From 198df660708a877fcf91021579ce9064b1443508 Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Fri, 22 Mar 2024 11:52:59 +0800 Subject: [PATCH 1/8] enhance(sdk): remove the `fuzzyArn`, instead use the lazy value from pulumi Before, the type used to construct the permission list was `aws.types.input.iam.GetPolicyDocumentStatement` type, it only allows passing strings as arguments, and we used `fuzzyArn` to build the ARN through string concatenation. This approach is messy and unsafe. Now, we've switched the permission type to `aws.types.input.iam.GetPolicyDocumentStatementArgs`, which allows us to pass lazy values as arguments. This means we can configure the permission using the ARN from the Pulumi resource. --- .../pluto-infra/src/aws/function.lambda.ts | 19 ++++++++++--------- .../pluto-infra/src/aws/kvstore.dynamodb.ts | 6 +----- packages/pluto-infra/src/aws/permission.ts | 8 +++----- packages/pluto-infra/src/aws/queue.sns.ts | 6 +----- packages/pluto-infra/src/aws/sagemaker.ts | 12 ++++-------- 5 files changed, 19 insertions(+), 32 deletions(-) diff --git a/packages/pluto-infra/src/aws/function.lambda.ts b/packages/pluto-infra/src/aws/function.lambda.ts index 3965e953..a374cf37 100644 --- a/packages/pluto-infra/src/aws/function.lambda.ts +++ b/packages/pluto-infra/src/aws/function.lambda.ts @@ -1,5 +1,6 @@ -import * as fs from "fs-extra"; import * as path from "path"; +import * as fs from "fs-extra"; +import assert from "node:assert"; import { Context } from "aws-lambda"; import * as aws from "@pulumi/aws"; import * as pulumi from "@pulumi/pulumi"; @@ -24,7 +25,7 @@ import { } from "@plutolang/pluto"; import { genAwsResourceName } from "@plutolang/pluto/dist/clients/aws"; import { dumpClosureToDir_python, serializeClosureToDir } from "../utils"; -import { currentAwsRegion } from "./utils"; +import { Permission } from "./permission"; export enum Ops { WATCH_LOG = "WATCH_LOG", @@ -37,7 +38,7 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, private readonly lambda: AwsLambda; private readonly lambdaUrl: aws.lambda.FunctionUrl; private readonly iam: Role; - private readonly statements: aws.types.input.iam.GetPolicyDocumentStatement[]; + private readonly statements: aws.types.input.iam.GetPolicyDocumentStatementArgs[]; public readonly lambdaName: string; public readonly lambdaArn: pulumi.Output; @@ -85,6 +86,7 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, }); // Serialize the closure with its dependencies to a directory. + assert(process.env.WORK_DIR, "WORK_DIR is not set."); const workdir = path.join(process.env.WORK_DIR!, "assets", `${this.id}}`); fs.rmSync(workdir, { recursive: true, force: true }); fs.ensureDirSync(workdir); @@ -123,7 +125,7 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, entrypointFilePathP.then(() => { const dependentStatements = closure.dependencies ?.filter((dep) => dep.type === "method") - .map((dep) => dep.resourceObject.grantPermission(dep.operation, this)); + .map((dep) => dep.resourceObject.grantPermission(dep.operation, this) as Permission); if (dependentStatements !== undefined) { this.statements.push(...dependentStatements); } @@ -137,7 +139,7 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, return this.lambdaUrl.functionUrl as any; } - public grantPermission(op: string): aws.types.input.iam.GetPolicyDocumentStatement { + public grantPermission(op: string): Permission { const WATCH_LOG_ARN = "arn:aws:logs:*:*:*"; switch (op.toUpperCase()) { case Ops.WATCH_LOG: @@ -147,11 +149,10 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, resources: [WATCH_LOG_ARN], }; case Ops.INVOKE: { - const fuzzyArn = `arn:aws:lambda:${currentAwsRegion()}:*:function:${this.lambdaName}`; return { effect: "Allow", actions: ["lambda:InvokeFunction"], - resources: [fuzzyArn], + resources: [this.lambda.arn], }; } default: @@ -238,7 +239,7 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, } private grantIAMPermission(): void { - const policyDocument = aws.iam.getPolicyDocument( + const policyDocument = aws.iam.getPolicyDocumentOutput( { statements: this.statements, }, @@ -251,7 +252,7 @@ export class Lambda extends pulumi.ComponentResource implements IResourceInfra, name: genAwsResourceName(this.id, "policy"), path: "/", description: "IAM policy", - policy: policyDocument.then((policyDocument) => policyDocument.json), + policy: policyDocument.json, }, { parent: this } ); diff --git a/packages/pluto-infra/src/aws/kvstore.dynamodb.ts b/packages/pluto-infra/src/aws/kvstore.dynamodb.ts index aa03542e..cb6bd282 100644 --- a/packages/pluto-infra/src/aws/kvstore.dynamodb.ts +++ b/packages/pluto-infra/src/aws/kvstore.dynamodb.ts @@ -59,13 +59,9 @@ export class DynamoKVStore return { effect: "Allow", actions: actions, - resources: [this.fuzzyArn()], + resources: [this.arn], }; } public postProcess(): void {} - - private fuzzyArn() { - return `arn:aws:dynamodb:*:*:table/${genAwsResourceName(this.id)}`; - } } diff --git a/packages/pluto-infra/src/aws/permission.ts b/packages/pluto-infra/src/aws/permission.ts index 679488d9..a8ff3440 100644 --- a/packages/pluto-infra/src/aws/permission.ts +++ b/packages/pluto-infra/src/aws/permission.ts @@ -1,5 +1,3 @@ -export interface Permission { - effect: string; - actions: string[]; - resources: string[]; -} +import * as aws from "@pulumi/aws"; + +export type Permission = aws.types.input.iam.GetPolicyDocumentStatementArgs; diff --git a/packages/pluto-infra/src/aws/queue.sns.ts b/packages/pluto-infra/src/aws/queue.sns.ts index 51fbbf9b..11617883 100644 --- a/packages/pluto-infra/src/aws/queue.sns.ts +++ b/packages/pluto-infra/src/aws/queue.sns.ts @@ -77,15 +77,11 @@ export class SNSQueue extends pulumi.ComponentResource implements IResourceInfra return { effect: "Allow", actions: actions, - resources: [this.fuzzyArn()], + resources: [this.topic.arn], }; } public postProcess() {} - - private fuzzyArn() { - return `arn:aws:sns:*:*:${genAwsResourceName(this.id)}`; - } } /** diff --git a/packages/pluto-infra/src/aws/sagemaker.ts b/packages/pluto-infra/src/aws/sagemaker.ts index a489d43c..78735dab 100644 --- a/packages/pluto-infra/src/aws/sagemaker.ts +++ b/packages/pluto-infra/src/aws/sagemaker.ts @@ -15,6 +15,8 @@ export enum SageMakerOps { export class SageMaker extends pulumi.ComponentResource implements IResourceInfra, ISageMakerInfra { public readonly id: string; + private readonly endpoint: aws.sagemaker.Endpoint; + public readonly endpointName: string; constructor(name: string, imageUri: string, options?: SageMakerOptions) { @@ -55,7 +57,7 @@ export class SageMaker extends pulumi.ComponentResource implements IResourceInfr // Define the SageMaker endpoint this.endpointName = genAwsResourceName(this.id, "endpoint"); - new aws.sagemaker.Endpoint( + this.endpoint = new aws.sagemaker.Endpoint( this.endpointName, { name: this.endpointName, @@ -119,15 +121,9 @@ export class SageMaker extends pulumi.ComponentResource implements IResourceInfr return { effect: "Allow", actions: actions, - resources: [this.fuzzyArn()], + resources: [this.endpoint.arn], }; } public postProcess(): void {} - - private fuzzyArn() { - return `arn:aws:sagemaker:${currentAwsRegion()}:*:endpoint/${ - this.endpointName - }`.toLocaleLowerCase(); - } } From 395b319c911ec68c9b9411b43b0a20b12e1fc1ec Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Fri, 22 Mar 2024 12:00:37 +0800 Subject: [PATCH 2/8] fix(deducer): incorrectly generate duplicate client statements when one code section accesses a single resource multiple times --- components/deducers/static/src/closure/store-closure.ts | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/components/deducers/static/src/closure/store-closure.ts b/components/deducers/static/src/closure/store-closure.ts index 5bac0963..f09a2136 100644 --- a/components/deducers/static/src/closure/store-closure.ts +++ b/components/deducers/static/src/closure/store-closure.ts @@ -47,8 +47,14 @@ function generateSourceCode( ): string { let cirCode = imports + "\n"; + const resources = new Set(); // Find the dependencies of this CIR and build corresponding instances. for (const dependentRes of dependentResources) { + if (resources.has(dependentRes.name)) { + continue; + } + resources.add(dependentRes.name); + // TODO: verify if the buildClient function exists. If it does not, use the original statement. cirCode += dependentRes.imports + "\n"; cirCode += `const ${dependentRes.name} = ${dependentRes.type}.buildClient(${dependentRes.parameters});\n`; From fc6a6dc7c9007e681bcc37de715f1da2d4cbff3f Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Fri, 22 Mar 2024 14:56:14 +0800 Subject: [PATCH 3/8] feat(sdk): export the table name and partition key of the created dynamodb instance to users --- .../pluto_client/clients/aws/kvstore_dynamodb.py | 14 +++++++++++--- packages/pluto-py/pluto_client/kvstore.py | 12 +++++++++++- packages/pluto-py/pluto_client/sagemaker.py | 9 ++++++--- packages/pluto-py/pyproject.toml | 2 +- packages/pluto/src/clients/aws/kvstore.dynamodb.ts | 10 +++++++++- packages/pluto/src/kvstore.ts | 7 ++++++- packages/pluto/src/sagemaker.aws.ts | 6 +++--- 7 files changed, 47 insertions(+), 13 deletions(-) diff --git a/packages/pluto-py/pluto_client/clients/aws/kvstore_dynamodb.py b/packages/pluto-py/pluto_client/clients/aws/kvstore_dynamodb.py index c2492cf0..bbea197c 100644 --- a/packages/pluto-py/pluto_client/clients/aws/kvstore_dynamodb.py +++ b/packages/pluto-py/pluto_client/clients/aws/kvstore_dynamodb.py @@ -8,11 +8,19 @@ class DynamoKVStore(IKVStoreClient): def __init__(self, name: str, opts: Optional[KVStoreOptions] = None): self.__id = utils.gen_resource_id(KVStore.fqn, name) - table_name = gen_aws_resource_name(self.__id) - self.__client = boto3.resource("dynamodb").Table(table_name) + self.__table_name = gen_aws_resource_name(self.__id) + self.__client = boto3.resource("dynamodb").Table(self.__table_name) + + @property + def aws_table_name(self) -> str: + return self.__table_name + + @property + def aws_partition_key(self) -> str: + return "Id" def get(self, key: str) -> str: - response = self.__client.get_item(Key={"Id": key}) + response = self.__client.get_item(Key={self.aws_partition_key: key}) if "Item" not in response: raise ValueError(f"There is no target key-value pair, Key: {key}.") return response["Item"]["Value"] diff --git a/packages/pluto-py/pluto_client/kvstore.py b/packages/pluto-py/pluto_client/kvstore.py index 2363265d..c05490fc 100644 --- a/packages/pluto-py/pluto_client/kvstore.py +++ b/packages/pluto-py/pluto_client/kvstore.py @@ -15,6 +15,16 @@ class KVStoreOptions: pass +class IKVStoreRegularApi: + @property + def aws_table_name(self) -> str: + raise NotImplementedError + + @property + def aws_partition_key(self) -> str: + raise NotImplementedError + + class IKVStoreClientApi(IResourceClientApi): def get(self, key: str) -> str: raise NotImplementedError @@ -31,7 +41,7 @@ class IKVStoreCapturedProps(IResourceCapturedProps): pass -class IKVStoreClient(IKVStoreClientApi, IKVStoreCapturedProps): +class IKVStoreClient(IKVStoreClientApi, IKVStoreCapturedProps, IKVStoreRegularApi): pass diff --git a/packages/pluto-py/pluto_client/sagemaker.py b/packages/pluto-py/pluto_client/sagemaker.py index f25c3bb4..daba3ec2 100644 --- a/packages/pluto-py/pluto_client/sagemaker.py +++ b/packages/pluto-py/pluto_client/sagemaker.py @@ -49,11 +49,12 @@ class HuggingFaceTaskType(Enum): @dataclass class SageMakerOptions: - instance_type: str = "ml.m5.large" + # The member name must be camelCase to match the name in the infrastructure SDK. + instanceType: str = "ml.m5.large" envs: Optional[Dict[str, Any]] = None -class ISageMakerNormalApi: +class ISageMakerRegularApi: @property def endpoint_name(self) -> str: raise NotImplementedError @@ -69,12 +70,14 @@ class ISageMakerInfraApi(IResourceInfraApi): class ISageMakerCapturedProps(IResourceCapturedProps): + # The method name doesn't have to be CamelCase since it can correctly match environment variable + # names in the implementation. def endpoint_url(self) -> str: raise NotImplementedError class ISageMakerClient( - ISageMakerClientApi, ISageMakerCapturedProps, ISageMakerNormalApi + ISageMakerClientApi, ISageMakerCapturedProps, ISageMakerRegularApi ): pass diff --git a/packages/pluto-py/pyproject.toml b/packages/pluto-py/pyproject.toml index 4c118ed9..e7fcb424 100644 --- a/packages/pluto-py/pyproject.toml +++ b/packages/pluto-py/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "pluto-client" -version = "0.0.3" +version = "0.0.4" description = "The Client Library for Pluto Programming Language." authors = ["Jade Zheng "] license = "Apache-2.0" diff --git a/packages/pluto/src/clients/aws/kvstore.dynamodb.ts b/packages/pluto/src/clients/aws/kvstore.dynamodb.ts index 07f0f9bb..fb8baf7d 100644 --- a/packages/pluto/src/clients/aws/kvstore.dynamodb.ts +++ b/packages/pluto/src/clients/aws/kvstore.dynamodb.ts @@ -23,12 +23,20 @@ export class DynamoKVStore implements IKVStoreClient { opts; } + public get awsTableName(): string { + return this.tableName; + } + + public get awsPartitionKey(): string { + return "Id"; + } + public async get(key: string): Promise { const result = await this.docClient.send( new GetCommand({ TableName: this.tableName, Key: { - Id: key, + [this.awsPartitionKey]: key, }, }) ); diff --git a/packages/pluto/src/kvstore.ts b/packages/pluto/src/kvstore.ts index b19ea86a..6082a19a 100644 --- a/packages/pluto/src/kvstore.ts +++ b/packages/pluto/src/kvstore.ts @@ -15,6 +15,11 @@ import { aws, k8s } from "./clients"; */ export interface KVStoreOptions {} +export interface IKVStoreRegularApi { + readonly awsTableName?: string; + readonly awsPartitionKey?: string; +} + /** * Define the access methods for KVStore that operate during runtime. */ @@ -34,7 +39,7 @@ export interface IKVStoreCapturedProps extends IResourceCapturedProps {} * Construct a type that includes all the necessary methods required to be implemented within the * client implementation class of a resource type. */ -export type IKVStoreClient = IKVStoreClientApi & IKVStoreCapturedProps; +export type IKVStoreClient = IKVStoreClientApi & IKVStoreCapturedProps & IKVStoreRegularApi; /** * Construct a type that includes all the necessary methods required to be implemented within the diff --git a/packages/pluto/src/sagemaker.aws.ts b/packages/pluto/src/sagemaker.aws.ts index 3b69118c..3074f01a 100644 --- a/packages/pluto/src/sagemaker.aws.ts +++ b/packages/pluto/src/sagemaker.aws.ts @@ -61,11 +61,11 @@ export interface SageMakerOptions { /** * The methods of this interface can be used at runtime, but it doesn't belong to Client API or Captured Props. */ -export interface ISageMakerNormalApi { +export interface ISageMakerRegularApi { /** * TODO: There are two bug within current deducer. One is that the deducer doesn't reduce the * repeated dependencies when the closure accesses same resource object multiple times. The other - * is that if the closure only accesses the normal api (not client api, infra api or capatured + * is that if the closure only accesses the regular api (not client api, infra api or capatured * property), the deducer doesn't add the resource object to the dependencies. After the bugs are * fixed, the `endpointUrl` method should be moved from `ISageMakerCapturedProps` to this * interface. @@ -84,7 +84,7 @@ export interface ISageMakerCapturedProps extends IResourceCapturedProps { endpointUrl(): string; } -export type ISageMakerClient = ISageMakerClientApi & ISageMakerCapturedProps & ISageMakerNormalApi; +export type ISageMakerClient = ISageMakerClientApi & ISageMakerCapturedProps & ISageMakerRegularApi; export type ISageMakerInfra = ISageMakerInfraApi & ISageMakerCapturedProps; From 9fe7f9c92e0b5b0ce1c65288802b548504e47413 Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Fri, 22 Mar 2024 17:06:29 +0800 Subject: [PATCH 4/8] feat(example): add an example, building a llama2 conversational chatbot with aws and langchain --- ...angchain-llama2-chatbot-sagemaker-arch.png | Bin 0 -> 647642 bytes .../.gitignore | 38 +++ .../.pluto/pluto.yml | 7 + .../README.md | 73 +++++ .../README_zh.md | 74 +++++ .../comparison/application/main.ts | 99 +++++++ .../comparison/pulumi/pulumi.ts | 203 ++++++++++++++ .../comparison/terraform/terraform.tf | 263 ++++++++++++++++++ .../package.json | 23 ++ .../src/index.ts | 182 ++++++++++++ .../tsconfig.json | 35 +++ 11 files changed, 997 insertions(+) create mode 100644 assets/langchain-llama2-chatbot-sagemaker-arch.png create mode 100644 examples/langchain-llama2-chatbot-sagemaker/.gitignore create mode 100644 examples/langchain-llama2-chatbot-sagemaker/.pluto/pluto.yml create mode 100644 examples/langchain-llama2-chatbot-sagemaker/README.md create mode 100644 examples/langchain-llama2-chatbot-sagemaker/README_zh.md create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf create mode 100644 examples/langchain-llama2-chatbot-sagemaker/package.json create mode 100644 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zJKAv&2ud`H@hb))uhjNGzk0C{B~p|%#6c(R{@K#|pLhP-3%pRxmt-|&DKfWR5{GcG OFI7biXqo)up#KMDue*x? literal 0 HcmV?d00001 diff --git a/examples/langchain-llama2-chatbot-sagemaker/.gitignore b/examples/langchain-llama2-chatbot-sagemaker/.gitignore new file mode 100644 index 00000000..9e6a4d69 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/.gitignore @@ -0,0 +1,38 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +node_modules +.pnp +.pnp.js + +# testing +coverage + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# local env files +.env +.env.local +.env.development.local +.env.test.local +.env.production.local + +# turbo +.turbo + +# vercel +.vercel + +# compilation result +**/dist/ + +# pluto +.pluto/**/state/ +.pluto/*.test.