Mohamed Abdelwahab
I’m an AI Automation Engineer with 5+ years of experience designing and implementing intelligent, scalable automation systems. I specialize in streamlining operations, reducing manual tasks, and building AI-powered solutions using Python, Node.js, and no-code/low-code platforms n8n. Whether you need to integrate complex APIs, build automated workflows, or deploy AI chatbots, I can deliver robust solutions tailored to your business needs. https://www.freelancer.com/u/MohmedAbdelwahab
Templates by Mohamed Abdelwahab
End-to-End Blog Generation for WordPress with Featured Image - GPT-5 Optimized
🧠 Automate end-to-end SEO blog creation and WordPress publishing using a GPT-5 multi-agent workflow with real-time research, metadata generation, and optional featured images. 🧠 AI-Powered SEO Blog Automation for WordPress — n8n + GPT-5 Category: WordPress . Content Creation · Marketing Automation · AI Agents · Publishing Use Case: End-to-End SEO Blog Generation & Publishing This production-ready n8n workflow automates the entire blog content lifecycle — from keyword input to WordPress draft publishing — using a multi-agent GPT-5 orchestration architecture. Designed for content teams, SEO professionals, agencies, and founders, the workflow transforms structured inputs into fact-checked, SEO-optimized, publication-ready blog posts with metadata, tags, excerpts, and optional featured images. Simply copy, paste, and configure — no custom coding required. --- 🚀 What This Workflow Does This workflow automates content creation at scale by coordinating specialized AI agents that each handle a single responsibility in a strict execution order. It supports both manual content requests and scheduled batch publishing, making it suitable for editorial pipelines, programmatic SEO, and marketing automation. --- ✨ Key Features & Capabilities 🧠 AI Content Creation (GPT-5 Optimized) Multi-agent content pipeline with clearly defined roles SEO-driven outline and section planning Expert-level long-form writing Inline citations and verified sources Final editorial HTML polishing 🔍 Real-Time Web Research Live Q&A and fact retrieval (OpenRouter / Perplexity Sonar) Context-aware, up-to-date information Reduced hallucinations and outdated content 📝 SEO & Publishing Automation SEO-optimized titles, slugs, excerpts, and meta descriptions Automatic WordPress category selection Dynamic tag creation and updates Draft publishing via WordPress REST API 🖼️ Optional Featured Image Pipeline AI-generated realistic cover image prompts Alt-text generation for accessibility and SEO Image upload, resizing, and WordPress attachment 📊 Workflow Tracking Google Sheets integration for batch automation Automatic status update (created = yes) Suitable for editorial calendars and content tracking --- 🔁 Supported Triggers 1️⃣ Form Trigger (Manual Publishing) Collects: Keywords Target word count Number of sections Writing style Website URL Website context Featured image toggle 2️⃣ Google Sheets + Schedule Trigger (Batch Publishing) Automatically processes rows containing: Keywords Word count Sections count Writing style Website Website context Featured image toggle created status flag (auto-updated after publishing) --- 🧠 AI Agent Orchestration (GPT-5) A central Orchestration Agent coordinates specialized sub-agents: GetOnlineInfo — Live research and Q&A OutlinePlanner — SEO-structured table of contents createSections — Detailed section definitions SectionsWriter — Expert-level content drafting Editor — Final HTML refinement and compliance checks MetaInfo — Title, slug, excerpt, and category selection Tags Agent — SEO-friendly WordPress tag creation and updates ImagePrompt — Cover image prompt and alt text (optional) Each agent executes once, deterministically, ensuring consistent and predictable output quality. --- ✍️ End-to-End Writing Flow Input parsing (Form or Google Sheets) Real-time data retrieval SEO-optimized outline generation Section-by-section expert writing Editorial refinement into valid HTML Metadata and excerpt generation Tag creation and update in WordPress Optional image generation and upload Draft post publishing to WordPress Google Sheets row update (created = yes) --- 📦 Output Structure (Validated JSON) json { "toc": "Table of Contents", "post_html": "<final HTML content>", "meta_data": { "title": "...", "slug": "...", "excerpt": "...", "category_id": 0 }, "image_data": { "image_prompt": "...", "alt_text": "..." }, "tags": [ { "name": "...", "description": "..." } ], "ready": true } --- 🌐 WordPress Integration REST API endpoints: /wp-json/wp/v2/posts /wp-json/wp/v2/media /wp-json/wp/v2/tags Draft publishing with author and category assignment Automatic excerpt and tag updates Featured image handling (optional) --- 🧩 Setup Requirements 🔑 Required Credentials WordPress API credentials OpenAI API key OpenRouter API key (optional) Google Sheets API credentials (optional) 🧠 Required Sub-Workflows OutlinePlanner createSections SectionsWriter Editor MetaInfo Tags Agent ImagePrompt (optional) --- 💡 Why Use This Workflow? Purpose-built for WordPress Automation and Content Creation Eliminates manual writing, formatting, and publishing steps Scales from single posts to batch content pipelines Modular design for easy testing and customization Suitable for SEO blogs, SaaS content, affiliate sites, and agencies --- 🏁 Production-Ready Copy-paste deployable Validated HTML, JSON, tags, and excerpts Modular and debuggable architecture Optimized for GPT-5 quality, consistency, and reliability
By Mohamed AbdelwahabAutomate document ingestion & RAG system with Google Drive, Sheets & OpenAI
