Build a Visa Requirement Checker with n8n & LangChain

Want to automate country-specific visa guidance for travelers or customers? In this guide you’ll learn how to build a robust, searchable “Visa Requirement Checker” using n8n, LangChain components, vector search (Weaviate), Cohere embeddings, Anthropic chat, and Google Sheets for logging. This modular workflow makes it easy to ingest official visa policies, respond to user queries, and record results for analytics.

Why build a Visa Requirement Checker?

Visa rules are complex and frequently updated. An automated checker saves time, reduces mistakes, and gives consistent answers to questions like “Do I need a visa to travel from Germany to Japan?” or “What documents are required for a US tourist visa?” Using embeddings and vector search ensures the system can retrieve the most relevant policy snippets even if a user asks in natural language.

Architecture overview

The workflow uses n8n as the orchestrator for the following high-level steps:

Accept user queries via a webhook
Split and embed policy documents
Index embeddings in a vector store (Weaviate)
Query the vector store to retrieve relevant context
Use an AI chat model (Anthropic) via a LangChain agent to produce the answer
Log the interaction to Google Sheets

Key components in the template image and JSON:

Webhook node (POST /visa_requirement_checker)
Splitter (character-based) — chunkSize 400, chunkOverlap 40
Cohere Embeddings node
Weaviate vector store Insert & Query nodes
Tool node configured for Weaviate (used by the Agent)
Memory buffer window to store session context
Anthropic chat model node
Agent node (promptType: define, text: ={{ $json }})
Google Sheets append for logging

Step-by-step setup

1. Prepare data and credentials

Collect official visa policy documents and convert them into text files or a single document. You will need API credentials for:

n8n (self-hosted or cloud)
Cohere (embeddings)
Weaviate (vector database)
Anthropic (chat model)
Google Sheets OAuth2 (for logging)

2. Configure the Webhook node

Create a POST webhook at path visa_requirement_checker. This endpoint will accept user requests with fields like origin country, destination country, passport type, and travel purpose. Example POST body:

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

3. Split large documents

Use the Splitter node to chunk long policy pages into manageable text segments (chunkSize 400, chunkOverlap 40). This improves embedding quality and retrieval accuracy.

4. Create embeddings

Send chunks to the Cohere Embeddings node. Cohere transforms text chunks into vector representations that Weaviate can index and search.

5. Index in Weaviate

The Insert node writes embedding vectors and their metadata (country, document source, effective date) to a Weaviate index named visa_requirement_checker. Store useful metadata fields so you can filter or rank results later.

6. Query and retrieval

When a user query arrives, the Query node looks up relevant vectors in Weaviate and returns the best-matching document chunks. The Tool node (Weaviate) is registered as an external tool for the LangChain agent to call when needed.

7. Agent & Memory

The Agent node combines the retrieved context with the incoming user query. It uses the Anthropic Chat model to produce a clear, policy-backed response. A Memory Buffer Window node keeps recent session context so follow-up clarifications can be handled naturally.

8. Logging and analytics

Append each interaction to Google Sheets: timestamp, query, matched sources, recommended answer, and confidence. This log supports audits and metric tracking.

Best practices

Include metadata

For every indexed chunk store metadata like source URL, publication date, country or region, and exact policy clause. This allows you to cite sources in the agent response: “According to [Ministry of Foreign Affairs — Japan, 2024], …”

Keep your data current

Visa policies change. Schedule periodic re-ingestion of official sources and update embeddings in Weaviate. You can implement a daily or weekly sync job in n8n to fetch and reindex updated pages.

Design agent prompts for accuracy

Use conservative, source-citing prompts and include fallback logic when the system is uncertain. For example, if vector similarity is below a threshold, ask the user to confirm details or surface a “consult embassy” suggestion.

Security & privacy

Only store non-sensitive metadata. If you must store PII, ensure consent and encryption at rest.
Protect API keys using n8n credentials and environment secrets.

Testing and evaluation

Test with a wide variety of queries: direct country-to-country checks, multi-leg itineraries, work and student visas, and edge cases (special passport types). Track precision, recall, and user feedback in your Google Sheets log and iterate on prompts.

Scaling & performance

To scale, monitor Weaviate resource usage and tune embedding chunk sizes. Cache popular query results and use pre-filtering by country metadata to reduce vector search costs. Use batching when inserting embeddings to minimize API calls.

Example response flow

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

Webhook receives request → Splitter ensures relevant policy chunks are available
Query node retrieves matching Spanish visa policy paragraphs for Brazilian passport holders
Agent composes an answer: short summary, required documents, maximum stay, and a citation
Sheet logs the interaction and the matched sources

Common pitfalls

Not enough or inconsistent metadata makes retrieval noisy.
Chunking too large or too small harms embedding quality — start with 400/40 and iterate.
Over-reliance on generative models without citing original policies — always surface source links.

Further enhancements

Add language detection and translation to support queries in multiple languages
Provide a UI that calls the webhook for public use
Integrate scheduled checks against embassy feeds or RSS for automated updates
Expose an admin dashboard tracking change frequency, top queries, and model confidence

Conclusion & call to action

With n8n as the workflow backbone and LangChain components for retrieval and reasoning, you can build a reliable Visa Requirement Checker that scales and remains auditable. The template provided (webhook → splitter → embeddings → Weaviate → agent → sheet) is a strong starting point for production systems.

Ready to try it? Deploy the n8n workflow, connect your Cohere and Weaviate accounts, and start ingesting visa policy documents. If you’d like the template exported, or help customizing prompts and metadata fields, get in touch or subscribe for step-by-step templates and updates.

Call to action: Download the workflow template, clone it into your n8n instance, and start indexing official visa sources today. Subscribe for more tutorials and real-world automation templates.

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