Build a Visa Requirement Checker with n8n & Weaviate

This guide explains how to implement a scalable, conversational visa requirement checker using n8n workflow automation, Cohere embeddings, a Weaviate vector database, Anthropic’s chat model, and Google Sheets for logging and analytics. The solution accepts requests via webhook, transforms and indexes policy content into a vector store, and orchestrates an AI agent that answers user questions with traceable references.

Use Case: Why Automate Visa Requirement Checks?

Visa and immigration rules are dynamic, often changing with little notice. Manually tracking these changes and responding to user queries does not scale. By combining vector search with a conversational AI agent, you can:

• Provide fast, contextual answers sourced from your latest policy documents
• Reduce manual workload for support and legal teams
• Maintain an auditable trail of queries and responses for compliance and quality control

n8n serves as the orchestration layer that connects data ingestion, vector indexing, retrieval, AI reasoning, and logging into a single automated workflow.

Solution Architecture

The n8n template brings together several specialized components into one cohesive workflow:

• Webhook (n8n) – Entry point for incoming visa queries via HTTP POST.
• Text Splitter – Pre-processing step that segments long policy documents into manageable chunks.
• Cohere Embeddings node – Converts text chunks into dense vector representations.
• Weaviate (Vector Store) – Stores embeddings and supports semantic similarity search.
• Query node + Tool wrapper – Exposes Weaviate search as a callable tool for the AI agent.
• Memory buffer – Maintains short-term conversational context across turns.
• Anthropic Chat (Agent) – Large language model that reasons over retrieved context and generates responses.
• Google Sheets – Persistent logging of inputs, outputs, and status for auditing and analytics.

The workflow can be used both for indexing new or updated policy content and for serving user queries against the existing vector index.

Workflow Implementation in n8n

1. Webhook Entry Point

Begin by configuring an n8n Webhook node to accept HTTP POST requests on a path such as /visa_requirement_checker. The payload typically contains details such as nationality, destination, and travel context:

{  "nationality": "India",  "destination": "Germany",  "travel_dates": "2025-11-01 to 2025-11-10",  "purpose": "tourism"
}

Best practices at this stage include:

• Validating all required fields server side (e.g. nationality, destination, purpose).
• Returning an immediate acknowledgement to the caller.
• Optionally processing the request asynchronously if the full workflow is long running.

2. Preparing Policy Documents with Text Splitting

Visa policies are often lengthy, with complex legal phrasing and multiple sections. To optimize retrieval quality, the workflow uses the Text Splitter node to divide large documents into smaller, semantically coherent chunks.

In the template, typical parameters are:

• chunkSize: 400
• chunkOverlap: 40

This configuration:

• Improves embedding quality for long documents.
• Ensures that relevant sections are small enough to be retrieved precisely.
• Reduces the risk of truncation or loss of context when passed to the language model.

3. Generating Text Embeddings with Cohere

Each chunk from the Text Splitter is then processed by a Cohere Embeddings node (or another compatible embedding provider). In the reference template, the embedding model is set to default. For production use, consider:

• Selecting a multilingual model if your policy corpus spans multiple languages.
• Storing metadata alongside each embedding, such as:
  • Document identifier
  • Source system or URL
  • Section titles or headings
  • Version or effective date

The output of this stage is a set of embeddings, one per chunk, enriched with metadata that supports transparent and auditable responses.

4. Indexing Embeddings in Weaviate

The embeddings are then inserted into a Weaviate index. In the template, a dedicated index such as visa_requirement_checker is used.

Recommended configuration patterns:

• Store the original text chunk as a metadata field so the agent can quote or reference it directly.
• Include a version or timestamp field for each record to:
  • Support re-indexing when rules change.
  • Filter out outdated content during retrieval.
• Define a schema that captures key attributes, such as jurisdiction, visa type, or traveler category if applicable.

5. Vector Search and Tool Integration for the Agent

To answer user questions, the AI agent must be able to query the vector store. The workflow uses:

• A Query node to perform similarity search in Weaviate.
• A Tool wrapper that exposes this query capability as a callable tool for the agent.

Configure the Query node to return the top K most relevant chunks. A typical starting point is:

• K = 3 to 5

Ensure that metadata is returned with each result, including the original text and any identifiers. This gives the agent enough context to:

• Generate accurate and grounded answers.
• Provide citations or references from the underlying policies.

6. Orchestrating the Agent, Memory, and Chat Model

The core reasoning component is the Agent node, which coordinates between the language model and the available tools (in this case, the Weaviate query tool). The workflow also includes:

• A Memory buffer node that preserves recent conversation history, enabling follow up questions such as:
  • “Do I also need a visa for Schengen transit?”

  to be answered in context.

