Build a Visa Requirement Checker with n8n & LangChain

This guide walks you through building a practical Visa Requirement Checker using n8n, LangChain components, Weaviate vector search, Cohere embeddings, Anthropic chat, and Google Sheets. You will learn not only how to assemble the workflow, but also why each part exists and how they work together.

What you will learn

By the end of this tutorial, you will be able to:

• Set up an n8n workflow that answers visa questions like “Do I need a visa to travel from Germany to Japan?”
• Ingest and process official visa policy documents into a searchable format
• Use Cohere embeddings and Weaviate for semantic, vector-based search
• Connect an Anthropic chat model through a LangChain-style agent to generate accurate, policy-backed answers
• Log all interactions into Google Sheets for auditing and analytics

Why build an automated Visa Requirement Checker?

Visa rules are complex, detailed, and often updated. Manually checking every request is slow and error-prone. An automated checker helps you:

• Save time by handling routine visa questions automatically
• Reduce mistakes by consistently using the same official sources
• Handle natural language queries like “What documents do I need for a US tourist visa?”

By combining embeddings with a vector database, the system can find the most relevant policy snippets even if the user’s question does not exactly match the wording in the documents.

Concepts and architecture

High-level workflow in n8n

n8n acts as the central orchestrator for the entire Visa Requirement Checker. At a high level, the workflow does the following:

• Receive user questions via a Webhook
• Split your visa policy documents into smaller chunks
• Create embeddings for each chunk using Cohere
• Store those embeddings in a Weaviate vector database
• Retrieve the most relevant chunks for each new query
• Use an Anthropic chat model through an Agent node to generate an answer
• Log the full interaction into Google Sheets for later review and analysis

Key nodes and components in the template

The n8n workflow template is built around these main nodes:

• Webhook node (POST /visa_requirement_checker) to accept user requests
• Splitter node (character-based) with chunkSize: 400 and chunkOverlap: 40 for document chunking
• Cohere Embeddings node to convert text chunks into vectors
• Weaviate Insert and Weaviate Query nodes for vector indexing and retrieval
• Tool node configured for Weaviate so the Agent can call it as an external tool
• Memory buffer window node to store recent conversation context
• Anthropic chat model node to generate natural language responses
• Agent node with promptType: define and text: ={{ $json }} to orchestrate reasoning and tool usage
• Google Sheets Append node to log each interaction

Before you start: data and credentials

Collect visa policy data

First, gather your source material. You will need:

• Official visa policy pages, PDFs, or documents from government or embassy sites
• Cleaned text versions of these documents, either as separate text files or a combined master document

The quality and freshness of these documents directly affect how reliable your Visa Requirement Checker will be.

Required accounts and API credentials

To run the workflow end to end, prepare credentials for:

• n8n (self-hosted or n8n cloud)
• Cohere for text embeddings
• Weaviate as the vector database
• Anthropic for the chat model
• Google Sheets OAuth2 for logging queries and responses

Step-by-step: building the Visa Requirement Checker in n8n

Step 1 – Create the Webhook endpoint

Start by creating a Webhook node in n8n that will receive user questions.

• HTTP method: POST
• Path: visa_requirement_checker

The webhook should accept JSON data that describes the user’s travel scenario. For example:

{  "origin": "Germany",  "destination": "Japan",  "passport_type": "ordinary",  "purpose": "tourism",  "arrival_date": "2025-06-10"
}

You can extend this schema with other fields, such as duration of stay or transit countries, depending on your use case.

Step 2 – Split visa policy documents into chunks

Long policy documents are difficult to search directly, so you will break them into smaller pieces.

Use a Splitter node configured as follows:

• Splitter type: character-based
• chunkSize: 400
• chunkOverlap: 40

This configuration produces overlapping chunks of about 400 characters, with 40 characters of overlap between them. Overlap helps preserve context that might otherwise be cut off at chunk boundaries and improves both embedding quality and retrieval accuracy.

Step 3 – Generate embeddings with Cohere

Next, convert each chunk into a numerical vector representation using the Cohere Embeddings node.

For each chunk:

• Send the text content to the Cohere Embeddings node
• Store the resulting vector along with the original text and metadata

These embeddings capture semantic meaning, which allows Weaviate to find the most relevant chunks even when the user’s question uses different wording than the original policy.

Step 4 – Index embeddings in Weaviate

Now you will store the embeddings in a vector database so that they can be searched efficiently.

Use a Weaviate Insert node to write each embedding and its associated data into an index. For this project, you can use an index (class) named:

visa_requirement_checker

Along with the vector, store helpful metadata such as:

• Country or region
• Source URL or document name
• Publication or effective date
• Exact policy clause or section identifier

This metadata will later allow you to filter search results and provide clear citations in the final answers.

Step 5 – Query Weaviate when a user asks a question

When a new request comes in via the Webhook, the workflow should:

1. Take the user’s structured data (origin, destination, purpose, etc.)
2. Formulate a query text or use the raw question
3. Send that query to a Weaviate Query node

The Query node searches the visa_requirement_checker index and returns the most relevant chunks based on vector similarity.

To enable the LangChain-style agent to call Weaviate on demand, configure a Tool node that wraps the Weaviate query. The Agent will treat this as an external tool it can invoke when it needs more context.

