{"uid":"cap_CpOT_6dXfV0yfdr8S4VmW","slug":"vektorwerk-text-embedding-be3d80cb","name":"Vektorwerk Text Embedding","description":"Embeds up to 32 texts on the local Ollama runtime. Returns explicit model and dimensions; no paid third-party API is called.","url":"https://vektor.netzhandwerker.de/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"},"model":{"enum":["nomic","bge_m3"],"type":"string","default":"nomic"},"texts":{"type":"array","items":{"type":"string"},"maxItems":32,"minItems":1}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_9jRfx-J8W6isP2MbLqA_u","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Embeds up to 32 texts locally using Ollama (nomic or bge-m3 model), returning dense vector representations with explicit model name and dimensions — no external paid API involved.","exampleAgentPrompt":"Embed these 3 product descriptions using the nomic model so I can store them as vectors: 'wireless noise-cancelling headphones', 'over-ear studio monitor headphones', 'true wireless earbuds with ANC'.","exampleUseCases":[{"title":"RAG document ingestion pipeline","prompt":"Embed these 10 chunk texts from my documentation using the bge_m3 model so I can index them in my vector database: [chunk1, chunk2, …chunk10]."},{"title":"Semantic deduplication of user reviews","prompt":"I have 25 customer reviews I need vectorized with the nomic model — can you embed them all in one shot so I can cluster similar ones together?"},{"title":"Multilingual query embedding for search","prompt":"Embed this search query using bge_m3 so I can find semantically similar documents across languages: 'how do I reset my password?'"}],"resultDescription":"Returns an array of embedding vectors (one per input text), along with the model name used and the dimensionality of the vectors, enabling immediate downstream use in vector stores or similarity computations.","failureModes":["Exceeding 32 texts in the texts array returns a validation error","Supplying an invalid model value (not 'nomic' or 'bge_m3') returns a schema error","Empty texts array (minItems:1 violated) returns a validation error","Ollama runtime unavailability on the server causes a 5xx error","Very long individual texts may time out or exceed model context windows"],"whenToPreferThis":"Choose this endpoint when you need text embeddings without incurring OpenAI or Cohere API costs, when you want explicit model metadata (name + dimensions) in the response, or when batching up to 32 texts at once. Particularly suitable for self-sovereignty use cases, local-first RAG pipelines, or multilingual embedding needs via bge-m3. Prefer over cloud embedding APIs when privacy, cost-zero third-party calls, or Ollama-compatible model outputs are priorities.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:36:55.047Z","isFirstParty":false}