IBM watsonx.ai¶

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

This example shows how to communicate with watsonx.ai embedding models using the LlamaIndex Embeddings API.

Setting up¶

Install the llama-index-embeddings-ibm package:

In [ ]:

!pip install -qU llama-index-embeddings-ibm

!pip install -qU llama-index-embeddings-ibm

The cell below defines the credentials required to work with watsonx Embeddings.

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

In [ ]:

import os
from getpass import getpass

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

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

Additionally, you can pass additional secrets as an environment variable:

In [ ]:

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"

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 embedding parameters for different tasks:

In [ ]:

truncate_input_tokens = 3

truncate_input_tokens = 3

Initialize the WatsonxEmbeddings class with the previously set parameter.

Note:

• To provide context for the API call, you must pass the project_id or space_id. To get your project or space ID, open your project or space, go to the Manage tab, and click General. For more information see: Project documentation or Deployment space documentation.
• Depending on the region of your provisioned service instance, use one of the urls listed in watsonx.ai API Authentication.

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

You need to specify the model_id that will be used for inferencing. You can find the list of all the available models in Supported foundation models.

In [ ]:

from llama_index.embeddings.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",
    truncate_input_tokens=truncate_input_tokens,
)

from llama_index.embeddings.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", truncate_input_tokens=truncate_input_tokens, )

Alternatively, you can use Cloud Pak for Data credentials. For details, see watsonx.ai software setup.

In [ ]:

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",
    truncate_input_tokens=truncate_input_tokens,
)

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", truncate_input_tokens=truncate_input_tokens, )

Usage¶

Embed query¶

In [ ]:

query = "Example query."

query_result = watsonx_embedding.get_query_embedding(query)
print(query_result[:5])

query = "Example query." query_result = watsonx_embedding.get_query_embedding(query) print(query_result[:5])

[-0.05538924, 0.05161056, 0.01207759, 0.0017501727, -0.017691258]

Embed list of texts¶

In [ ]:

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

doc_result = watsonx_embedding.get_text_embedding_batch(texts)
print(doc_result[0][:5])

texts = ["This is a content of one document", "This is another document"] doc_result = watsonx_embedding.get_text_embedding_batch(texts) print(doc_result[0][:5])

[0.009447167, -0.024981938, -0.02601326, -0.04048393, -0.05780444]