{"uid":"cap_fos71zViZsmpzMGj4ETPq","slug":"quickbuild-text-embeddings-api-3c9250a8","name":"QuickBuild Text Embeddings API","description":"Landing pages ($15), Google Sheets/Apps Script automation ($10), scrapers, scripts & Telegram bots (from $15), broken-link + sitemap audit reports ($10). See a working demo first, pay after.","url":"https://quickbuild.quynguyenbac.workers.dev/v1/embeddings?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":[],"properties":{"input":{"type":["string","array"],"items":{"type":"string"},"description":"Text or array of texts to embed (max 100). Optional: defaults to a sample sentence."},"model":{"enum":["bge-m3","bge-large-en-v1.5","bge-base-en-v1.5","bge-small-en-v1.5","embeddinggemma-300m"],"type":"string","description":"Embedding model alias (default bge-m3)."},"dimensions":{"type":"integer","maximum":1024,"minimum":1,"description":"Optional output size; vectors are truncated and re-normalized."},"encoding_format":{"enum":["float","base64"],"type":"string","description":"float (default) or base64 (little-endian float32)."}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","properties":{"data":{"type":"array","items":{"type":"object","properties":{"index":{"type":"integer"},"object":{"type":"string"},"embedding":{"type":["array","string"],"items":{"type":"number"}}}}},"model":{"type":"string"},"usage":{"type":"object","properties":{"total_tokens":{"type":"integer"},"prompt_tokens":{"type":"integer"}}},"object":{"type":"string"},"modelId":{"type":"string"},"dimensions":{"type":"integer"}}}}}}},"responseSchema":{"type":"json","example":{"data":[{"index":0,"object":"embedding","embedding":[-0.0291,0.0137,-0.0402,0.0088]}],"model":"bge-m3","usage":{"total_tokens":12,"prompt_tokens":12},"object":"list","modelId":"@cf/baai/bge-m3","dimensions":1024}},"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_DWjHl3t78Sa_Z7XvtW7jg","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 text strings into dense float vector embeddings using BGE and Gemma models, with optional dimension truncation.","exampleAgentPrompt":"Embed this sentence using the bge-m3 model and give me the 1024-dimensional float vector: 'The quick brown fox jumps over the lazy dog'.","exampleUseCases":[{"title":"Semantic search index population","prompt":"Take these 50 product descriptions and convert them all into bge-m3 embeddings so I can store them in my vector database for semantic search."},{"title":"Sentence similarity scoring","prompt":"Generate embeddings for these two sentences using bge-large-en-v1.5 so I can compute their cosine similarity: 'I love machine learning' and 'Deep learning is fascinating'."},{"title":"RAG document chunking pipeline","prompt":"I need to vectorize these document chunks for my retrieval-augmented generation system — use bge-m3 with 512 dimensions and return float arrays."}],"resultDescription":"Returns a JSON object containing an array of embedding objects (each with index, object type, and the embedding vector as float array or base64 string), the model name used, model ID, output dimensions, and token usage counts (prompt and total tokens).","failureModes":["Missing required 'input' query parameter returns default sample embedding rather than an error","Requesting more than 100 texts in a single call may be rejected or silently truncated","Invalid model alias returns an error or falls back to default","Dimension value exceeding 1024 is rejected by schema validation","Network timeout on Cloudflare Worker cold start for large batches"],"whenToPreferThis":"Choose this endpoint when you need cheap ($0.001/call), pay-per-use text embeddings via x402 micropayments without API key signup, especially when working with multilingual content (bge-m3) or needing dimension-truncated vectors for memory-constrained vector stores. Prefer over OpenAI embeddings when minimizing cost per call or when a Cloudflare Workers-hosted solution reduces latency for edge deployments.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T07:19:50.019Z","isFirstParty":false,"canonicalSlug":"quickbuild-text-embeddings-api-3c9250a8"}