Integrate n8n with GCP for Efficient Document Management

Integrating n8n with GCP for Document Management

This mirrors the Azure RAG architecture but uses Google Cloud Platform services — Vertex AI for embeddings, Vertex AI Search (or AlloyDB/Cloud SQL with pgvector) for vector storage, and n8n as the orchestration layer.

The Full Architecture

			
Your Documents (PDFs, Docs, Sheets)
              ↓
   Google Cloud Storage (GCS)
              ↓
   Document AI / Dataflow (chunk + clean)
              ↓
   Vertex AI Embeddings (text → vector)
              ↓
   Vertex AI Search / pgvector (store vectors)
              ↓
         n8n Workflow
              ↓
   User gets grounded answer + sources

		

GCP Services Mapping

Azure Service	GCP Equivalent	Role
Azure Data Lake	Google Cloud Storage (GCS)	Store raw documents
Azure Data Factory	Cloud Dataflow / Document AI	Process & chunk text
Azure OpenAI Embeddings	Vertex AI Embeddings	Convert text → vectors
Azure AI Search	Vertex AI Search / pgvector	Store & search vectors
Azure OpenAI Chat	Vertex AI Gemini / PaLM	Generate answers
n8n	n8n	Orchestrate everything

Step-by-Step Implementation

Step 1 — Store Documents in GCS

Upload all your PDFs, Word docs, and text files to a GCS bucket:

			
# Create a bucket
gsutil mb gs://my-company-docs
# Upload documents
gsutil cp *.pdf gs://my-company-docs/raw/

Bucket structure:

			
gs://my-company-docs/
  ├── raw/              ← original documents
  ├── processed/        ← cleaned text chunks
  └── embeddings/       ← vector JSON files

Step 2 — Process & Chunk Documents

Use Google Document AI to extract clean text from PDFs, then split into chunks:

			
# Cloud Function or Dataflow job
from google.cloud import documentai, storage
def chunk_document(text, chunk_size=500, overlap=50):
    words = text.split()
    chunks = []
    for i in range(0, len(words), chunk_size - overlap):
        chunk = " ".join(words[i:i + chunk_size])
        chunks.append({
            "chunk_id": f"chunk_{i}",
            "text": chunk,
            "source": "refund_policy.pdf",
            "page": i // chunk_size + 1
        })
    return chunks

		

Output chunk format:

			
{
  "chunk_id": "refund_policy_001",
  "text": "Refunds are available within 30 days of purchase...",
  "source": "refund_policy.pdf",
  "page": 1,
  "metadata": {
    "department": "finance",
    "last_updated": "2026-01-15"
  }
}

		

Step 3 — Generate Embeddings with Vertex AI

Call the Vertex AI Embeddings API to convert each chunk into a vector:

			
# REST API call
POST https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT/
     locations/us-central1/publishers/google/models/text-embedding-004:predict
Headers:
  Authorization: Bearer $(gcloud auth print-access-token)
  Content-Type: application/json
Body:
{
  "instances": [
    { "content": "Refunds are available within 30 days of purchase..." }
  ]
}

		

Response:

			
{
  "predictions": [
    {
      "embeddings": {
        "values": [0.023, -0.841, 0.334, ...],
        "statistics": { "truncated": false, "token_count": 42 }
      }
    }
  ]
}

		

Vertex AI embedding models:

Model	Dimensions	Best for
`text-embedding-004`	768	General text, RAG
`text-multilingual-embedding-002`	768	Multi-language docs
`text-embedding-preview-0815`	768	Latest preview

Step 4 — Store Vectors

You have two main options on GCP:

Option A — Vertex AI Search (fully managed)

			
# Create a data store
gcloud alpha discovery-engine data-stores create \
  --project=YOUR_PROJECT \
  --location=global \
  --display-name="company-docs" \
  --industry-vertical=GENERIC \
  --solution-types=SOLUTION_TYPE_SEARCH

		

Option B — AlloyDB / Cloud SQL with pgvector (more control)

