Files
t6_mem0/embedchain/embedchain/llm/aws_bedrock.py
2024-09-07 22:39:28 +05:30

58 lines
1.8 KiB
Python

import os
from typing import Optional
try:
from langchain_aws import BedrockLLM
except ModuleNotFoundError:
raise ModuleNotFoundError(
"The required dependencies for AWSBedrock are not installed." "Please install with `pip install langchain_aws`"
) from None
from embedchain.config import BaseLlmConfig
from embedchain.helpers.json_serializable import register_deserializable
from embedchain.llm.base import BaseLlm
@register_deserializable
class AWSBedrockLlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config)
def get_llm_model_answer(self, prompt) -> str:
response = self._get_answer(prompt, self.config)
return response
def _get_answer(self, prompt: str, config: BaseLlmConfig) -> str:
try:
import boto3
except ModuleNotFoundError:
raise ModuleNotFoundError(
"The required dependencies for AWSBedrock are not installed."
"Please install with `pip install boto3==1.34.20`."
) from None
self.boto_client = boto3.client(
"bedrock-runtime", os.environ.get("AWS_REGION", os.environ.get("AWS_DEFAULT_REGION", "us-east-1"))
)
kwargs = {
"model_id": config.model or "amazon.titan-text-express-v1",
"client": self.boto_client,
"model_kwargs": config.model_kwargs
or {
"temperature": config.temperature,
},
}
if config.stream:
from langchain.callbacks.streaming_stdout import (
StreamingStdOutCallbackHandler,
)
kwargs["streaming"] = True
kwargs["callbacks"] = [StreamingStdOutCallbackHandler()]
llm = BedrockLLM(**kwargs)
return llm.invoke(prompt)