Visa Requirement Checker with n8n and AI

Overview

This reference guide explains how to implement a scalable Visa Requirement Checker using n8n, vector search, and LLM-based conversational agents. The workflow template combines a webhook-based entry point, text splitting, semantic embeddings, a Weaviate vector database, an Anthropic-powered agent, and Google Sheets logging to deliver accurate and auditable visa guidance.

The automation pipeline:

• Accepts structured user queries via a Webhook
• Splits and embeds policy text or query content using a Text Splitter and Cohere embeddings
• Persists and retrieves visa policy knowledge from Weaviate as a vector store
• Uses an Agent + Tool setup to inject relevant context into an Anthropic chat model
• Logs the full interaction lifecycle into Google Sheets for analytics and compliance

Use Case Rationale

Visa and entry rules change frequently and depend on multiple parameters such as nationality, destination, purpose of travel, and passport validity. A dedicated Visa Requirement Checker built on n8n and AI helps:

• Travel agencies validate eligibility before bookings
• Customer support teams respond faster with consistent answers
• Enterprise mobility teams manage employee travel compliance

Using n8n and AI-driven vector search makes the solution:

• Fast to deploy with minimal custom code
• Context-aware, handling natural-language questions and follow-ups
• Scalable via webhook-based concurrent request handling
• Auditable through structured logging in Google Sheets

Workflow Architecture

At a high level, the n8n workflow template follows this architecture:

1. Webhook receives POST requests from a front-end or external API client.
2. Text Splitter segments long text into manageable chunks with overlap.
3. Embeddings (Cohere) convert each chunk into a vector representation.
4. Weaviate Insert stores embeddings and metadata in the vector database.
5. Weaviate Query retrieves the most relevant policy chunks for a given query.
6. Vector Store Tool exposes Weaviate search to the Agent as a tool.
7. Memory (Buffer Window) preserves recent conversation state for follow-ups.
8. Agent + Chat (Anthropic) generate the final user-facing response.
9. Google Sheets appends a log entry for each interaction.

Node-by-Node Breakdown

1. Webhook (n8n Trigger)

The Webhook node is the entry point for all user queries. It listens for incoming HTTP POST requests from your UI, form, or back-end service.

Typical configuration

• HTTP Method: POST
• Path: for example /visa_requirement_checker
• Response Mode: usually On Received or Last Node depending on whether you want to return the AI answer directly

Expected request payload

The workflow expects structured JSON with user and trip parameters, for example:

{  "name": "John Doe",  "nationality": "India",  "destination": "Germany",  "purpose": "tourism",  "passport_validity_months": 6
}

You can enforce required fields either:

• Upstream in your client or API gateway (recommended), or
• Downstream in n8n using additional nodes (e.g. IF nodes for validation and error responses)

Make sure the Webhook node is configured to accept application/json and that your client sends a valid JSON body. For production, consider validating types and value ranges (for example, passport_validity_months >= 0).

2. Text Splitter

The Text Splitter node prepares text for embedding by splitting it into smaller, overlapping segments. This is important both for:

• Long visa policy documents that you index into Weaviate
• Complex multi-field user inputs that may be combined into a single text string

Key parameters

• Chunk Size: for example 400 tokens or characters
• Chunk Overlap: for example 40, to preserve context between adjacent chunks

Choosing appropriate chunk sizes helps mitigate tokenization issues and improves retrieval quality. If chunks are too large, you may hit token limits in the LLM. If they are too small, you risk losing contextual meaning.

3. Embeddings (Cohere)

The Embeddings node calls Cohere to transform each text chunk into a high-dimensional vector. These embeddings are later used by Weaviate to perform semantic similarity search.

Configuration details

• Credentials: Cohere API key configured in n8n credentials
• Model: choose a Cohere embedding model optimized for semantic search
• Input: array of text chunks from the Text Splitter node

All embeddings generated here should be consistent with those used at query time. Avoid switching models mid-way, as it will degrade vector similarity results.

4. Weaviate Vector Store (Insert & Query)

4.1 Insert node

The Insert operation writes embeddings and associated metadata into Weaviate. This step is usually used when:

• Seeding the vector store with visa policy documents
• Updating or extending existing policies

Typical metadata fields include:

• country or destination
• date_updated or version information
• source (for example, official government site URL)

This metadata is crucial for filtering and auditing, and it can also be surfaced to the LLM as part of the context.

4.2 Query node

At query time, the workflow:

1. Computes an embedding for the user question using the same Cohere model
2. Executes a Weaviate Query to retrieve the top-N most similar chunks

The query node typically returns:

• The matched text snippets
• Associated metadata (country, source, last updated date, etc.)
• Similarity scores

The top-N results, sometimes referred to as top_snippets, are passed downstream to the Agent to ground its response in real policy data.

5. Vector Store Tool Layer

The Tool node wraps the Weaviate Query so that the Agent can call it as a tool. In n8n, this layer:

• Defines an interface for the Agent, for example a “search_visa_policies” tool
• Maps tool inputs to Weaviate query parameters
• Formats tool outputs into a structure the Agent can consume in its reasoning loop

This abstraction allows the LLM to request additional context from the vector store when needed, instead of embedding all data into the prompt upfront.

6. Memory (Buffer Window)

The Memory node, configured as a Buffer Window, stores recent conversation turns so the Agent can handle follow-up queries such as:

“What about family members?”

