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236 | class WatsonxEmbeddings(BaseEmbedding):
"""
IBM watsonx.ai embeddings.
Example:
`pip install llama-index-embeddings-ibm`
```python
from llama_index.embeddings.ibm import WatsonxEmbeddings
watsonx_llm = WatsonxEmbeddings(
model_id="ibm/slate-125m-english-rtrvr",
url="https://us-south.ml.cloud.ibm.com",
apikey="*****",
project_id="*****",
)
```
"""
model_id: str = Field(
default=DEFAULT_EMBED_MODEL,
description="""Type of model to use.""",
allow_mutation=False,
)
truncate_input_tokens: Optional[int] = Field(
default=None,
description="""Represents the maximum number of input tokens accepted.""",
)
project_id: Optional[str] = Field(
default=None,
description="ID of the Watson Studio project.",
allow_mutation=False,
)
space_id: Optional[str] = Field(
default=None,
description="""ID of the Watson Studio space.""",
allow_mutation=False,
)
url: Optional[SecretStr] = Field(
default=None,
description="""Url to Watson Machine Learning or CPD instance""",
allow_mutation=False,
)
apikey: Optional[SecretStr] = Field(
default=None,
description="""Apikey to Watson Machine Learning or CPD instance""",
allow_mutation=False,
)
token: Optional[SecretStr] = Field(
default=None, description="""Token to CPD instance""", allow_mutation=False
)
password: Optional[SecretStr] = Field(
default=None, description="""Password to CPD instance""", allow_mutation=False
)
username: Optional[SecretStr] = Field(
default=None, description="""Username to CPD instance""", allow_mutation=False
)
instance_id: Optional[SecretStr] = Field(
default=None,
description="""Instance_id of CPD instance""",
allow_mutation=False,
)
version: Optional[SecretStr] = Field(
default=None, description="""Version of CPD instance""", allow_mutation=False
)
verify: Union[str, bool, None] = Field(
default=None,
description="""User can pass as verify one of following:
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""",
allow_mutation=False,
)
_embed_model: Embeddings = PrivateAttr()
def __init__(
self,
model_id: str,
truncate_input_tokens: Optional[int] = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
project_id: Optional[str] = None,
space_id: Optional[str] = None,
url: Optional[str] = None,
apikey: Optional[str] = None,
token: Optional[str] = None,
password: Optional[str] = None,
username: Optional[str] = None,
instance_id: Optional[str] = None,
version: Optional[str] = None,
verify: Union[str, bool, None] = None,
api_client: Optional[APIClient] = None,
callback_manager: Optional[CallbackManager] = None,
**kwargs: Any,
):
callback_manager = callback_manager or CallbackManager([])
if isinstance(api_client, APIClient):
project_id = api_client.default_project_id or project_id
space_id = api_client.default_space_id or space_id
creds = {}
else:
creds = resolve_watsonx_credentials(
url=url,
apikey=apikey,
token=token,
username=username,
password=password,
instance_id=instance_id,
)
super().__init__(
model_id=model_id,
truncate_input_tokens=truncate_input_tokens,
project_id=project_id,
space_id=space_id,
url=creds.get("url"),
apikey=creds.get("apikey"),
token=creds.get("token"),
password=creds.get("password"),
username=creds.get("username"),
instance_id=creds.get("instance_id"),
version=version,
verify=verify,
callback_manager=callback_manager,
embed_batch_size=embed_batch_size,
**kwargs,
)
self._embed_model = Embeddings(
model_id=model_id,
params=self.params,
credentials=(
Credentials.from_dict(
{
key: value.get_secret_value() if value else None
for key, value in self._get_credential_kwargs().items()
},
_verify=self.verify,
)
if creds
else None
),
project_id=self.project_id,
space_id=self.space_id,
api_client=api_client,
)
class Config:
validate_assignment = True
@classmethod
def class_name(cls) -> str:
return "WatsonxEmbedding"
def _get_credential_kwargs(self) -> Dict[str, SecretStr | None]:
return {
"url": self.url,
"apikey": self.apikey,
"token": self.token,
"password": self.password,
"username": self.username,
"instance_id": self.instance_id,
"version": self.version,
}
@property
def params(self) -> Dict[str, int] | None:
return (
{"truncate_input_tokens": self.truncate_input_tokens}
if self.truncate_input_tokens
else None
)
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._embed_model.embed_query(text=query, params=self.params)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
return self._get_query_embedding(query=text)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return self._embed_model.embed_documents(texts=texts, params=self.params)
### Async methods
# Asynchronous evaluation is not yet supported for watsonx.ai embeddings
async def _aget_query_embedding(self, query: str) -> List[float]:
"""The asynchronous version of _get_query_embedding."""
return self._get_query_embedding(query)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
return self._get_text_embedding(text)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
return self._get_text_embeddings(texts)
|