• An Anthropic Chat node that acts as the language model producing the final response.

Provide the agent with a clear and robust system prompt that defines behavior, for example:

You are an assistant that answers visa requirement questions using retrieved policy documents. Always include the source passage and a citation. If you are unsure, say so and recommend official embassy or consulate sources.

Good prompt design should instruct the model to:

• Use retrieved documents as the primary source of truth.
• Avoid speculation when information is missing or ambiguous.
• Highlight when travelers should verify with official government or consular websites.

7. Logging and Analytics with Google Sheets

Finally, the workflow appends each interaction to a Google Sheets document. Typical fields to log include:

• Input parameters (nationality, destination, travel dates, purpose)
• User question and any follow up queries
• Generated answer from the agent
• Success or failure status, including error messages if applicable
• Timestamps and request identifiers

This logging layer provides:

• An audit trail for compliance and internal review.
• Data for monitoring model quality and response consistency.
• A convenient source for usage analytics and reporting.

Operational Best Practices

Keeping Visa Data Current

Because visa rules change frequently, you should design a content management process around the vector index:

• Schedule periodic ingestion of updated policy documents.
• Trigger re-indexing when authoritative sources publish changes.
• Use versioning in Weaviate to retire or filter outdated content.

This approach reduces the risk of serving obsolete guidance to users.

Prompt Engineering for Safety and Clarity

For a compliance sensitive use case such as visa guidance, prompts should explicitly enforce safe behavior:

• Require the agent to include citations from Weaviate search results.
• Instruct the model to acknowledge uncertainty when documents do not clearly answer a question.
• Direct users to official resources (embassy, consulate, or government websites) when necessary.

This reduces the likelihood of overconfident or unsupported answers.

Optimizing Embeddings and Retrieval

Retrieval quality has a direct impact on answer quality. Consider:

• Experimenting with chunk size and overlap to balance context and precision.
• Selecting an embedding model tuned for your language coverage and domain.
• Exploring hybrid search (keyword plus vector) or semantic reranking when exact legal wording is critical.

Iterate based on real user queries and observed failure modes.

Scalability and Error Handling

In production environments, you should harden the workflow against external dependencies and load spikes:

• Implement retries with exponential backoff for calls to embedding providers, Weaviate, and Google Sheets.
• Monitor job queue lengths and throttle or scale resources as needed.
• Batch insert operations into Weaviate when re-indexing large document sets.
• Log and alert on failed insertions or query errors for proactive maintenance.

Security, Privacy, and Compliance

Visa queries can contain sensitive personal information. When designing your workflow:

• Ensure compliance with relevant privacy regulations such as GDPR or CCPA.
• Avoid storing unnecessary personal identifiers in logs or the vector store.
• Encrypt sensitive data at rest and in transit.
• Restrict access to logs and configuration to authorized personnel only.
• Consider anonymizing user inputs before indexing them, especially for long term storage.

Testing and Evaluation Strategy

Before exposing the visa checker to end users, invest in a structured evaluation process:

• Create a test suite of representative queries across:
  • Common travel scenarios
  • Edge cases and exemptions
  • Multiple nationalities and destinations
• Evaluate:
  • Accuracy of the answers against known ground truth
  • Citation correctness and relevance
  • Latency from request to response
• Identify gaps in coverage and add targeted documents or rule based checks where the agent underperforms.

Continuous evaluation helps maintain reliability as policies and models evolve.

Example System Prompt for the Agent

The following prompt snippet can be used as a starting point for the agent’s system message:

You are an assistant that answers visa requirement questions using retrieved policy documents. Always include the source passage and a citation. If you are unsure, say so and recommend official embassy or consulate sources.

Adapt this prompt to reflect your organization’s risk tolerance, legal guidance, and tone of voice.

Conclusion and Next Steps

By combining n8n for orchestration, Cohere for embeddings, Weaviate for vector search, and an Anthropic chat model for reasoning, you can implement a robust, auditable visa requirement checker that scales with your content and user base.

Use the provided template as a foundation, then iterate on:

• Retrieval configuration and embedding strategy
• Prompt engineering and safety controls
• Monitoring, logging, and evaluation processes

Call to action: Integrate your own policy documents into this n8n template to deploy a live visa requirement checker tailored to your organization. For more automation blueprints and guidance on building production ready AI assistants, consider subscribing to ongoing updates and best practices.