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@ -1,3 +1,4 @@
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import os
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from typing import Any, Dict, List, Mapping, Optional, Union
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from langchain_core.callbacks import CallbackManagerForLLMRun
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@ -17,13 +18,15 @@ class Predibase(LLM):
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An optional `adapter_id` parameter is the Predibase ID or HuggingFace ID of a
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fine-tuned LLM adapter, whose base model is the `model` parameter; the
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fine-tuned adapter must be compatible with its base model;
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otherwise, an error is raised. If a Predibase ID references the
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fine-tuned adapter, then the `adapter_version` in the adapter repository can
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be optionally specified; omitting it defaults to the most recent version.
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otherwise, an error is raised. If the fine-tuned adapter is hosted at Predibase,
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then `adapter_version` in the adapter repository must be specified.
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An optional `predibase_sdk_version` parameter defaults to latest SDK version.
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"""
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model: str
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predibase_api_key: SecretStr
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predibase_sdk_version: Optional[str] = None
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adapter_id: Optional[str] = None
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adapter_version: Optional[int] = None
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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@ -46,65 +49,139 @@ class Predibase(LLM):
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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try:
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from predibase import PredibaseClient
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from predibase.pql import get_session
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from predibase.pql.api import (
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ServerResponseError,
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Session,
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)
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from predibase.resource.llm.interface import (
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HuggingFaceLLM,
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LLMDeployment,
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)
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from predibase.resource.llm.response import GeneratedResponse
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from predibase.resource.model import Model
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session: Session = get_session(
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token=self.predibase_api_key.get_secret_value(),
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gateway="https://api.app.predibase.com/v1",
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serving_endpoint="serving.app.predibase.com",
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)
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pc: PredibaseClient = PredibaseClient(session=session)
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except ImportError as e:
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raise ImportError(
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"Could not import Predibase Python package. "
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"Please install it with `pip install predibase`."
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) from e
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except ValueError as e:
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raise ValueError("Your API key is not correct. Please try again") from e
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options: Dict[str, Union[str, float]] = (
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self.model_kwargs or self.default_options_for_generation
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)
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base_llm_deployment: LLMDeployment = pc.LLM(
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uri=f"pb://deployments/{self.model}"
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if self._is_deprecated_sdk_version():
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try:
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from predibase import PredibaseClient
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from predibase.pql import get_session
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from predibase.pql.api import (
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ServerResponseError,
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Session,
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)
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from predibase.resource.llm.interface import (
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HuggingFaceLLM,
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LLMDeployment,
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)
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from predibase.resource.llm.response import GeneratedResponse
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from predibase.resource.model import Model
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session: Session = get_session(
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token=self.predibase_api_key.get_secret_value(),
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gateway="https://api.app.predibase.com/v1",
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serving_endpoint="serving.app.predibase.com",
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)
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pc: PredibaseClient = PredibaseClient(session=session)
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except ImportError as e:
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raise ImportError(
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"Could not import Predibase Python package. "
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"Please install it with `pip install predibase`."
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) from e
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except ValueError as e:
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raise ValueError("Your API key is not correct. Please try again") from e
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base_llm_deployment: LLMDeployment = pc.LLM(
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uri=f"pb://deployments/{self.model}"
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)
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result: GeneratedResponse
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if self.adapter_id:
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"""
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Attempt to retrieve the fine-tuned adapter from a Predibase
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repository. If absent, then load the fine-tuned adapter
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from a HuggingFace repository.
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"""
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adapter_model: Union[Model, HuggingFaceLLM]
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try:
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adapter_model = pc.get_model(
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name=self.adapter_id,
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version=self.adapter_version,
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model_id=None,
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)
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except ServerResponseError:
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# Predibase does not recognize the adapter ID (query HuggingFace).