Step 6 – Add memory for multi-turn conversations

To support follow-up questions like “What about a business visa instead?” you can use a Memory buffer window node.

This node stores recent messages in the current session so that the Agent and the Anthropic model can:

• Remember what the user asked previously
• Maintain context across multiple turns
• Avoid repeating the same questions for each follow-up

Step 7 – Configure the Agent and Anthropic chat model

Now connect the reasoning engine that generates the final answer.

• Use an Anthropic chat model node as the underlying LLM
• Set up an Agent node that:
  • Uses promptType: define
  • Has text: ={{ $json }} so it receives the combined context and query
  • Can call the Weaviate Tool node as needed

The Agent’s job is to:

1. Interpret the user’s query
2. Decide when to call the Weaviate tool to retrieve more policy context
3. Use the Anthropic chat model to synthesize a clear answer
4. Cite the relevant policy snippets and sources found via Weaviate

The result should be a concise, policy-backed explanation such as:

“According to the Ministry of Foreign Affairs of Japan (2024), German citizens traveling for tourism for up to X days do not require a visa, provided that…”

Step 8 – Log interactions in Google Sheets

Finally, track every interaction for analysis, debugging, and audits.

Use a Google Sheets Append node to record:

• Timestamp of the request
• Original query or structured input
• Countries and purpose involved
• Matched sources or policy references
• Final answer returned to the user
• Optional confidence scores or similarity metrics

This log makes it easy to review how the system is performing, identify gaps in your data, and refine prompts or metadata over time.

Example end-to-end response flow

To see how everything connects, consider this example query:

User request: POST to /visa_requirement_checker with the question: “Do I need a visa to travel from Brazil to Spain for tourism?”

1. Webhook node receives the request and passes the JSON payload into the workflow.
2. Weaviate Query node searches for Spanish visa policies that mention Brazilian passport holders and tourism.
3. Agent node uses the retrieved chunks plus the user’s details to generate an answer with the Anthropic chat model, including:
   • A short summary of visa requirements
   • Required documents
   • Maximum stay allowed
   • Clear citation of the policy source
4. Google Sheets Append node logs the full interaction, including which policy chunks were used.

Best practices for a reliable Visa Requirement Checker

Use rich and consistent metadata

Good metadata makes retrieval more precise and explanations more trustworthy. For each chunk you index in Weaviate, include fields like:

• Source URL or document name
• Publication or effective date
• Country or region the policy applies to
• Specific policy clause or section title

This lets the Agent respond with citations such as: “According to [Ministry of Foreign Affairs – Japan, 2024]…”

Keep your policy data up to date

Visa rules change frequently. To maintain accuracy:

• Schedule regular re-ingestion of official sources using n8n (daily or weekly)
• Update or reindex embeddings in Weaviate after each sync
• Monitor which policies users ask about most often and prioritize those sources

Design prompts for safety and accuracy

Prompt design has a large impact on how the Agent behaves. Consider:

• Instructing the model to always cite its sources
• Including explicit instructions to avoid guessing when information is unclear
• Defining a fallback when vector similarity is low, for example:
  • Ask the user to confirm missing details
  • Suggest consulting the nearest embassy or consulate

Security and privacy considerations

• Store only non-sensitive metadata by default. If you must log personal data, ensure:
  • Explicit user consent
  • Encryption at rest and in transit
• Keep all API keys and credentials secure using:
  • n8n credentials storage
  • Environment variables or secrets management

Testing, evaluation, and tuning

How to test your workflow

To validate your Visa Requirement Checker:

• Test simple country-to-country cases, such as “Germany to Japan, tourism.”
• Try multi-leg trips or special cases, like transit visas.
• Include different visa types, for example:
  • Tourist visas
  • Work permits
  • Student visas
• Cover edge cases such as:
  • Diplomatic or service passports
  • Long-term stays

Use your Google Sheets log to track:

• Where the model is highly accurate
• Where it seems uncertain or incomplete
• User feedback or corrections

Scaling and performance tips

As usage grows, you may need to tune for performance:

• Monitor Weaviate resource usage and response times
• Adjust chunkSize and chunkOverlap if retrieval quality or speed suffers
• Use metadata filters (such as country) to narrow down search scope before vector search
• Batch embedding inserts to reduce API overhead when indexing large document sets
• Optionally cache answers to very common questions to reduce repeated queries

Common pitfalls to avoid

• Weak or inconsistent metadata Makes it harder to filter and cite results, which can lead to noisy or irrelevant answers.
• Poor chunking configuration Chunks that are too large or too small can harm embedding quality. Start with 400 character chunks and 40 overlap, then adjust based on tests.
• Over-reliance on the model without citations Always surface links or references to the original policies so users can verify the information themselves.

Ideas for further enhancements

Once the basic Visa Requirement Checker is working, you can extend it with additional features:

• Language detection and translation Detect the user’s language and translate queries or responses so you can support a global audience.
• Public-facing UI Build a simple web or mobile interface that sends requests to your n8n Webhook endpoint.
• Automated policy updates Integrate RSS feeds or embassy APIs, then schedule n8n workflows to re-ingest and reindex content automatically