{"uid":"cap_gSYhpgSE_9mpCx3WouAU_","slug":"agishub-text-embedding-api-1ddc2e9a","name":"AGISHub Text Embedding API","description":"Turn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual.","url":"https://api.agishub.com/v1/embed","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input","output"],"properties":{"input":{"type":"object","required":["type","method","queryParams"],"properties":{"type":{"enum":["http"],"type":"string"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["text"],"properties":{"text":{"type":"string","minLength":1,"description":"Text to embed into a numeric vector for semantic search / RAG."}},"additionalProperties":false}},"additionalProperties":false},"output":{"type":"object","required":["type","example"],"properties":{"type":{"enum":["json"],"type":"string"},"example":{"type":"object"}},"additionalProperties":false}}},"responseSchema":{"type":"json","example":{"result":"Turn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual."}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_xTpbfqW7OyUEpTMtWpQrM","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts input text into a numeric embedding vector suitable for semantic search, retrieval-augmented generation (RAG), and similarity comparison, with multilingual support.","exampleAgentPrompt":"Embed this text into a numeric vector so I can do a semantic similarity search: 'What are the best practices for microservices architecture?'","exampleUseCases":[{"title":"Semantic search over knowledge base","prompt":"Convert this user query into an embedding vector so I can find the most semantically similar articles in our knowledge base: 'how do I reset my account password?'"},{"title":"RAG pipeline document indexing","prompt":"I need to embed this product description into a numeric vector for storing in our Pinecone index: 'Lightweight running shoes with breathable mesh upper and cushioned sole, ideal for daily training.'"},{"title":"Multilingual similarity matching","prompt":"Turn this French sentence into a vector embedding so I can compare it with English documents in my similarity search: 'Quels sont les meilleurs restaurants à Paris?'"}],"resultDescription":"Returns a JSON object containing the embedding result — a numeric vector representation of the input text, usable for cosine similarity, nearest-neighbor search, and RAG retrieval pipelines. The response includes the embedded representation under the 'result' key.","failureModes":["Empty or missing 'text' query parameter returns a validation error","Text too long may exceed model context window and return an error","Non-UTF-8 or malformed text may cause processing failures","Service unavailability or rate limiting returns a 4xx/5xx response","Payment not completed (x402) results in access denial"],"whenToPreferThis":"Choose this endpoint when you need a simple, pay-per-call multilingual text embedding with no API key setup, particularly well-suited for agents that need on-demand vectorization without managing embedding model infrastructure. Ideal for RAG pipelines, semantic search, and similarity tasks where cost predictability ($0.01/call) matters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T18:49:52.474Z","isFirstParty":false}