run_dynamically, undo_last, analyze_feedback, apply_reponse_filters, interactive_run, streamed_generation,

Former-commit-id: 200c580e37

playground/posmed/positive_med.py

@@ -1,7 +1,7 @@
 from swarms import OpenAIChat
 
 DRAFT_PROMPT = """# MISSION
-Write a 100% unique, creative and in human-like style article of a minimum of 2000 words using headings and sub-headings.
+Write a 100% unique, creative and in human-like style article of a minimum of 5,000 words using headings and sub-headings.
 Ensure your tone is Professional and casual while focusing on presenting information and analysis without excessive embellishment.
 
 The topic is: {{TOPIC}}

swarms/structs/flow.py

@@ -44,17 +44,60 @@ flow.save("path/flow.yaml")
 import json
 import logging
 import time
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple
 from termcolor import colored
 
-
 # Custome stopping condition
 def stop_when_repeats(response: str) -> bool:
     # Stop if the word stop appears in the response
     return "Stop" in response.lower()
 
+def parse_done_token(response: str) -> bool:
+    """Parse the response to see if the done token is present"""
+    return "<DONE>" in response
+
 
 class Flow:
+    """
+    Flow is a chain like structure from langchain that provides the autonomy to language models
+    to generate sequential responses.
+
+    Features:
+    * User defined queries
+    * Dynamic keep generating until <DONE> is outputted by the agent
+    * Interactive, AI generates, then user input
+    * Message history and performance history fed -> into context
+    * Ability to save and load flows
+    * Ability to provide feedback on responses
+    * Ability to provide a stopping condition
+    * Ability to provide a retry mechanism
+    * Ability to provide a loop interval
+    
+    Args:
+        llm (Any): The language model to use
+        max_loops (int): The maximum number of loops to run
+        stopping_condition (Optional[Callable[[str], bool]]): A stopping condition
+        loop_interval (int): The interval between loops
+        retry_attempts (int): The number of retry attempts
+        retry_interval (int): The interval between retry attempts
+        interactive (bool): Whether or not to run in interactive mode
+        **kwargs (Any): Any additional keyword arguments
+
+    Example:
+    >>> from swarms.models import OpenAIChat
+    >>> from swarms.structs import Flow
+    >>> llm = OpenAIChat(
+    ...     openai_api_key=api_key,
+    ...     temperature=0.5,
+    ... )
+    >>> flow = Flow(
+    ...     llm=llm, max_loops=5,
+    ...     #system_prompt=SYSTEM_PROMPT,
+    ...     #retry_interval=1,
+    ... )
+    >>> flow.run("Generate a 10,000 word blog")
+    >>> flow.save("path/flow.yaml")
+    """
     def __init__(
         self,
         llm: Any,
@@ -149,6 +192,33 @@ class Flow:
         """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
+    ):
+        """
+        Run the autonomous agent loop dynamically based on the <DONE>
+
+        # Usage Example
+
+        # 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)
+
+        response = flow.run_dynamically("Generate a report <DONE>")
+        print(response)
+
+        """
+        if "<DONE>" in task:
+            self.stopping_condition = parse_done_token
+        self.max_loops = max_loops or float('inf')
+        response = self.run(task)
+        return response
+
     @staticmethod
     def from_llm_and_template(llm: Any, template: str) -> "Flow":
         """Create FlowStream from LLM and a string template."""
@@ -170,3 +240,139 @@ class Flow:
         with open(file_path, 'r') as f:
             self.memory = json.load(f)
         print(f"Loaded flow history from {file_path}")
+    
+    def validate_response(self, response: str) -> bool:
+        """Validate the response based on certain criteria"""
+        if len(response) < 5:
+            print("Response is too short")
+            return False
+        return True
+
+    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()
+        response = self.run(task)
+        end_time = time.time()
+        if end_time - start_time > timeout:
+            print("Operaiton timed out")
+            return 'Timeout'
+        return response
+    
+    def backup_memory_to_s3(
+        self,
+        bucket_name: str,
+        object_name: str
+    ):
+        """Backup the memory to S3"""
+        import boto3
+        s3 = boto3.client('s3')
+        s3.put_object(Bucket=bucket_name, Key=object_name, Body=json.dumps(self.memory))
+        print(f"Backed up memory to S3: {bucket_name}/{object_name}")
+
+    def analyze_feedback(self):
+        """Analyze the feedback for issues"""
+        feedback_counts = {}
+        for feedback in self.feedback:
+            if feedback in feedback_counts:
+                feedback_counts[feedback] += 1
+            else:
+                feedback_counts[feedback] = 1
+        print(f"Feedback counts: {feedback_counts}")
+
+    def undo_last(self) -> Tuple[str, str]:
+        """
+        Response the last response and return the previous state
+
+        Example:
+        # Feature 2: Undo functionality
+        response = flow.run("Another task")
+        print(f"Response: {response}")
+        previous_state, message = flow.undo_last()
+        print(message)
+                
+        """
+        if len(self.memory) < 2:
+            return None, None
+
+        # Remove the last response
+        self.memory.pop()
+
+        # Get the previous state
+        previous_state = self.memory[-1][-1]
+        return previous_state, f"Restored to {previous_state}"
+    
+    # Response Filtering
+    def add_response_filter(self, filter_word: str) -> None:
+        """
+        Add a response filter to filter out certain words from the response
+
+        Example:
+        flow.add_response_filter("Trump")
+        flow.run("Generate a report on Trump")
+
+        
+        """
+        self.reponse_filters.append(filter_word)
+    
+    def apply_reponse_filters(self, response: str) -> str:
+        """
+        Apply the response filters to the response
+
+        
+        """
+        for word in self.response_filters:
+            response = response.replace(word, "[FILTERED]")
+        return response
+
+    def filtered_run(self, task: str) -> str:
+        """
+        # Feature 3: Response filtering
+        flow.add_response_filter("report")
+        response = flow.filtered_run("Generate a report on finance")
+        print(response)
+        """
+        raw_response = self.run(task)
+        return self.apply_response_filters(raw_response)
+    
+    def interactive_run(
+        self,
+        max_loops: int = 5
+    ) -> None:
+        """Interactive run mode"""
+        response = input("Start the cnversation")
+
+        for i in range(max_loops):
+            ai_response = self.streamed_generation(response)
+            print(f"AI: {ai_response}")
+
+            # Get user input
+            response = input("You: ")
+    
+    def streamed_generation(
+        self,
+        prompt: str
+    ) -> str:
+        """
+        Stream the generation of the response
+        
+        Args:
+            prompt (str): The prompt to use
+        
+        Example:
+        # Feature 4: Streamed generation
+        response = flow.streamed_generation("Generate a report on finance")
+        print(response)
+        
+        """
+        tokens = list(prompt)
+        response = ""
+        for token in tokens:
+            time.sleep(0.1)
+            response += token
+            print(token, end="", flush=True)
+        print()
+        return response