Overview The IngestionDocs workflow is a fully automated document ingestion and knowledge management system built with n8n. Its purpose is to continuously ingest organizational documents from Google Drive, transform them into vector embeddings using OpenAI, store them in Pinecone, and make them searchable and retrievable through an AI-powered Q&A interface. This ensures that employees always have access to the most up-to-date knowledge base without requiring manual intervention. ------------------------------------------------------------------------ Key Objectives Automated Ingestion → Seamlessly process new and updated documents from Google Drive.\ Change Detection → Track and differentiate between new, updated, and previously processed documents.\ Knowledge Base Construction → Convert documents into embeddings for semantic search.\ AI-Powered Assistance → Provide an intelligent Q&A system for employees to query manuals.\ Scalable & Maintainable → Modular design using n8n, LangChain, and Pinecone. ------------------------------------------------------------------------ Workflow Breakdown A. Document Monitoring and Retrieval The workflow begins with two Google Drive triggers: File Created Trigger → Fires when a new document is uploaded.\ File Updated Trigger → Fires when an existing document is modified.\ A search operation lists the files in the designated Google Drive folder.\ Non-downloadable items (e.g., subfolders) are filtered out.\ For valid files: The file is downloaded.\ A SHA256 hash is generated to uniquely identify the file's content. ------------------------------------------------------------------------ B. Record Management (Google Sheets Integration) To keep track of ingestion states, the workflow uses a Google Sheets--based Record Manager:\ Each file entry contains:\ Id (Google Drive file ID)\ Name (file name)\ hashId (SHA256 checksum)\ The workflow compares the current file's hash with the stored one:\ New Document → File not found in records → Inserted into the Record Manager.\ Already Processed → File exists and hash matches → Skipped.\ Updated Document → File exists but hash differs → Record is updated. This guarantees that only new or modified content is processed, avoiding duplication. ------------------------------------------------------------------------ C. Document Processing and Vectorization Once a document is marked as new or updated:\ Default Data Loader extracts its content (binary files supported).\ Pages are split into individual chunks.\ Metadata such as file ID and name are attached.\ Recursive Character Text Splitter divides the content into manageable segments with overlap.\ OpenAI Embeddings (text-embedding-3-large) transform each text chunk into a semantic vector.\ Pinecone Vector Store stores these vectors in the configured index:\ For new documents, embeddings are inserted into a namespace based on the file name.\ For updated documents, the namespace is cleared first, then re-ingested with fresh embeddings. This process builds a scalable and queryable knowledge base. ------------------------------------------------------------------------ D. Knowledge Base Q&A Interface The workflow also provides an interactive form-based user interface:\ Form Trigger → Collects employee questions.\ LangChain AI Agent:\ Receives the question.\ Retrieves relevant context from Pinecone using vector similarity search.\ Processes the response using OpenAI Chat Model (gpt-4.1-mini).\ Answer Formatting:\ Responses are returned in HTML format for readability.\ A custom CSS theme ensures a modern, user-friendly design.\ Answers may include references to page numbers when available. This creates a self-service knowledge base assistant that employees can query in natural language. ------------------------------------------------------------------------ Technologies Used n8n → Orchestration of the entire workflow.\ Google Drive API → File monitoring, listing, and downloading.\ Google Sheets API → Record manager for tracking file states.\ OpenAI API: text-embedding-3-large for semantic vector creation.\ gpt-4.1-mini for conversational Q&A.\ Pinecone → Vector database for embedding storage and retrieval.\ LangChain → Document loaders, text splitters, vector store connectors, and agent logic.\ Crypto (SHA256) → File hash generation for change detection.\ Form Trigger + Form Node → Employee-facing Q&A submission and answer display.\ Custom CSS → Provides a modern, responsive, styled UI for the knowledge base. ------------------------------------------------------------------------ End-to-End Data Flow Employee uploads or updates a document → Google Drive detects the change.\ Workflow downloads and hashes the file → Ensures uniqueness and detects modifications.\ Record Manager (Google Sheets) → Decides whether to skip, insert, or update the record.\ Document Processing → Splitting + Embedding + Storing into Pinecone.\ Knowledge Base Updated → The latest version of documents is indexed.\ Employee asks a question via the web form.\ AI Agent retrieves embeddings from Pinecone + uses GPT-4.1-mini → Generates a contextual answer.\ Answer displayed in styled HTML → Delivered back to the employee through the form interface. ------------------------------------------------------------------------ Benefits Always Up-to-Date → Automatically syncs documents when uploaded or changed.\ No Duplicates → Smart hashing ensures only relevant updates are reprocessed.\ Searchable Knowledge Base → Employees can query documents semantically, not just by keywords.\ Enhanced Productivity → Answers are immediate, reducing time spent browsing manuals.\ Scalable → New documents and users can be added without workflow redesign. ------------------------------------------------------------------------ ✅ In summary, IngestionDocs is a robust AI-driven document ingestion and retrieval system that integrates Google Drive, Google Sheets, OpenAI, and Pinecone within n8n. It continuously builds and maintains a knowledge base of manuals while offering employees an intelligent, user-friendly Q&A assistant for fast and accurate knowledge retrieval.
