dosubot[bot]
commented
1 year ago Answer generated by a 🤖
Answer
Hello @3coins, @jasondotparse, @hwchase17,
I understand that you're having trouble with the Bedrock class not being able to load your AWS credentials when called from a Lambda function. This issue is likely due to the difference in how AWS credentials are loaded in a local environment versus a Lambda function.
In your local environment, the Bedrock class is able to load your AWS credentials from the ~/.aws/credentials file or the ~/.aws/config file. However, when running in a Lambda function, these files are not available. Instead, AWS Lambda functions use IAM roles for permissions. The IAM role associated with your Lambda function needs to have the necessary permissions to access the Bedrock service.
To resolve this issue, you should ensure that the IAM role associated with your Lambda function has the necessary permissions to access the Bedrock service. If you're using the default AWS SDK for Python (boto3) client, it will automatically use the credentials associated with the IAM role of the Lambda function.
Here's the relevant part of the code that loads the credentials:
try:
import boto3
if values["credentials_profile_name"] is not None:
session = boto3.Session(profile_name=values["credentials_profile_name"])
else:
# use default credentials
session = boto3.Session()
client_params = {}
if values["region_name"]:
client_params["region_name"] = values["region_name"]
if values["endpoint_url"]:
client_params["endpoint_url"] = values["endpoint_url"]
values["client"] = session.client("bedrock", **client_params)
except ImportError:
raise ModuleNotFoundError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from eIn this code, if credentials_profile_name is not provided, it defaults to using the default credentials which, in the case of a Lambda function, would be the IAM role associated with the function.
There was a similar issue raised in the LangChain repository: AmazonKendraRetriever "Could not load credentials" error in latest release which was resolved by ensuring the IAM role associated with the Lambda function had the necessary permissions.
I hope this helps! Let me know if you have any other questions.
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Issue you'd like to raise.
Bedrock can't seem to load my credentials when used within a Lambda function. My AWS credentials were set up in my local environment using environment variables. When I use the bedrock class directly, it is able to load my credentials and my code runs smoothly. Here is the RetrievalQA function that utilizes the bedrock class:
The above code runs without issues when used directly. However, when used within a lambda function, it fails. The scripts file used to build my lambda function uses the bedrock class as written above, however, I run into the
validation errorswhen I invoke the Lambda function.{ "errorMessage": "1 validation error for Bedrock\n__root__\n Could not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. (type=value_error)", "errorType": "ValidationError", "requestId": "b772f236-f582-4308-8af5-b5a418d4327f", "stackTrace": [ " File \"/var/task/main.py\", line 62, in handler\n response = qa(query)\n", " File \"/var/task/main.py\", line 32, in qa\n llm = Bedrock(model_id=\"amazon.titan-tg1-large\", region_name='us-east-1',) #client=BEDROCK_CLIENT)\n", " File \"/var/task/langchain/load/serializable.py\", line 74, in __init__\n super().__init__(**kwargs)\n", " File \"pydantic/main.py\", line 341, in pydantic.main.BaseModel.__init__\n raise validation_error\n" ]As clearly indicated by the error message, bedrock couldn't load credentials.
Suggestion:
I have looked at the official documentation of the bedrock class but still do not understand why my code fails. Any help will be appreciated. @3coins @jasondotparse @hwchase17