mirror of
https://github.com/hwchase17/langchain.git
synced 2026-04-05 03:48:48 +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),
}
```
102 lines
2.9 KiB
Python
102 lines
2.9 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_community.llms.utils import enforce_stop_tokens
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class ContentHandlerAmazonAPIGateway:
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"""Adapter to prepare the inputs from Langchain to a format
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that LLM model expects.
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It also provides helper function to extract
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the generated text from the model response."""
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@classmethod
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def transform_input(
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cls, prompt: str, model_kwargs: Dict[str, Any]
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) -> Dict[str, Any]:
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return {"inputs": prompt, "parameters": model_kwargs}
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@classmethod
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def transform_output(cls, response: Any) -> str:
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return response.json()[0]["generated_text"]
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class AmazonAPIGateway(LLM):
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"""Amazon API Gateway to access LLM models hosted on AWS."""
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api_url: str
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"""API Gateway URL"""
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headers: Optional[Dict] = None
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"""API Gateway HTTP Headers to send, e.g. for authentication"""
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model_kwargs: Optional[Dict] = None
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"""Keyword arguments to pass to the model."""
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content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
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"""The content handler class that provides an input and
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output transform functions to handle formats between LLM
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and the endpoint.
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"""
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class Config:
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extra = "forbid"
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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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_model_kwargs = self.model_kwargs or {}
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return {
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**{"api_url": self.api_url, "headers": self.headers},
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**{"model_kwargs": _model_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 "amazon_api_gateway"
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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 Amazon API Gateway model.
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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 = se("Tell me a joke.")
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"""
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_model_kwargs = self.model_kwargs or {}
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payload = self.content_handler.transform_input(prompt, _model_kwargs)
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try:
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response = requests.post(
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self.api_url,
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headers=self.headers,
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json=payload,
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)
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text = self.content_handler.transform_output(response)
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except Exception as error:
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raise ValueError(f"Error raised by the service: {error}")
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if stop is not None:
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text = enforce_stop_tokens(text, stop)
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return text
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