By Mohamed AbdelwahabGenerate LinkedIn posts from books using OpenAI, LangChain & Pinecone vector search
Automates the process of generating, storing, and publishing engaging LinkedIn posts derived from books (PDFs) using AI and vector search. --- 🧠 Overview This workflow: Watches a Google Drive folder for new or updated book PDFs. Extracts and embeds the content using OpenAI. Stores the data in a Pinecone vector database. Uses a LangChain agent to generate post ideas. Creates concise LinkedIn posts with hook, insight, CTA. Updates a Google Sheet and posts to LinkedIn. --- 🛠 Workflow Breakdown 📥 1. Google Drive Trigger Trigger: Watches a folder for new or updated PDF files. Action: Downloads the updated PDF. 📄 2. Extract and Embed Content Extract from File: Parses PDF to extract text. Text Splitter: Breaks text into chunks. Embeddings (OpenAI): Converts chunks into vector embeddings. Pinecone Vector Store: Saves the embeddings with the book name as namespace. 🧠 3. Post Idea Generation (LangChain Agent) Uses a prompt to: Search Pinecone DB Extract insights Format into 5 LinkedIn post ideas with: Hook Insight CTA Memory buffer and structured output parser are used for clean AI interaction. ✍️ 4. Post Creation Each idea is: Split Rewritten with a GPT model prompt to match LinkedIn tone Styled for under 600 characters Includes emojis, hashtags, and tone guidelines 📊 5. Google Sheet Integration Saves all generated posts to a Google Sheet. Marks status: "published" or "no". 🔁 6. Scheduled Publishing Every day: Pulls an unpublished post Publishes it to LinkedIn Updates the post's status and timestamp in the Google Sheet --- ⚙️ Setup Guide 📂 Google Drive Create a folder for book PDFs Connect your Google Drive account to n8n Provide access token with file read permission 📊 Google Sheets Create a Google Sheet with columns: bookname, hook, insight, cta, postContent, published, date Add credentials in n8n with read/write permission 🧠 Pinecone Set up a Pinecone project and index (linkdenpost) Namespace will be auto-named using the book filename 🔑 API Credentials Required OpenAI API (for embeddings and post generation) Pinecone API (for vector storage and retrieval) LinkedIn OAuth2 (to publish posts) Google Drive & Sheets credentials --- 🔁 Flow Summary mermaid graph TD A[Google Drive Trigger] --> B[Download PDF] B --> C[Extract Text] C --> D[Text Splitter] D --> E[Create Embeddings] E --> F[Pinecone Vector Store] F --> G[LangChain Agent] G --> H[Structured Output (5 Post Ideas)] H --> I[Split Ideas] I --> J[Format as LinkedIn Post (GPT)] J --> K[Store in Google Sheet] L[Schedule Trigger] --> M[Get Unpublished Post] M --> N[Post to LinkedIn] N --> O[Mark as Published] --- 🧪 Prompt Example (Used in LangChain Agent) text You are a content strategist. Search the Pinecone vector DB containing a book. Generate 5 unique LinkedIn post ideas with: A Hook (curiosity driven) Insight (summary < 100 words) CTA ("Agree or disagree?", etc.) Respond in structured JSON: [ { "Hook": "...", "Insight": "...", "CTA": "..." }, ... ] --- ✅ Output Sample json { "Hook": "Why your lab's results might be invalid 😱", "Insight": "ISO/IEC 17025 stresses that labs must plan and address risks to impartiality and validity.", "CTA": "Does your lab audit for these risks?" } --- 📆 Schedule Control Uses Schedule Trigger to post daily at a set time. Ensures automation with LinkedIn and accurate Google Sheet syncing. --- 📝 Notes Posts remain professional and concise for a LinkedIn audience Works with any PDF book Supports multi-book pipelines You can filter and tag books by filename or folder for segmenting post styles
By Mohamed Abdelwahab