{"uid":"cap_Lh9qFjRWarCuZvJUUfi0g","slug":"qdrant-vector-collection-query-f2f4fc64","name":"Qdrant Vector Collection Query","description":"Query a Qdrant collection for the nearest points to a given vector.","url":"https://sovereign-x402-zeta.vercel.app/api/v1/vector/qdrant/query","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"type":"object","properties":{},"additionalProperties":true},"type":{"type":"string","const":"http"},"method":{"enum":["POST","PUT","PATCH"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_FWEBBwodVqtz-6l5RA_uf","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Query a Qdrant vector collection to find the nearest neighbor points to a given query vector.","exampleAgentPrompt":"Search my Qdrant collection called 'product-embeddings' for the 10 nearest points to this query vector: [0.12, 0.45, 0.78, ...] — return the IDs, scores, and payloads.","exampleUseCases":[{"title":"Semantic document retrieval","prompt":"I have a Qdrant collection called 'docs-index' with embedded article chunks. Given this query vector representing a user's question, find the 5 most semantically similar document chunks and return their text payloads and scores."},{"title":"Product recommendation by embedding","prompt":"Query my 'product-vectors' Qdrant collection and return the top 10 nearest neighbor items to this embedding vector — I want to show users similar products to the one they just viewed."},{"title":"Duplicate detection in vector store","prompt":"Search the 'user-submissions' Qdrant collection for points closest to this embedding and return anything with a similarity score above 0.95 — I need to flag potential duplicate submissions."}],"resultDescription":"Returns a list of nearest neighbor points from the specified Qdrant collection, each including the point ID, distance/similarity score, and associated payload data stored alongside the vector.","failureModes":["Collection not found — if the specified collection name does not exist in the Qdrant instance","Vector dimension mismatch — query vector dimension must match the collection's configured vector size","Invalid filter syntax — malformed filter expressions return a 400-level error","Payment failure — x402 micropayment of $0.003 USDC not completed or rejected","Timeout — large collections or high top-k values may cause latency issues","Empty results — collection exists but contains no indexed vectors yet"],"whenToPreferThis":"Choose this endpoint when you need to perform nearest-neighbor vector search against a Qdrant collection and want a simple pay-per-use API without managing your own Qdrant infrastructure. Prefer this over Pinecone-based siblings on this platform if your vector store is hosted in Qdrant. Ideal for semantic search, RAG pipelines, and recommendation systems that already use Qdrant as the backing store.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:54:54.287Z","isFirstParty":false}