			
-- Enable pgvector extension
CREATE EXTENSION vector;
-- Create table with vector field
CREATE TABLE document_chunks (
  chunk_id     TEXT PRIMARY KEY,
  text         TEXT,
  source       TEXT,
  page         INT,
  metadata     JSONB,
  embedding    VECTOR(768)    -- matches Vertex AI output dimensions
);
-- Create HNSW index for fast similarity search
CREATE INDEX ON document_chunks
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

		

Insert a chunk with its vector:

			
INSERT INTO document_chunks
  (chunk_id, text, source, embedding)
VALUES (
  'refund_policy_001',
  'Refunds are available within 30 days...',
  'refund_policy.pdf',
  '[0.023, -0.841, 0.334, ...]'::vector
);

		

Step 5 — Build the n8n Workflow

The n8n workflow has these nodes:

			
Webhook Trigger
      ↓
HTTP Request → Vertex AI Embeddings
      ↓
HTTP Request → pgvector / Vertex AI Search
      ↓
Code Node → Format retrieved context
      ↓
HTTP Request → Vertex AI Gemini (chat)
      ↓
Respond to Webhook

		

Step 6 — Webhook Receives User Question

Incoming request to n8n:

			
{
  "question": "What is the refund policy?",
  "user_id": "user_123"
}

Step 7 — n8n Calls Vertex AI Embeddings

HTTP Request node configuration:

			
Method:  POST
URL:     https://us-central1-aiplatform.googleapis.com/v1/projects/
         {{ $env.GCP_PROJECT }}/locations/us-central1/publishers/google/
         models/text-embedding-004:predict
Headers:
  Authorization: Bearer {{ $env.GCP_ACCESS_TOKEN }}
  Content-Type:  application/json
Body:
{
  "instances": [
    { "content": "{{ $json.question }}" }
  ]
}

		

Output stored in state:

{ "query_vector": [0.021, -0.834, 0.291, ...] }

Step 8 — n8n Searches pgvector

HTTP Request node (calling Cloud SQL proxy or AlloyDB REST):

			
-- n8n Code Node generates this query
SELECT
  chunk_id,
  text,
  source,
  page,
  1 - (embedding <=> '[0.021, -0.834, 0.291, ...]'::vector) AS similarity
FROM document_chunks
ORDER BY embedding <=> '[0.021, -0.834, 0.291, ...]'::vector
LIMIT 5;

		

pgvector distance operators:

Operator	Metric	Use case
`<=>`	Cosine distance	Text similarity (recommended)
`<->`	Euclidean distance	Image embeddings
`<#>`	Negative dot product	Normalized vectors

Results returned:

			
[
  { "chunk_id": "refund_policy_001", "text": "Refunds are available within 30 days...", "source": "refund_policy.pdf", "similarity": 0.97 },
  { "chunk_id": "returns_guide_003", "text": "To initiate a return, visit our portal...", "source": "returns_guide.pdf", "similarity": 0.81 }
]

Step 9 — Format Context in n8n Code Node

			
// n8n Code Node
const results = items[0].json.results;
const question = $node["Webhook Trigger"].json.question;
const context = results
  .map(r => `Source: ${r.source} (Page ${r.page})\nContent: ${r.text}`)
  .join("\n\n---\n\n");
return [{
  json: {
    question: question,
    context: context,
    sources: results.map(r => r.source)
  }
}];

		

Step 10 — Send Grounded Prompt to Vertex AI Gemini

HTTP Request node:

			
Method: POST
URL:    https://us-central1-aiplatform.googleapis.com/v1/projects/
        {{ $env.GCP_PROJECT }}/locations/us-central1/publishers/google/
        models/gemini-1.5-pro:generateContent
Body:
{
  "contents": [{
    "role": "user",
    "parts": [{
      "text": "You are an internal company assistant.\nAnswer ONLY using the context below.\nIf the answer is not in the context, say: I don't know.\nAlways cite the source document.\n\nContext:\n{{ $json.context }}\n\nQuestion: {{ $json.question }}"
    }]
  }],
  "generationConfig": {
    "temperature": 0.2,
    "maxOutputTokens": 512
  }
}

		

Step 11 — Return Answer to User

n8n Respond to Webhook node:

			
{
  "answer": "Refunds are available within 30 days of purchase. To initiate a return, visit our returns portal.",
  "sources": ["refund_policy.pdf", "returns_guide.pdf"],
  "confidence": "high"
}

