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This could be Amazon Elastic Compute Cloud (Amazon EC2), AWS Lambda , AWS SDK , Amazon SageMaker notebooks, or your workstation if you are doing a quick proof of concept. We aim to target and simplify them using generative AI with Amazon Bedrock. This is a proof of concept setup. CUR data stored in an S3 bucket.
Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. The Lambda function associated with the Amazon Lex chatbot contains the logic and business rules required to process the user’s intent. For more details on supported data sources, refer to Data sources. ConversationTable – Stores conversation history.
For more details, refer to the Primer on Retrieval Augmented Generation, Embeddings, and Vector Databases section in Preview – Connect Foundation Models to Your Company Data Sources with Agents for Amazon Bedrock. For more information, refer to Model access. You will use this Lambda layer code later to create the Lambda function.
The generation of different responses for a given prompt is possible due to the use of a stochastic, rather than greedy, decoding strategy. For more information, refer to Set up permissions for batch inference. Refer to Run batch inference to access batch inference APIs via custom SDKs. Lambda function B. SQS queue C.
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Scaling and State This is Part 9 of Learning Lambda, a tutorial series about engineering using AWS Lambda. So far in this series we’ve only been talking about processing a small number of events with Lambda, one after the other. Finally I mention Lambda’s limited, but not trivial, vertical scaling capability.
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