The "Schrems II" Problem in Your AI Stack

It is June 2023, and the hype cycle for Large Language Models (LLMs) has officially hit the enterprise. Every CTO I talk to in Oslo wants to integrate GPT-4 into their customer support workflow. They all face the same problem: Hallucinations and lack of domain knowledge.

The standard fix is RAG (Retrieval-Augmented Generation). You take your proprietary data, chunk it, embed it, and stuff the relevant context into the prompt before the LLM generates an answer. Simple.

But here is the trap.

Most tutorials tell you to spin up a managed vector database instance in the US cloud (Pinecone, Weaviate Cloud, etc.). If you are processing data for Norwegian citizens, you just sent PII (Personally Identifiable Information) across the Atlantic to a third-party sub-processor. Under current GDPR interpretations and the Schrems II ruling, this is a compliance minefield. The Datatilsynet (Norwegian Data Protection Authority) does not care how cool your chatbot is.

The solution isn't to abandon AI. It's to own the infrastructure. Specifically, the retrieval layer.

The Architecture: Hybrid Cloud RAG

We need a pragmatic compromise. We can use OpenAI's API for the generation (sending only anonymized context), but we must host the knowledge base and the retrieval logic on sovereign Norwegian soil. This minimizes latency and maximizes control.

We will build a vector store using PostgreSQL with the pgvector extension. Why Postgres? because managing a niche vector DB adds unnecessary complexity to your stack. Postgres is rock solid, and since version 0.4.0 (released April 2023), pgvector supports HNSW (Hierarchical Navigable Small World) indexing, which makes it fast enough for production.

Prerequisites:

• Python 3.10+
• Docker & Docker Compose
• A CoolVDS NVMe instance (Ubuntu 22.04 LTS recommended)

Step 1: The Infrastructure (Docker + Pgvector)

First, we need a database that supports vector math. Standard Postgres doesn't do this. We need to compile the extension or use a pre-built image. For a production deployment on a VPS, I prefer using a custom Dockerfile to ensure we have control over the version.

Here is the Dockerfile to get Postgres 15 with the latest vector extensions:

FROM postgres:15

# Install build dependencies
RUN apt-get update && apt-get install -y \
    git \
    make \
    gcc \
    postgresql-server-dev-15

# Clone and install pgvector (v0.4.1 stable as of June 2023)
RUN git clone --branch v0.4.1 https://github.com/pgvector/pgvector.git \
    && cd pgvector \
    && make \
    && make install

# Cleanup
RUN rm -rf pgvector && apt-get remove -y git make gcc postgresql-server-dev-15 && apt-get autoremove -y

Next, let's define the service in docker-compose.yml. Note the volume mapping. On CoolVDS, your /var/lib/postgresql/data should sit on the local NVMe storage, not networked block storage, to ensure high IOPS during index builds.

version: '3.8'
services:
  vectordb:
    build: .
    environment:
      POSTGRES_USER: admin
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_DB: rag_production
    volumes:
      - ./pgdata:/var/lib/postgresql/data
    ports:
      - "5432:5432"
    restart: unless-stopped
    shm_size: '1gb'  # Crucial for parallel workers

Deploy it:

docker-compose up -d --build

Step 2: Database Configuration

Once the container is running, we need to enable the extension and tune the database. Connect to your instance:

psql -h localhost -U admin -d rag_production

Run the activation command:

CREATE EXTENSION vector;

Now, let's talk about performance configuration. Vector search is memory-intensive. If your working set (the HNSW index) doesn't fit in RAM, performance falls off a cliff. On a standard 8GB RAM VPS, you should tune your shared_buffers and work_mem aggressively.

Pro Tip: Unlike standard B-Tree indexes, HNSW graph construction is CPU heavy. Ensure your VPS isn't suffering from "noisy neighbor" CPU steal. We monitor this via top looking at the %st value. If it's above 5%, move hosts. (CoolVDS guarantees <1% steal due to strict KVM isolation).

