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),
}
```
128 lines
4.2 KiB
Python
128 lines
4.2 KiB
Python
import logging
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from typing import Any, Dict, List, 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.utils import get_from_dict_or_env, pre_init
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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 PredictionGuard(LLM):
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"""Prediction Guard large language models.
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To use, you should have the ``predictionguard`` python package installed, and the
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environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, or pass
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it as a named parameter to the constructor. To use Prediction Guard's API along
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with OpenAI models, set the environment variable ``OPENAI_API_KEY`` with your
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OpenAI API key as well.
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Example:
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.. code-block:: python
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pgllm = PredictionGuard(model="MPT-7B-Instruct",
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token="my-access-token",
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output={
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"type": "boolean"
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})
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"""
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client: Any #: :meta private:
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model: Optional[str] = "MPT-7B-Instruct"
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"""Model name to use."""
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output: Optional[Dict[str, Any]] = None
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"""The output type or structure for controlling the LLM output."""
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max_tokens: int = 256
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"""Denotes the number of tokens to predict per generation."""
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temperature: float = 0.75
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"""A non-negative float that tunes the degree of randomness in generation."""
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token: Optional[str] = None
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"""Your Prediction Guard access token."""
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stop: Optional[List[str]] = None
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class Config:
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extra = "forbid"
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@pre_init
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that the access token and python package exists in environment."""
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token = get_from_dict_or_env(values, "token", "PREDICTIONGUARD_TOKEN")
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try:
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import predictionguard as pg
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values["client"] = pg.Client(token=token)
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except ImportError:
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raise ImportError(
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"Could not import predictionguard python package. "
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"Please install it with `pip install predictionguard`."
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling the Prediction Guard API."""
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return {
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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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 "predictionguard"
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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 out to Prediction Guard's model API.
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Args:
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prompt: The prompt to pass into the model.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = pgllm.invoke("Tell me a joke.")
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"""
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import predictionguard as pg
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params = self._default_params
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if self.stop is not None and stop is not None:
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raise ValueError("`stop` found in both the input and default params.")
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elif self.stop is not None:
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params["stop_sequences"] = self.stop
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else:
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params["stop_sequences"] = stop
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response = pg.Completion.create(
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model=self.model,
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prompt=prompt,
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output=self.output,
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temperature=params["temperature"],
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max_tokens=params["max_tokens"],
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**kwargs,
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)
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text = response["choices"][0]["text"]
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# If stop tokens are provided, Prediction Guard's endpoint returns them.
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# In order to make this consistent with other endpoints, we strip them.
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if stop is not None or self.stop is not None:
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text = enforce_stop_tokens(text, params["stop_sequences"])
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return text
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