How a Frustrated Founder Built a Visa Requirement Checker With n8n and Vector Search

On a rainy Tuesday evening, Lena stared at her support inbox and sighed.

She was the founder of a small travel startup that helped digital nomads plan long stays abroad. Business was growing, but one problem refused to go away. Every day, dozens of people asked the same types of questions in slightly different ways:

• “Do I need a visa to work from Portugal for 60 days?”
• “Can I transit through Canada on my Indian passport?”
• “Is a 10-day tourist trip to Germany from India visa free?”

Her team had collected official embassy pages, PDFs, and policy documents. They tried keyword search, tags, even a basic FAQ chatbot. Still, the answers were often incomplete, outdated, or simply wrong when users phrased questions differently or in another language.

Lena knew she needed something more reliable. Something that could read complex visa rules, understand nuance, and still be auditable. That is when she discovered an n8n workflow template that used embeddings and a vector database to power a Visa Requirement Checker.

The Problem: When Keyword Search Fails Real Travelers

Lena’s existing setup relied on simple keyword matching. It worked fine for obvious queries, but visa rules are rarely obvious. Policies change, exceptions are buried in footnotes, and documents exist in multiple languages. Keyword search kept missing the point.

She saw issues like:

• Subtle differences between “tourist visit,” “business visit,” and “remote work” not captured by keywords
• Multilingual content where the query language did not match the document language
• Users asking natural questions that did not contain the exact words in the policy text

She needed semantic understanding, not just string matching. That is when she learned about embeddings and vector search, and how n8n could orchestrate everything into a single, automated workflow.

The Breakthrough: Semantic Search With Embeddings and Weaviate

Lena discovered an n8n template that promised exactly what she needed: a scalable Visa Requirement Checker built on semantic search. The idea was simple but powerful.

Instead of searching by keywords, the system would:

1. Convert visa documents into vector embeddings
2. Store them in a vector database (Weaviate in the template)
3. Compare user questions with document embeddings using semantic similarity
4. Feed the most relevant snippets into an LLM to generate a clear answer

By turning text into embeddings, the workflow could understand that “10-day tourist stay” is related to “short-term Schengen tourism visit,” even if the wording was different. That was exactly the nuance Lena was missing.

Inside the Workflow: The Invisible Machine Behind the Assistant

As Lena imported the template into her n8n instance, she saw a compact but powerful architecture. Each node played a specific role in the journey from user question to final answer.

The Core Components

The workflow relied on several key building blocks:

• Webhook – The public entry point that accepts POST requests from her frontend or any automation trigger
• Splitter – A helper that breaks long legal documents into smaller chunks to improve embedding quality
• Embeddings – A node that sends text chunks to Cohere (or another provider) to create vector representations
• Insert – The bridge to Weaviate that stores embeddings with rich metadata in a class named visa_requirement_checker
• Query and Tool – The retrieval layer that pulls the most relevant chunks for each user query
• Memory – A short-term context buffer for multi-turn conversations
• Chat/Agent – The orchestrator that calls an LLM (Anthropic in the template) and composes the final response
• Sheet – A Google Sheets integration that logs questions and answers for analytics and auditing

On the surface, users would just see a simple “Ask about visa requirements” box. Underneath, this workflow would quietly handle everything.

Rising Action: Lena Starts Wiring the System Together

To turn the template into a working Visa Requirement Checker, Lena followed the steps, but in her mind, they became part of a story: a user asks a question, the system reads through the law, and returns a grounded, explainable answer.

Step 1 – The Webhook: Opening the Door

First, she configured an n8n Webhook node to receive POST requests from her web app. The payload included:

• user_question – The natural language query
• country_of_origin
• destination_country
• Optional document uploads or URLs to official sources

If processing needed to be asynchronous, the webhook returned a 202 status code, confirming that the question was accepted and being processed. Input validation here helped her avoid incomplete or malformed requests.

Step 2 – The Splitter: Teaching the System to Read Long Documents

Lena’s data sources were long PDFs and dense legal pages. Feeding them into an embeddings model as a single block would be inefficient and noisy. The template solved this with a Splitter pattern.