*/ \ No newline at end of file diff --git a/examples/langchain-llama2-chatbot-sagemaker/.pluto/pluto.yml b/examples/langchain-llama2-chatbot-sagemaker/.pluto/pluto.yml new file mode 100644 index 00000000..f985319a --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/.pluto/pluto.yml @@ -0,0 +1,7 @@ +current: dev +language: typescript +stacks: + - configs: {} + name: dev + platformType: AWS + provisionType: Pulumi diff --git a/examples/langchain-llama2-chatbot-sagemaker/README.md b/examples/langchain-llama2-chatbot-sagemaker/README.md new file mode 100644 index 00000000..08731639 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/README.md @@ -0,0 +1,73 @@ +# Building a Llama2 Conversational Chatbot with AWS and LangChain + +In this guide, you'll discover how to create a simple conversational chatbot by integrating Pluto, AWS services, LangChain, and Llama2. We’ll walk you through the architecture components of our example application and how to deploy and operate it using Pluto. + +## Architecture Overview + +![Chatbot Architecture Diagram](../../assets/langchain-llama2-chatbot-sagemaker-arch.png) + +As illustrated above, our example application relies on the following AWS services and resources: + +- **Amazon SageMaker**: Deploying the Llama2 model + - Model, EndpointConfiguration, Endpoint, Role +- **Amazon DynamoDB**: Persisting session messages + - Table +- **AWS Lambda**: Executing backend business logic + - Function, Role, Policy +- **Amazon API Gateway**: Providing an HTTP API endpoint for user access + - Api, Route, Integration, Deployment, Stage + +## Comparison + +The table below shows a comparison between Pluto and other tools regarding the amount and type of code: + +| | Code Type | Lines of Code | +| ----------- | ---------------------------------- | ------------- | +| Application | Business Logic | 79 | +| Pulumi | Infrastructure Configuration | 159 | +| Terraform | Infrastructure Configuration | 201 | +| **Pluto** | **Business Logic + Configuration** | **87** | + +Using Pluto, you can integrate business logic and infrastructure configuration with only an 8-line increase compared to pure business logic code, significantly reducing the amount of code. When building with Pulumi or Terraform, the total lines of code for business logic plus infrastructure configuration reach 238 and 280 lines, respectively, with Pluto's code amounting to only about a third of that. + +## Deployment Steps + +To deploy this chatbot, please follow the steps below: + +1. Install Pluto and configure AWS access credentials. Detailed instructions can be found in the [Pluto Quick Start Guide](https://github.com/pluto-lang/pluto#-quick-start). + +2. Run the following commands in the root directory of the example application to initialize: + + ```bash + npm install + pip install -r requirements.txt + ``` + +3. Replace `HUGGING_FACE_HUB_TOKEN` in the `src/index.ts` file with your Hugging Face Hub Token. If you don’t have a token yet, generate one [here](https://huggingface.co/settings/tokens). + +4. Deploy the application: + + ```bash + pluto deploy + ``` + +Be patient, as deploying the model in SageMaker can take some time. Once the deployment is complete, the console will display the URL for ApiGateway, which you can access via a browser or a curl command. Here is a simple test command: + +```bash +CHATBOT_API_URL=your ApiGateway URL +time curl --get "$CHATBOT_API_URL/chat" \ + --data-urlencode "sessionid=session_1" \ + --data-urlencode "query='What is the capital of China?'" +``` + +By using the same `sessionid`, you can conduct multiple rounds of conversation testing. + +To clean up resources, simply run the `pluto destroy` command in the root directory of the example application. + +## Extending the Example Application + +This example application serves as a basic chatbot, but you can expand it based on your needs to make it more powerful and practical. For instance, you could: + +- **Add custom business logic**: Write more Lambda functions to execute more complex business logic tailored to specific business needs. +- **Integrate additional AWS services**: Take advantage of the wide array of services provided by AWS, such as Amazon SNS as a message queue for asynchronous message processing. +- **Enhance user experience**: Develop a frontend user interface to allow users to interact with the chatbot through a web page or mobile app, rather than solely through API requests. diff --git a/examples/langchain-llama2-chatbot-sagemaker/README_zh.md b/examples/langchain-llama2-chatbot-sagemaker/README_zh.md new file mode 100644 index 00000000..ea370496 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/README_zh.md @@ -0,0 +1,74 @@ +# 基于 AWS 和 LangChain 的 Llama2 会话聊天机器人 + +在本指南中，你将了解如何结合 Pluto、AWS 服务、LangChain 和 Llama2 构建一个简易的会话型聊天机器人。我们将介绍示例应用的架构组件，以及如何使用 Pluto 进行部署和使用。 + +## 架构概览 + +![聊天机器人架构图](../../assets/langchain-llama2-chatbot-sagemaker-arch.png) + +如图所示，该示例应用依赖以下 AWS 服务和资源： + +- **Amazon SageMaker**: 用于部署 Llama2 模型 + - Model, EndpointConfiguration, Endpoint, Role +- **Amazon DynamoDB**: 用来持久化会话消息 + - Table +- **AWS Lambda**: 执行后端业务逻辑 + - Function, Role, Policy +- **Amazon API Gateway**: 提供 HTTP API 端点以供用户访问服务 + - Api, Route, Integration, Deployment, Stage + +## 比较 + +以下表格展示了 Pluto 与其他工具在代码量和类型上的对比： + +| | 代码类型 | 代码量/行 | +| --------- | ------------------- | --------- | +| 应用程序 | 业务逻辑 | 79 | +| Pulumi | 基础设施配置 | 159 | +| Terraform | 基础设施配置 | 201 | +| **Pluto** | **业务逻辑 + 配置** | **87** | + +使用 Pluto 可以整合业务逻辑和基础设施配置，与纯业务逻辑代码相比，仅增加了 8 行代码，显著减少代码量。而采用 Pulumi 或 Terraform 构建时，业务逻辑加基础设施配置的整体代码量分别达到 238 行和 280 行，Pluto 的代码量仅为其约三分之一。 + +## 部署步骤 + +要部署这个聊天机器人，请按照以下步骤操作： + +1. 安装 Pluto 并配置 AWS 访问凭证，详细步骤请参阅 [Pluto 快速开始指南](https://github.com/pluto-lang/pluto#-quick-start)。 + +2. 在示例应用的根目录下运行以下命令进行初始化： + + ```bash + npm install + pip install -r requirements.txt + ``` + +3. 将 `src/index.ts` 文件中的 `HUGGING_FACE_HUB_TOKEN` 修改成你的 Hugging Face Hub Token。如果你还没有 Token，可以在 [这里](https://huggingface.co/settings/tokens) 生成一个。 + +4. 部署应用： + + ```bash + pluto deploy + ``` + +这个过程需要耐心等待一会，因为 SageMaker 部署模型需要一定时间。一旦部署完成，控制台会显示出 ApiGateway 的 URL，你可以通过浏览器或 curl 命令来访问它。以下是一个简单的测试命令： + +```bash +CHATBOT_API_URL=你的 ApiGateway URL +time curl --get "$CHATBOT_API_URL/chat" \ + --data-urlencode "sessionid=session_1" \ + --data-urlencode "query='What is the capital of China?'" +``` + +通过使用相同的 `sessionid`，你可以进行连续的多轮对话测试。 + +如果你想要清理资源，只需要在示例应用根目录执行 `pluto destroy` 命令即可。 + +## 扩展示例应用 + +这个示例应用只是一个简单的聊天机器人，但是你可以根据自己的需求进行扩展，使其更加强大和实用，例如： + +- **增加自定义业务逻辑**：根据特定的业务需求，你可以编写更多的 Lambda 函数，实现更复杂的业务逻辑。 +- **整合更多 AWS 服务**：你可以利用 AWS 提供的丰富服务，例如 Amazon SNS 作为消息队列，实现异步消息处理。 + +- **增强用户体验**：你可以开发一个前端用户界面，让用户能够通过网页或移动应用与聊天机器人互动，而不仅仅是通过 API 请求。 diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts new file mode 100644 index 00000000..ad02bec4 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts @@ -0,0 +1,99 @@ +import { BufferMemory } from "langchain/memory"; +import { PromptTemplate } from "langchain/prompts"; +import { ConversationChain } from "langchain/chains"; +import { + SageMakerEndpoint, + SageMakerLLMContentHandler, +} from "@langchain/community/llms/sagemaker_endpoint"; +import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb"; + +// Replace the following constants with your own values from AWS Console or AWS CLI output. +const SAGEMAKER_ENDPONIT_NAME = "llama-2-7b"; +const DYNAMODB_TABLE_NAME = "chatbot"; +const DYNAMODB_PARTITION_KEY = "Id"; + +class LLama27BHandler implements SageMakerLLMContentHandler { + contentType = "application/json"; + + accepts = "application/json"; + + async transformInput(prompt: string, modelKwargs: Record): Promise { + const payload = { + inputs: prompt, + parameters: modelKwargs, + }; + const stringifiedPayload = JSON.stringify(payload); + return new TextEncoder().encode(stringifiedPayload); + } + + async transformOutput(output: any): Promise { + const response_json = JSON.parse(new TextDecoder("utf-8").decode(output)); + const content: string = response_json[0]["generated_text"] ?? ""; + const answer = content + .substring(content.indexOf("<|assistant|>") + "<|assistant|>".length) + .trim(); + return answer; + } +} + +const model = new SageMakerEndpoint({ + endpointName: SAGEMAKER_ENDPONIT_NAME, + modelKwargs: { + temperature: 0.5, + max_new_tokens: 700, + top_p: 0.9, + }, + endpointKwargs: { + CustomAttributes: "accept_eula=true", + }, + contentHandler: new LLama27BHandler(), + clientOptions: { + // In theory, there's no need to supply the following details as the code will be executed within + // the AWS Lambda environment. However, due to the way SageMakerEndpoint is implemented, it's + // required to specify a region. + region: process.env["AWS_REGION"], + // credentials: { + // accessKeyId: "YOUR AWS ACCESS ID", + // secretAccessKey: "YOUR AWS SECRET ACCESS KEY", + // }, + }, +}); + +async function handler(event, context) { + const queries = event.queryStringParameters ?? {}; + const sessionId = Array.isArray(queries["sessionid"]) + ? queries["sessionid"][0] + : queries["sessionid"]; + const query = Array.isArray(queries["query"]) ? queries["query"][0] : queries["query"]; + if (!sessionId || !query) { + return { + statusCode: 400, + body: "Both sessionid and query are required.", + }; + } + + const memory = new BufferMemory({ + chatHistory: new DynamoDBChatMessageHistory({ + tableName: DYNAMODB_TABLE_NAME, + partitionKey: DYNAMODB_PARTITION_KEY, + sessionId: sessionId, + }), + }); + + const promptTemplate = PromptTemplate.fromTemplate(`<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>`); + + const llmChain = new ConversationChain({ llm: model, memory: memory, prompt: promptTemplate }); + + const result = await llmChain.invoke({ query }); + return { + statusCode: 200, + body: result["response"], + }; +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts new file mode 100644 index 00000000..4801269c --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts @@ -0,0 +1,203 @@ +import * as aws from "@pulumi/aws"; +import * as pulumi from "@pulumi/pulumi"; + +// Initialize Pulumi configuration +const config = new pulumi.Config(); +const stageName = config.require("stageName"); +const codeDir = config.require("codeDir"); +const huggingFaceToken = config.requireSecret("huggingFaceToken"); // Use Pulumi's secrets for sensitive data + +// Lambda IAM role policy document +const lambdaPolicyDoc = aws.iam.getPolicyDocumentOutput({ + statements: [ + { + actions: ["sts:AssumeRole"], + effect: "Allow", + principals: [ + { + identifiers: ["lambda.amazonaws.com"], + type: "Service", + }, + ], + }, + ], +}); + +// Lambda IAM role +const lambdaRole = new aws.iam.Role("lambda-role", { + assumeRolePolicy: lambdaPolicyDoc.json, +}); + +// Lambda function +const lambdaFunction = new aws.lambda.Function("lambda-function", { + code: new pulumi.asset.FileArchive(codeDir), + role: lambdaRole.arn, + handler: "main.handler", + runtime: aws.lambda.Runtime.NodeJS18dX, + environment: { + variables: {}, + }, + timeout: 600, // 10 minutes +}); + +// API Gateway +const api = new aws.apigatewayv2.Api("api", { + protocolType: "HTTP", +}); + +// API Gateway integration +const integration = new aws.apigatewayv2.Integration("integration", { + apiId: api.id, + integrationType: "AWS_PROXY", + integrationMethod: "POST", + integrationUri: lambdaFunction.invokeArn, + timeoutMilliseconds: 30000, +}); + +// API Gateway route +const route = new aws.apigatewayv2.Route("route", { + apiId: api.id, + routeKey: "POST /hello", + target: pulumi.interpolate`integrations/${integration.id}`, +}); + +// API Gateway permissions for Lambda +const permission = new aws.lambda.Permission("api-lambda-permission", { + action: "lambda:InvokeFunction", + function: lambdaFunction.name, + principal: "apigateway.amazonaws.com", + sourceArn: pulumi.interpolate`${api.executionArn}/*`, +}); + +// API Gateway deployment +const deployment = new aws.apigatewayv2.Deployment( + "deployment", + { + apiId: api.id, + }, + { dependsOn: [route] } +); + +// API Gateway stage +const stage = new aws.apigatewayv2.Stage( + "api-stage", + { + apiId: api.id, + deploymentId: deployment.id, + name: stageName, + accessLogSettings: { + destinationArn: pulumi.interpolate`${ + new aws.cloudwatch.LogGroup("api-loggroup", { + retentionInDays: 7, + }).arn + }`, + format: `$context.identity.sourceIp - - [$context.requestTime] "$context.httpMethod $context.routeKey $context.protocol" $context.status $context.responseLength $context.requestId $context.integrationErrorMessage $context.error.messageString $context.error.responseType`, + }, + }, // Depends on deployment to ensure the API stage is created after deployment + { dependsOn: [deployment] } +); + +// SageMaker execution role +const sageMakerExecutionRole = new aws.iam.Role("sagemaker-role", { + assumeRolePolicy: JSON.stringify({ + Version: "2012-10-17", + Statement: [ + { + Action: "sts:AssumeRole", + Effect: "Allow", + Principal: { + Service: "sagemaker.amazonaws.com", + }, + }, + ], + }), +}); + +// SageMaker policy attachment +new aws.iam.RolePolicyAttachment("sagemaker-policy-attachment", { + role: