Merge pull request #158 from elder-plinius/master

AutoTemp

playground/demos/autotemp/autotemp.py

@@ -0,0 +1,67 @@
+import re
+from swarms.models.openai_models import OpenAIChat
+
+class AutoTemp:
+    """
+    AutoTemp is a tool for automatically selecting the best temperature setting for a given task.
+    It generates responses at different temperatures, evaluates them, and ranks them based on quality.
+    """
+
+    def __init__(self, api_key, default_temp=0.0, alt_temps=None, auto_select=True, max_workers=6):
+        self.api_key = api_key
+        self.default_temp = default_temp
+        self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
+        self.auto_select = auto_select
+        self.max_workers = max_workers
+        self.llm = OpenAIChat(openai_api_key=self.api_key, temperature=self.default_temp)
+
+    def evaluate_output(self, output, temperature):
+        print(f"Evaluating output at temperature {temperature}...")
+        eval_prompt = f"""
+        Evaluate the following output which was generated at a temperature setting of {temperature}. Provide a precise score from 0.0 to 100.0, considering the following criteria:
+
+        - Relevance: How well does the output address the prompt or task at hand?
+        - Clarity: Is the output easy to understand and free of ambiguity?
+        - Utility: How useful is the output for its intended purpose?
+        - Pride: If the user had to submit this output to the world for their career, would they be proud?
+        - Delight: Is the output likely to delight or positively surprise the user?
+
+        Be sure to comprehensively evaluate the output, it is very important for my career. Please answer with just the score with one decimal place accuracy, such as 42.0 or 96.9. Be extremely critical.
+
+        Output to evaluate:
+        ---
+        {output}
+        ---
+        """
+        score_text = self.llm(eval_prompt, temperature=0.5)
+        score_match = re.search(r'\b\d+(\.\d)?\b', score_text)
+        return round(float(score_match.group()), 1) if score_match else 0.0
+
+    def run(self, prompt, temperature_string):
+        print("Starting generation process...")
+        temperature_list = [float(temp.strip()) for temp in temperature_string.split(',') if temp.strip()]
+        outputs = {}
+        scores = {}
+        for temp in temperature_list:
+            print(f"Generating at temperature {temp}...")
+            output_text = self.llm(prompt, temperature=temp)
+            if output_text:
+                outputs[temp] = output_text
+                scores[temp] = self.evaluate_output(output_text, temp)
+
+        print("Generation process complete.")
+        if not scores:
+            return "No valid outputs generated.", None
+
+        sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
+        best_temp, best_score = sorted_scores[0]
+        best_output = outputs[best_temp]
+
+        return (
+            f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}"
+            if self.auto_select
+            else "\n".join(
+                f"Temp {temp} | Score: {score}:\n{outputs[temp]}"
+                for temp, score in sorted_scores
+            )
+        )

playground/demos/autotemp/autotemp_example.py

@@ -0,0 +1,22 @@
+from swarms.models import OpenAIChat
+from swarms.models.autotemp import AutoTemp
+
+# Your OpenAI API key
+api_key = ""
+
+autotemp_agent = AutoTemp(
+    api_key=api_key,
+    alt_temps=[0.4, 0.6, 0.8, 1.0, 1.2],
+    auto_select=False,
+    # model_version="gpt-3.5-turbo"  # Specify the model version if needed
+)
+
+# Define the task and temperature string
+task = "Generate a short story about a lost civilization."
+temperature_string = "0.4,0.6,0.8,1.0,1.2,"
+
+# Run the AutoTempAgent
+result = autotemp_agent.run(task, temperature_string)
+
+# Print the result
+print(result)

