Source code for langchain_community.llms.oci_generative_ai

from __future__ import annotations

from abc import ABC
from enum import Enum
from typing import Any, Dict, List, Mapping, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator

from langchain_community.llms.utils import enforce_stop_tokens

CUSTOM_ENDPOINT_PREFIX = "ocid1.generativeaiendpoint"
VALID_PROVIDERS = ("cohere", "meta")

[docs]class OCIAuthType(Enum): """OCI authentication types as enumerator.""" API_KEY = 1 SECURITY_TOKEN = 2 INSTANCE_PRINCIPAL = 3 RESOURCE_PRINCIPAL = 4
[docs]class OCIGenAIBase(BaseModel, ABC): """Base class for OCI GenAI models""" client: Any #: :meta private: 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 """ auth_profile: Optional[str] = "DEFAULT" """The name of the profile in ~/.oci/config If not specified , DEFAULT will be used """ model_id: str = None # type: ignore[assignment] """Id of the model to call, e.g., cohere.command""" provider: str = None # type: ignore[assignment] """Provider name of the model. Default to None, will try to be derived from the model_id otherwise, requires user input """ model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" service_endpoint: str = None # type: ignore[assignment] """service endpoint url""" compartment_id: str = None # type: ignore[assignment] """OCID of compartment""" is_stream: bool = False """Whether to stream back partial progress""" llm_stop_sequence_mapping: Mapping[str, str] = { "cohere": "stop_sequences", "meta": "stop", } @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that OCI config and python package exists in environment.""" # Skip creating new client if passed in constructor if values["client"] is not None: return values try: import oci client_kwargs = { "config": {}, "signer": None, "service_endpoint": values["service_endpoint"], "retry_strategy": oci.retry.DEFAULT_RETRY_STRATEGY, "timeout": (10, 240), # default timeout config for OCI Gen AI service } if values["auth_type"] == OCIAuthType(1).name: client_kwargs["config"] = oci.config.from_file( profile_name=values["auth_profile"] ) client_kwargs.pop("signer", None) elif values["auth_type"] == OCIAuthType(2).name: def make_security_token_signer(oci_config): # type: ignore[no-untyped-def] pk = oci.signer.load_private_key_from_file( oci_config.get("key_file"), None ) with open( oci_config.get("security_token_file"), encoding="utf-8" ) as f: st_string = return oci.auth.signers.SecurityTokenSigner(st_string, pk) client_kwargs["config"] = oci.config.from_file( profile_name=values["auth_profile"] ) client_kwargs["signer"] = make_security_token_signer( oci_config=client_kwargs["config"] ) elif values["auth_type"] == OCIAuthType(3).name: client_kwargs[ "signer" ] = oci.auth.signers.InstancePrincipalsSecurityTokenSigner() elif values["auth_type"] == OCIAuthType(4).name: client_kwargs[ "signer" ] = oci.auth.signers.get_resource_principals_signer() else: raise ValueError("Please provide valid value to auth_type") values["client"] = oci.generative_ai_inference.GenerativeAiInferenceClient( **client_kwargs ) except ImportError as ex: raise ImportError( "Could not import oci python package. " "Please make sure you have the oci package installed." ) from ex except Exception as e: raise ValueError( "Could not authenticate with OCI client. " "Please check if ~/.oci/config exists. " "If INSTANCE_PRINCIPLE or RESOURCE_PRINCIPLE is used, " "Please check the specified " "auth_profile and auth_type are valid." ) from e return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"model_kwargs": _model_kwargs}, } def _get_provider(self) -> str: if self.provider is not None: provider = self.provider else: provider = self.model_id.split(".")[0].lower() if provider not in VALID_PROVIDERS: raise ValueError( f"Invalid provider derived from model_id: {self.model_id} " "Please explicitly pass in the supported provider " "when using custom endpoint" ) return provider
[docs]class OCIGenAI(LLM, OCIGenAIBase): """OCI large language models. To authenticate, the OCI client uses the methods described in 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 (from ~/.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: .. code-block:: python from langchain_community.llms import OCIGenAI llm = OCIGenAI( model_id="MY_MODEL_ID", service_endpoint="", compartment_id="MY_OCID" ) """ class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @property def _llm_type(self) -> str: """Return type of llm.""" return "oci" def _prepare_invocation_object( self, prompt: str, stop: Optional[List[str]], kwargs: Dict[str, Any] ) -> Dict[str, Any]: from oci.generative_ai_inference import models oci_llm_request_mapping = { "cohere": models.CohereLlmInferenceRequest, "meta": models.LlamaLlmInferenceRequest, } provider = self._get_provider() _model_kwargs = self.model_kwargs or {} if stop is not None: _model_kwargs[self.llm_stop_sequence_mapping[provider]] = stop if self.model_id.startswith(CUSTOM_ENDPOINT_PREFIX): serving_mode = models.DedicatedServingMode(endpoint_id=self.model_id) else: serving_mode = models.OnDemandServingMode(model_id=self.model_id) inference_params = {**_model_kwargs, **kwargs} inference_params["prompt"] = prompt inference_params["is_stream"] = self.is_stream invocation_obj = models.GenerateTextDetails( compartment_id=self.compartment_id, serving_mode=serving_mode, inference_request=oci_llm_request_mapping[provider](**inference_params), ) return invocation_obj def _process_response(self, response: Any, stop: Optional[List[str]]) -> str: provider = self._get_provider() if provider == "cohere": text =[0].text elif provider == "meta": text =[0].text else: raise ValueError(f"Invalid provider: {provider}") if stop is not None: text = enforce_stop_tokens(text, stop) return text def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to OCIGenAI generate endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = llm.invoke("Tell me a joke.") """ invocation_obj = self._prepare_invocation_object(prompt, stop, kwargs) response = self.client.generate_text(invocation_obj) return self._process_response(response, stop)