raplaced .yaml files with prompt file

cookbook/examples/workflows/social_media_content_planner/prompts.py

@@ -0,0 +1,110 @@
+# Planner Agents Configuration
+agents_config = {
+    "blog_analyzer": {
+        "role": "Blog Analyzer",
+        "goal": "Analyze blog and identify key ideas, sections, and technical concepts",
+        "backstory": (
+            "You are a technical writer with years of experience writing, editing, and reviewing technical blogs. "
+            "You have a talent for understanding and documenting technical concepts.\n\n"
+        ),
+        "verbose": False,
+    },
+    "twitter_thread_planner": {
+        "role": "Twitter Thread Planner",
+        "goal": "Create a Twitter thread plan based on the provided blog analysis",
+        "backstory": (
+            "You are a technical writer with years of experience in converting long technical blogs into Twitter threads. "
+            "You have a talent for breaking longform content into bite-sized tweets that are engaging and informative. "
+            "And identify relevant URLs to media that can be associated with a tweet.\n\n"
+        ),
+        "verbose": False,
+    },
+    "linkedin_post_planner": {
+        "role": "LinkedIn Post Planner",
+        "goal": "Create an engaging LinkedIn post based on the provided blog analysis",
+        "backstory": (
+            "You are a technical writer with extensive experience crafting technical LinkedIn content. "
+            "You excel at distilling technical concepts into clear, authoritative posts that resonate with a professional audience "
+            "while maintaining technical accuracy. You know how to balance technical depth with accessibility and incorporate "
+            "relevant hashtags and mentions to maximize engagement.\n\n"
+        ),
+        "verbose": False,
+    },
+}
+
+# Planner Tasks Configuration
+tasks_config = {
+    "analyze_blog": {
+        "description": (
+            "Analyze the markdown file at {blog_path} to create a developer-focused technical overview\n\n"
+            "1. Map out the core idea that the blog discusses\n"
+            "2. Identify key sections and what each section is about\n"
+            "3. For each section, extract all URLs that appear inside image markdown syntax ![](image_url)\n"
+            "4. You must associate these identified image URLs to their corresponding sections, so that we can use them with the tweets as media pieces\n\n"
+            "Focus on details that are important for a comprehensive understanding of the blog.\n\n"
+        ),
+        "expected_output": (
+            "A technical analysis containing:\n"
+            "- Blog title and core concept/idea\n"
+            "- Key technical sections identified with their main points\n"
+            "- Important code examples or technical concepts covered\n"
+            "- Key takeaways for developers\n"
+            "- Relevant URLs to media that are associated with the key sections and can be associated with a tweet, this must be done.\n\n"
+        ),
+    },
+    "create_twitter_thread_plan": {
+        "description": (
+            "Develop an engaging Twitter thread based on the blog analysis provided and closely follow the writing style provided in the {path_to_example_threads}\n\n"
+            "The thread should break down complex technical concepts into digestible, tweet-sized chunks "
+            "that maintain technical accuracy while being accessible.\n\n"
+            "Plan should include:\n"
+            "- A strong hook tweet that captures attention, it should be under 10 words, it must be the same as the title of the blog\n"
+            "- Logical flow from basic to advanced concepts\n"
+            "- Code snippets or key technical highlights that fit Twitter's format\n"
+            "- Relevant URLs to media that are associated with the key sections and must be associated with their corresponding tweets\n"
+            "- Clear takeaways for engineering audience\n\n"
+            "Make sure to cover:\n"
+            "- The core problem being solved\n"
+            "- Key technical innovations or approaches\n"
+            "- Interesting implementation details\n"
+            "- Real-world applications or benefits\n"
+            "- Call to action for the conclusion\n"
+            "- Add relevant URLs to each tweet that can be associated with a tweet\n\n"
+            "Focus on creating a narrative that technical audiences will find valuable "
+            "while keeping each tweet concise, accessible, and impactful.\n\n"
+        ),
+        "expected_output": (
+            "A Twitter thread with a list of tweets, where each tweet has the following:\n"
+            "- content\n"
+            "- URLs to media that are associated with the tweet, whenever possible\n"
+            "- is_hook: true if the tweet is a hook tweet, false otherwise\n\n"
+        ),
+    },
+    "create_linkedin_post_plan": {
+        "description": (
+            "Develop a comprehensive LinkedIn post based on the blog analysis provided\n\n"
+            "The post should present technical content in a professional, long-form format "
+            "while maintaining engagement and readability.\n\n"
+            "Plan should include:\n"
+            "- An attention-grabbing opening statement, it should be the same as the title of the blog\n"
+            "- Well-structured body that breaks down the technical content\n"
+            "- Professional tone suitable for LinkedIn's business audience\n"
+            "- One main blog URL placed strategically at the end of the post\n"
+            "- Strategic use of line breaks and formatting\n"
+            "- Relevant hashtags (3-5 maximum)\n\n"
+            "Make sure to cover:\n"
+            "- The core technical problem and its business impact\n"
+            "- Key solutions and technical approaches\n"
+            "- Real-world applications and benefits\n"
+            "- Professional insights or lessons learned\n"
+            "- Clear call to action\n\n"
+            "Focus on creating content that resonates with both technical professionals "
+            "and business leaders while maintaining technical accuracy.\n\n"
+        ),
+        "expected_output": (
+            "A LinkedIn post plan containing:\n"
+            "- content\n"
+            "- a main blog URL that is associated with the post\n\n"
+        ),
+    },
+}

