_done_token
-        self.max_loops = max_loops or float("inf")
-        response = self.run(task)
-        return response
+        return agent_history_prompt
+
+    def bulk_run(self, inputs: List[Dict[str, Any]]) -> List[str]:
+        """Generate responses for multiple input sets."""
+        return [self.run(**input_data) for input_data in inputs]
 
     @staticmethod
     def from_llm_and_template(llm: Any, template: str) -> "Flow":
@@ -339,6 +344,60 @@ class Flow:
             return False
         return True
 
+    def print_history_and_memory(self):
+        """
+        Prints the entire history and memory of the flow.
+        Each message is colored and formatted for better readability.
+        """
+        print(colored("Flow History and Memory", "cyan", attrs=["bold"]))
+        print(colored("========================", "cyan", attrs=["bold"]))
+        for loop_index, history in enumerate(self.memory, start=1):
+            print(colored(f"\nLoop {loop_index}:", "yellow", attrs=["bold"]))
+            for message in history:
+                speaker, _, message_text = message.partition(": ")
+                if "Human" in speaker:
+                    print(colored(f"{speaker}:", "green") + f" {message_text}")
+                else:
+                    print(colored(f"{speaker}:", "blue") + f" {message_text}")
+            print(colored("------------------------", "cyan"))
+        print(colored("End of Flow History", "cyan", attrs=["bold"]))
+
+    def step(self, task: str, **kwargs):
+        """
+
+        Executes a single step in the flow interaction, generating a response
+        from the language model based on the given input text.
+
+        Args:
+            input_text (str): The input text to prompt the language model with.
+
+        Returns:
+            str: The language model's generated response.
+
+        Raises:
+            Exception: If an error occurs during response generation.
+
+        """
+        try:
+            # Generate the response using lm
+            response = self.llm(task, **kwargs)
+
+            # Update the flow's history with the new interaction
+            if self.interactive:
+                self.memory.append(f"AI: {response}")
+                self.memory.append(f"Human: {task}")
+            else:
+                self.memory.append(f"AI: {response}")
+
+            return response
+        except Exception as error:
+            logging.error(f"Error generating response: {error}")
+            raise
+
+    def graceful_shutdown(self):
+        """Gracefully shutdown the system saving the state"""
+        return self.save_state("flow_state.json")
+
     def run_with_timeout(self, task: str, timeout: int = 60) -> str:
         """Run the loop but stop if it takes longer than the timeout"""
         start_time = time.time()
@@ -455,23 +514,97 @@ class Flow:
         print()
         return response
 
-    def streamed_token_generation(self, prompt: str) -> Generator[str, None, None]:
+    def get_llm_params(self):
+        """
+        Extracts and returns the parameters of the llm object for serialization.
+        It assumes that the llm object has an __init__ method with parameters that can be used to recreate it.
         """
-        Generate tokens in real-time for a given prompt.
+        if not hasattr(self.llm, "__init__"):
+            return None
 
-        This method simulates the real-time generation of each token.
-        For simplicity, we treat each character of the input as a token
-        and yield them with a slight delay. In a real-world scenario,
-        this would involve using the LLM's internal methods to generate
-        the response token by token.
+        init_signature = inspect.signature(self.llm.__init__)
+        params = init_signature.parameters
+        llm_params = {}
+
+        for name, param in params.items():
+            if name == "self":
+                continue
+            if hasattr(self.llm, name):
+                value = getattr(self.llm, name)
+                if isinstance(
+                    value, (str, int, float, bool, list, dict, tuple, type(None))
+                ):
+                    llm_params[name] = value
+                else:
+                    llm_params[name] = str(
+                        value
+                    )  # For non-serializable objects, save their string representation.
+
+        return llm_params
+
+    def save_state(self, file_path: str) -> None:
+        """
+        Saves the current state of the flow to a JSON file, including the llm parameters.
 
         Args:
-            prompt (str): The input prompt for which the tokens should be generated.
+            file_path (str): The path to the JSON file where the state will be saved.
 
