{"uid":"cap_1zbCm6m3M8ps_7MM68oj0","slug":"netintel-multilingual-text-embeddings-9fa7126c","name":"NetIntel Multilingual Text Embeddings","description":"Multilingual text embeddings (384-dim) served in-house, no OpenAI dependency, no API key. NetIntel's cheapest embeddings at $0.001/call, batches up to 256 texts. Supports query/passage input types for retrieval (e5). Pairs with /text/chunk for a full chunk→embed RAG pipeline on one provider.","url":"https://netintel.dev/embeddings","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"type":"object","required":["input"],"properties":{"input":{"type":"string","description":"Text to embed. Also accepts an array of up to 256 strings (200000 chars total). Aliases: text, inputs."},"input_type":{"type":"string","description":"query or passage (default). Aliases: task, type."},"encoding_format":{"type":"string","description":"float (default) or base64."}}},"type":{"type":"string","const":"http"},"method":{"enum":["POST"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","properties":{"data":{"type":"array","description":"[{object:'embedding', index, embedding:[…384 floats]}]"},"model":{"type":"string","description":"Always 'netintel-embed-small'"},"usage":{"type":"object","description":"prompt_tokens, total_tokens (input-only, estimated)"},"object":{"type":"string","description":"Always 'list'"},"dimensions":{"type":"number","description":"Always 384"}}}}}}},"responseSchema":{"type":"json","example":{"data":[{"index":0,"object":"embedding","embedding":[0.0123,-0.0456,0.0789]}],"model":"netintel-embed-small","usage":{"total_tokens":6,"prompt_tokens":6},"object":"list","findings":[],"provider":"netintel","dimensions":384,"input_type":"passage","normalized":true,"source_model":"multilingual-e5-small"}},"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_I7NtiAg0YIu3k8JLqk6Up","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 (or batches up to 256 strings) into 384-dimensional multilingual embeddings using a hosted e5 model, with no OpenAI dependency or API key required.","exampleAgentPrompt":"Embed this passage using NetIntel's multilingual embeddings so I can store it in my vector database: 'Climate change is accelerating the melting of polar ice caps.' — use passage input type.","exampleUseCases":[{"title":"RAG pipeline chunk embedding","prompt":"I've split my document into chunks using netintel's chunker — now embed all these chunks as passages so I can store them in my vector DB for retrieval. Here are the 50 text chunks."},{"title":"Multilingual semantic search index","prompt":"I need to embed these 200 product descriptions in multiple languages as passage vectors so users can search them semantically. Batch them all in one call and give me the 384-dim float arrays."},{"title":"Query vector for similarity lookup","prompt":"Convert this user search query — 'best noise-cancelling headphones under $200' — into a query embedding so I can find the closest matching passages in my vector store."}],"resultDescription":"Returns a JSON object with a 'data' array of embedding objects (each with index, object type, and a 384-float embedding array), the model name 'netintel-embed-small', dimensions (384), normalized flag, source model identifier, input_type used, and token usage counts (prompt_tokens, total_tokens).","failureModes":["Input array exceeds 256 strings or 200,000 total characters — request rejected","Invalid input_type value outside 'query'/'passage' — defaults or errors","Payment not received or x402 handshake fails — 402 response with payment details","Malformed JSON body or missing required 'input' field — 400 error","Very long individual strings may be truncated or cause token limit errors"],"whenToPreferThis":"Choose this endpoint when you need cheap ($0.001/call), dependency-free multilingual text embeddings without an OpenAI account, especially when already using other NetIntel endpoints (e.g. /text/chunk) to build a self-contained RAG pipeline on a single provider. Prefer it for batch embedding up to 256 texts per call and for retrieval tasks requiring query/passage asymmetry via the e5 input_type parameter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:49:43.494Z","isFirstParty":false}