Long documents were cut into smaller chunks, for example:

• Chunk size: about 400 characters
• Overlap: about 40 characters to preserve context between chunks

This approach kept enough context for the model to understand the text while staying within token limits and improving retrieval accuracy.

Step 3 – Embeddings: Turning Text Into Vectors

Each chunk then flowed into an Embeddings node. In the template, Cohere was used by default, but the node could be easily switched to OpenAI, Anthropic, or another provider.

Crucially, Lena kept metadata attached to every embedding:

• Source URL
• Document title
• Chunk index
• Country and language
• Last-updated date
• Source type, such as official government page or third-party explanation

This metadata would later allow precise filtering and explainable answers. She wanted to know exactly which paragraph a response came from.

Step 4 – Insert: Building the Knowledge Base in Weaviate

Next, the Insert node wrote the embeddings and metadata into Weaviate. She created a class called visa_requirement_checker, which became the central index for all visa-related knowledge.

Weaviate’s approximate nearest neighbor (ANN) search meant that, when a user asked a question, the system could quickly find the most semantically similar chunks, not just those with matching keywords. Metadata filters made it easy to restrict results to a specific country, region, or date range, so outdated or irrelevant rules stayed out of the answer.

Step 5 – Query + Tool: Finding the Right Paragraphs

With the knowledge base in place, Lena turned to the retrieval flow. When a user asked a question, the workflow ran a Query against Weaviate to fetch the top-k most relevant chunks.

Those retrieved snippets were then wrapped by a Tool node. This node made the context available to the agent so the LLM could “see” the exact text that explained the policy. The assistant was no longer guessing. It was reading.

Step 6 – Memory: Remembering the Conversation

Visa questions are rarely one-and-done. Users often ask follow-ups like “What if I stay longer?” or “Does this apply if I have a US visa?”

To handle this, Lena enabled a Memory buffer. It stored short-term session context such as previous questions, clarifications, and decisions. This allowed the assistant to support multi-turn conversations without having to fully reshape the retrieval step on every interaction.

Step 7 – Chat/Agent: Crafting the Final Answer

Finally, the agent node pulled everything together. It built a prompt that included:

• The user’s current question
• The retrieved document chunks from Weaviate
• Relevant session memory

The node then called an LLM, Anthropic in the template, to generate a clear, citation-backed answer. Lena configured the prompt to enforce several rules:

• Answer concisely and reference which document or paragraph the information came from
• Be explicit when information is missing, uncertain, or possibly outdated
• Offer concrete next steps, such as linking to official embassy pages or contact forms

Behind the scenes, a Sheet node logged each question and answer to Google Sheets, giving Lena a transparent audit trail and analytics for future improvements.

The Turning Point: A Real Query Puts It to the Test

With everything wired up, Lena was ready to test the workflow with a real-world scenario that her support team saw almost daily.

A user asked:

“Do I need a visa to travel from India to Germany for a 10-day tourist stay?”

Here is how the workflow handled it, step by step:

1. The Webhook node received the question and metadata such as origin country (India) and destination (Germany).
2. The Query node searched Weaviate for the most relevant chunks related to Germany entry requirements and Schengen short-stay rules.
3. The Tool node packaged these snippets, and the agent composed an answer that referenced the official German foreign office page and the specific paragraph that applied to short-term tourism.
4. The Sheet node logged the entire exchange to Google Sheets for later review and analytics.

For the first time, Lena saw a response that was both natural and grounded in verifiable sources, with citations pointing back to the exact policy text.

Staying Safe and Accurate: Best Practices Lena Adopted

As she prepared to roll this out to real users, Lena focused on reliability, security, and long-term maintainability.

Making Metadata Work Harder

She expanded the metadata stored with each embedding to include:

• Country and region
• Last-updated date
• Language
• Source type, such as official embassy, government gazette, or third-party explanation

This allowed her to filter retrieval results by country or date and avoid surfacing stale or irrelevant guidance. If a rule changed, she could re-index documents and be sure that older chunks would not sneak back into answers.

Handling Rate Limits and Batching

Since embeddings and LLM calls can be rate limited, Lena configured the workflow to:

• Batch embedding requests where feasible
• Use backoff and retry logic in n8n to handle transient errors
• Monitor usage to avoid hitting provider limits unexpectedly

This kept the system stable, even as more users began to rely on it.

