---
title: Aleph Alpha Embeddings
 | Developer Documentation
---

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-embeddings-alephalpha
```

```
!pip install llama-index
```

```
# Initialise with your AA token
import os


os.environ["AA_TOKEN"] = "your_token_here"
```

#### With `luminous-base` embeddings.

- representation=“Document”: Use this for texts (documents) you want to store in your vector database
- representation=“Query”: Use this for search queries to find the most relevant documents in your vector database
- representation=“Symmetric”: Use this for clustering, classification, anomaly detection or visualisation tasks.

```
from llama_index.embeddings.alephalpha import AlephAlphaEmbedding


# To customize your token, do this
# otherwise it will lookup AA_TOKEN from your env variable
# embed_model = AlephAlpha(token="<aa_token>")


# with representation='query'
embed_model = AlephAlphaEmbedding(
    model="luminous-base",
    representation="Query",
)


embeddings = embed_model.get_text_embedding("Hello Aleph Alpha!")


print(len(embeddings))
print(embeddings[:5])
```

```
representation_enum: SemanticRepresentation.Query




5120
[0.14257812, 2.59375, 0.33203125, -0.33789062, -0.94140625]
```

```
# with representation='Document'
embed_model = AlephAlphaEmbedding(
    model="luminous-base",
    representation="Document",
)


embeddings = embed_model.get_text_embedding("Hello Aleph Alpha!")


print(len(embeddings))
print(embeddings[:5])
```

```
representation_enum: SemanticRepresentation.Document




5120
[0.14257812, 2.59375, 0.33203125, -0.33789062, -0.94140625]
```