sageMakerExecutionRole.name, + policyArn: "arn:aws:iam::aws:policy/AmazonSageMakerFullAccess", +}); + +// SageMaker model +const sagemakerModel = new aws.sagemaker.Model("sagemaker-model", { + executionRoleArn: sageMakerExecutionRole.arn, + primaryContainer: { + image: + "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:pytorch-1.7.1-transformers-4.6.1-gpu-py36-cu110-ubuntu18.04", + environment: { + HF_MODEL_ID: "meta-llama/Llama-2-7b-chat-hf", + HF_TASK: "text-classification", + HUGGING_FACE_HUB_TOKEN: huggingFaceToken, + }, + }, +}); + +// SageMaker endpoint configuration +const endpointConfig = new aws.sagemaker.EndpointConfiguration("sagemaker-endpoint-config", { + productionVariants: [ + { + instanceType: "ml.g5.2xlarge", + modelName: sagemakerModel.name, + initialInstanceCount: 1, + variantName: "AllTraffic", + }, + ], +}); + +// SageMaker endpoint +const sagemakerEndpoint = new aws.sagemaker.Endpoint("sagemaker-endpoint", { + endpointConfigName: endpointConfig.name, +}); + +// DynamoDB table +const dynamoDbTable = new aws.dynamodb.Table("dynamodb-table", { + attributes: [ + { + name: "Id", + type: "S", + }, + ], + hashKey: "Id", + billingMode: "PAY_PER_REQUEST", + readCapacity: 10, + writeCapacity: 10, +}); + +// IAM policy for Lambda function to access DynamoDB and SageMaker +const policyDocument = aws.iam.getPolicyDocumentOutput({ + statements: [ + { + actions: ["dynamodb:*"], + effect: "Allow", + resources: [dynamoDbTable.arn], + }, + { + actions: ["sagemaker:InvokeEndpoint"], + effect: "Allow", + resources: [sagemakerEndpoint.arn], + }, + ], +}); + +// IAM policy +const lambdaAccessPolicy = new aws.iam.Policy("lambda-access-policy", { + path: "/", + description: "Policy for Lambda Function to access resources", + policy: policyDocument.json, +}); + +// Attach the IAM policy to Lambda role +new aws.iam.RolePolicyAttachment("lambda-policy-attachment", { + role: lambdaRole.name, + policyArn: lambdaAccessPolicy.arn, +}); + +// Export the API endpoint URL +export const apiEndpointUrl = api.apiEndpoint; + +// Export the SageMaker endpoint name +export const sagemakerEndpointName = sagemakerEndpoint.name; + +// Export the DynamoDB table name +export const dynamoDbTableName = dynamoDbTable.name; diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf b/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf new file mode 100644 index 00000000..114d1eec --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf @@ -0,0 +1,263 @@ +terraform { + required_version = ">= 0.12" + + required_providers { + aws = { + source = "hashicorp/aws" + version = "~> 3.0" + } + } +} + +provider "aws" { + region = "us-east-1" +} + +# Initialize Terraform variables +variable "stageName" { + type = string + description = "The name of the stage for the API Gateway." +} + +variable "codeDir" { + type = string + description = "The directory where the Lambda function code is located." +} + +variable "huggingFaceToken" { + type = string + description = "Secret token for Hugging Face API." + sensitive = true +} + +# Use locals for common tags +locals { + common_tags = { + ManagedBy = "Terraform" + } +} + +# Lambda IAM role policy document +data "aws_iam_policy_document" "lambda_assume_role_policy" { + statement { + actions = ["sts:AssumeRole"] + effect = "Allow" + principals { + type = "Service" + identifiers = ["lambda.amazonaws.com"] + } + } +} + +# Lambda IAM role +resource "aws_iam_role" "lambda_role" { + name = "lambda-role" + assume_role_policy = data.aws_iam_policy_document.lambda_assume_role_policy.json + tags = local.common_tags +} + +# Lambda function +resource "aws_lambda_function" "lambda_function" { + function_name = "lambda-function" + handler = "main.handler" + role = aws_iam_role.lambda_role.arn + runtime = "nodejs18.x" + + source_code_hash = filebase64sha256("${var.codeDir}/lambda.zip") + filename = "${var.codeDir}/lambda.zip" + + environment { + variables = { + HUGGING_FACE_TOKEN = var.huggingFaceToken + } + } + + timeout = 600 # 10 minutes + tags = local.common_tags +} + +# API Gateway +resource "aws_apigatewayv2_api" "api" { + name = "api" + protocol_type = "HTTP" + tags = local.common_tags +} + +# API Gateway integration +resource "aws_apigatewayv2_integration" "integration" { + api_id = aws_apigatewayv2_api.api.id + integration_type = "AWS_PROXY" + integration_method = "POST" + integration_uri = aws_lambda_function.lambda_function.invoke_arn + timeout_milliseconds = 30000 +} + +# API Gateway route +resource "aws_apigatewayv2_route" "route" { + api_id = aws_apigatewayv2_api.api.id + route_key = "POST /hello" + target = "integrations/${aws_apigatewayv2_integration.integration.id}" +} + +# API Gateway permissions for Lambda +resource "aws_lambda_permission" "api_lambda_permission" { + action = "lambda:InvokeFunction" + function_name = aws_lambda_function.lambda_function.function_name + principal = "apigateway.amazonaws.com" + source_arn = "${aws_apigatewayv2_api.api.execution_arn}/*" +} + +# API Gateway deployment +resource "aws_apigatewayv2_deployment" "deployment" { + api_id = aws_apigatewayv2_api.api.id + # A lifecycle block is used to ensure the deployment is re-created when the route changes + lifecycle { + create_before_destroy = true + } + depends_on = [ + aws_apigatewayv2_route.route + ] +} + +# API Gateway stage +resource "aws_apigatewayv2_stage" "stage" { + api_id = aws_apigatewayv2_api.api.id + name = var.stageName + deployment_id = aws_apigatewayv2_deployment.deployment.id + + access_log_settings { + destination_arn = aws_cloudwatch_log_group.api_loggroup.arn + format = "$context.identity.sourceIp - - [$context.requestTime] \"$context.httpMethod $context.routeKey $context.protocol\" $context.status $context.responseLength $context.requestId $context.integrationErrorMessage $context.error.messageString $context.error.responseType" + } + + tags = local.common_tags +} + +# CloudWatch log group for API Gateway +resource "aws_cloudwatch_log_group" "api_loggroup" { + name = "api-loggroup" + retention_in_days = 7 + tags = local.common_tags +} + +# SageMaker execution role +resource "aws_iam_role" "sagemaker_role" { + name = "sagemaker-role" + assume_role_policy = jsonencode({ + Version = "2012-10-17", + Statement = [{ + Action = "sts:AssumeRole", + Effect = "Allow", + Principal = { + Service = "sagemaker.amazonaws.com" + } + }] + }) + tags = local.common_tags +} + +# SageMaker policy attachment +resource "aws_iam_role_policy_attachment" "sagemaker_policy_attachment" { + role = aws_iam_role.sagemaker_role.name + policy_arn = "arn:aws:iam::aws:policy/AmazonSageMakerFullAccess" +} + +# SageMaker model +resource "aws_sagemaker_model" "sagemaker_model" { + name = "sagemaker-model" + execution_role_arn = aws_iam_role.sagemaker_role.arn + + primary_container { + image = "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:pytorch-1.7.1-transformers-4.6.1-gpu-py36-cu110-ubuntu18.04" + environment = { + HF_MODEL_ID = "meta-llama/Llama-2-7b-chat-hf" + HF_TASK = "text-classification" + // Sensitive data is referenced through variables and marked as sensitive + HUGGING_FACE_HUB_TOKEN = var.huggingFaceToken + } + } + tags = local.common_tags +} + +# SageMaker endpoint configuration +resource "aws_sagemaker_endpoint_configuration" "endpoint_config" { + name = "sagemaker-endpoint-config" + + production_variants { + instance_type = "ml.g5.2xlarge" + model_name = aws_sagemaker_model.sagemaker_model.name + initial_instance_count = 1 + variant_name = "AllTraffic" + } + tags = local.common_tags +} + +# SageMaker endpoint +resource "aws_sagemaker_endpoint" "sagemaker_endpoint" { + name = "sagemaker-endpoint" + endpoint_config_name = aws_sagemaker_endpoint_configuration.endpoint_config.name + tags = local.common_tags +} + +# DynamoDB table +resource "aws_dynamodb_table" "dynamodb_table" { + name = "dynamodb-table" + billing_mode = "PAY_PER_REQUEST" + hash_key = "Id" + + attribute { + name = "Id" + type = "S" + } + tags = local.common_tags +} + +# IAM policy for Lambda function to access DynamoDB and SageMaker +data "aws_iam_policy_document" "lambda_access_policy_doc" { + statement { + actions = [ + "dynamodb:GetItem", + "dynamodb:PutItem", + "dynamodb:UpdateItem" + ] + effect = "Allow" + resources = [aws_dynamodb_table.dynamodb_table.arn] + } + + statement { + actions = ["sagemaker:InvokeEndpoint"] + effect = "Allow" + resources = [aws_sagemaker_endpoint.sagemaker_endpoint.arn] + } +} + +# IAM policy +resource "aws_iam_policy" "lambda_access_policy" { + name = "lambda-access-policy" + policy = data.aws_iam_policy_document.lambda_access_policy_doc.json + description = "IAM policy for Lambda to access DynamoDB and SageMaker" + tags = local.common_tags +} + +# Attach the IAM policy to Lambda role +resource "aws_iam_role_policy_attachment" "lambda_policy_attachment" { + role = aws_iam_role.lambda_role.name + policy_arn = aws_iam_policy.lambda_access_policy.arn +} + +# Outputs +output "api_endpoint_url" { + value = aws_apigatewayv2_api.api.api_endpoint + description = "The URL of the API endpoint." +} + +output "sagemaker_endpoint_name" { + value = aws_sagemaker_endpoint.sagemaker_endpoint.name + description = "The name of the SageMaker endpoint." +} + +output "dynamodb_table_name" { + value = aws_dynamodb_table.dynamodb_table.name + description = "The name of the DynamoDB table." +} + diff --git a/examples/langchain-llama2-chatbot-sagemaker/package.json b/examples/langchain-llama2-chatbot-sagemaker/package.json new file mode 100644 index 00000000..597e5d37 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/package.json @@ -0,0 +1,23 @@ +{ + "name": "langchain-llama2-chatbot-sagemaker", + "version": "0.0.0", + "scripts": { + "test:dev": "pluto test --sim", + "test:prod": "pluto test", + "deploy": "pluto deploy", + "destroy": "pluto destroy" + }, + "dependencies": { + "@langchain/community": "^0.0.33", + "@plutolang/pluto": "latest", + "langchain": "^0.1.23" + }, + "devDependencies": { + "@plutolang/base": "latest", + "@plutolang/pluto-infra": "latest", + "@pulumi/pulumi": "^3.88.0", + "@types/node": "^20", + "typescript": "^5.2.2" + }, + "main": "dist/index.js" +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/src/index.ts b/examples/langchain-llama2-chatbot-sagemaker/src/index.ts new file mode 100644 index 00000000..1f882868 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/src/index.ts @@ -0,0 +1,182 @@ +import { SageMaker, KVStore, Router, HttpRequest, HttpResponse } from "@plutolang/pluto"; +import { BufferMemory } from "langchain/memory"; +import { PromptTemplate } from "langchain/prompts"; +import { ConversationChain } from "langchain/chains"; +import { + SageMakerEndpoint, + SageMakerLLMContentHandler, +} from "@langchain/community/llms/sagemaker_endpoint"; +import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb"; + +/** + * Deploy the Llama2 model on AWS SageMaker using the Hugging Face Text Generation Inference (TGI) + * container. Here will deploy the TinyLlama-1.1B-Chat-v1.0 model, which can be run on the + * ml.m5.xlarge instance. + * + * Below is a set up minimum requirements for each model size of Llama2 model: + * ``` + * Model Instance Type Quantization # of GPUs per replica + * Llama 7B ml.g5.2xlarge - 1 + * Llama 13B ml.g5.12xlarge - 4 + * Llama 70B ml.g5.48xlarge bitsandbytes 8 + * Llama 70B ml.p4d.24xlarge - 8 + * ``` + * The initial limit set for these instances is zero. If you need more, you can request an increase + * in quota via the [AWS Management Console](https://console.aws.amazon.com/servicequotas/home). + */ +const sagemaker = new SageMaker( + "llama-2-7b", + "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.1.1-tgi1.4.0-gpu-py310-cu121-ubuntu20.04", + { + // instanceType: "ml.m5.xlarge", + instanceType: "ml.g5.2xlarge", + envs: { + HF_MODEL_ID: "meta-llama/Llama-2-7b-chat-hf", + // HF_MODEL_ID: "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + HF_TASK: "text-generation", + // If you want to deploy the Meta Llama2 model, you need to request a permission and prepare the + // token. You can get the token from https://huggingface.co/settings/tokens + HUGGING_FACE_HUB_TOKEN: "hf_EmXPwpnyHoNrxxxxxxxxx", + }, + } +); + +const router = new Router("chatbot"); +const conversions = new KVStore("conversions"); + +/** + * TODO: Given the constraints of the current version