mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
98779797fe
Change all usages of __fields__ with get_fields adapter merged into
langchain_core.
Code mod generated using the following grit pattern:
```
engine marzano(0.1)
language python
`$X.__fields__` => `get_fields($X)` where {
add_import(source="langchain_core.utils.pydantic", name="get_fields")
}
```
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
import logging
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from typing import Any, Dict, List, Mapping, Optional, cast
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
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from langchain_core.utils.pydantic import get_fields
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from langchain_community.llms.utils import enforce_stop_tokens
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logger = logging.getLogger(__name__)
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class CerebriumAI(LLM):
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"""CerebriumAI large language models.
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To use, you should have the ``cerebrium`` python package installed.
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You should also have the environment variable ``CEREBRIUMAI_API_KEY``
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set with your API key or pass it as a named argument in the constructor.
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Any parameters that are valid to be passed to the call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_community.llms import CerebriumAI
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cerebrium = CerebriumAI(endpoint_url="", cerebriumai_api_key="my-api-key")
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"""
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endpoint_url: str = ""
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"""model endpoint to use"""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not
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explicitly specified."""
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cerebriumai_api_key: Optional[SecretStr] = None
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class Config:
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extra = "forbid"
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in get_fields(cls).values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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@pre_init
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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cerebriumai_api_key = convert_to_secret_str(
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get_from_dict_or_env(values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY")
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)
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values["cerebriumai_api_key"] = cerebriumai_api_key
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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**{"endpoint_url": self.endpoint_url},
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**{"model_kwargs": self.model_kwargs},
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "cerebriumai"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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headers: Dict = {
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"Authorization": cast(
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SecretStr, self.cerebriumai_api_key
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).get_secret_value(),
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"Content-Type": "application/json",
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}
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params = self.model_kwargs or {}
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payload = {"prompt": prompt, **params, **kwargs}
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response = requests.post(self.endpoint_url, json=payload, headers=headers)
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if response.status_code == 200:
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data = response.json()
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text = data["result"]
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if stop is not None:
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# I believe this is required since the stop tokens
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# are not enforced by the model parameters
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text = enforce_stop_tokens(text, stop)
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return text
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else:
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response.raise_for_status()
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return ""
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