This article provides a complete, practical walkthrough of the Splitout Limit Import Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Building an AI Essay Chatbot with n8n, Milvus, and OpenAI
   
   Meta Description:
   Explore how a low-code workflow using n8n can fetch, process, embed, and query essays from Paul Graham’s website using OpenAI and Milvus. Learn to build an AI-powered chatbot that cites its sources.
   
   Keywords:
   n8n workflow, Paul Graham essays, Milvus vector store, OpenAI embeddings, GPT-4, AI chatbot, LangChain, semantic search, web scraping, document retrieval, vector database, AI with citations, low-code automation, RAG architecture
   
   Third-Party APIs Used:
   - OpenAI API (for embeddings via text-embedding-ada-002 and LLM chat via GPT-4)
   - Milvus Vector Database API (for document storage and retrieval)
   
   Article:
   
   Unlocking AI-Powered Essay Search with n8n, Milvus, and OpenAI
   
   In the growing field of AI-assisted content retrieval, the combination of powerful tools like n8n, OpenAI, and Milvus can deliver a surprisingly sophisticated ReAct-style (retrieve-and-act) chatbot—all without writing tons of boilerplate code. This article showcases an n8n workflow that automates the fetching, embedding, storing, and querying of essays from famed writer Paul Graham, turning them into an interactive knowledge base that can answer questions and cite sources.
   
   Let’s unpack how it works.
   
   🌐 Step 1: Scraping Paul Graham’s Essays
   
   The workflow begins with a Manual Trigger node—the user initiates data ingestion by clicking “Execute Workflow.” It then sends a request to Paul Graham’s archive (http://www.paulgraham.com/articles.html), fetching the HTML contents of his essay list.
   
   An HTML Extract node parses this page and grabs all links nested in the article tables. These anchor elements, representing essay links, are extracted with their href attributes to build a list of essay URLs. To keep this demo manageable, the dataset is limited to the first three essays using a Limit node.
   
   Each of those URLs is fetched in full HTML using another HTTP Request node, followed by a second HTML Extract node that isolates the body text while excluding images and navigation elements to get clean content.
   
   🧠 Step 2: Embedding & Loading into Milvus
   
   Once the plain text of the essays is extracted, the workflow prepares the data for vector operations—starting with a Recursive Character Text Splitter. This LangChain-based component breaks the documents into manageable text chunks (e.g., 6000 characters per chunk) that fit within LLM context windows.
   
   A Document Loader then structures each chunk before passing them to an Embeddings node powered by OpenAI’s ADA model (text-embedding-ada-002). These embeddings are fundamental for semantic understanding, letting the AI “understand” the text in vector form.
   
   The final ingestion step pushes these embeddings and associated documents into a Milvus vector store collection called my_collection using the Milvus Insert node. The option to clear the collection each time ensures a fresh index on repeat loads.
   
   💬 Step 3: Enabling Chat with Knowledge + Citations
   
   With the data now indexed in Milvus, the workflow transitions into chatbot mode. When a user submits a query via a chat interface (hooked through the LangChain Chat Trigger node), the system retrieves the most relevant chunks from the Milvus store (topK = 2) using vector similarity search.
   
   Those chunks are compiled in a JavaScript Code node into a coherent context string. A second OpenAI chat model (configured to GPT-4-mini, but replaceable as needed) then receives the prompt:
   
   Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know. Don’t try to make up an answer.
   
   The model returns both an answer and the indexes of chunks used. To make the responses more transparent and trustworthy, a "Compose Citations" node pairs each index with its metadata—e.g. filename and line numbers.
   
   Finally, the generated answer is formatted and presented, complete with its citation list.
   
   📦 Summary: What This Workflow Demonstrates
   
   This n8n workflow exemplifies a simple but powerful retrieval-augmented generation (RAG) pipeline. It integrates:
   
   - Web scraping with built-in HTTP and HTML parsing
   - Text preprocessing and chunking via LangChain components
   - Vector embeddings using OpenAI
   - Persistent storage with Milvus
   - Query answering and citation generation using GPT models
   
   All are orchestrated within n8n’s visual, node-based interface.
   
   This makes it not only a practical tool for automating knowledge ingestion, but also a very approachable blueprint for anyone looking to experiment with AI-driven content search, citation-based assistants, or educational chatbots.
   
   🛠️ Getting Started
   
   To replicate this workflow:
   
   1. Set up Milvus using Docker Compose with a collection named my_collection.
   2. Install and configure your OpenAI API and Milvus credentials in n8n.
   3. Copy and paste or import the workflow into your n8n instance.
   4. Click “Execute Workflow” to load essays.
   5. Use the chatbot trigger to start asking questions.
   
   And voilà—you have an AI chatbot trained on Paul Graham essays, ready to chat and cite.
   
   💡 Pro Tip:
   With a few tweaks, this workflow can be adapted to other data sources: GitHub repos, PDFs, or even legal documents. Swapping input and embedding logic makes it endlessly reconfigurable.
   
   In an era hungry for explainable, grounded AI, few things are more elegant than a chatbot that not only responds—but shows its work.
   
   —
   
   Want to dive deeper or extend this automation? Head over to the official Milvus and LangChain docs, or explore the n8n community forums for reusable components and real-world use cases.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.