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adapter_model = pc.LLM(uri=f"hf://{self.adapter_id}")
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result = base_llm_deployment.with_adapter(model=adapter_model).generate(
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prompt=prompt,
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options=options,
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)
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else:
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result = base_llm_deployment.generate(
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prompt=prompt,
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options=options,
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)
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return result.response
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from predibase import Predibase
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os.environ["PREDIBASE_GATEWAY"] = "https://api.app.predibase.com"
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predibase: Predibase = Predibase(
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api_token=self.predibase_api_key.get_secret_value()
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)
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import requests
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from lorax.client import Client as LoraxClient
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from lorax.errors import GenerationError
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from lorax.types import Response
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lorax_client: LoraxClient = predibase.deployments.client(
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deployment_ref=self.model
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)
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result: GeneratedResponse
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response: Response
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if self.adapter_id:
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"""
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Attempt to retrieve the fine-tuned adapter from a Predibase repository.
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If absent, then load the fine-tuned adapter from a HuggingFace repository.
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"""
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adapter_model: Union[Model, HuggingFaceLLM]
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if self.adapter_version:
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# Since the adapter version is provided, query the Predibase repository.
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pb_adapter_id: str = f"{self.adapter_id}/{self.adapter_version}"
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try:
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response = lorax_client.generate(
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prompt=prompt,
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adapter_id=pb_adapter_id,
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**options,
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)
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except GenerationError as ge:
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raise ValueError(
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f"""An adapter with the ID "{pb_adapter_id}" cannot be \
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found in the Predibase repository of fine-tuned adapters."""
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) from ge
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else:
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# The adapter version is omitted,
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# hence look for the adapter ID in the HuggingFace repository.
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try:
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response = lorax_client.generate(
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prompt=prompt,
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adapter_id=self.adapter_id,
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adapter_source="hub",
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**options,
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)
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except GenerationError as ge:
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raise ValueError(
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f"""Either an adapter with the ID "{self.adapter_id}" \
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cannot be found in a HuggingFace repository, or it is incompatible with the \
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base model (please make sure that the adapter configuration is consistent).
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"""
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) from ge
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else:
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try:
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adapter_model = pc.get_model(
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name=self.adapter_id,
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version=self.adapter_version,
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model_id=None,
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response = lorax_client.generate(
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prompt=prompt,
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**options,
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)
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except ServerResponseError:
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# Predibase does not recognize the adapter ID (query HuggingFace).
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adapter_model = pc.LLM(uri=f"hf://{self.adapter_id}")
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result = base_llm_deployment.with_adapter(model=adapter_model).generate(
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prompt=prompt,
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options=options,
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)
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else:
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result = base_llm_deployment.generate(
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prompt=prompt,
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options=options,
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)
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return result.response
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except requests.JSONDecodeError as jde:
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raise ValueError(
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f"""An LLM with the deployment ID "{self.model}" cannot be found \
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at Predibase (please refer to \
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"https://docs.predibase.com/user-guide/inference/models" for the list of \
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supported models).
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"""
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) from jde
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response_text = response.generated_text
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return response_text
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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@ -112,3 +189,26 @@ class Predibase(LLM):
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return {
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**{"model_kwargs": self.model_kwargs},
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}
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def _is_deprecated_sdk_version(self) -> bool:
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try:
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import semantic_version
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from predibase.version import __version__ as current_version
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from semantic_version.base import Version
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sdk_semver_deprecated: Version = semantic_version.Version(
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version_string="2024.4.8"
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)
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actual_current_version: str = self.predibase_sdk_version or current_version
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sdk_semver_current: Version = semantic_version.Version(
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version_string=actual_current_version
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)
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return not (
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(sdk_semver_current > sdk_semver_deprecated)
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or ("+dev" in actual_current_version)
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)
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except ImportError as e:
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raise ImportError(
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"Could not import Predibase Python package. "
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"Please install it with `pip install semantic_version predibase`."
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) from e
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Alex Sherstinsky
2 months ago
committed by
GitHub