initial commit

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

@@ -0,0 +1,121 @@
+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, pre_init
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    SecretStr,
+    model_validator,
+)
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+logger = logging.getLogger(__name__)
+
+
+class PipelineAI(LLM, BaseModel):
+    """PipelineAI large language models.
+
+    To use, you should have the ``pipeline-ai`` python package installed,
+    and the environment variable ``PIPELINE_API_KEY`` set with your API key.
+
+    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 PipelineAI
+            pipeline = PipelineAI(pipeline_key="")
+    """
+
+    pipeline_key: str = ""
+    """The id or tag of the target pipeline"""
+
+    pipeline_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any pipeline parameters valid for `create` call not
+    explicitly specified."""
+
+    pipeline_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("pipeline_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 pipeline_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+        values["pipeline_kwargs"] = extra
+        return values
+
+    @pre_init
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        pipeline_api_key = convert_to_secret_str(
+            get_from_dict_or_env(values, "pipeline_api_key", "PIPELINE_API_KEY")
+        )
+        values["pipeline_api_key"] = pipeline_api_key
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            **{"pipeline_key": self.pipeline_key},
+            **{"pipeline_kwargs": self.pipeline_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "pipeline_ai"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call to Pipeline Cloud endpoint."""
+        try:
+            from pipeline import PipelineCloud
+        except ImportError:
+            raise ImportError(
+                "Could not import pipeline-ai python package. "
+                "Please install it with `pip install pipeline-ai`."
+            )
+        client = PipelineCloud(token=self.pipeline_api_key.get_secret_value())  # type: ignore[union-attr]
+        params = self.pipeline_kwargs or {}
+        params = {**params, **kwargs}
+
+        run = client.run_pipeline(self.pipeline_key, [prompt, params])
+        try:
+            text = run.result_preview[0][0]
+        except AttributeError:
+            raise AttributeError(
+                f"A pipeline run should have a `result_preview` attribute."
+                f"Run was: {run}"
+            )
+        if stop is not None:
+            # I believe this is required since the stop tokens
+            # are not enforced by the pipeline parameters
+            text = enforce_stop_tokens(text, stop)
+        return text