AI MEETS
QDRANT.
VECTOR SEARCH
AT FULL SPEED.
THE HIGHEST-PERFORMANCE VECTOR DATABASE AVAILABLE — MINIMAL LATENCY, MAXIMUM ACCURACY, FAST INDEXING, AND FULL CONTROL. THE BACKBONE OF THE SMBSERVICES AI KNOWLEDGE BASE.
AN INTELLIGENT, SUPER-FAST FILING CABINET FOR AI
QDRANT IS THE VECTOR DATABASE AT THE HEART OF THE SMBSERVICES AI SEARCH LAYER. IT STORES AND RETRIEVES INFORMATION NOT BY EXACT KEYWORD — BUT BY MEANING. ASK A QUESTION IN PLAIN LANGUAGE AND QDRANT FINDS THE MOST SEMANTICALLY RELEVANT CONTENT FROM EVERY DOCUMENT, STATEMENT, AND RECORD IN YOUR KNOWLEDGE BASE.
- HIGHEST REQUESTS-PER-SECOND WITH MINIMAL QUERY LATENCY
- FAST INDEXING — NEW DOCUMENTS BECOME SEARCHABLE IMMEDIATELY AFTER INGESTION
- HIGH CONTROL OVER ACCURACY VIA CONFIGURABLE SIMILARITY THRESHOLDS
- ALL YOUR STORED DATA AND DOCUMENTS MADE INSTANTLY RETRIEVABLE VIA RAG
- MULTILINGUAL SUPPORT — ENGLISH AND FRENCH OUT OF THE BOX
- 1024-DIMENSION EMBEDDING VECTORS FOR MAXIMUM SEMANTIC PRECISION
MEANING-BASED RETRIEVAL — NOT KEYWORD MATCHING
WHEN A DOCUMENT IS INGESTED, IT IS CONVERTED INTO A HIGH-DIMENSIONAL NUMERIC VECTOR THAT CAPTURES ITS SEMANTIC MEANING. WHEN YOU ASK A QUESTION, YOUR QUESTION IS CONVERTED THE SAME WAY. QDRANT FINDS THE STORED VECTORS THAT ARE GEOMETRICALLY CLOSEST TO YOUR QUERY VECTOR — MEANING THE MOST CONTEXTUALLY RELEVANT CONTENT, REGARDLESS OF EXACT WORDING.
- DOCUMENTS ARE EMBEDDED INTO 1024-DIMENSION VECTORS ON INGESTION
- QUESTIONS ARE EMBEDDED AT QUERY TIME USING THE SAME MODEL
- QDRANT RETURNS TOP MATCHES RANKED BY SIMILARITY SCORE
- A CONFIGURABLE SCORE THRESHOLD FILTERS OUT LOW-CONFIDENCE RESULTS
- WORKS ACROSS LANGUAGES — A FRENCH QUESTION CAN MATCH AN ENGLISH DOCUMENT