Build a Visa Requirement Checker with n8n, Embeddings and Weaviate

This guide describes how to implement a production-ready Visa Requirement Checker using n8n as the workflow engine, an embeddings provider and Weaviate as the vector database, and an AI agent for reasoning over retrieved policies. The workflow also logs all queries and responses to Google Sheets for auditing and analytics.

1. Solution overview

The Visa Requirement Checker automates the evaluation of visa rules based on structured input such as nationality, destination, passport type, trip purpose, and stay duration. The n8n workflow exposes a webhook endpoint, retrieves relevant regulations from a vector store, uses an AI agent to interpret the rules, then returns a structured answer and records the interaction.

1.1 Core capabilities

• Automated visa requirement evaluation based on traveler and trip attributes
• Semantic search over a centralized corpus of visa policies stored as embeddings
• AI agent that interprets retrieved policy text and generates clear, actionable responses
• Persistent logging of all queries and answers in Google Sheets for audit trails and reporting
• Extensible design that can accommodate new countries, document types, or policy changes

1.2 Typical use cases

• Travel platforms providing instant visa guidance at checkout or trip planning stages
• Corporate HR and mobility teams supporting employee travel compliance
• Internal tools for support teams to quickly answer visa-related questions

2. High-level architecture

At a high level, the workflow follows this sequence:

1. Incoming POST request hits an n8n Webhook node.
2. Visa policy documents are pre-processed, split into chunks, and converted into embeddings.
3. Embeddings and associated metadata are stored in Weaviate as a vector store.
4. For each request, n8n queries Weaviate to retrieve the most relevant policy chunks.
5. An AI agent consumes the retrieved content, applies domain logic, and produces a final answer.
6. n8n appends the request and response to Google Sheets for logging, then returns the answer to the caller.

2.1 Main workflow components

• n8n Webhook node – Exposes an HTTP endpoint for POST requests from your UI or API.
• Text splitting logic – Breaks large policy documents into embedding-friendly segments with controlled overlap.
• Embeddings provider – Generates vector representations of each text chunk using a model such as Cohere or OpenAI.
• Weaviate vector store – Persists vectors and metadata and provides semantic search capabilities.
• AI agent – Uses the vector store as a tool, plus optional memory, to answer queries using retrieved context.
• Google Sheets integration – Stores query parameters, AI responses, and citations for analysis and compliance.

3. Data model and request format

3.1 Incoming request payload

The Webhook node is configured to receive a JSON payload with traveler and trip details. A typical POST body looks like:

{  "nationality": "India",  "destination": "Germany",  "passport_type": "Ordinary",  "purpose": "Tourism",  "arrival_date": "2025-11-01",  "stay_days": 10
}

You can extend this schema with additional attributes (for example, transit-only flag, dual nationality, or vaccination status) as needed, but the core workflow assumes at least nationality, destination, passport type, purpose, and stay duration.

3.2 Policy document representation

Source regulations such as country rules, bilateral agreements, and exemptions are stored as text documents. Before insertion into Weaviate, each document is:

• Split into chunks based on character length and overlap.
• Converted to embeddings.
• Enriched with metadata such as country, document type, effective date, and source URL.

Example metadata object:

{  "country": "Germany",  "type": "visa_policy",  "source": "gov.de"
}

4. Node-by-node workflow breakdown in n8n

4.1 Webhook node – Request entry point

Purpose: Receive visa check requests from your front end or API clients.

• Method: POST
• Path: for example /visa_requirement_checker
• Response mode: Typically “On Received” or “Last Node” depending on how you want to control the HTTP response.
• Expected content type: application/json

The Webhook node parses the JSON payload and makes the fields (such as nationality and destination) available to subsequent nodes via item data. You can add basic validation here, for example checking that required fields are present before proceeding.

4.2 Text splitting for embeddings

Purpose: Pre-process long policy documents into segments suitable for embedding models and vector search.

The template uses a character-based text splitter with:

• chunkSize: 400 characters
• chunkOverlap: 40 characters

These parameters balance context preservation with embedding length and cost. You can tune them according to your corpus:

• Chunk size: typically 300-800 characters depending on how dense and structured the source text is.
• Overlap: typically 20-100 characters to avoid breaking sentences and to carry context across chunks.

In n8n, this logic is usually implemented via a dedicated text-splitting node or a combination of Function nodes and the built-in text splitter integration, depending on your setup. The important aspect is that each resulting item contains:

• The text chunk.
• Identifiers linking back to the original document.
• Any static or derived metadata you want to store with the embedding.

4.3 Embeddings node – Vector generation

Purpose: Convert each text chunk into a numeric vector using an external embeddings API.

You can use an embeddings node configured with providers such as:

• Cohere – via the Cohere credentials in n8n.
• OpenAI – via the OpenAI credentials in n8n.
• Any other embeddings-capable provider supported by your n8n instance.

Configuration points:

• Select the appropriate model name based on your provider.
• Map the input field to the text chunk property produced by the text splitter.
• Ensure your API keys are stored in n8n credentials and not hard-coded in nodes.

The node returns vectors that are then paired with the original text and metadata for insertion into Weaviate.

4.4 Weaviate insertion – Building the vector store

Purpose: Persist embeddings and metadata in Weaviate for later semantic search.