cookbook/examples/workflows/social_media_content_planner/social_media_content_planner.py

@@ -4,26 +4,19 @@
 from phi.tools.firecrawl import FirecrawlTools
 from pydantic import BaseModel, Field
 from typing import List, Optional
-import yaml
 import json
 from dotenv import load_dotenv
 import scheduler
+from prompts import agents_config, tasks_config
 
 # Load environment variables
 load_dotenv()
 
-# Load agent and task configurations
-with open('planner_agents.yaml', 'r') as f:
-    agents_config = yaml.safe_load(f)
-
-with open('planner_tasks.yaml', 'r') as f:
-    tasks_config = yaml.safe_load(f)
-
 
 # Define Pydantic models to structure responses
 class BlogAnalyzer(BaseModel):
     """
-    Represents the response from the Draft Analyzer agent.
+    Represents the response from the Blog Analyzer agent.
     Includes the blog title and content in Markdown format.
     """
     title: str
@@ -61,13 +54,13 @@ class LinkedInPost(BaseModel):
 blog_analyzer = Agent(
     model=OpenAIChat(id="gpt-4o"),
     tools=[FirecrawlTools(scrape=True, crawl=False)],  # Enables blog scraping capabilities
-    description=f"{agents_config['draft_analyzer']['role']} - {agents_config['draft_analyzer']['goal']}",
+    description=f"{agents_config['blog_analyzer']['role']} - {agents_config['blog_analyzer']['goal']}",
     instructions=[
-        f"{agents_config['draft_analyzer']['backstory']}",
-        tasks_config['analyze_draft']['description'],  # Task-specific instructions for draft analysis
+        f"{agents_config['blog_analyzer']['backstory']}",
+        tasks_config['analyze_blog']['description'],  # Task-specific instructions for blog analysis
     ],
     response_model=BlogAnalyzer,  # Expects response to follow the BlogAnalyzer Pydantic model
-    verbose=agents_config['draft_analyzer']['verbose'],
+    verbose=agents_config['blog_analyzer']['verbose'],
 )
 
 # Twitter Thread Planner:
@@ -116,7 +109,7 @@ def scrape_blog_post(self, blog_post_url: str):
             print(f"Blog title: {result.title}")
             return result.blog_content_markdown
         else:
-            raise ValueError("Unexpected content type received from draft analyzer.")
+            raise ValueError("Unexpected content type received from blog analyzer.")
 
     def generate_plan(self, blog_content: str, post_type: str) -> dict:
         if post_type == "twitter":
@@ -158,7 +151,7 @@ def run(self):
         # Scrape the blog post
         blog_content = self.scrape_blog_post(self.blog_post_url)
 
-        # Generate the plan based on the draft and post type
+        # Generate the plan based on the blog and post type
         plan = self.generate_plan(blog_content, self.post_type)
 
         # Schedule and publish the content
@@ -172,3 +165,4 @@ def run(self):
     # Initialize and run the workflow
     workflow = ContentPlanningWorkflow(blog_post_url=blog_post_url, post_type=post_type)
     workflow.run()
+