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aws-langchain-common-layer

Stability: Experimental

All classes are under active development and subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

Language Package
Typescript Logo TypeScript @cdklabs/generative-ai-cdk-constructs
Python Logo Python cdklabs.generative_ai_cdk_constructs

Table of contents

Credits

This construct is a modified version of the following construct: https://github.com/aws-samples/aws-genai-llm-chatbot/tree/main/lib/model-interfaces/langchain

Thanks to the original authors:

Overview

This construct provides an AWS Lambda layer which contains needed python pip packages to build generative AI applications based on the LangChain client. The list of libraries installed and their version is available here

Building with Differing Architectures

Use Docker's default platform environmental variable, DOCKER_DEFAULT_PLATFORM, when synthesizing or deploying on different architectures. For example: ARM chips (such as Apple silicon series macOS M1, M2, and M3) are by default arm64 architectures and may bundle unsupported packages into x86_64 Lambda Layers.

A solution is:

DOCKER_DEFAULT_PLATFORM=linux/amd64 cdk deploy

Here is a minimal deployable pattern definition:

TypeScript

import { Construct } from 'constructs';
import { Stack, StackProps, Aws } from 'aws-cdk-lib';
import { LangchainCommonDepsLayer } from '@cdklabs/generative-ai-cdk-constructs';

const lambdaArchitecture = lambda.Architecture.ARM_64;
const lambdaRuntime = lambda.Runtime.PYTHON_3_10;

const lambdaDepsLayer = new LangchainCommonDepsLayer(this, 'lambdagenaidepslayer', {
        runtime: lambdaRuntime,
        architecture: lambdaArchitecture,
        autoUpgrade: true
      });

//Then pass the layer above to your lambda function constructor

Python

from constructs import Construct
from aws_cdk import Aws, aws_lambda as lambda_
from cdklabs.generative_ai_cdk_constructs import (
    LangchainCommonDepsLayer,
    LangchainCommonLayer,
)

lambda_architecture = lambda_.Architecture.ARM_64
lambda_runtime = lambda_.Runtime.PYTHON_3_10


lambda_deps_layer = LangchainCommonDepsLayer(
    self,
    'lambdagenaidepslayer',
    runtime=lambda_runtime,
    architecture=lambda_architecture,
    auto_upgrade=True,
)

Initializer

new LangchainCommonDepsLayer(scope: Construct, id: string, props: LangchainLayerProps)

Parameters

Pattern Construct Props

Name Type Required Description
runtime lambda.Runtime Required Lambda function runtime compatible with this layer.
architecture lambda.Architecture Required Lambda function architecture compatible with this layer.
autoUpgrade boolean Optional Add '--upgrade' to pip install requirements.txt. In case of a LangchainCommonLayer, this parameter is not used.
additionalPackages string[] Optional A prop allowing additional python pip libraries to be installed with this langchain layer.
description string Optional Default: Dependencies to build gen ai applications with the langchain client
layerVersionName string Optional The name of the layer
license string Optional The SPDX licence identifier or URL to the license file for this layer
removalPolicy RemovalPolicy Optional Whether to retain this version of the layer when a new version is added or when the stack is deleted. Default: DESTROY

Default properties

Out-of-the-box implementation of the construct without any override will not set any default values. Depending on the features enabled, user will need to provide environmental variable values to the AWS Lambda function used by the LangchainCommonLayer.

Architecture

Architecture Diagram

Cost

You are responsible for the cost of the AWS services used while running this construct. As of this revision, the cost for running this construct with the default settings in the US East (N. Virginia) Region is approximately $0.25 per month.

We recommend creating a budget through AWS Cost Explorer to help manage costs. Prices are subject to change. For full details, refer to the pricing webpage for each AWS service used in this solution.

Security

When you build systems on AWS infrastructure, security responsibilities are shared between you and AWS. This shared responsibility model reduces your operational burden because AWS operates, manages, and controls the components including the host operating system, virtualization layer, and physical security of the facilities in which the services operate. For more information about AWS security, visit AWS Cloud Security.

This construct requires you to provide an existing AWS Lambda function. Please refer to the official documentation on best practices to secure this service:

If choosing to interface with a third-party LLM provider (outside of Amazon Bedrock), customers must evaluate the security considerations of data leaving their AWS account. The customer is required to provide a valid API key at the time of deployment.

The primary piece of data sent to the third-party LLM provider is the prompt to perform inference on. Depending on the use case, the prompt can contain the user’s input, previous interactions (for example, chat history), and document excerpts sourced from the configured knowledge base (for example, Amazon OpenSearch search result).

AWS CloudTrail provides a number of security features to consider as you develop and implement your own security policies. Please follow the related best practices through the official documentation.

Warning This construct allows you to interact with models from third party providers. Your use of the third-party generative AI (GAI) models is governed by the terms provided to you by the third-party GAI model providers when you acquired your license to use them (for example, their terms of service, license agreement, acceptable use policy, and privacy policy).

You are responsible for ensuring that your use of the third-party GAI models comply with the terms governing them, and any laws, rules, regulations, policies, or standards that apply to you.

You are also responsible for making your own independent assessment of the third-party GAI models that you use, including their outputs and how third-party GAI model providers use any data that might be transmitted to them based on your deployment configuration. AWS does not make any representations, warranties, or guarantees regarding the third-party GAI models, which are “Third-Party Content” under your agreement with AWS. This construct is offered to you as “AWS Content” under your agreement with AWS.

Supported AWS Regions

This solution optionally uses the Amazon Bedrock and Amazon OpenSearch service, which is not currently available in all AWS Regions. You must launch this construct in an AWS Region where these services are available. For the most current availability of AWS services by Region, see the AWS Regional Services List.

Note You need to explicity enable access to models before they are available for use in the Amazon Bedrock service. Please follow the Amazon Bedrock User Guide for steps related to enabling model access.

Quotas

Service quotas, also referred to as limits, are the maximum number of service resources or operations for your AWS account.

Make sure you have sufficient quota for each of the services implemented in this solution. For more information, refer to AWS service quotas.

To view the service quotas for all AWS services in the documentation without switching pages, view the information in the Service endpoints and quotas page in the PDF instead.

Clean up

When deleting your stack which uses this construct, do not forget to go over the following instructions to avoid unexpected charges:

  • delete the lambda layers uploaded to the account

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