WatsonxEmbeddings#

class langchain_ibm.embeddings.WatsonxEmbeddings[source]#

Bases: BaseModel, Embeddings

IBM watsonx.ai embedding model integration.

???+ info “Setup”

To use, you should have langchain_ibm python package installed, and the environment variable WATSONX_APIKEY set with your API key, or pass it as a named parameter apikey to the constructor.

```bash pip install -U langchain-ibm

# or using uv uv add langchain-ibm ```

`bash export WATSONX_APIKEY="your-api-key" `

??? info “Instantiate”

```python from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(

model_id=”ibm/granite-embedding-278m-multilingual”, url=”https://us-south.ml.cloud.ibm.com”, project_id=”*”, # apikey=”*”

??? info “Embed single text”

`python input_text = "The meaning of life is 42" vector = embeddings.embed_query("hello") print(vector[:3]) `

`python [-0.0020519258, 0.0147288125, -0.0090887165] `

??? info “Embed multiple texts”

`python vectors = embeddings.embed_documents(["hello", "goodbye"]) # Showing only the first 3 coordinates print(len(vectors)) print(vectors[0][:3]) `

`python 2 [-0.0020519265, 0.01472881, -0.009088721] `

??? info “Async”

```python await embeddings.aembed_query(input_text) print(vector[:3])

# multiple: # await embeddings.aembed_documents(input_texts) ```

`python [-0.0020519258, 0.0147288125, -0.0090887165] `

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param apikey: SecretStr | None [Optional]#

API key to the Watson Machine Learning or CPD instance.

param instance_id: SecretStr | None [Optional]#

Instance_id of the CPD instance.

param model: str | None = None#

Name or alias of the foundation model to use. When using IBM’s watsonx.ai Model Gateway (public preview), you can specify any supported third-party model—OpenAI, Anthropic, NVIDIA, Cerebras, or IBM’s own Granite series—via a single, OpenAI-compatible interface. Models must be explicitly provisioned (opt-in) through the Gateway to ensure secure, vendor-agnostic access and easy switch-over without reconfiguration.

For more details on configuration and usage, see [IBM watsonx Model Gateway docs](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-model-gateway.html?context=wx&audience=wdp)

param model_id: str | None = None#

Type of model to use.

param params: dict | None = None#

Model parameters to use during request generation.

param password: SecretStr | None [Optional]#

Password to the CPD instance.

param project_id: str | None = None#

ID of the Watson Studio project.

param space_id: str | None = None#

ID of the Watson Studio space.

param token: SecretStr | None [Optional]#

Token to the CPD instance.

param url: SecretStr [Optional]#

URL to the Watson Machine Learning or CPD instance.

param username: SecretStr | None [Optional]#

Username to the CPD instance.

param verify: str | bool | None = None#

You can pass one of following as verify: * the path to a CA_BUNDLE file * the path of directory with certificates of trusted CAs * True - default path to truststore will be taken * False - no verification will be made

param version: SecretStr | None = None#

Version of the CPD instance.

async aembed_documents(
texts: list[str],
**kwargs: Any,
) → list[list[float]][source]#

Asynchronous Embed search docs.

Parameters:

  • texts (list[str])

  • kwargs (Any)

Return type:

list[list[float]]

async aembed_query(
text: str,
**kwargs: Any,
) → list[float][source]#

Asynchronous Embed query text.

Parameters:

  • text (str)

  • kwargs (Any)

Return type:

list[float]

embed_documents(
texts: list[str],
**kwargs: Any,
) → list[list[float]][source]#

Embed search docs.

Parameters:

  • texts (list[str])

  • kwargs (Any)

Return type:

list[list[float]]

embed_query(
text: str,
**kwargs: Any,
) → list[float][source]#

Embed query text.

Parameters:

  • text (str)

  • kwargs (Any)

Return type:

list[float]