{"uid":"cap_lgjtBrewdTRxGD2iS7W_q","slug":"animica-text-embedding-api-e5004136","name":"Animica Text Embedding API","description":"Embed up to 256 texts in ONE call with all-MiniLM-L6-v2 (384-dimensional, normalised for cosine similarity), up to 8192 characters each. Sold as a batch on purpose: a single settlement costs real gas, so a per-string price would be almost entirely payment overhead — one call, many vectors, is the only honest shape for this. This is a small fast local sentence-transformer, not a frontier embedding model; the model id rides on every response so a future change is visible rather than silent.","url":"https://animica.dev/x402/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"texts":{"type":"array","description":"1..256 strings"},"normalize":{"type":"boolean","description":"vectors are already unit-normalised; set false only if you want them raw (default true)"}}},"responseSchema":{"type":"json"},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001796","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":"$0.001796/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001796","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001796","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_FYxIV74rwiwpihQyx_cRc","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001796","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates dense vector embeddings for up to 256 texts in a single call, returning unit-normalized vectors with dimension and character count metadata.","exampleAgentPrompt":"Embed these 3 product descriptions for me so I can store them in my vector database and run similarity searches: 'Lightweight running shoes with foam sole', 'Waterproof hiking boots for rough terrain', 'Classic leather dress shoes for formal occasions' — return them unit-normalized.","exampleUseCases":[{"title":"RAG pipeline document chunking","prompt":"I have 50 text chunks from my knowledge base docs — can you embed all of them into vectors so I can load them into my Pinecone index for retrieval-augmented generation?"},{"title":"Semantic search query vectorization","prompt":"Embed this user search query so I can find the nearest matching products in my vector store: 'comfortable shoes for long walks in the city'."},{"title":"Customer review clustering","prompt":"I have 200 short customer reviews — embed all of them into vectors so I can run k-means clustering and group them by topic automatically."}],"resultDescription":"A JSON object containing the embedding model name, vector dimensionality, count of embedded texts, a 2D array of float vectors (one per input text, in request order), total character count across all inputs, and per-text character counts. Vectors are unit-normalized by default for direct cosine similarity use.","failureModes":["Exceeding 256 texts in a single request returns a validation error","Empty texts array returns an error","Payment failure via x402 protocol blocks the request","Very long individual strings may cause timeout or character-limit errors","Malformed JSON body returns a 400 error"],"whenToPreferThis":"Choose this endpoint when you need batch text embeddings (up to 256 at once) via a pay-per-call x402 micropayment model without managing an OpenAI API key. It is ideal for AI agents with crypto wallets that need on-demand embeddings for semantic search, RAG pipelines, or clustering tasks at low per-call cost ($0.005 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:45:57.013Z","isFirstParty":false}