Embeddings with Clarifai¶

LlamaIndex has support for Clarifai embeddings models.

You must have a Clarifai account and a Personal Access Token (PAT) key. Check here to get or create a PAT.

Set CLARIFAI_PAT as an environment variable or You can pass PAT as argument to ClarifaiEmbedding class

In [ ]:

%pip install llama-index-embeddings-clarifai

%pip install llama-index-embeddings-clarifai

In [ ]:

!export CLARIFAI_PAT=YOUR_KEY

!export CLARIFAI_PAT=YOUR_KEY

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

In [ ]:

!pip install llama-index

!pip install llama-index

Models can be referenced either by the full URL or by the model_name, user ID, and app ID combination.

In [ ]:

from llama_index.embeddings.clarifai import ClarifaiEmbedding

# Create a clarifai embedding class just with model_url, assuming that CLARIFAI_PAT is set as an environment variable
embed_model = ClarifaiEmbedding(
    model_url="https://clarifai.com/clarifai/main/models/BAAI-bge-base-en"
)

# Alternatively you can initialize the class with model_name, user_id, app_id and pat as well.
embed_model = ClarifaiEmbedding(
    model_name="BAAI-bge-base-en",
    user_id="clarifai",
    app_id="main",
    pat=CLARIFAI_PAT,
)

from llama_index.embeddings.clarifai import ClarifaiEmbedding # Create a clarifai embedding class just with model_url, assuming that CLARIFAI_PAT is set as an environment variable embed_model = ClarifaiEmbedding( model_url="https://clarifai.com/clarifai/main/models/BAAI-bge-base-en" ) # Alternatively you can initialize the class with model_name, user_id, app_id and pat as well. embed_model = ClarifaiEmbedding( model_name="BAAI-bge-base-en", user_id="clarifai", app_id="main", pat=CLARIFAI_PAT, )

In [ ]:

embeddings = embed_model.get_text_embedding("Hello World!")
print(len(embeddings))
print(embeddings[:5])

embeddings = embed_model.get_text_embedding("Hello World!") print(len(embeddings)) print(embeddings[:5])

Embed list of texts

In [ ]:

text = "roses are red violets are blue."
text2 = "Make hay while the sun shines."

text = "roses are red violets are blue." text2 = "Make hay while the sun shines."

In [ ]:

embeddings = embed_model._get_text_embeddings([text2, text])
print(len(embeddings))
print(embeddings[0][:5])
print(embeddings[1][:5])

embeddings = embed_model._get_text_embeddings([text2, text]) print(len(embeddings)) print(embeddings[0][:5]) print(embeddings[1][:5])