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c2a3021bb0
Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
104 lines
2.9 KiB
Python
104 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 pydantic import ConfigDict
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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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model_config = ConfigDict(
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extra="forbid",
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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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_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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