# Language distribution
top_80_df["lang"].value_counts()
```

```mdx
lang
Spanish    33
English    29
Danish     18
Name: count, dtype: int64
```

## Embed and index documents

Let's embed our documents and store the embeddings. These embeddings are high-dimensional vectors (1,024 dimensions) that capture the semantic meaning of each document. We'll use Cohere's Embed 4 model that we have defined in the client setup.

The Embed 4 model require us to specify an `input_type` parameter that indicates what we're embedding. For semantic search, we use `search_document` for the documents we want to search through, and `search_query` for the search queries we'll make later.

We'll also keep track information about each document's language and translation to provide richer search results.

Finally, we'll build a search index with the `hnsw` vector library to store these embeddings efficiently, enabling faster document searches.

```python PYTHON