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"""Wrapper around Modal API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
logger = logging.getLogger(__name__)
class Modal(LLM, BaseModel):
"""Wrapper around Modal large language models.
To use, you should have the ``modal-client`` python package installed.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import Modal
modal = Modal(endpoint_url="")
"""
endpoint_url: str = ""
"""model endpoint to use"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not
explicitly specified."""
class Config:
"""Configuration for this pydantic config."""
extra = Extra.forbid
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transfered to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"endpoint_url": self.endpoint_url},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "modal"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call to Modal endpoint."""
params = self.model_kwargs or {}
response = requests.post(
url=self.endpoint_url,
headers={
"Content-Type": "application/json",
},
json={"prompt": prompt, **params},
)
try:
if prompt in response.json()["prompt"]:
response_json = response.json()
except KeyError:
raise ValueError("LangChain requires 'prompt' key in response.")
text = response_json["prompt"]
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text
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