playground/demos/blog_gen/blog_gen.py

@@ -0,0 +1,110 @@
+import os
+from termcolor import colored
+from swarms.models import OpenAIChat
+from swarms.models.autotemp import AutoTemp
+from swarms.structs import SequentialWorkflow
+
+
+class BlogGen:
+    def __init__(
+        self, api_key, blog_topic, temperature_range: str = "0.4,0.6,0.8,1.0,1.2"
+    ):  # Add blog_topic as an argument
+        self.openai_chat = OpenAIChat(openai_api_key=api_key, temperature=0.8)
+        self.auto_temp = AutoTemp(api_key)
+        self.temperature_range = temperature_range
+        self.workflow = SequentialWorkflow(max_loops=5)
+
+        # Formatting the topic selection prompt with the user's topic
+        self.TOPIC_SELECTION_SYSTEM_PROMPT = f"""
+        Given the topic '{blog_topic}', generate an engaging and versatile blog topic. This topic should cover areas related to '{blog_topic}' and might include aspects such as current events, lifestyle, technology, health, and culture related to '{blog_topic}'. Identify trending subjects within this realm. The topic must be unique, thought-provoking, and have the potential to draw in readers interested in '{blog_topic}'.
+        """
+
+        self.DRAFT_WRITER_SYSTEM_PROMPT = """
+        Create an engaging and comprehensive blog article of at least 1,000 words on '{{CHOSEN_TOPIC}}'. The content should be original, informative, and reflective of a human-like style, with a clear structure including headings and sub-headings. Incorporate a blend of narrative, factual data, expert insights, and anecdotes to enrich the article. Focus on SEO optimization by using relevant keywords, ensuring readability, and including meta descriptions and title tags. The article should provide value, appeal to both knowledgeable and general readers, and maintain a balance between depth and accessibility. Aim to make the article engaging and suitable for online audiences.
+        """
+
+        self.REVIEW_AGENT_SYSTEM_PROMPT = """
+        Critically review the drafted blog article on '{{ARTICLE_TOPIC}}' to refine it to high-quality content suitable for online publication. Ensure the article is coherent, factually accurate, engaging, and optimized for search engines (SEO). Check for the effective use of keywords, readability, internal and external links, and the inclusion of meta descriptions and title tags. Edit the content to enhance clarity, impact, and maintain the authors voice. The goal is to polish the article into a professional, error-free piece that resonates with the target audience, adheres to publication standards, and is optimized for both search engines and social media sharing.
+        """
+
+        self.DISTRIBUTION_AGENT_SYSTEM_PROMPT = """
+        Develop an autonomous distribution strategy for the blog article on '{{ARTICLE_TOPIC}}'. Utilize an API to post the article on a popular blog platform (e.g., WordPress, Blogger, Medium) commonly used by our target audience. Ensure the post includes all SEO elements like meta descriptions, title tags, and properly formatted content. Craft unique, engaging social media posts tailored to different platforms to promote the blog article. Schedule these posts to optimize reach and engagement, using data-driven insights. Monitor the performance of the distribution efforts, adjusting strategies based on engagement metrics and audience feedback. Aim to maximize the article's visibility, attract a diverse audience, and foster engagement across digital channels.
+        """
+
+    def run_workflow(self):
+        try:
+            # Topic generation using OpenAIChat
+            topic_result = self.openai_chat.generate(
+                [self.TOPIC_SELECTION_SYSTEM_PROMPT]
+            )
+            topic_output = topic_result.generations[0][0].text
+            print(
+                colored(
+                    f"\nTopic Selection Task Output:\n----------------------------\n{topic_output}\n",
+                    "white",
+                )
+            )
+
+            chosen_topic = topic_output.split("\n")[0]
+            print(colored("Selected topic: " + chosen_topic, "yellow"))
+
+            # Initial draft generation with AutoTemp
+            initial_draft_prompt = self.DRAFT_WRITER_SYSTEM_PROMPT.replace(
+                "{{CHOSEN_TOPIC}}", chosen_topic
+            )
+            auto_temp_output = self.auto_temp.run(
+                initial_draft_prompt, self.temperature_range
+            )
+            initial_draft_output = auto_temp_output  # Assuming AutoTemp.run returns the best output directly
+            print(
+                colored(
+                    f"\nInitial Draft Output:\n----------------------------\n{initial_draft_output}\n",
+                    "white",
+                )
+            )
+
+            # Review process using OpenAIChat
+            review_prompt = self.REVIEW_AGENT_SYSTEM_PROMPT.replace(
+                "{{ARTICLE_TOPIC}}", chosen_topic
+            )
+            review_result = self.openai_chat.generate([review_prompt])
+            review_output = review_result.generations[0][0].text
+            print(
+                colored(
+                    f"\nReview Output:\n----------------------------\n{review_output}\n",
+                    "white",
+                )
+            )
+
+            # Distribution preparation using OpenAIChat
+            distribution_prompt = self.DISTRIBUTION_AGENT_SYSTEM_PROMPT.replace(
+                "{{ARTICLE_TOPIC}}", chosen_topic
+            )
+            distribution_result = self.openai_chat.generate([distribution_prompt])
+            distribution_output = distribution_result.generations[0][0].text
+            print(
+                colored(
+                    f"\nDistribution Output:\n----------------------------\n{distribution_output}\n",
+                    "white",
+                )
+            )
+
+            # Final compilation of the blog
+            final_blog_content = (
+                f"{initial_draft_output}\n\n{review_output}\n\n{distribution_output}"
+            )
+            print(
+                colored(
+                    f"\nFinal Blog Content:\n----------------------------\n{final_blog_content}\n",
+                    "green",
+                )
+            )
+
+        except Exception as e:
+            print(colored(f"An error occurred: {str(e)}", "red"))
+
+
+if __name__ == "__main__":
+    api_key = os.environ["OPENAI_API_KEY"]
+    blog_generator = BlogGen(api_key)
+    blog_generator.run_workflow()

