canonical: https://jentic.com/apis/nomic.ai/nomic

# Nomic AI Embedding API

The Nomic AI Embedding API generates vector embeddings for text and images using Nomic's embedding models. It embeds text inputs for a chosen task type such as search, classification, or clustering, embeds image inputs into the same vector space, and lists the embedding models available. Applications use the resulting vectors for semantic search, retrieval, and similarity grouping.

## For AI agents

Generate text and image embeddings with Nomic's models for semantic search, classification, and clustering. Returns vectors in a shared space for cross-modal retrieval.

## Scope

Does not handle text generation, vector storage, or chat completion. Use for generating text and image embeddings only.

## Capabilities

- Generate text embeddings for a task type such as search, classification, or clustering
- Embed images into the same vector space as text for cross-modal retrieval
- List the available Nomic embedding models
- Produce vectors sized for a chosen Nomic model to power a semantic search index

## Use cases

### AI Retrieval Agent

An AI agent connected through Jentic builds and queries a retrieval index by embedding documents and search queries with Nomic, so a question gets matched to the most relevant passages without the developer wiring the embedding calls by hand. The agent picks a model and the search task type, then stores or compares the returned vectors.

Example prompt: Embed a set of documents and a user query with the search task type and return the top matches by vector similarity

### Multimodal Image Search

A visual search product embeds both images and text descriptions into Nomic's shared vector space so a text query can retrieve matching images and vice versa. Because image and text vectors are comparable, the product runs cross-modal similarity without maintaining two separate models.

Example prompt: Embed a catalogue of product images and a text query, then return the images whose vectors are closest to the query

### Clustering and Classification

A data-labelling or analytics tool embeds records with the clustering or classification task type and groups them by vector proximity, surfacing themes or routing items to categories. Choosing the task type tunes the vectors so downstream grouping and labelling stay accurate.

Example prompt: Embed a batch of support tickets with the clustering task type and group them by similarity to surface common themes

## Key endpoints

| Method | Path | Description |
| --- | --- | --- |
| POST | `/v1/embedding/text` | Generate embeddings for text inputs |
| POST | `/v1/embedding/image` | Generate embeddings for image inputs |
| GET | `/v1/models` | List available embedding models |

## Key resources

- **Text Embeddings** — Vectors for text inputs with a selectable task type
- **Image Embeddings** — Vectors for image inputs in the same space as text
- **Models** — The embedding models available to call

## AI readiness

This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.

- **Score:** 63 / 100
- **Maturity:** AI-Aware
- **Dimensions:**
  - Foundational Compliance: 100 / 100
  - Developer Experience & Jentic Compatibility: 62 / 100
  - AI-Readiness & Agent Experience: 45 / 100
  - Agent Usability: 94 / 100
  - Security: 60 / 100
  - AI Discoverability: 54 / 100
- **View full report:** https://jentic.com/apis/nomic.ai/nomic/scorecard
- **How the score is calculated:** https://docs.jentic.com/reference/api-readiness-framework/overview/
- **More about the dimensions:** https://docs.jentic.com/reference/api-readiness-framework/specification/#dimensional-model-overview

### Score it yourself

Every API in the directory is allowlisted, so you can re-score it with no key required.

- **Score your own API:** https://jentic.com/scorecard.md
- **Scoring CLI agent skill:** https://github.com/jentic/jentic-api-scorecard/blob/main/skills/jentic-api-scorecard/SKILL.md

```sh
npx @jentic/api-scorecard-cli score <openapi-url>
```

## Why Jentic

- **Setup:** Wiring the Nomic Embedding API by hand means handling its bearer token, picking a model and task type, and batching inputs yourself. Through Jentic you install once, import Nomic from the API Directory, store the token once, and your agent calls it.
- **Permission scoping:** The API only reads inputs and returns vectors, so a rule can keep your agent to just the text and image embedding operations. You choose which operations it may call, and nothing it does changes account state.
- **Credential handling:** Your Nomic API token is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
- **Discovery method:** Agents search Jentic by intent such as 'embed text for search' or 'embed an image', and Jentic returns the matching Nomic operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

## Related APIs

- **OpenAI API** — Text embedding models alongside chat and completion endpoints.
- **Cohere API** — Embedding and reranking models tuned for search and retrieval.
- **Jina AI API** — Text and multimodal embedding models with a REST interface.
- **Pinecone API** — Managed vector database for storing and querying embeddings.

## FAQ

### What authentication does the Nomic AI Embedding API use?

The Nomic AI Embedding API authenticates with a bearer token in the Authorization header, per its OpenAPI spec. Through Jentic that token is held encrypted by your own Jentic One instance and injected at call time, so it never appears in the agent's prompt or logs.

### Can I embed images with the Nomic AI Embedding API?

Yes. The image embedding operation maps images into the same vector space as text, so you can run cross-modal similarity and retrieval. The text embedding operation accepts a task type such as search or clustering so the vectors suit your downstream use.

### What are the rate limits for the Nomic AI Embedding API?

The OpenAPI spec does not define rate limits for the Nomic AI Embedding API. Nomic documents current limits and model details at https://docs.nomic.ai, so check there before embedding large batches.

### Can I limit what my agent is allowed to do with the Nomic AI Embedding API?

Yes. Write a rule that allows only the text and image embedding operations, so the agent generates vectors and nothing else. None of the operations change account state, and every call the agent makes is logged.

### How do I generate embeddings with the Nomic AI Embedding API through Jentic?

Search by intent in Jentic with a query like 'embed text for search', add the Nomic AI Embedding API from the Jentic API Directory, and your agent calls the text or image embedding operation with your chosen model. To run it on your own infrastructure, install Jentic One from its GitHub repo.