{"uid":"cap_RkKthXDQZ2EC6W1P5Wg3A","slug":"nojunk-llm-embeddings-bge-m3-5b622e75","name":"NoJunk LLM Embeddings (BGE-M3)","description":"NoJunk LLM embeddings: OpenAI-compatible text embeddings on BGE-M3 (1024 dimensions, multilingual, up to 32 inputs per call).","url":"https://nojunk-llm.mick8511.workers.dev/v1/embeddings?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{"type":["string","array"],"description":"A string, or an array of 1-32 strings (each 1-8000 characters)"},"model":{"type":"string","description":"Optional: bge-m3 (the only embedding model)"}}},"responseSchema":{"type":"json","example":{"data":[{"index":0,"object":"embedding","embedding":[0.0213,-0.0471,0.0088,0.0329]},{"index":1,"object":"embedding","embedding":[0.0213,-0.0471,0.0088,0.0329]}],"model":"bge-m3","usage":{"total_tokens":11,"prompt_tokens":11},"object":"list"}},"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_CFY9zHnas-fBTNQviDG9B","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":"Generates 1024-dimensional vector embeddings for up to 32 text strings per call using BGE-M3, paid per-request in USDC with no API key required.","exampleAgentPrompt":"Generate BGE-M3 embeddings for these two sentences: 'The quick brown fox jumps over the lazy dog' and 'A fast auburn fox leaps above a sleepy hound' — I need the 1024-dim vectors to compare their semantic similarity.","exampleUseCases":[{"title":"RAG document ingestion pipeline","prompt":"Embed these 10 product review snippets so I can store them in my vector database for semantic search — use BGE-M3 and give me back the 1024-dimensional vectors for each."},{"title":"Semantic similarity scoring","prompt":"I have two customer support tickets: 'My order never arrived' and 'Package not delivered to my address' — embed both so I can compute how semantically similar they are and decide if they should be merged."},{"title":"Zero-signup embeddings for a prototype","prompt":"I'm prototyping a search feature and need embeddings for these 5 article titles right now without setting up an account — just embed them with BGE-M3 and charge me per call in USDC."}],"resultDescription":"Returns a JSON object conforming to the OpenAI embeddings response format: a list of embedding objects each containing the embedding index, a 1024-dimensional float array, and the object type 'embedding'; plus the model name ('bge-m3') and token usage stats (prompt_tokens and total_tokens).","failureModes":["Input array exceeds 32 strings — request rejected","Individual string exceeds 8000 characters — may be truncated or rejected","Payment via x402 fails or USDC balance insufficient — no charge, no result returned","Invalid JSON body or missing 'input' field — request fails","Model field set to unsupported model name — falls back or errors"],"whenToPreferThis":"Choose this endpoint when you need BGE-M3 embeddings (multilingual, strong cross-lingual retrieval) with zero onboarding friction — no API key, no account, no rate limits — and you want to pay only when a valid result is returned, billed per-call in USDC on Base via x402. Ideal for agents, prototypes, or low-volume workloads where provisioning a traditional embedding API account is overkill. Also suitable when your stack already uses an OpenAI-compatible client and you just need to swap the base URL.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T20:32:39.059Z","isFirstParty":false,"canonicalSlug":"nojunk-llm-embeddings-bge-m3-5b622e75"}