mirror of
https://github.com/hwchase17/langchain
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122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
import logging
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from typing import Any, Dict, List, Mapping, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.pydantic_v1 import (
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BaseModel,
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Extra,
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Field,
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SecretStr,
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root_validator,
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)
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_community.llms.utils import enforce_stop_tokens
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logger = logging.getLogger(__name__)
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class PipelineAI(LLM, BaseModel):
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"""PipelineAI large language models.
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To use, you should have the ``pipeline-ai`` python package installed,
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and the environment variable ``PIPELINE_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_community.llms import PipelineAI
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pipeline = PipelineAI(pipeline_key="")
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"""
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pipeline_key: str = ""
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"""The id or tag of the target pipeline"""
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pipeline_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any pipeline parameters valid for `create` call not
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explicitly specified."""
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pipeline_api_key: Optional[SecretStr] = None
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class Config:
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"""Configuration for this pydantic config."""
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extra = Extra.forbid
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("pipeline_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""{field_name} was transferred to pipeline_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["pipeline_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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pipeline_api_key = convert_to_secret_str(
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get_from_dict_or_env(values, "pipeline_api_key", "PIPELINE_API_KEY")
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)
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values["pipeline_api_key"] = pipeline_api_key
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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**{"pipeline_key": self.pipeline_key},
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**{"pipeline_kwargs": self.pipeline_kwargs},
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "pipeline_ai"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call to Pipeline Cloud endpoint."""
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try:
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from pipeline import PipelineCloud
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except ImportError:
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raise ImportError(
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"Could not import pipeline-ai python package. "
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"Please install it with `pip install pipeline-ai`."
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)
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client = PipelineCloud(token=self.pipeline_api_key.get_secret_value())
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params = self.pipeline_kwargs or {}
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params = {**params, **kwargs}
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run = client.run_pipeline(self.pipeline_key, [prompt, params])
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try:
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text = run.result_preview[0][0]
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except AttributeError:
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raise AttributeError(
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f"A pipeline run should have a `result_preview` attribute."
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f"Run was: {run}"
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)
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if stop is not None:
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# I believe this is required since the stop tokens
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# are not enforced by the pipeline parameters
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text = enforce_stop_tokens(text, stop)
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return text
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