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Create Embeddings (LLM API)
Create Embeddings (LLM API) is a paid API for AI agents from agents.llmapi.ai, paid per call via MPP, price unknown, status unknown (last checked 2026-10-14).
Generates vector embeddings for input text using OpenAI or Jina embedding models, returning dense float vectors suitable for semantic search, clustering, or retrieval.
Price / call—
Success—
StatusUnknown
How this service works
Generates vector embeddings for the provided input text.
Output
Returns a dense float vector (array of numbers) representing the semantic content of the input text. The dimensionality depends on the model chosen — text-embedding-3-large produces 3072-dim vectors, text-embedding-3-small produces 1536-dim vectors, and jina-embeddings-v4 varies. The response follows OpenAI embedding API conventions including the embedding array, model name, and token usage counts.
Example use cases
Semantic search over product catalog
“Embed this user query 'wireless noise-cancelling headphones under $200' using text-embedding-3-large so I can find the closest matching products in my vector database.”
Document clustering for research papers
“I need vector embeddings for this abstract — 'This paper explores transformer architectures for low-resource NLP tasks' — use jina-embeddings-v4 so I can cluster it with similar papers.”
RAG pipeline query encoding
“Before I retrieve context from my knowledge base, embed my question 'What are the cancellation terms for premium subscriptions?' using text-embedding-3-small so I can do a nearest-neighbor lookup.”
Technical details
Endpoint
POSThttps://agents.llmapi.ai/zc/v1/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need pay-per-call embedding inference without a subscription, especially if you're already using the LLM API ecosystem for chat completions or other inference. It's ideal for agents that need OpenAI-compatible embeddings (text-embedding-3-small/large) or Jina v4 embeddings on demand without managing API keys for multiple providers. Prefer alternatives if you need streaming, batch processing of thousands of documents at discounted rates, or models not in the supported enum.
Side effects
- Invalid model name — must be exactly 'text-embedding-3-large', 'text-embedding-3-small', or 'jina-embeddings-v4'
- Missing required 'input' or 'model' field returns 422 validation error
- Input text exceeds model token limit causing a 400 or 413 error
- Payment or authentication failure returns 402 or 401
- Upstream LLM API timeout or outage returns 502 or 503
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | string | Text or array of texts to embed. |
| modelrequired | string | The model id exactly as written in the service description: text-embedding-3-large, text-embedding-3-small, or jina-embeddings-v4. |
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