initial commit

venv/Lib/site-packages/langchain_community/llms/cerebriumai.py

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+import logging
+from typing import Any, Dict, List, Mapping, Optional, cast
+
+import requests
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from pydantic import ConfigDict, Field, SecretStr, model_validator
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+logger = logging.getLogger(__name__)
+
+
+class CerebriumAI(LLM):
+    """CerebriumAI large language models.
+
+    To use, you should have the ``cerebrium`` python package installed.
+    You should also have the environment variable ``CEREBRIUMAI_API_KEY``
+    set with your API key or pass it as a named argument in the constructor.
+
+    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_community.llms import CerebriumAI
+            cerebrium = CerebriumAI(endpoint_url="", cerebriumai_api_key="my-api-key")
+
+    """
+
+    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."""
+
+    cerebriumai_api_key: Optional[SecretStr] = None
+
+    model_config = ConfigDict(
+        extra="forbid",
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def build_extra(cls, values: Dict[str, Any]) -> Any:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = set(list(cls.model_fields.keys()))
+
+        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 transferred 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
+
+    @pre_init
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        cerebriumai_api_key = convert_to_secret_str(
+            get_from_dict_or_env(values, "cerebriumai_api_key", "CEREBRIUMAI_API_KEY")
+        )
+        values["cerebriumai_api_key"] = cerebriumai_api_key
+        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 "cerebriumai"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        headers: Dict = {
+            "Authorization": cast(
+                SecretStr, self.cerebriumai_api_key
+            ).get_secret_value(),
+            "Content-Type": "application/json",
+        }
+        params = self.model_kwargs or {}
+        payload = {"prompt": prompt, **params, **kwargs}
+        response = requests.post(self.endpoint_url, json=payload, headers=headers)
+        if response.status_code == 200:
+            data = response.json()
+            text = data["result"]
+            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
+        else:
+            response.raise_for_status()
+        return ""