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 an AI-Powered Movie Recommendation Chatbot with n8n, OpenAI & Qdrant
   
   Meta Description:
   Learn how to build a Retrieval-Augmented Generation (RAG) movie recommendation chatbot using n8n, OpenAI's GPT-4 and Embedding API, and Qdrant as a vector database. A step-by-step breakdown of the workflow that powers personalized movie suggestions.
   
   Keywords:
   n8n, OpenAI, GPT-4, Qdrant, RAG pipeline, movie recommendation chatbot, AI chatbot, vector database, LangChain, embeddings, automation, machine learning, natural language processing, recommender system
   
   Third-Party APIs Used:
   - OpenAI API (for GPT-4 and embeddings)
   - Qdrant API (vector store and recommendation engine)
   - GitHub API (data retrieval of movie dataset)
   
   —
   
   Article:
   
   Build Your Own AI Movie Recommendation Chatbot Using n8n, OpenAI, and Qdrant
   
   In today’s fast-paced world, consumers increasingly rely on intelligent chatbots to guide their entertainment choices. From Netflix's recommendation systems to Spotify's music curation, the demand for smart assistants is booming. Now, imagine having your own AI-powered chatbot that can understand user preferences and suggest the perfect movie. Thanks to the power of low-code automation with n8n, OpenAI’s intelligent embedding and language models, and Qdrant’s blazing-fast vector database—this is completely possible, and here’s how.
   
   In this article, we’ll explore a Retrieval-Augmented Generation (RAG) workflow built using n8n that queries a vector-based movie dataset, analyzes user preferences, and recommends movies in a human-like, conversational format. 
   
   Let’s dive in.
   
   Step 1: Data Retrieval from GitHub
   
   The first step in the workflow begins with a manual trigger inside n8n or a chat command to initiate the process. Here, the workflow uses the GitHub API to fetch a CSV file named Top_1000_IMDB_movies.csv from a public repository. This dataset includes critical metadata such as movie names, release years, and descriptions, essential for fine-tuned recommendations.
   
   Step 2: Embedding Movie Descriptions with OpenAI
   
   Once the data is extracted, each movie's description is embedded using OpenAI’s Embedding API (specifically using the text-embedding-3-small model). Embeddings are numerical vector representations of text data, allowing for semantic comparisons between movie plots and a user's preferences. n8n’s built-in LangChain nodes interact seamlessly with the OpenAI API to process these embeddings.
   
   Step 3: Storing Vectors in Qdrant
   
   The resulting embedded vectors, bundled with contextual metadata (like title and release year), are ingested into a Qdrant vector store. Qdrant allows high-performance, real-time similarity searches among vectorized data—ideal for recommendation systems. Once the movie dataset is vectorized and stored, the chatbot is review-ready.
   
   Step 4: Chat-Driven User Preference Recognition
   
   The chatbot interface is initiated using a LangChain-powered AI agent within n8n. When a user types a request—for example, “I love sci-fi but hate horror”—the agent parses the sentiment and formulates both a “positive example” (preferred genre/narrative type) and a “negative example” (genres or features to avoid). These preferences are sent to OpenAI's Embedding API to convert the text descriptions into vectors for comparison.
   
   Step 5: Personalized Recommendation via Qdrant
   
   With the user's preference vectors generated, Qdrant’s recommendation API is called using the “average_vector” strategy. The API then looks for top-matching movie vectors to the user's request, subtracting those close to the anti-preferences. The top 3 nearest neighbors in vector space are identified as recommended movies.
   
   Step 6: Retrieving and Formatting Movie Metadata
   
   Once the top recommendations are retrieved from Qdrant, an additional API request retrieves the full metadata for each film—title, description, release year—which are then structured and formatted using n8n’s “Set” and “Aggregate” nodes. These results are fed back into the AI agent.
   
   Step 7: Conversational Output with GPT-4
   
   Finally, using the GPT-4o-mini model from OpenAI, the chatbot crafts a friendly, conversational response embedding the top 3 movie suggestions, tailored to the original user request. Thanks to LangChain’s memory buffer and workflow tools in n8n, each conversation maintains context, enabling multiple rounds of dialogue with the user.
   
   Workflow Power Summary
   
   This no-code/low-code automation blends the following technologies:
   
   - n8n for orchestration and automation.
   - OpenAI for language modeling and semantic embedding.
   - Qdrant for intelligent, high-performance vector search and retrieval.
   
   Conclusion
   
   This workflow exemplifies the power of integrating large language models (LLMs) with vector databases in real-world applications. With a few well-orchestrated tools and APIs, anyone can build a powerful, RAG-based chatbot that truly understands user preferences and offers intelligent, conversational movie guidance.
   
   Whether you're an automation enthusiast, a developer experimenting with vector search, or a company building intelligent assistants, this use case offers a scalable and adaptable foundation for knowledge-driven recommendations.
   
   Ready to build yours? This is just the beginning.
   
   —
   Got questions or want help setting up your own chatbot workflow? Join the n8n community and start transforming the way information is searched and delivered.
   
   

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.