Rather than re-sending all previous messages, the buffer window:

• Maintains only the last N messages
• Controls prompt size and cost
• Preserves enough context for coherent multi-turn interactions

Choose a window size that balances context richness with token usage. For one-shot or stateless queries, you can keep this small or even disable multi-turn memory.

7. Chat & Agent (Anthropic)

The Agent node orchestrates:

• The Anthropic chat model
• The Vector Store Tool
• The Memory buffer
• The prompt template that structures the task

Anthropic (or another supported LLM) generates the final natural-language answer by combining:

• User-provided parameters (nationality, destination, purpose, passport validity)
• Retrieved policy snippets from Weaviate
• Conversation history stored in the Memory node

Prompting guidelines

When configuring the Agent, ensure that the system and user prompts instruct the model to:

• Prioritize official policy text and authoritative sources
• Clearly state assumptions or uncertainties when policies conflict
• Optionally return structured JSON if your UI needs machine-readable output

8. Google Sheets (Logging and Analytics)

The final node in the workflow appends a row to a Google Sheets spreadsheet for each interaction. This provides:

• Traceability for compliance and audits
• Data for analytics and quality monitoring
• A simple way to review or correct answers manually

Typical columns include:

• Raw user input
• Parsed or normalized parameters (nationality, destination, purpose, validity)
• Retrieved policy snippets or document IDs
• Model response text
• Timestamp and any relevant request IDs

Configure Google Sheets credentials in n8n and ensure the target sheet has a stable schema to avoid write errors.

Prompt Template Example

Below is a sample prompt template that can be used within the Agent node to structure the LLM’s behavior:

You are an assistant that determines visa requirements. Use the user data and the policy snippets below. If policies conflict, prioritize official government sources and state uncertainty.

User data:
- Nationality: {nationality}
- Destination: {destination}
- Purpose: {purpose}
- Passport validity: {passport_validity_months} months

Policy snippets:
{top_snippets}

Answer with:
1) Short recommendation
2) Required documents
3) Any notes and source citations

You can adapt this template for your UI needs, for example by adding a requirement to return JSON keys such as recommendation, required_documents, and notes.

Configuration Notes & Integration Details

Credentials

• Cohere: API key for embeddings
• Weaviate: endpoint URL, API key or auth token, and any TLS settings
• Anthropic: API key for the chat model
• Google Sheets: OAuth or service account credentials

All credentials should be stored in n8n’s credential manager, not hard-coded in nodes.

Data flow and mapping

• Webhook JSON fields are mapped into the Agent prompt variables and, if needed, into the text that is embedded.
• Weaviate metadata fields should align with how you filter or display results (for example, destination matching the user’s chosen country).
• Google Sheets columns should be mapped explicitly from node outputs to avoid schema drift.

Error handling and edge cases

Typical scenarios to consider:

• Missing or invalid input: handle via conditional nodes (IF / Switch) after the Webhook and return a clear error payload.
• No relevant policy snippets found: define Agent behavior for an empty or low-confidence Weaviate result set, for example instruct the model to say that information is unavailable.
• Timeouts or API errors: configure retry strategies or send a fallback message to the user, and log the error to Google Sheets or an additional error log.

Advanced Customization

Scaling and performance

• Place a reverse proxy with TLS in front of n8n for secure public access.
• Use rate limiting at the proxy or API gateway to prevent abuse and control external API costs.
• Batch insert documents when seeding or refreshing large policy sets to speed up indexing in Weaviate.
• Monitor vector store usage and tune chunk size, overlap, and top-K retrieval parameters for accuracy and latency.

Security, compliance, and data handling

Visa-related queries can contain personally identifiable information. Apply the following practices:

• Avoid embedding unnecessary PII in the vector store. Prefer storing a reference ID as metadata and keeping PII in a separate, secured system if needed.
• Enable access controls for both n8n and Weaviate, and use encryption at rest where supported.
• Define a log retention policy for Google Sheets, periodically removing or anonymizing old entries.
• Display a clear disclaimer to users indicating that the checker provides guidance and not legal or immigration advice.

Testing and troubleshooting

Before going live, test the workflow with a broad range of scenarios:

• Different nationalities and destinations
• Dual nationality, diplomatic or service passports
• Short layovers, transit-only trips, and long stays

Common issues and how to address them:

• Outdated or incorrect information: verify the underlying sources in Weaviate and refresh the index with updated policy documents.
• Poor retrieval quality: experiment with a different embedding model, adjust chunk size and overlap, or increase top-K in Weaviate queries.
• Unexpected cost spikes: cache frequent queries upstream, limit model response length, or configure usage quotas in your LLM provider account.

Business Impact and Typical Deployments

Organizations commonly use this Visa Requirement Checker pattern to:

• Automate pre-booking eligibility checks in travel portals
• Provide suggested answers to customer support agents for faster response times
• Standardize travel compliance checks for employees across multiple regions

Because the solution is built on n8n with a no-code / low-code approach, it is easier to maintain and extend than a fully custom-coded system.

Getting Started with the Template

To implement this workflow in your own environment:

1. Import the provided n8n workflow template.
2. Configure credentials for Cohere, Weaviate, Anthropic, and Google Sheets.
3. Seed the Weaviate index with authoritative visa