{"uid":"cap_S-j0eXoi-ui0rHemC8g6V","slug":"agentrxi-text-embeddings-bge-m3-97366569","name":"AgentRXI Text Embeddings (BGE-M3)","description":"Text embeddings for semantic search and RAG (BGE-M3, multilingual, 1024 dimensions). Up to 100 texts per call, one flat price. Priced by size: $0.003 + 3x the model token cost. Also callable as GET with the same fields as query parameters.","url":"https://agentrxi.com/v1/embed?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$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","properties":{"text":{"type":"string"},"texts":{"type":"string","description":"Up to 100 texts; separate items with |"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.003","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":"$0.003/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.003","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_Ved-AOhmdrb-RVZhvPI8D","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.003","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates multilingual BGE-M3 text embeddings (1024 dimensions) for up to 100 texts per call, optimized for semantic search and RAG pipelines.","exampleAgentPrompt":"Embed these three sentences for me using BGE-M3 so I can store them in my vector database: 'The sky is blue', 'El cielo es azul', 'Le ciel est bleu'.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I'm building a RAG system — can you embed these five document chunks so I can store them in my vector DB? Here they are separated by pipes: 'Quantum computing uses qubits|Classical computers use bits|Superposition allows multiple states|Entanglement links qubits|Decoherence is a key challenge'."},{"title":"Multilingual semantic search index","prompt":"I need to vectorize these product descriptions in English, Spanish, and French so users can search across languages — embed all of them: 'Wireless headphones with noise cancellation|Auriculares inalámbricos con cancelación de ruido|Casque sans fil avec réduction de bruit'."},{"title":"Sentence similarity scoring setup","prompt":"Can you get me the BGE-M3 embeddings for both of these sentences so I can measure how similar they are: 'How do I reset my password?' and 'I forgot my login credentials and need to get back into my account'?"}],"resultDescription":"Returns 1024-dimensional float embedding vectors for each input text, produced by the multilingual BGE-M3 model. Each text maps to one dense vector suitable for cosine similarity, nearest-neighbor search, or storage in a vector database.","failureModes":["Input exceeds 100 texts per call — request is rejected","Individual text too long for model token limit — truncation or error","Malformed pipe-separated texts field — parsing failure","Payment not included or insufficient — 402 response","Empty input texts — returns error or empty array"],"whenToPreferThis":"Choose this endpoint when you need production-ready multilingual embeddings via a simple pay-per-call API without managing GPU infrastructure. BGE-M3's 1024-dimensional output is well-suited for cross-lingual semantic search and RAG. Best when batching up to 100 texts at once to maximize cost efficiency at $0.003 base price.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:38:57.731Z","isFirstParty":false,"canonicalSlug":"agentrxi-text-embeddings-bge-m3-97366569"}