mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-11 01:56:12 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
from __future__ import annotations
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import warnings
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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 BaseModel
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# Ignoring type because below is valid pydantic code
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# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
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class Params(BaseModel, extra="allow"): # type: ignore[call-arg]
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"""Parameters for the MLflow AI Gateway LLM."""
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temperature: float = 0.0
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candidate_count: int = 1
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"""The number of candidates to return."""
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stop: Optional[List[str]] = None
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max_tokens: Optional[int] = None
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class MlflowAIGateway(LLM):
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"""MLflow AI Gateway LLMs.
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To use, you should have the ``mlflow[gateway]`` python package installed.
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For more information, see https://mlflow.org/docs/latest/gateway/index.html.
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Example:
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.. code-block:: python
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from langchain_community.llms import MlflowAIGateway
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completions = MlflowAIGateway(
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gateway_uri="<your-mlflow-ai-gateway-uri>",
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route="<your-mlflow-ai-gateway-completions-route>",
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params={
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"temperature": 0.1
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}
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)
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"""
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route: str
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gateway_uri: Optional[str] = None
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params: Optional[Params] = None
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def __init__(self, **kwargs: Any):
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warnings.warn(
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"`MlflowAIGateway` is deprecated. Use `Mlflow` or `Databricks` instead.",
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DeprecationWarning,
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)
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try:
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import mlflow.gateway
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except ImportError as e:
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raise ImportError(
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"Could not import `mlflow.gateway` module. "
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"Please install it with `pip install mlflow[gateway]`."
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) from e
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super().__init__(**kwargs)
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if self.gateway_uri:
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mlflow.gateway.set_gateway_uri(self.gateway_uri)
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@property
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def _default_params(self) -> Dict[str, Any]:
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params: Dict[str, Any] = {
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"gateway_uri": self.gateway_uri,
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"route": self.route,
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**(self.params.dict() if self.params else {}),
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}
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return params
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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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try:
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import mlflow.gateway
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except ImportError as e:
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raise ImportError(
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"Could not import `mlflow.gateway` module. "
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"Please install it with `pip install mlflow[gateway]`."
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) from e
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data: Dict[str, Any] = {
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"prompt": prompt,
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**(self.params.dict() if self.params else {}),
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}
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if s := (stop or (self.params.stop if self.params else None)):
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data["stop"] = s
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resp = mlflow.gateway.query(self.route, data=data)
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return resp["candidates"][0]["text"]
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@property
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def _llm_type(self) -> str:
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return "mlflow-ai-gateway"
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