Clarifai

Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference.

This example goes over how to use LangChain to interact with Clarifai models. Text embedding models in particular can be found here.

To use Clarifai, you must have an account and a Personal Access Token (PAT) key. Check here to get or create a PAT.

Dependencies

# Install required dependencies
pip install clarifai

Imports

Here we will be setting the personal access token. You can find your PAT under settings/security in your Clarifai account.

# Please login and get your API key from  https://clarifai.com/settings/security
from getpass import getpass

CLARIFAI_PAT = getpass()

     ········

# Import the required modules
from langchain.embeddings import ClarifaiEmbeddings
from langchain import PromptTemplate, LLMChain

API Reference:

• ClarifaiEmbeddings from langchain.embeddings

Input

Create a prompt template to be used with the LLM Chain:

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

Setup

Set the user id and app id to the application in which the model resides. You can find a list of public models on https://clarifai.com/explore/models

You will have to also initialize the model id and if needed, the model version id. Some models have many versions, you can choose the one appropriate for your task.

USER_ID = "openai"
APP_ID = "embed"
MODEL_ID = "text-embedding-ada"

# You can provide a specific model version as the model_version_id arg.
# MODEL_VERSION_ID = "MODEL_VERSION_ID"

# Initialize a Clarifai embedding model
embeddings = ClarifaiEmbeddings(
    pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID
)

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])