playground/demos/blog_gen/blog_gen_example.py

@@ -0,0 +1,23 @@
+import os
+from swarms.swarms.blog_gen import BlogGen
+
+
+def main():
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        raise ValueError("OPENAI_API_KEY environment variable not set.")
+
+    blog_topic = input("Enter the topic for the blog generation: ")
+
+    blog_generator = BlogGen(api_key, blog_topic)
+    blog_generator.TOPIC_SELECTION_SYSTEM_PROMPT = (
+        blog_generator.TOPIC_SELECTION_SYSTEM_PROMPT.replace(
+            "{{BLOG_TOPIC}}", blog_topic
+        )
+    )
+
+    blog_generator.run_workflow()
+
+
+if __name__ == "__main__":
+    main()

swarms/models/autotemp.py

@@ -1,101 +0,0 @@
-import re
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from swarms.models.openai_models import OpenAIChat
-
-
-class AutoTempAgent:
-    """
-    AutoTemp is a tool for automatically selecting the best temperature setting for a given task.
-
-    Flow:
-        1. Generate outputs at a range of temperature settings.
-        2. Evaluate each output using the default temperature setting.
-        3. Select the best output based on the evaluation score.
-        4. Return the best output.
-
-
-    Args:
-        temperature (float, optional): The default temperature setting to use. Defaults to 0.5.
-        api_key (str, optional): Your OpenAI API key. Defaults to None.
-        alt_temps ([type], optional): A list of alternative temperature settings to try. Defaults to None.
-        auto_select (bool, optional): If True, the best temperature setting will be automatically selected. Defaults to True.
-        max_workers (int, optional): The maximum number of workers to use when generating outputs. Defaults to 6.
-
-    Returns:
-        [type]: [description]
-
-    Examples:
-        >>> from swarms.demos.autotemp import AutoTemp
-        >>> autotemp = AutoTemp()
-        >>> autotemp.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.", "0.4,0.6,0.8,1.0,1.2,1.4")
-        Best AutoTemp Output (Temp 0.4 | Score: 100.0):
-        Generate a 10,000 word blog on mental clarity and the benefits of meditation.
-
-    """
-
-    def __init__(
-        self,
-        temperature: float = 0.5,
-        api_key: str = None,
-        alt_temps=None,
-        auto_select=True,
-        max_workers=6,
-    ):
-        self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
-        self.auto_select = auto_select
-        self.max_workers = max_workers
-        self.temperature = temperature
-        self.alt_temps = alt_temps
-        self.llm = OpenAIChat(
-            openai_api_key=api_key,
-            temperature=temperature,
-        )
-
-    def evaluate_output(self, output: str):
-        """Evaluate the output using the default temperature setting."""
-        eval_prompt = f"""
-            Evaluate the following output which was generated at a temperature setting of {self.temperature}. 
-            Provide a precise score from 0.0 to 100.0, considering the criteria of relevance, clarity, utility, pride, and delight.
-
-            Output to evaluate:
-            ---
-            {output}
-            ---
-            """
-        score_text = self.llm(prompt=eval_prompt)
-        score_match = re.search(r"\b\d+(\.\d)?\b", score_text)
-        return round(float(score_match.group()), 1) if score_match else 0.0
-
-    def run(self, task: str, temperature_string):
-        """Run the AutoTemp agent."""
-        temperature_list = [
-            float(temp.strip()) for temp in temperature_string.split(",")
-        ]
-        outputs = {}
-        scores = {}
-        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-            future_to_temp = {
-                executor.submit(self.llm.generate, task, temp): temp
-                for temp in temperature_list
-            }
-            for future in as_completed(future_to_temp):
-                temp = future_to_temp[future]
-                output_text = future.result()
-                outputs[temp] = output_text
-                scores[temp] = self.evaluate_output(output_text, temp)
-
-        if not scores:
-            return "No valid outputs generated.", None
-
-        sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
-        best_temp, best_score = sorted_scores[0]
-        best_output = outputs[best_temp]
-
-        return (
-            f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}"
-            if self.auto_select
-            else "\n".join(
-                f"Temp {temp} | Score: {score}:\n{outputs[temp]}"
-                for temp, score in sorted_scores
-            )
-        )