{"uid":"cap_SnpOUC43FuzbYc8paIjtH","slug":"vektorwerk-text-embedding-e33616df","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.halowerk.com/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_j5XVFVrp43QiqcXK5wM-_","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 into vector representations using a local Ollama runtime with nomic or bge_m3 models, returning the model name and embedding dimensions.","exampleAgentPrompt":"Embed these 5 product descriptions into vectors using the nomic model so I can store them in my vector database: 'Wireless headphones with noise cancellation', 'Bluetooth speaker for outdoor use', 'USB-C charging cable 6ft', 'Laptop stand adjustable height', 'Mechanical keyboard with RGB lighting'.","exampleUseCases":[{"title":"RAG document ingestion pipeline","prompt":"Convert these 10 knowledge base paragraphs into embeddings using the bge_m3 model so I can index them in my vector store for retrieval-augmented generation."},{"title":"Semantic similarity scoring setup","prompt":"I need to embed these 20 customer support tickets using the nomic model so I can cluster them by topic — can you turn them all into vectors at once?"},{"title":"Privacy-safe local embedding","prompt":"Embed this batch of 8 internal confidential memos into vectors using the nomic model — I need this done locally without sending anything to a third-party API."}],"resultDescription":"Returns embedding vectors for each input text along with the model name used and the dimensionality of the embeddings, enabling downstream vector database storage, similarity search, or clustering.","failureModes":["More than 32 texts submitted — maxItems constraint violated, request rejected","Invalid model name provided — only 'nomic' and 'bge_m3' are accepted","Empty texts array or minItems not met — at least 1 text required","Local Ollama runtime unavailable — service may be temporarily down","Text inputs too long for the selected model's context window — truncation or error possible"],"whenToPreferThis":"Prefer this endpoint when you need local, privacy-preserving text embeddings without calling paid third-party APIs like OpenAI or Cohere. Ideal for batch embedding up to 32 texts per call with a choice between nomic (general purpose) and bge-m3 (multilingual) models. Best suited for RAG pipelines, semantic search indexing, and clustering tasks where data privacy or cost is a concern.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:33:52.278Z","isFirstParty":false}