initial commit

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

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+import logging
+from typing import Any, Dict, List, Mapping, Optional, cast
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.utils import (
+    secret_from_env,
+)
+from pydantic import ConfigDict, Field, SecretStr, model_validator
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+logger = logging.getLogger(__name__)
+
+
+class Banana(LLM):
+    """Banana large language models.
+
+    To use, you should have the ``banana-dev`` python package installed,
+    and the environment variable ``BANANA_API_KEY`` set with your API key.
+    This is the team API key available in the Banana dashboard.
+
+    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 Banana
+            banana = Banana(model_key="", model_url_slug="")
+    """
+
+    model_key: str = ""
+    """model key to use"""
+
+    model_url_slug: 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."""
+
+    banana_api_key: Optional[SecretStr] = Field(
+        default_factory=secret_from_env("BANANA_API_KEY", default=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
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            **{"model_key": self.model_key},
+            **{"model_url_slug": self.model_url_slug},
+            **{"model_kwargs": self.model_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "bananadev"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call to Banana endpoint."""
+        try:
+            from banana_dev import Client
+        except ImportError:
+            raise ImportError(
+                "Could not import banana-dev python package. "
+                "Please install it with `pip install banana-dev`."
+            )
+        params = self.model_kwargs or {}
+        params = {**params, **kwargs}
+        api_key = cast(SecretStr, self.banana_api_key)
+        model_key = self.model_key
+        model_url_slug = self.model_url_slug
+        model_inputs = {
+            # a json specific to your model.
+            "prompt": prompt,
+            **params,
+        }
+        model = Client(
+            # Found in main dashboard
+            api_key=api_key.get_secret_value(),
+            # Both found in model details page
+            model_key=model_key,
+            url=f"https://{model_url_slug}.run.banana.dev",
+        )
+        response, meta = model.call("/", model_inputs)
+        try:
+            text = response["outputs"]
+        except (KeyError, TypeError):
+            raise ValueError(
+                "Response should be of schema: {'outputs': 'text'}."
+                "\nTo fix this:"
+                "\n- fork the source repo of the Banana model"
+                "\n- modify app.py to return the above schema"
+                "\n- deploy that as a custom repo"
+            )
+        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