		

Complete n8n Workflow Diagram

			
┌─────────────────────────────────────────────────────────┐
│                    n8n WORKFLOW                         │
│                                                         │
│  [Webhook]──→[Vertex AI Embed]──→[pgvector Search]     │
│                                         ↓               │
│                                  [Code: Format]         │
│                                         ↓               │
│                                  [Gemini Chat]          │
│                                         ↓               │
│                                  [Respond]              │
└─────────────────────────────────────────────────────────┘

		

GCP vs Azure — Side by Side

Step	Azure	GCP
Document storage	Azure Data Lake	Google Cloud Storage
Text extraction	Azure Form Recognizer	Document AI
Chunking	Azure Data Factory	Cloud Dataflow / Functions
Embedding model	`text-embedding-ada-002`	`text-embedding-004`
Vector dimensions	1,536	768
Vector store	Azure AI Search	AlloyDB pgvector / Vertex AI Search
Search algorithm	HNSW (built-in)	HNSW via pgvector
LLM	Azure OpenAI Chat	Vertex AI Gemini
Orchestration	n8n	n8n

Security Best Practices on GCP

			
n8n running on GCP VM / Cloud Run
         ↓
Uses Workload Identity (no hardcoded keys)
         ↓
Accesses GCS, Vertex AI, AlloyDB
via IAM roles:
  - roles/aiplatform.user
  - roles/storage.objectViewer
  - roles/cloudsql.client

		

Store secrets in Google Secret Manager, not in n8n environment variables directly:

			
# Store API credentials securely
gcloud secrets create vertex-ai-key --data-file=key.json
# n8n fetches at runtime via HTTP Request node
GET https://secretmanager.googleapis.com/v1/projects/YOUR_PROJECT/
    secrets/vertex-ai-key/versions/latest:access

		

Key Takeaway

The GCP RAG pipeline with n8n gives you:

GCS for durable, scalable document storage
Document AI for accurate PDF/text extraction
Vertex AI Embeddings for state-of-the-art semantic vectors
pgvector on AlloyDB for flexible, SQL-native vector search
Gemini for grounded, citation-aware answer generation
n8n as the glue — zero custom application code needed

The result is a fully managed, enterprise-grade document Q&A system where every answer is grounded in your actual documents, with sources always cited.

Infra Cloud Solutions

Integrate n8n with GCP for Efficient Document Management

Integrating n8n with GCP for Document Management

The Full Architecture

GCP Services Mapping

Step-by-Step Implementation

Step 1 — Store Documents in GCS

Step 2 — Process & Chunk Documents

Step 3 — Generate Embeddings with Vertex AI

Step 4 — Store Vectors

Option A — Vertex AI Search (fully managed)

Option B — AlloyDB / Cloud SQL with pgvector (more control)

Step 5 — Build the n8n Workflow

Step 6 — Webhook Receives User Question

Step 7 — n8n Calls Vertex AI Embeddings

Step 8 — n8n Searches pgvector

Step 9 — Format Context in n8n Code Node

Step 10 — Send Grounded Prompt to Vertex AI Gemini

Step 11 — Return Answer to User

Complete n8n Workflow Diagram

GCP vs Azure — Side by Side

Security Best Practices on GCP

Key Takeaway

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Integrating n8n with GCP for Document Management

The Full Architecture

GCP Services Mapping

Step-by-Step Implementation

Step 1 — Store Documents in GCS

Step 2 — Process & Chunk Documents

Step 3 — Generate Embeddings with Vertex AI

Step 4 — Store Vectors

Option A — Vertex AI Search (fully managed)

Option B — AlloyDB / Cloud SQL with pgvector (more control)

Step 5 — Build the n8n Workflow

Step 6 — Webhook Receives User Question

Step 7 — n8n Calls Vertex AI Embeddings

Step 8 — n8n Searches pgvector

Step 9 — Format Context in n8n Code Node

Step 10 — Send Grounded Prompt to Vertex AI Gemini

Step 11 — Return Answer to User

Complete n8n Workflow Diagram

GCP vs Azure — Side by Side

Security Best Practices on GCP

Key Takeaway

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