{"uid":"cap_bQyQJmxnn1zxceWYLIw7r","slug":"agent-data-tools-text-embedding-47010af0","name":"Agent Data Tools: Text Embedding","description":"A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication.","url":"https://agent-data-tools.revmesh2074.workers.dev/ai/embed?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","required":["text"],"properties":{"text":{"type":"string","description":"Text to embed, up to 4,000 characters"}}},"responseSchema":{"type":"object","properties":{"model":{"type":"string"},"embedding":{"type":"array","items":{"type":"number"}},"dimensions":{"type":"number"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USD","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_4c5kOEn2fl0Dsgf_fDfW2","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 (up to 4,000 characters) into a 1024-dimensional numerical embedding vector using the BAAI BGE-M3 model","exampleAgentPrompt":"Embed this text for me using BGE-M3: 'Retrieval-augmented generation combines large language models with external knowledge sources to produce more accurate and grounded responses.' I need the 1024-dimensional vector so I can store it in my vector database.","exampleUseCases":[{"title":"RAG pipeline document chunking","prompt":"I have this paragraph I want to index in my vector store: 'The Federal Reserve raised interest rates by 25 basis points in its latest meeting, citing persistent inflation concerns.' Can you embed it so I can store it alongside my other document chunks?"},{"title":"Semantic similarity between two phrases","prompt":"I need to check how semantically similar two pieces of text are — can you embed both 'machine learning model training' and 'AI neural network optimization' so I can compute their cosine similarity?"},{"title":"Query vectorization for search","prompt":"Embed this user search query so I can run a nearest-neighbor lookup against my knowledge base: 'What are the side effects of ibuprofen when taken with blood thinners?'"}],"resultDescription":"Returns a JSON object containing the model name ('model': '@cf/baai/bge-m3'), a 1024-dimensional floating-point array ('embedding': [float, ...]) representing the semantic content of the input text, and a dimension count ('dimensions': 1024). The embedding can be used directly for cosine similarity, nearest-neighbor search, or storage in a vector database.","failureModes":["Text exceeds 4,000 character limit — embedding may be truncated or rejected","Missing required 'text' query parameter returns an error","Payment failure in x402 protocol results in no charge and no response","Model inference timeout on very dense or unusual text","Network errors to Cloudflare Workers endpoint"],"whenToPreferThis":"Choose this endpoint when you need a pay-per-call, no-account embedding service using the multilingual BGE-M3 model at $0.002 per call with x402 USDC micropayments on Base. Ideal for AI agents that need on-demand embeddings without managing API keys or subscriptions, and that already use the Agent Data Tools suite for crypto, web, or business data. BGE-M3 provides strong multilingual and cross-lingual retrieval performance at 1024 dimensions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T12:37:25.665Z","isFirstParty":false,"canonicalSlug":"agent-data-tools-text-embedding-47010af0"}