> ## Documentation Index
> Fetch the complete documentation index at: https://thenile.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Cohere Embedding Models

## Availble models

Cohere has multiple models with various sizes and languages, here's the one we use in our example.
You can replace it with any embedding model supported by Cohere:

| Model              | Dimensions | Max Tokens | Cost               | MTEB Avg Score | Similarity Metrics |
| ------------------ | ---------- | ---------- | ------------------ | -------------- | ------------------ |
| embed-english-v3.0 | 1024       | 512        | \$0.10 / 1M Tokens | 64.5           | cosine             |

## Usage

Cohere's API includes an `inputType` that allows you to specify the type of input you are embedding.
For example, is it a document that you will later retrieve? a question that you use for searching documents?
Texts for classification or clustering?

### Installing dependencies

```bash theme={null}
npm install @niledatabase/server cohere-ai
```

### Generating embeddings with Cohere

```javascript theme={null}
const { CohereClient } = require('cohere-ai');
const COHERE_API_KEY = 'your cohere api key';

const cohere = new CohereClient({
  token: COHERE_API_KEY,
});

const model = 'embed-english-v3.0'; // replace with your favorite cohere model

const input_text = `The future belongs to those who believe in the beauty of their 
                    dreams.`;

const doc_vec_resp = await cohere.embed({
  texts: [input_text], // you can pass multiple texts to embed in one batch call
  model: model,
  inputType: 'search_document', // for embedding of documents stored in pg_vector
});

const question = 'Who does the future belong to?';

const question_vec_resp = await cohere.embed({
  texts: [question],
  model: model,
  inputType: 'search_query', // for embedding of questions used to search documents
});

// Cohere's response is an object with an array of vector embeddings
// The object has other useful info like the model used, input text, billing, etc.
const doc_vec = doc_vec_resp.embeddings[0];
const question_vec = question_vec_resp.embeddings[0];
```

### Storing and retrieving the embeddings

```javascript theme={null}
// set up Nile as the vector store
const { Nile } = await import('@niledatabase/server');
const NILEDB_USER = 'you need a nile user with access to a nile database';
const NILEDB_PASSWORD = 'and a password for that user';
const nile = Nile({
  user: NILEDB_USER,
  password: NILEDB_PASSWORD,
});

// store vector in a table
await nile.db.query('INSERT INTO embeddings (embedding) values ($1)', [
  JSON.stringify(doc_vec.map((v) => Number(v))),
]);

// search for similar vectors
let db_resp = await nile.db.query(
  'select embedding from embeddings order by embedding<=>$1 limit 1',
  [JSON.stringify(question_vec.map((v) => Number(v)))],
);

// Postgres returns an object, with array of rows each column is a
// property of the row the vector is represented as a string
let similar_str = db_resp.rows[0].embedding;
let similar = similar_str
  .substring(1, similar_str.length - 1)
  .split(',')
  .map((v) => parseFloat(v));

// check that we got the same vector back
let same = true;

for (let i = 0; i < similar.length; i++) {
  if (Math.abs(similar[i] - doc_vec[i]) > 0.000001) {
    same = false;
  }
}

console.log('got same vector? ' + same);
```