{"uid":"cap_wTYLDmHkKZ9bMGxG_yWdp","slug":"tanod-mlpeek-text-embeddings-7510dab9","name":"Tanod mlpeek Text Embeddings","description":"One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.","url":"https://tanod.dev/v1/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"model":{"enum":["small-en","multilingual"],"type":"string","default":"small-en","description":"small-en = BAAI/bge-small-en-v1.5 (English); multilingual = intfloat/multilingual-e5-small (~100 languages). 384 dimensions both."},"texts":{"type":"array","items":{"type":"string","maxLength":8000,"minLength":1},"maxItems":64,"minItems":1,"description":"1-64 texts, each at most 8,000 characters; each is truncated at 512 tokens (reported per text). Priced per text, minimum USD 0.001 per call."},"encoding":{"enum":["float","base64"],"type":"string","default":"float","description":"base64 = little-endian float32 bytes."},"normalize":{"type":"boolean","default":true,"description":"L2-normalise (cosine similarity = dot product)."},"input_type":{"enum":["none","query","passage"],"type":"string","default":"none","description":"Retrieval prefix: query / passage (e5: 'query: ' / 'passage: '; bge: its query instruction / nothing). none = symmetric use."}}},"responseSchema":{"type":"json","example":{"data":[{"index":0,"tokens":8,"embedding":[-0.017226,0.004483,0.019969,0.00569],"truncated":false}],"note":"float values rounded to 6 decimal places","model":{"id":"bge-small-en-v1.5","alias":"small-en","source":"BAAI/bge-small-en-v1.5","license":"MIT","revision":"5c38ec7c405ec4b44b94cc5a9bb96e735b38267a"},"usage":{"texts":1,"tokens":8,"truncated":0},"encoding":"float","dimensions":384,"input_type":"none","normalized":true,"prefix_applied":null,"max_tokens_per_text":512}},"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_76UErS6Vv3dJWJu2kZbjq","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":"Converts 1–64 texts into 384-dimensional float vectors using bge-small-en or multilingual-e5-small models, with support for query/passage retrieval prefixes and L2 normalization.","exampleAgentPrompt":"Embed this text using the English bge-small-en model with the query prefix so I can use it for semantic search: 'What are the best practices for securing a Solidity smart contract?'","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I need to vectorize these 10 passage chunks from my documentation so I can store them in my vector database — use the multilingual model with the passage prefix and return normalized float embeddings."},{"title":"Semantic search query encoding","prompt":"Encode this user search query into a 384-dim vector using the English model with query prefix so I can find the most similar documents in my Pinecone index: 'how do I reset my account password?'"},{"title":"Multilingual product similarity","prompt":"Embed these product descriptions — some are in Spanish, French, and German — using the multilingual model so I can cluster them by semantic similarity."}],"resultDescription":"Returns a JSON object with an array of embedding objects (each containing index, token count, 384-dimensional float or base64-encoded vector, and truncation flag), plus model metadata (id, alias, source, license, revision), usage stats (texts, tokens, truncated), encoding format, dimensions (384), input_type, normalization status, and any prefix applied.","failureModes":["Texts exceeding 8,000 characters are rejected at the input level","Texts longer than 512 tokens are silently truncated (reported via truncated flag)","Batches exceeding 64 texts are rejected","Empty strings or zero-length texts are rejected","Payment failure (x402) if insufficient USDC balance","Invalid model alias returns a validation error","Malformed JSON body returns a 400 error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, fast 384-dimensional embeddings for English (bge-small-en) or ~100-language (multilingual-e5-small) text with built-in retrieval prefix support (query/passage), pay-per-use pricing starting at $0.001 per call, and no need to manage model infrastructure. Ideal for RAG pipelines, semantic search, clustering, and similarity tasks where a compact 384-dim vector is sufficient and cost efficiency matters over larger models.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T18:41:34.303Z","isFirstParty":false,"canonicalSlug":"tanod-mlpeek-text-embeddings-7510dab9"}