mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-19 13:26:32 -04:00
Baichuan Sun
6157bdf9d9
Add API Headers support for Amazon API Gateway to enable Authentication using DynamoDB. <!-- Thank you for contributing to LangChain! Replace this comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out! If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @dev2049 - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @dev2049 - Memory: @hwchase17 - Agents / Tools / Toolkits: @vowelparrot - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->
103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
from typing import Any, Dict, List, Mapping, Optional
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import requests
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from pydantic import Extra
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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class ContentHandlerAmazonAPIGateway:
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"""Adapter class to prepare the inputs from Langchain to a format
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that LLM model expects. 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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"""Wrapper around custom Amazon API Gateway"""
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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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"""Key word 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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"""Configuration for this pydantic object."""
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extra = 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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**{"endpoint_name": self.api_url},
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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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