-        Yields:
-            str: The next token (character) from the generated response.
+        Example:
+        >>> flow.save_state('saved_flow.json')
         """
-        tokens = list(prompt)
-        for token in tokens:
-            time.sleep(0.1)
-            yield token
+        state = {
+            "memory": self.memory,
+            # "llm_params": self.get_llm_params(),
+            "loop_interval": self.loop_interval,
+            "retry_attempts": self.retry_attempts,
+            "retry_interval": self.retry_interval,
+            "interactive": self.interactive,
+            "dashboard": self.dashboard,
+            "dynamic_temperature": self.dynamic_temperature,
+        }
+
+        with open(file_path, "w") as f:
+            json.dump(state, f, indent=4)
+
+        saved = colored("Saved flow state to", "green")
+        print(f"{saved} {file_path}")
+
+    def load_state(self, file_path: str):
+        """
+        Loads the state of the flow from a json file and restores the configuration and memory.
+
+
+        Example:
+        >>> flow = Flow(llm=llm_instance, max_loops=5)
+        >>> flow.load_state('saved_flow.json')
+        >>> flow.run("Continue with the task")
+
+        """
+        with open(file_path, "r") as f:
+            state = json.load(f)
+
+        # Assuming 'llm_class' is a class reference to the language
+        # llm_params = state.get("llm_params", {})
+        # self.llm = self.llm(**llm_params)
+
+        # Restore other saved attributes
+        self.memory = state.get("memory", [])
+        self.max_loops = state.get("max_loops", 5)
+        self.loop_interval = state.get("loop_interval", 1)
+        self.retry_attempts = state.get("retry_attempts", 3)
+        self.retry_interval = state.get("retry_interval", 1)
+        self.interactive = state.get("interactive", False)
+
+        print(f"Flow state loaded from {file_path}")
+
+    def retry_on_failure(self, function, retries: int = 3, retry_delay: int = 1):
+        """Retry wrapper for LLM calls."""
+        attempt = 0
+        while attempt < retries:
+            try:
+                return function()
+            except Exception as error:
+                logging.error(f"Error generating response: {error}")
+                attempt += 1
+                time.sleep(retry_delay)
+        raise Exception("All retry attempts failed")

swarms/structs/sequential_workflow.py

@@ -18,3 +18,82 @@ workflow.add("Create a report on these metrics", mistral)
 workflow.run()
 
 """
+from dataclasses import dataclass, field
+from typing import List, Any, Dict, Callable, Union
+from swarms.models import OpenAIChat
+from swarms.structs import Flow
+
+
+# Define a generic Task that can handle different types of callable objects
+@dataclass
+class Task:
+    description: str
+    model: Union[Callable, Flow]
+    args: List[Any] = field(default_factory=list)
+    kwargs: Dict[str, Any] = field(default_factory=dict)
+    result: Any = None
+
+    def execute(self):
+        if isinstance(self.model, Flow):
+            self.result = self.model.run(*self.args, **self.kwargs)
+        else:
+            self.result = self.model(*self.args, **self.kwargs)
+
+
+# SequentialWorkflow class definition using dataclasses
+@dataclass
+class SequentialWorkflow:
+    tasks: List[Task] = field(default_factory=list)
+    max_loops: int = 1
+
+    def add(
+        self, description: str, model: Union[Callable, Flow], *args, **kwargs
+    ) -> None:
+        self.tasks.append(
+            Task(description=description, model=model, args=list(args), kwargs=kwargs)
+        )
+
+    def run(self) -> None:
+        for _ in range(self.max_loops):
+            for task in self.tasks:
+                # Check if the current task can be executed
+                if task.result is None:
+                    task.execute()
+                    # Pass the result as an argument to the next task if it exists
+                    next_task_index = self.tasks.index(task) + 1
+                    if next_task_index < len(self.tasks):
+                        next_task = self.tasks[next_task_index]
+                        next_task.args.insert(0, task.result)
+
+
+# Example usage
+api_key = ""  # Your actual API key here
+
+# Initialize the language model
+llm = OpenAIChat(
+    openai_api_key=api_key,
+    temperature=0.5,
+    max_tokens=3000,
+)
+
+# Initialize the Flow with the language model
+flow1 = Flow(llm=llm, max_loops=5, dashboard=True)
+
+# Create another Flow for a different task
+flow2 = Flow(llm=llm, max_loops=5, dashboard=True)
+
+# Create the workflow
+workflow = SequentialWorkflow(max_loops=1)
+
+# Add tasks to the workflow
+workflow.add("Generate a 10,000 word blog on health and wellness.", flow1)
+
+# Suppose the next task takes the output of the first task as input
+workflow.add("Summarize the generated blog", flow2)
+
+# Run the workflow
+workflow.run()
+
+# Output the results
+for task in workflow.tasks:
+    print(f"Task: {task.description}, Result: {task.result}")

tests/models/ada.py

@@ -3,12 +3,15 @@
 import pytest
 import openai
 from unittest.mock import patch
-from swarms.models.simple_ada import get_ada_embeddings  # Adjust this import path to your project structure
+from swarms.models.simple_ada import (
+    get_ada_embeddings,
+)  # Adjust this import path to your project structure
 from os import getenv
 from dotenv import load_dotenv
 
 load_dotenv()
 
+
 # Fixture for test texts
 @pytest.fixture
 def test_texts():
@@ -18,20 +21,24 @@ def test_texts():
         "A quick brown fox jumps over the lazy dog",
     ]
 