Each item sent to Weaviate typically includes:

• The embedding vector generated by the embeddings node.
• The original text chunk.
• Metadata such as country, type, effective_date, and source.

Storing metadata is important for:

• Filtering searches by destination country or policy type.
• Tracking which source and version of the policy a result came from.
• Supporting future analytics and debugging.

This ingestion process usually runs as a separate one-time or scheduled workflow to keep the Weaviate corpus updated, independent from the real-time visa check requests.

4.5 Weaviate query – Retrieving relevant policies

Purpose: For each incoming visa check request, retrieve the most relevant policy chunks from Weaviate.

After the Webhook receives a query, the workflow constructs a search prompt or query vector that reflects:

• Nationality.
• Destination.
• Passport type.
• Trip purpose and stay duration.

The Weaviate query node then:

• Performs a semantic search against the stored embeddings.
• Returns the top K results (typically K = 3-5) that are most relevant to the query.

The template treats the vector store as a tool that the AI agent can call to fetch these policy passages. The retrieved items, including their text and metadata, are passed forward to the agent node as context.

4.6 AI agent configuration and memory

Purpose: Interpret the retrieved policy text, apply domain logic, and generate a user-friendly answer.

The AI agent is configured to:

• Read the policy chunks returned from Weaviate.
• Reason about rules such as visa-free durations, distinctions between transit and entry, and differences by passport type.
• Produce a structured, concise explanation of whether a visa is required and what the next steps are.

The template uses a memory buffer to maintain short-term conversation history. This is useful if your UI supports follow-up questions, for example clarifying stay duration or purpose after the initial answer.

When designing the agent prompt, instruct it to:

• Always cite sources based on the metadata (for example, government website or official document name).
• Include the effective date of the policy if available in the metadata.
• Provide clear next steps, such as links to embassy application pages or a list of required supporting documents.

While the agent can handle a range of scenarios, it is important not to overstate certainty. For ambiguous or conflicting policy text, you can instruct the agent to highlight uncertainty and recommend contacting an embassy.

4.7 Google Sheets node – Logging and auditing

Purpose: Persist each interaction for monitoring, analytics, and compliance.

After the AI agent generates a response, the workflow appends a new row to a Google Sheet. Typical columns include:

• Timestamp of the request.
• Nationality.
• Destination.
• Passport type and trip purpose.
• Stay duration.
• Recommended visa action (for example, “No visa required for stays up to X days”, “Apply for Schengen tourist visa”).
• Source citations or URLs returned by the agent.

This log supports:

• Quality checks by sampling responses and verifying them against official sources.
• Internal reporting on query volume and patterns.
• Regulatory or compliance audits where you must show how advice was generated.

5. Configuration and security considerations

5.1 Credentials and access control

• Store embeddings provider keys (Cohere, OpenAI, etc.) in n8n credentials, not in plain text within nodes.
• Restrict access to the Weaviate instance using appropriate authentication and network controls.
• Use IAM roles or service accounts where possible and rotate API keys regularly.

5.2 Webhook security

• Expose the webhook endpoint only over HTTPS.
• Validate incoming payloads, including content type and required fields.
• Implement rate limiting and basic abuse detection to mitigate misuse or automated attacks.

5.3 Data privacy and compliance

• Avoid storing unnecessary personally identifiable information (PII) in logs or vector stores.
• Mask or anonymize data in Google Sheets where feasible.
• Ensure that your data handling practices align with regulations such as GDPR, particularly if logs contain identifiers that could be linked back to individuals.

6. Testing, edge cases, and monitoring

6.1 Functional test coverage

Before deploying to production, run comprehensive tests across a range of scenarios:

• Different nationality-destination combinations, including:
  • Visa-free entries.
  • Visa-on-arrival cases.
  • eVisa availability.
  • Embassy or consulate-issued visas required in advance.
• Transit-only itineraries and multi-leg trips where the traveler may not technically “enter” a country.
• Special passport types, such as diplomatic or service passports, and expired or soon-to-expire passports where rules may differ.

6.2 Edge cases and error handling

Consider how the workflow responds when:

• No relevant documents are returned from Weaviate (for example, new country not yet in the corpus).
• The embeddings provider or Weaviate is temporarily unavailable.
• The input payload is incomplete or malformed.

In these cases, you can:

• Return a fallback message that indicates manual review is required.
• Log the failure in Google Sheets for later investigation.
• Trigger alerts or notifications via separate n8n workflows if needed.

6.3 Monitoring and quality assurance

Use the Google Sheets log or a dedicated logging system to:

• Monitor query volume and response times.
• Track error rates or empty results from Weaviate.
• Periodically sample agent outputs and verify their accuracy against official government sources.

7. Enhancements and advanced customization

7.1 Automated policy ingestion

Once the core checker is stable, you can build additional n8n workflows to:

• Regularly fetch updated visa policies from official websites or APIs.
• Re-split and re-embed documents when changes are detected.
• Update or upsert records in Weaviate on a schedule.

7.2 Domain-specific extensions

• Create country-specific modules or microservices that monitor legal changes and push updates into your corpus.
• Add a UI layer or integrations with Slack, WhatsApp, or internal chat tools to make visa checks easily accessible to end users or staff.
• Introduce structured forms that capture additional parameters such as vaccination requirements or dual nationality.