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IBM watsonx.ai

WatsonxEmbeddings is a wrapper for IBM watsonx.ai foundation models.

This example shows how to communicate with watsonx.ai models using LangChain.

Setting up​

Install the package langchain-ibm.

!pip install -qU langchain-ibm

This cell defines the WML credentials required to work with watsonx Embeddings.

Action: Provide the IBM Cloud user API key. For details, see documentation.

import os
from getpass import getpass

watsonx_api_key = getpass()
os.environ["WATSONX_APIKEY"] = watsonx_api_key

Additionaly you are able to pass additional secrets as an environment variable. 

import os

os.environ["WATSONX_URL"] = "your service instance url"
os.environ["WATSONX_TOKEN"] = "your token for accessing the CPD cluster"
os.environ["WATSONX_PASSWORD"] = "your password for accessing the CPD cluster"
os.environ["WATSONX_USERNAME"] = "your username for accessing the CPD cluster"
os.environ["WATSONX_INSTANCE_ID"] = "your instance_id for accessing the CPD cluster"

Load the model​

You might need to adjust model parameters for different models.

from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames

embed_params = {
    EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: 3,
    EmbedTextParamsMetaNames.RETURN_OPTIONS: {"input_text": True},
}

Initialize the WatsonxEmbeddings class with previously set parameters.

Note:

  • To provide context for the API call, you must add project_id or space_id. For more information see documentation.
  • Depending on the region of your provisioned service instance, use one of the urls described here.

In this example, we’ll use the project_id and Dallas url.

You need to specify model_id that will be used for inferencing.

from langchain_ibm import WatsonxEmbeddings

watsonx_embedding = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
    params=embed_params,
)

Alternatively you can use Cloud Pak for Data credentials. For details, see documentation. 

watsonx_embedding = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    url="PASTE YOUR URL HERE",
    username="PASTE YOUR USERNAME HERE",
    password="PASTE YOUR PASSWORD HERE",
    instance_id="openshift",
    version="4.8",
    project_id="PASTE YOUR PROJECT_ID HERE",
    params=embed_params,
)

For certain requirements, there is an option to pass the IBM's APIClient object into the WatsonxEmbeddings class.

from ibm_watsonx_ai import APIClient

api_client = APIClient(...)

watsonx_llm = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    watsonx_client=api_client,
)

Usage​

Embed query​

text = "This is a test document."

query_result = watsonx_embedding.embed_query(text)
query_result[:5]
[0.0094472, -0.024981909, -0.026013248, -0.040483925, -0.057804465]

Embed documents​

texts = ["This is a content of the document", "This is another document"]

doc_result = watsonx_embedding.embed_documents(texts)
doc_result[0][:5]
[0.009447193, -0.024981918, -0.026013244, -0.040483937, -0.057804447]

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