+
 # Basic Test
 def test_get_ada_embeddings_basic(test_texts):
-    with patch('openai.Embedding.create') as mock_create:
+    with patch("openai.Embedding.create") as mock_create:
         # Mocking the OpenAI API call
-        mock_create.return_value = {
-            "data": [
-                {"embedding": [0.1, 0.2, 0.3]}
-            ]
-        }
-        
+        mock_create.return_value = {"data": [{"embedding": [0.1, 0.2, 0.3]}]}
+
         for text in test_texts:
             embedding = get_ada_embeddings(text)
-            assert embedding == [0.1, 0.2, 0.3], "Embedding does not match expected output"
-            mock_create.assert_called_with(input=[text.replace("\n", " ")], model="text-embedding-ada-002")
+            assert embedding == [
+                0.1,
+                0.2,
+                0.3,
+            ], "Embedding does not match expected output"
+            mock_create.assert_called_with(
+                input=[text.replace("\n", " ")], model="text-embedding-ada-002"
+            )
+
 
 # Parameterized Test
 @pytest.mark.parametrize(
@@ -42,27 +49,28 @@ def test_get_ada_embeddings_basic(test_texts):
     ],
 )
 def test_get_ada_embeddings_models(text, model, expected_call_model):
-    with patch('openai.Embedding.create') as mock_create:
-        mock_create.return_value = {
-            "data": [
-                {"embedding": [0.1, 0.2, 0.3]}
-            ]
-        }
+    with patch("openai.Embedding.create") as mock_create:
+        mock_create.return_value = {"data": [{"embedding": [0.1, 0.2, 0.3]}]}
 
         _ = get_ada_embeddings(text, model=model)
         mock_create.assert_called_with(input=[text], model=expected_call_model)
 
+
 # Exception Test
 def test_get_ada_embeddings_exception():
-    with patch('openai.Embedding.create') as mock_create:
+    with patch("openai.Embedding.create") as mock_create:
         mock_create.side_effect = openai.error.OpenAIError("Test error")
         with pytest.raises(openai.error.OpenAIError):
             get_ada_embeddings("Some text")
 
+
 # Tests for environment variable loading
 def test_env_var_loading(monkeypatch):
     monkeypatch.setenv("OPENAI_API_KEY", "testkey123")
-    with patch('openai.Embedding.create'):
-        assert getenv("OPENAI_API_KEY") == "testkey123", "Environment variable for API key is not set correctly"
+    with patch("openai.Embedding.create"):
+        assert (
+            getenv("OPENAI_API_KEY") == "testkey123"
+        ), "Environment variable for API key is not set correctly"
+
 
 # ... more tests to cover other aspects such as different input types, large inputs, invalid inputs, etc.

tests/models/huggingface.py

@@ -70,11 +70,14 @@ def test_llm_memory_consumption(llm_instance):
 
 
 # Test different initialization parameters
-@pytest.mark.parametrize("model_id, max_length", [
-    ("gpt2-small", 100),
-    ("gpt2-medium", 200),
-    ("gpt2-large", None)  # None to check default behavior
-])
+@pytest.mark.parametrize(
+    "model_id, max_length",
+    [
+        ("gpt2-small", 100),
+        ("gpt2-medium", 200),
+        ("gpt2-large", None),  # None to check default behavior
+    ],
+)
 def test_llm_initialization_params(model_id, max_length):
     if max_length:
         instance = HuggingfaceLLM(model_id=model_id, max_length=max_length)
@@ -157,11 +160,14 @@ def test_llm_timeout_handling(mock_run, llm_instance):
 @patch("swarms.models.huggingface.HuggingfaceLLM.run")
 def test_llm_response_time(mock_run, llm_instance):
     import time
+
     mock_run.return_value = "mocked output"
     start_time = time.time()
     llm_instance.run("test task for response time")
     end_time = time.time()
-    assert end_time - start_time < 1  # Assuming the response should be faster than 1 second
+    assert (
+        end_time - start_time < 1
+    )  # Assuming the response should be faster than 1 second
 
 
 # Test the logging of a warning for long inputs
@@ -173,7 +179,9 @@ def test_llm_long_input_warning(mock_warning, llm_instance):
 
 
 # Test for run method behavior when model raises an exception
-@patch("swarms.models.huggingface.HuggingfaceLLM._model.generate", side_effect=RuntimeError)
+@patch(
+    "swarms.models.huggingface.HuggingfaceLLM._model.generate", side_effect=RuntimeError
+)
 def test_llm_run_model_exception(mock_generate, llm_instance):
     with pytest.raises(RuntimeError):
         llm_instance.run("test task when model fails")
@@ -219,6 +227,7 @@ def test_llm_multilingual_input(mock_run, llm_instance):
     result = llm_instance.run(multilingual_input)
     assert isinstance(result, str)  # Simple check to ensure output is string type
 
+
 # Test caching mechanism to prevent re-running the same inputs
 @patch("swarms.models.huggingface.HuggingfaceLLM.run")
 def test_llm_caching_mechanism(mock_run, llm_instance):