{"uid":"cap_h05Cj3r9mglWAc76oBHSt","slug":"citiview-agent-market-text-embeddings-bge-m3-5caaff76","name":"Citiview Agent Market – 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: $0.003 + 3x the model token cost. Also callable as GET with the same fields as query parameters.","url":"https://agents.citiviewgroup.com/v1/embed?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$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_8XnKFq7bxXoVV2paq6u3M","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":"Converts up to 100 texts per call into 1024-dimensional multilingual semantic embeddings using BGE-M3, suitable for semantic search and RAG pipelines.","exampleAgentPrompt":"Embed these 3 passages for me so I can store them in my vector database and run semantic search: 'Climate change affects global temperatures', 'Renewable energy reduces carbon emissions', 'Solar panels are increasingly affordable'.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"I need to embed all these product descriptions so I can store them in Pinecone and retrieve the most relevant ones when a user asks a question — can you vectorize these 50 texts for me?"},{"title":"Multilingual semantic search","prompt":"I have a mix of English, French, and Spanish customer reviews — can you generate embeddings for all of them so I can cluster similar feedback together regardless of language?"},{"title":"Query-document similarity scoring","prompt":"Embed this user query 'best practices for API rate limiting' and these 5 documentation snippets so I can compute cosine similarity and find the most relevant doc."}],"resultDescription":"Returns 1024-dimensional float embedding vectors for each input text, produced by the BGE-M3 multilingual model. Each vector captures the semantic meaning of the input and can be used directly for cosine similarity, nearest-neighbor search, clustering, or as input to downstream RAG retrieval steps.","failureModes":["More than 100 texts submitted in one call — batch limit exceeded","Malformed pipe-separated texts string causing parsing errors","Empty input text resulting in zero or degenerate vectors","Payment failure or insufficient USDC balance blocking the x402 call","Text exceeding model's token context window producing truncated embeddings"],"whenToPreferThis":"Choose this endpoint when you need multilingual, high-quality 1024-dimensional dense embeddings from BGE-M3 at a flat per-call price regardless of batch size (up to 100 texts). It is especially well-suited for RAG pipelines, multilingual corpora, and scenarios where you want a single API call to handle a batch of passages. Prefer it over OpenAI embeddings when you want a flat-rate, non-token-based pricing model or need BGE-M3's specific multilingual representation quality.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-14T06:48:30.938Z","isFirstParty":false,"canonicalSlug":"citiview-agent-market-text-embeddings-bge-m3-5caaff76"}