langchain_community.embeddings.oci_generative_ai.OCIGenAIEmbeddings

class langchain_community.embeddings.oci_generative_ai.OCIGenAIEmbeddings

Bases: BaseModel, Embeddings

OCI embedding models.

To authenticate, the OCI client uses the methods described in https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm

The authentifcation method is passed through auth_type and should be one of: API_KEY (default), SECURITY_TOKEN, INSTANCE_PRINCIPLE, RESOURCE_PRINCIPLE

Make sure you have the required policies (profile/roles) to access the OCI Generative AI service. If a specific config profile is used, you must pass the name of the profile (~/.oci/config) through auth_profile.

To use, you must provide the compartment id along with the endpoint url, and model id as named parameters to the constructor.

Example

from langchain.embeddings import OCIGenAIEmbeddings

embeddings = OCIGenAIEmbeddings(
    model_id="MY_EMBEDDING_MODEL",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="MY_OCID"
)

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

Raises ValidationError if the input data cannot be parsed to form a valid model.

param auth_profile: Optional[str] = 'DEFAULT'

The name of the profile in ~/.oci/config If not specified , DEFAULT will be used

param auth_type: Optional[str] = 'API_KEY'

Authentication type, could be

API_KEY, SECURITY_TOKEN, INSTANCE_PRINCIPLE, RESOURCE_PRINCIPLE

If not specified, API_KEY will be used

param compartment_id: str = None

OCID of compartment

param model_id: str = None

Id of the model to call, e.g., cohere.embed-english-light-v2.0

param model_kwargs: Optional[Dict] = None

Keyword arguments to pass to the model

param service_endpoint: str = None

service endpoint url

param truncate: Optional[str] = 'END'

Truncate embeddings that are too long from start or end (“NONE”|”START”|”END”)

async aembed_documents(texts: List[str]) → List[List[float]]

Asynchronous Embed search docs.

async aembed_query(text: str) → List[float]

Asynchronous Embed query text.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

embed_documents(texts: List[str]) → List[List[float]]

Call out to OCIGenAI’s embedding endpoint.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float]

Call out to OCIGenAI’s embedding endpoint.

Parameters

text – The text to embed.

Returns

Embeddings for the text.

classmethod from_orm(obj: Any) → Model

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod parse_obj(obj: Any) → Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model