This article provides a complete, practical walkthrough of the Splitout Github Create 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 a RAG Chatbot for Movie Recommendations Using n8n, Qdrant, and OpenAI
   
   Meta Description:  
   Learn how to build a powerful Retrieval-Augmented Generation (RAG) chatbot for personalized movie recommendations using n8n, Qdrant vector database, and OpenAI embeddings. A step-by-step guide to integrating AI tools in automated workflows.
   
   Keywords:  
   n8n, AI chatbot, movie recommender system, Qdrant, OpenAI, vector database, RAG, GPT-4, text embeddings, Langchain, automation workflow, machine learning, IMDB movie data, GitHub automation
   
   Third-Party APIs Used:
   
   - GitHub API (for fetching IMDB movie dataset)
   - OpenAI API (for computing embeddings and generating responses using GPT models)
   - Qdrant API (for storing and querying vector embeddings in the vector database)
   
   Article:
   
   Building an AI Movie Recommendation Chatbot Using n8n, Qdrant, and OpenAI  
   
   In the ever-expanding domain of conversational AI and recommendation systems, combining Retrieval-Augmented Generation (RAG) with vector search engines creates highly intelligent and responsive tools. In this article, we explore how to create a movie recommendation chatbot using n8n's automation platform, powered by OpenAI's large language models and Qdrant’s high-performance vector database.
   
   This no-code workflow scrapes high-quality movie data from a GitHub repository, enriches it with language embeddings, stores it in a vector database, and uses OpenAI’s GPT-4 model in tandem with a memory buffer to provide contextual movie suggestions. Let’s dive into how each part of this workflow works and ties into the magical whole.
   
   Step 1: Data Ingestion & Preparation  
   
   The process begins with fetching a dataset titled "Top_1000_IMDB_movies.csv" from a GitHub repository. The file includes key metadata such as movie names, descriptions, and release years. This data is extracted via the GitHub API directly into n8n using the GitHub node.
   
   The extracted data then flows into a document data loader node, where the movie description acts as the main content, and metadata fields such as movie title and release year are associated as contextual enrichment. These documents are then passed through a token splitter to prepare them for embedding generation.
   
   Step 2: Embedding Movie Descriptions Using OpenAI  
   
   The parsed movie descriptions are converted into numerical vectors using OpenAI's text-embedding-3-small model. These high-dimensional vectors enable semantic understanding of each movie’s content. This setup allows the system to later retrieve "similar" movies based on purely textual input, even if the exact keywords or genres aren't explicitly stated.
   
   These embedded vectors—along with their corresponding metadata—are uploaded to a Qdrant vector store, indexed under the "imdb" collection. Qdrant here acts as a powerful, scalable search engine that can efficiently query similarity in vector space.
   
   Step 3: Enabling RAG: Chat Interface and Retrieval Logic  
   
   The core of this chatbot is a Retrieval-Augmented Generation (RAG) approach, where user input is not just relayed to the language model but augmented with context retrieved from Qdrant.
   
   A user sends a message via a chatbot interface (simulated via the Chat Trigger node in n8n). This query is processed via an OpenAI-powered language model (GPT-4o-mini) with memory persistence and an AI Agent logic built around tool usage.
   
   Within the tool execution, the user’s positive preference (e.g., “romantic comedy”) and negative preference (e.g., “horror bloody movie”) are embedded separately using OpenAI’s HTTP embedding API endpoint. These embeddings produce numerical representations of the user’s intent and aversions.
   
   Step 4: Querying with Qdrant for Recommendations  
   
   The positive and negative embedding vectors are sent to Qdrant’s “recommend” endpoint using an average vector strategy. This query returns the top 3 most relevant movies ranked by proximity in the vector space, ensuring recommendations closely match the user-specified traits while avoiding undesired characteristics.
   
   The system retrieves the metadata of these top recommended items using a follow-up query to the Qdrant collection. At this point, we have not only the IDs of the recommended movies but their name, description, release year, and embedded content.
   
   Step 5: Structuring the Final Response via AI Agent  
   
   The final response generation is left to another layer of OpenAI’s LLM, where the system prompt instructs the agent to generate a natural language reply with the top-3 movie recommendations without showing their internal similarity scores. This maintains a smooth user experience while ensuring AI-backed relevance.
   
   This RAG-based agent has access to memory, tools, and context, ensuring a coherent and intelligent dialogue over time. The recommendations are compiled using aggregation and formatting nodes before being returned to the user.
   
   Conclusion  
   
   Using n8n’s modular design, this workflow showcases the power of chaining APIs like GitHub, OpenAI, and Qdrant to build an intelligent, customizable, and interactive AI-powered chatbot. It emphasizes low-code/no-code automation while leveraging cutting-edge AI models and vector search techniques.
   
   Whether you're building a movie recommender or an AI assistant reliant on dynamic external data, this architecture proves scalable, efficient, and user-centric.
   
   By integrating NLP, embeddings, vector databases, and an orchestrating LLM agent, your n8n workflow becomes more than a simple automation—it becomes an intelligent AI application.
   
   Ready to build your own AI agent? With tools like OpenAI and Qdrant, the limits are only defined by your imagination.
   
   —  
   🚀 Explore the GitHub repo and try building this movie recommender chatbot yourself in n8n. Happy automating!

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.