Cohere ↗

    import chromadb.utils.embedding_functions as embedding_functions
    cohere_ef  = embedding_functions.CohereEmbeddingFunction(api_key="YOUR_API_KEY",  model_name="large")
    cohere_ef(input=["document1","document2"])
    ```
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  <span class="tab-start" data-tab-title="TypeScript"></span>
```typescript
    // npm install @chroma-core/cohere

    import { CohereEmbeddingFunction } from "@chroma-core/cohere";

    const embedder = new CohereEmbeddingFunction({ apiKey: "apiKey" });

    // use directly
    const embeddings = embedder.generate(["document1", "document2"]);

    // pass documents to query for .add and .query
    const collection = await client.createCollection({
        name: "name",
        embeddingFunction: embedder,
    });
    const collectionGet = await client.getCollection({
        name: "name",
        embeddingFunction: embedder,
    });
    ```
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You can pass in an optional `model_name` argument, which lets you choose which Cohere embeddings model to use. By default, Chroma uses `large` model. You can see the available models under `Get embeddings` section [here](https://docs.cohere.ai/reference/embed).

### Multilingual model example


  ```python
  cohere_ef  = embedding_functions.CohereEmbeddingFunction(
      api_key="YOUR_API_KEY",
      model_name="multilingual-22-12"
  )

  multilingual_texts  = [
      'Hello from Cohere!', 'مرحبًا من كوهير!',
      'Hallo von Cohere!', 'Bonjour de Cohere!',
      '¡Hola desde Cohere!', 'Olá do Cohere!',
      'Ciao da Cohere!', '您好，来自 Cohere！',
      'कोहिअर से नमस्ते!'
  ]

  cohere_ef(input=multilingual_texts)

import { CohereEmbeddingFunction } from "chromadb";

const embedder = new CohereEmbeddingFunction("apiKey");

multilingual_texts = [
    "Hello from Cohere!",
    "مرحبًا من كوهير!",
    "Hallo von Cohere!",
    "Bonjour de Cohere!",
    "¡Hola desde Cohere!",
    "Olá do Cohere!",
    "Ciao da Cohere!",
    "您好，来自 Cohere！",
    "कोहिअर से नमस्ते!",
];

const embeddings = embedder.generate(multilingual_texts);

import os
from datasets import load_dataset, Image


dataset = load_dataset(path="detection-datasets/coco", split="train", streaming=True)

IMAGE_FOLDER = "images"
N_IMAGES = 5

# Write the images to a folder
dataset_iter = iter(dataset)
os.makedirs(IMAGE_FOLDER, exist_ok=True)
for i in range(N_IMAGES):
    image = next(dataset_iter)['image']
    image.save(f"images/{i}.jpg")


multimodal_cohere_ef = CohereEmbeddingFunction(
    model_name="embed-english-v3.0",
    api_key="YOUR_API_KEY",
)
image_loader = ImageLoader()

multimodal_collection = client.create_collection(
    name="multimodal",
    embedding_function=multimodal_cohere_ef,
    data_loader=image_loader)

image_uris = sorted([os.path.join(IMAGE_FOLDER, image_name) for image_name in os.listdir(IMAGE_FOLDER)])
ids = [str(i) for i in range(len(image_uris))]
for i in range(len(image_uris)):
    # max images per add is 1, see cohere docs https://docs.cohere.com/v2/reference/embed#request.body.images
    multimodal_collection.add(ids=[str(i)], uris=[image_uris[i]])

retrieved = multimodal_collection.query(query_texts=["animals"], include=['data'], n_results=3)