I’ve developed an automated blog generation system that takes a simple topic and transforms it into a full-length blog post, using a two-step process powered by LangGraph. 🤖✨ How it Works: Step 1: Generate Outline 📝 The workflow first takes the topic provided by the user and generates a detailed blog outline. This helps structure the content by breaking it down into key sections. Step 2: Generate Blog Content 🖋️ With the outline in hand, the system then uses it to generate a detailed blog post, ensuring each section of the outline is fully fleshed out into a coherent and informative blog. LangGraph Workflow Design: The workflow is built with LangGraph and consists of two primary nodes: 🔧 Node 1: Create Outline 📋 Functionality: This node takes the user-provided topic and uses it to generate a structured outline for the blog. The model understands the topic and breaks it down into key points and sub-topics. Node 2: Create Blog Content 🖊️ Functionality: This node takes the topic and the outline and generates a full-length blog based on the outline. It ensures the content is well-organized and aligned with the initial structure, providing a comprehensive blog post. Key Features: Efficient Workflow 🚀: Seamlessly moves from generating a structured outline to creating a detailed blog. Node-based Process ⚙️: Clear and easy-to-understand flow with specific functionalities for each node. Real-Time Streaming ⏱️: The content is streamed progressively to the user, ensuring a dynamic and engaging experience. This system showcases the power of LangGraph in structuring complex workflows for content generation, allowing for automation and efficiency in content creation. 🌟 Check out the demo to see how the system works in action! 🎥 hashtag#AI hashtag#BlogGeneration hashtag#LangGraph hashtag#MachineLearning hashtag#Automation hashtag#ContentCreation hashtag#TechInnovation hashtag#Python hashtag#ArtificialIntelligence hashtag#NodeBasedWorkflow ✨

🧩Step-by-Step Breakdown: 🗨️Trigger: "When chat message received" --This node listens for incoming chat messages from the user, asking for a specific type of doctor on Facebook 🌐HTTP Request: "Searching from Apify" --Sends a POST request to the Apify API to search Facebook for the type of doctor mentioned by the user (e.g., “best gynecologist in Sukkur”). ⏳Wait: "Gather result" - Pauses the workflow until Apify completes the search. - Outputs 5 result items from Facebook. 🧠Code in JavaScript: "Make structured output" - Doctor’s name - Timings - Availability - Location ✏️Edit Fields: "Required output" --Get required output 📄Append row in sheet: "Saving results" --Appends the final structured data into a connected Google Sheet for record-keeping.

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