13 services carry this tag in their source record. Tags come from the provider, not from us — a tag is a claim about what the service does, not proof that it does it well.
Smart, auto-routed research paper search. A broad field term is grounded against the OpenAlex taxonomy and returned as a coherent field cluster; a natural-language question runs hybrid retrieval over OpenAlex and Semanti...
Email deliverability & domain security check — validates SPF, DKIM (multi-selector), and DMARC, detects spoofing and misconfigurations, and returns an A–F email security grade with transparent deductions. Anti-spam / ant...
Convert text into a 768-dimension embedding vector (nomic-embed-text) for semantic search, RAG retrieval, clustering, deduplication or similarity scoring. POST JSON {"input": string}, up to 3000 characters; returns {embe...
OpenAI-compatible text embeddings (text-embedding-3-small by default; 3-large and ada-002 supported), up to 64 inputs or 16k chars per call, returned in the standard OpenAI shape. Identical inputs repeated within 10 minu...
Generate a text embedding vector using OpenAI text-embedding-3-large (3072 dimensions). Higher accuracy than the small model. Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text...
Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingual input works best in English) for semantic search, clustering and deduplication, or rerank mode: pass query + documents to get documents s...
Generate a text embedding vector using OpenAI text-embedding-3-small (1536 dimensions). Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text capped at 32k chars.
OpenAI-compatible text embeddings over x402 (768-dim, up to 32 inputs per call) — Genesis402 / UnyKorn Operator Network
Generate sentence embeddings (all-MiniLM-L6-v2, 384-dim, mean-pooled + L2-normalized) for up to 96 texts per call. Drop-in compatible with any all-MiniLM-L6-v2 index. Pairs with /chunk for a complete RAG pipeline.
Text embeddings for semantic search and RAG: one vector per call, 1024 dimensions.
Breached password check: is this password in known data breaches, and how often? Send only the SHA-1 hash (never the password); just the first 5 characters reach Have I Been Pwned (k-anonymity). Up to 50 hashes per call.
Use when you need image embeddings or text embeddings in one 2048-dimension space for retrieval, RAG, semantic search, clustering, or dedup (Qwen3-VL-Embedding-2B, OpenAI-compatible). Batch up to 256 items per call, serv...
OpenAI-compatible chat completion with Qwen3 30B (fast mixture-of-experts model): set the base URL to /v1/llm/qwen3-30b in any OpenAI SDK. Up to 32,000 characters in and 2,048 tokens out per call, a fixed price per call,...