Security, Compliance, and PII

Visa-related questions sometimes included personal details. To stay compliant, Lena:

• Encrypted sensitive data at rest
• Restricted access to the Weaviate instance
• Avoided logging full passport numbers, national IDs, or other highly sensitive identifiers
• Implemented a policy to purge sensitive records when no longer needed

The goal was to provide helpful guidance without turning the system into a liability.

Dealing With Ambiguity and Edge Cases

No model is perfect. When the LLM expressed uncertainty or when the source documents were unclear, Lena’s workflow provided a safe fallback:

• Link to the relevant official embassy or government guidance
• Recommend contacting an immigration lawyer for complex cases
• Offer to collect more context, such as length of stay, visa type, or travel dates

This kept users safe and aligned the assistant with real-world legal complexity.

From Prototype to Production: Testing, Scaling, and Monitoring

Before deploying the Visa Requirement Checker broadly, Lena treated it like any other critical product feature.

Testing and Validation

She created a test suite of representative queries across multiple countries and visa types. For each query, she defined:

• Expected documents or paragraphs that should be cited
• Acceptable answer patterns

She then used automated checks to validate that:

• Retrieval results referenced the correct documents
• Precision and recall metrics stayed within acceptable ranges after content updates

This gave her confidence that changes to the knowledge base would not silently break answers.

Scaling and Monitoring in Production

As usage grew, Lena began tracking:

• Query latency through the workflow
• Embeddings throughput
• Vector store size and performance
• Prompt costs for the LLM

Weaviate’s sharding and horizontal scaling options helped her keep retrieval fast even as the number of documents increased. To control LLM costs, she experimented with truncating or compressing retrieval context while preserving the most relevant chunks.

Keeping Up With a Moving Target: Content Updates

Immigration rules do not stand still, and Lena knew that a one-time import of documents would not be enough. She set up a content ingestion pipeline that periodically fetched and re-indexed:

• Official embassy pages
• Government gazettes
• Other authoritative sources

Using the Insert node, she could upsert updated embeddings and maintain a changelog for traceability. When a rule changed, the new version would be reflected in answers without manual intervention in each node.

Deployment: How Lena Put It All Online

To move from her local experiments to a production-ready assistant, Lena followed a straightforward deployment checklist.

1. Provisioned a Weaviate instance and created a class called visa_requirement_checker.
2. Configured embeddings provider credentials, such as Cohere or OpenAI, inside n8n.
3. Added LLM credentials, using Anthropic in her case, for answer generation.
4. Set the Webhook URL in her frontend to point to the n8n webhook node.
5. Connected Google Sheets to log requests and answers for analytics and auditing.

She ran a final round of tests, checked logs, and gradually rolled out the new assistant to a segment of her users before making it available to everyone.

The Resolution: A Smarter, Trustworthy Visa Requirement Checker

Weeks later, Lena opened her support dashboard and smiled. Repetitive visa questions had dropped sharply. When they did appear, they were often more advanced or nuanced, because the basic entry requirements were already answered by the assistant.

Her team could now focus on edge cases and higher-value support, while travelers received fast, citation-backed guidance grounded in official documents.

By combining n8n with semantic search and a vector database, Lena had built a Visa Requirement Checker that was:

• Flexible – Easy to adapt to new countries, visa types, and languages
• Auditable – Every answer could be traced back to specific documents and paragraphs
• Accurate – Retrieval and generation were clearly separated, keeping responses tied to real sources

What started as a frustrating support problem became a competitive advantage for her travel startup.

Your Turn: Build Your Own Visa Requirement Checker With n8n

If you are facing similar challenges, you do not have to start from scratch. You can import the same n8n workflow template, connect your own data sources, and adapt it to your list of countries and visa types.

To get started:

1. Import the n8n workflow JSON into your n8n instance, self-hosted or on n8n.cloud.
2. Connect your Cohere or other embeddings provider credentials.
3. Set up Weaviate and create the visa_requirement_checker class.
4. Add your LLM credentials,