mirror of
https://github.com/hwchase17/langchain.git
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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),
}
```
118 lines
3.6 KiB
Python
118 lines
3.6 KiB
Python
from typing import Any, Dict, List, Mapping, Optional
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import requests
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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 SecretStr, root_validator
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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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class ForefrontAI(LLM):
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"""ForefrontAI large language models.
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To use, you should have the environment variable ``FOREFRONTAI_API_KEY``
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set with your API key.
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Example:
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.. code-block:: python
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from langchain_community.llms import ForefrontAI
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forefrontai = ForefrontAI(endpoint_url="")
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"""
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endpoint_url: str = ""
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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length: int = 256
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"""The maximum number of tokens to generate in the completion."""
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top_p: float = 1.0
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"""Total probability mass of tokens to consider at each step."""
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top_k: int = 40
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"""The number of highest probability vocabulary tokens to
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keep for top-k-filtering."""
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repetition_penalty: int = 1
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"""Penalizes repeated tokens according to frequency."""
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forefrontai_api_key: SecretStr
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base_url: Optional[str] = None
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"""Base url to use, if None decides based on model name."""
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class Config:
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extra = "forbid"
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key exists in environment."""
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values["forefrontai_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "forefrontai_api_key", "FOREFRONTAI_API_KEY")
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)
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return values
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling ForefrontAI API."""
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return {
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"temperature": self.temperature,
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"length": self.length,
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"top_p": self.top_p,
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"top_k": self.top_k,
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"repetition_penalty": self.repetition_penalty,
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}
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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 {**{"endpoint_url": self.endpoint_url}, **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 "forefrontai"
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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 ForefrontAI's complete endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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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 = ForefrontAI("Tell me a joke.")
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"""
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auth_value = f"Bearer {self.forefrontai_api_key.get_secret_value()}"
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response = requests.post(
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url=self.endpoint_url,
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headers={
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"Authorization": auth_value,
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"Content-Type": "application/json",
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},
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json={"text": prompt, **self._default_params, **kwargs},
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)
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response_json = response.json()
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text = response_json["result"][0]["completion"]
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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 model parameters
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text = enforce_stop_tokens(text, stop)
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return text
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