Use Qdrant Cloud (Free Tier)¶

Run RAGWire against a hosted Qdrant cluster — no Docker, no local storage, fully managed. Qdrant Cloud offers a free tier with 1 GB storage, enough for millions of vectors.

1. Create a Free Cluster¶

Sign up at cloud.qdrant.io
Create a cluster — select the Free tier
Copy your Cluster URL (e.g. https://xyz-abc.qdrant.io) and generate an API Key

2. Configure RAGWire¶

Store your credentials in a .env file (never commit this):

# .env
QDRANT_URL=https://xyz-abc.qdrant.io
QDRANT_API_KEY=your-api-key-here

Reference them in config.yaml:

vectorstore:
  url: "${QDRANT_URL}"
  api_key: "${QDRANT_API_KEY}"
  collection_name: "my_docs"
  use_sparse: true

RAGWire loads .env automatically via python-dotenv at startup.

3. Run¶

No other changes needed — the rest of your code is identical to a local setup:

from ragwire import RAGWire

rag = RAGWire("config.yaml")
stats = rag.ingest_directory("data/")
print(f"Processed: {stats['processed']}, Chunks: {stats['chunks_created']}")

results = rag.retrieve("Apple revenue 2025")
for doc in results:
    print(doc.page_content[:200])

Free Tier Limits¶

Limit	Value
Storage	1 GB
Collections	Unlimited
Vectors	~1M (depends on dimensions)
Uptime SLA	None (best effort)

For production workloads, upgrade to a paid plan or self-host with Docker.

Hybrid search works on Qdrant Cloud

Unlike local file storage, Qdrant Cloud fully supports sparse vectors. Set use_sparse: true and search_type: "hybrid" for the best retrieval quality.