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

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+import logging
+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.utils import (
+    convert_to_secret_str,
+    get_from_dict_or_env,
+    get_pydantic_field_names,
+)
+from pydantic import ConfigDict, Field, SecretStr, model_validator
+
+logger = logging.getLogger(__name__)
+
+
+class GooseAI(LLM):
+    """GooseAI large language models.
+
+    To use, you should have the ``openai`` python package installed, and the
+    environment variable ``GOOSEAI_API_KEY`` set with your API key.
+
+    Any parameters that are valid to be passed to the openai.create call can be passed
+    in, even if not explicitly saved on this class.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import GooseAI
+            gooseai = GooseAI(model_name="gpt-neo-20b")
+
+    """
+
+    client: Any = None
+
+    model_name: str = "gpt-neo-20b"
+    """Model name to use"""
+
+    temperature: float = 0.7
+    """What sampling temperature to use"""
+
+    max_tokens: int = 256
+    """The maximum number of tokens to generate in the completion.
+    -1 returns as many tokens as possible given the prompt and
+    the models maximal context size."""
+
+    top_p: float = 1
+    """Total probability mass of tokens to consider at each step."""
+
+    min_tokens: int = 1
+    """The minimum number of tokens to generate in the completion."""
+
+    frequency_penalty: float = 0
+    """Penalizes repeated tokens according to frequency."""
+
+    presence_penalty: float = 0
+    """Penalizes repeated tokens."""
+
+    n: int = 1
+    """How many completions to generate for each prompt."""
+
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for `create` call not explicitly specified."""
+
+    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
+    """Adjust the probability of specific tokens being generated."""
+
+    gooseai_api_key: Optional[SecretStr] = None
+
+    model_config = ConfigDict(
+        extra="ignore",
+    )
+
+    @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 = get_pydantic_field_names(cls)
+
+        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"""WARNING! {field_name} is not default parameter.
+                    {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
+
+        gooseai_api_key = convert_to_secret_str(
+            get_from_dict_or_env(values, "gooseai_api_key", "GOOSEAI_API_KEY")
+        )
+        values["gooseai_api_key"] = gooseai_api_key
+        try:
+            import openai
+
+            openai.api_key = gooseai_api_key.get_secret_value()
+            openai.api_base = "https://api.goose.ai/v1"
+            values["client"] = openai.Completion
+        except ImportError:
+            raise ImportError(
+                "Could not import openai python package. "
+                "Please install it with `pip install openai`."
+            )
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling GooseAI API."""
+        normal_params = {
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens,
+            "top_p": self.top_p,
+            "min_tokens": self.min_tokens,
+            "frequency_penalty": self.frequency_penalty,
+            "presence_penalty": self.presence_penalty,
+            "n": self.n,
+            "logit_bias": self.logit_bias,
+        }
+        return {**normal_params, **self.model_kwargs}
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {**{"model_name": self.model_name}, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "gooseai"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call the GooseAI API."""
+        params = self._default_params
+        if stop is not None:
+            if "stop" in params:
+                raise ValueError("`stop` found in both the input and default params.")
+            params["stop"] = stop
+
+        params = {**params, **kwargs}
+
+        response = self.client.create(engine=self.model_name, prompt=prompt, **params)
+        text = response.choices[0].text
+        return text