{"uid":"cap_vboad1MS-Wcx7qHdzDd8R","slug":"arbiter-embedding-api-bc236013","name":"ARBITER Embedding API","description":"Create deterministic persistent machine representations for memory, indexing, retrieval, clustering, reuse, cross-system meaning, and downstream coherence measurement.","url":"https://api.grip.fyi/v1/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"texts":{"type":"array","items":{"type":"string"},"minItems":1,"description":"Strings to represent in the deterministic ARBITER field."},"use_freq":{"type":"boolean","default":true}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/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_epF9zGJjkbaF3c0ksW-vy","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":"Generates deterministic vector embeddings for input text strings, suitable for memory storage, semantic indexing, retrieval, and clustering tasks.","exampleAgentPrompt":"Embed these five product description strings into deterministic vector representations so I can index them for semantic search: 'noise-cancelling headphones', 'wireless earbuds', 'studio monitor speakers', 'bluetooth soundbar', 'portable DAC amplifier'.","exampleUseCases":[{"title":"Semantic memory for AI agent","prompt":"Take these 10 conversation summaries and embed them into deterministic vectors so I can store them in my agent's long-term memory and retrieve the most relevant ones later."},{"title":"Document clustering pipeline","prompt":"I have 50 customer support tickets — embed all of them so I can cluster similar issues together and identify the top recurring problems."},{"title":"Duplicate content detection","prompt":"Embed these 30 product descriptions so I can measure which ones are semantically near-duplicate and clean up my catalog before publishing."}],"resultDescription":"Returns deterministic vector embeddings for each input string — fixed-length numerical representations that can be stored, compared via cosine similarity, clustered, or indexed in a vector database for downstream retrieval and coherence measurement tasks.","failureModes":["Empty texts array returns validation error","Strings exceeding model token limit may be truncated or rejected","Network timeout on large batches","Non-UTF-8 or malformed strings may cause processing errors","Rate limiting if too many calls in short succession"],"whenToPreferThis":"Choose this endpoint when you need deterministic, reproducible embeddings — meaning the same input always produces the same vector — for use cases like agent memory, cross-system semantic alignment, deduplication, or coherence measurement. Prefer it over non-deterministic embedding services when consistency across runs is critical.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:30:14.571Z","isFirstParty":false}