mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
from __future__ import annotations
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from typing import Any, Dict, List, Mapping, Optional
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from urllib.parse import urlparse
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import LLM
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from langchain_core.pydantic_v1 import Field, PrivateAttr
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class Mlflow(LLM):
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"""MLflow LLM service.
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To use, you should have the `mlflow[genai]` python package installed.
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For more information, see https://mlflow.org/docs/latest/llms/deployments.
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Example:
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.. code-block:: python
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from langchain_community.llms import Mlflow
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completions = Mlflow(
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target_uri="http://localhost:5000",
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endpoint="test",
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temperature=0.1,
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)
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"""
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endpoint: str
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"""The endpoint to use."""
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target_uri: str
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"""The target URI to use."""
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temperature: float = 0.0
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"""The sampling temperature."""
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n: int = 1
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"""The number of completion choices to generate."""
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stop: Optional[List[str]] = None
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"""The stop sequence."""
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max_tokens: Optional[int] = None
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"""The maximum number of tokens to generate."""
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extra_params: Dict[str, Any] = Field(default_factory=dict)
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"""Any extra parameters to pass to the endpoint."""
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"""Extra parameters such as `temperature`."""
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_client: Any = PrivateAttr()
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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self._validate_uri()
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try:
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from mlflow.deployments import get_deploy_client
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self._client = get_deploy_client(self.target_uri)
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except ImportError as e:
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raise ImportError(
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"Failed to create the client. "
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"Please run `pip install mlflow[genai]` to install "
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"required dependencies."
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) from e
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def _validate_uri(self) -> None:
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if self.target_uri == "databricks":
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return
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allowed = ["http", "https", "databricks"]
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if urlparse(self.target_uri).scheme not in allowed:
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raise ValueError(
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f"Invalid target URI: {self.target_uri}. "
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f"The scheme must be one of {allowed}."
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)
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@property
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def _default_params(self) -> Dict[str, Any]:
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return {
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"target_uri": self.target_uri,
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"endpoint": self.endpoint,
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"temperature": self.temperature,
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"n": self.n,
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"stop": self.stop,
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"max_tokens": self.max_tokens,
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"extra_params": self.extra_params,
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}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return self._default_params
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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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data: Dict[str, Any] = {
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"prompt": prompt,
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"temperature": self.temperature,
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"n": self.n,
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**self.extra_params,
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**kwargs,
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}
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if stop := self.stop or stop:
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data["stop"] = stop
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if self.max_tokens is not None:
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data["max_tokens"] = self.max_tokens
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resp = self._client.predict(endpoint=self.endpoint, inputs=data)
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return resp["choices"][0]["text"]
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@property
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def _llm_type(self) -> str:
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return "mlflow"
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