langchain_community.embeddings.oci_generative_ai
.OCIGenAIEmbeddings¶
- class langchain_community.embeddings.oci_generative_ai.OCIGenAIEmbeddings[source]¶
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 batch_size: int = 96¶
Batch size of OCI GenAI embedding requests. OCI GenAI may handle up to 96 texts per request
- 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.
- Parameters
texts (List[str]) – List of text to embed.
- Returns
List of embeddings.
- Return type
List[List[float]]
- async aembed_query(text: str) List[float] ¶
Asynchronous Embed query text.
- Parameters
text (str) – Text to embed.
- Returns
Embedding.
- Return type
List[float]