Qdrant: The Vector Database That Made Our RAG 10x More Accurate
When our RAG system returned irrelevant results on 30% of queries, the problem wasn't the LLM — it was the search. We tested 4 vector databases in 2 weeks. Qdrant won, and not by a little.
What Are Vector Databases (Explained Simply)
A traditional database searches for exact words: if you search "termination", it only finds documents containing that word. A vector database searches by meaning: if you search "termination", it also finds documents about "dissolution of employment relationship" or "risoluzione del rapporto di lavoro". How? Every piece of text is converted into a vector — a list of 1024 numbers representing the text's meaning in mathematical space. Texts with similar meanings have nearby vectors. Search becomes a distance calculation between vectors: fast, precise and language-independent. This is the heart of our RAG system. The user asks a question in Italian, the system converts it to a vector, and Qdrant finds the 10 most similar text pieces among thousands of documents in any language — in under 50 milliseconds.
Why Qdrant and Not Pinecone, Weaviate or Milvus
We tested 4 solutions with the same dataset (the Mueller Report, 448 pages, 12,000 chunks): Pinecone: great cloud service, but cloud-only. For our enterprise clients it was an automatic no. Data doesn't leave the company, period. Weaviate: good and open-source, but initial setup took us 2 days. RAM usage was 40% higher than Qdrant on the same dataset. Milvus: powerful and scalable, but designed for large-scale Kubernetes deployments. For a single on-premise server it's overkill. Qdrant: written in Rust (native performance), simple well-documented API, native on-premise support. Installs with a single Docker container. On our benchmark: 8ms average latency for search across 1 million vectors. Weaviate took 23ms, Milvus 15ms.
Filtering: The Killer Feature
Pure vector search isn't enough. When a lawyer searches for "non-compete clause", they don't want results from all 25,000 documents — they want only employment contracts from the last 3 years. Qdrant lets you combine semantic search with metadata filters, without performance loss. Every document in our system has metadata: date, type (contract, ruling, circular), client, language, department. Users can filter by any combination of these fields AND simultaneously run a semantic search on content. On a dataset of 1 million vectors, unfiltered search takes 8ms. A search filtered by date and document type takes 3ms — it's faster because the search space is smaller. Our users filter 90% of the time.
FAQ About Qdrant
Q: Can a vector database replace PostgreSQL? A: No, they're complementary. The vector database handles semantic search, the relational handles users, permissions and structured relationships. We use both in our stack. Q: How many documents can Qdrant handle on a single server? A: With 1024-dimension vectors, 1 million vectors need about 4 GB RAM. On a 32 GB server, you handle 5-6 million vectors, about 200,000 documents. Q: Is Qdrant free for commercial use? A: Yes, open-source under Apache 2.0. Use it freely in production without limits. A managed cloud offering exists as paid, but on-premise is completely free.
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