mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-29 07:19:59 +00:00
8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
from typing import Any, Dict, List, Mapping, Optional
|
|
|
|
import requests
|
|
from langchain_core.callbacks import CallbackManagerForLLMRun
|
|
from langchain_core.language_models.llms import LLM
|
|
from pydantic import ConfigDict
|
|
|
|
from langchain_community.llms.utils import enforce_stop_tokens
|
|
|
|
|
|
class ContentHandlerAmazonAPIGateway:
|
|
"""Adapter to prepare the inputs from Langchain to a format
|
|
that LLM model expects.
|
|
|
|
It also provides helper function to extract
|
|
the generated text from the model response."""
|
|
|
|
@classmethod
|
|
def transform_input(
|
|
cls, prompt: str, model_kwargs: Dict[str, Any]
|
|
) -> Dict[str, Any]:
|
|
return {"inputs": prompt, "parameters": model_kwargs}
|
|
|
|
@classmethod
|
|
def transform_output(cls, response: Any) -> str:
|
|
return response.json()[0]["generated_text"]
|
|
|
|
|
|
class AmazonAPIGateway(LLM):
|
|
"""Amazon API Gateway to access LLM models hosted on AWS."""
|
|
|
|
api_url: str
|
|
"""API Gateway URL"""
|
|
|
|
headers: Optional[Dict] = None
|
|
"""API Gateway HTTP Headers to send, e.g. for authentication"""
|
|
|
|
model_kwargs: Optional[Dict] = None
|
|
"""Keyword arguments to pass to the model."""
|
|
|
|
content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
|
|
"""The content handler class that provides an input and
|
|
output transform functions to handle formats between LLM
|
|
and the endpoint.
|
|
"""
|
|
|
|
model_config = ConfigDict(
|
|
extra="forbid",
|
|
)
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
_model_kwargs = self.model_kwargs or {}
|
|
return {
|
|
**{"api_url": self.api_url, "headers": self.headers},
|
|
**{"model_kwargs": _model_kwargs},
|
|
}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "amazon_api_gateway"
|
|
|
|
def _call(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Call out to Amazon API Gateway model.
|
|
|
|
Args:
|
|
prompt: The prompt to pass into the model.
|
|
stop: Optional list of stop words to use when generating.
|
|
|
|
Returns:
|
|
The string generated by the model.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
response = se("Tell me a joke.")
|
|
"""
|
|
_model_kwargs = self.model_kwargs or {}
|
|
payload = self.content_handler.transform_input(prompt, _model_kwargs)
|
|
|
|
try:
|
|
response = requests.post(
|
|
self.api_url,
|
|
headers=self.headers,
|
|
json=payload,
|
|
)
|
|
text = self.content_handler.transform_output(response)
|
|
|
|
except Exception as error:
|
|
raise ValueError(f"Error raised by the service: {error}")
|
|
|
|
if stop is not None:
|
|
text = enforce_stop_tokens(text, stop)
|
|
|
|
return text
|