Step 3: The Ingestion Pipeline

We will use Python and psycopg2 to interact with our store. While LangChain is the popular abstraction layer right now, for high-performance production systems, I prefer writing raw SQL for the insertion to handle conflicts and batching better.

We'll create a table capable of storing OpenAI's text-embedding-ada-002 vectors, which have 1536 dimensions.

CREATE TABLE documents (
    id bigserial PRIMARY KEY,
    content text,
    metadata jsonb,
    embedding vector(1536)
);

-- Create the HNSW index for fast approximate nearest neighbor search
-- 'm' is the max number of connections per layer (16 is standard)
-- 'ef_construction' trades build time for search accuracy
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64);

Now, the Python script to ingest data. We simulate the embedding generation here.

import psycopg2
from pgvector.psycopg2 import register_vector
import numpy as np

# Connect to the self-hosted DB
conn = psycopg2.connect(
    host="localhost",
    database="rag_production",
    user="admin",
    password="secure_password"
)

# Register the vector type with the driver
register_vector(conn)
cur = conn.cursor()

# Mock data - in reality, you fetch this from OpenAI API
def get_embedding(text):
    # Returns a list of 1536 floats
    return np.random.rand(1536).tolist()

doc_text = "CoolVDS offers low latency hosting in Oslo."
doc_vector = get_embedding(doc_text)

# Insert with SQL injection protection
cur.execute(
    "INSERT INTO documents (content, embedding) VALUES (%s, %s)",
    (doc_text, doc_vector)
)

conn.commit()
print("Document ingested successfully.")

Step 4: The Retrieval (The "R" in RAG)

When a user asks a question, we embed their query and search our local database. The magic of pgvector is the cosine similarity operator <=>.

query_text = "Where is the server located?"
query_vector = get_embedding(query_text)

# Semantic search query
# The <=> operator returns the cosine distance
# LIMIT 5 gives us the 5 most relevant chunks
sql = """
SELECT content, 1 - (embedding <=> %s) AS similarity
FROM documents
ORDER BY embedding <=> %s
LIMIT 5;
"""

cur.execute(sql, (query_vector, query_vector))
results = cur.fetchall()

for row in results:
    print(f"Score: {row[1]:.4f} | Content: {row[0]}")

Latency Matters: The 50ms Threshold

Why do we care about hosting this in Norway? Aside from GDPR, it is about the user experience. A RAG pipeline involves multiple network hops:

1. User to Server
2. Server to OpenAI (Embedding)
3. Server to Vector DB (Retrieval)
4. Server to OpenAI (Completion)
5. Server to User

You cannot control the latency to OpenAI's API (usually 200-500ms). However, you can control step 3. If your vector database is in us-east-1 and your application server is in Oslo, you are adding 80-100ms of latency per round trip. For a complex agent that might query the DB multiple times (Multi-hop RAG), this destroys the "chat" experience.

By hosting the vector store on CoolVDS in Oslo, the latency between your application logic and your database is essentially zero (localhost) or <2ms (LAN).

Hardware Considerations for 2023

Vector indexes are hungry. We aren't just storing text; we are storing thousands of float arrays.

• Storage: HDD is dead for this use case. Random seek times on spinning rust will kill your HNSW traversal speeds. NVMe is mandatory.
• CPU: AVX-512 instruction set support helps with vector calculations. CoolVDS nodes are powered by modern processors that support these instructions natively.
• Memory: Calculate your requirements roughly as: (Number of Vectors) * (Dimensions * 4 bytes) * 1.5 (overhead). For 1 million OpenAI vectors, you need about 6GB of RAM just for the index.

Conclusion

Building a RAG system isn't just about stringing API calls together. It is about data sovereignty, legal compliance, and raw performance. By moving the retrieval layer to a self-hosted Postgres instance on high-performance infrastructure, you satisfy the lawyers and the engineers simultaneously.

Don't let your data traverse the globe unnecessarily. Deploy a localized, GDPR-ready vector store.

Ready to build? Spin up a high-memory NVMe instance on CoolVDS today and get your latency down to where it belongs.