of Deducer, we have to place the following + * code within the separated function. Once we've upgraded Deducer, it'll be necessary to move this + * code outside of the function. + */ +async function createSageMakerModel() { + // Custom for whatever model you'll be using + class LLama27BHandler implements SageMakerLLMContentHandler { + contentType = "application/json"; + + accepts = "application/json"; + + async transformInput(prompt: string, modelKwargs: Record): Promise { + const payload = { + inputs: prompt, + parameters: modelKwargs, + }; + const stringifiedPayload = JSON.stringify(payload); + return new TextEncoder().encode(stringifiedPayload); + } + + async transformOutput(output: any): Promise { + const response_json = JSON.parse(new TextDecoder("utf-8").decode(output)); + const content: string = response_json[0]["generated_text"] ?? ""; + const answer = content + .substring(content.indexOf("<|assistant|>") + "<|assistant|>".length) + .trim(); + return answer; + } + } + + return new SageMakerEndpoint({ + endpointName: sagemaker.endpointName, + modelKwargs: { + temperature: 0.5, + max_new_tokens: 700, + top_p: 0.9, + }, + endpointKwargs: { + CustomAttributes: "accept_eula=true", + }, + contentHandler: new LLama27BHandler(), + clientOptions: { + // In theory, there's no need to supply the following details as the code will be executed within + // the AWS Lambda environment. However, due to the way SageMakerEndpoint is implemented, it's + // required to specify a region. + region: process.env["AWS_REGION"], + // credentials: { + // accessKeyId: "YOUR AWS ACCESS ID", + // secretAccessKey: "YOUR AWS SECRET ACCESS KEY", + // }, + }, + }); + + // TODO: Use the following statement to help the deducer identify the right relationship between + // Lambda and SageMaker. This will be used to grant permission for the Lambda instance to call + // upon the SageMaker endpoint. This code should be removed after the deducer supports the + // analysis of libraries. + // TODO: bug: only asynchrous function can be successfully analyzed by deducer. + await sagemaker.invoke({}); +} + +/** + * The ApiGateway comes with a built-in 30-second timeout limit, which unfortunately, can't be + * increased. This means if the generation process takes longer than this half-minute window, we'll + * end up getting hit with a 503 Service Unavailable error. Consequently, if your instance can't + * meet the requirements of the Llama2 model, you'll encounter a 503 error. To avoid this, you can + * directly use the Lambda function to handle the requests. + * + * For more details, check out: + * https://docs.aws.amazon.com/apigateway/latest/developerguide/limits.html + * + * You can send a POST HTTP request to the Lambda function using curl or Postman. The request body + * needs to be set as an array representing the function's arguments. Here's an example of a curl + * request: + * ```sh + * curl -X POST https://.lambda-url..on.aws/ \ + * -H "Content-Type: application/json" \ + * -d '["sessionid", "What is the capital of France?"]' + * ``` + * If you get an error message such as `{"code":400,"body":"Payload should be an array."}`, you can + * add a query parameter, such as `?n=1`, to the URL to resolve it. I don't know why it turns into a + * GET request when I don't include the query parameter, even though the curl log indicates it's a + * POST request. If you know the reason, please let me know. + */ + +/** + * The following code is creating a chain for the chatbot task. It can answer the user-provided question. + */ +router.get("/chat", async (req: HttpRequest): Promise => { + const sessionId = Array.isArray(req.query["sessionid"]) + ? req.query["sessionid"][0] + : req.query["sessionid"]; + const query = Array.isArray(req.query["query"]) ? req.query["query"][0] : req.query["query"]; + if (!sessionId || !query) { + return { + statusCode: 400, + body: "Both sessionid and query are required.", + }; + } + + const result = await chat(sessionId, query); + return { + statusCode: 200, + body: result, + }; +}); + +async function chat(sessionId: string, query: string) { + const memory = new BufferMemory({ + chatHistory: new DynamoDBChatMessageHistory({ + tableName: conversions.awsTableName!, + partitionKey: conversions.awsPartitionKey!, + sessionId: sessionId, + }), + }); + + const model = await createSageMakerModel(); + + const promptTemplate = PromptTemplate.fromTemplate(`<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>`); + + const llmChain = new ConversationChain({ llm: model, memory: memory, prompt: promptTemplate }); + + const result = await llmChain.invoke({ query }); + return result["response"]; + + await conversions.get("placeholder"); + await conversions.set("placeholder", "placeholder"); +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/tsconfig.json b/examples/langchain-llama2-chatbot-sagemaker/tsconfig.json new file mode 100644 index 00000000..957dddad --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/tsconfig.json @@ -0,0 +1,35 @@ +{ + "compilerOptions": { + "rootDir": "./src", + "outDir": "./dist", + "alwaysStrict": true, + "declaration": true, + "esModuleInterop": true, + "experimentalDecorators": true, + "inlineSourceMap": true, + "inlineSources": true, + "lib": [ + "es2019" + ], + "module": "CommonJS", + "noEmitOnError": false, + "noFallthroughCasesInSwitch": true, + "noImplicitAny": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "resolveJsonModule": true, + "strict": true, + "strictNullChecks": true, + "strictPropertyInitialization": true, + "stripInternal": true, + "target": "ES2019" + }, + "include": [ + "src/**/*.ts" + ], + "exclude": [ + "node_modules" + ] +} From daa6ef920bc38f3e12a1dee8481b97b4349ce3e1 Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Fri, 22 Mar 2024 17:19:18 +0800 Subject: [PATCH 5/8] chore: add changeset --- .changeset/brave-countries-build.md | 5 +++++ .changeset/breezy-cats-mate.md | 5 +++++ .changeset/config.json | 3 ++- .changeset/tough-swans-behave.md | 5 +++++ 4 files changed, 17 insertions(+), 1 deletion(-) create mode 100644 .changeset/brave-countries-build.md create mode 100644 .changeset/breezy-cats-mate.md create mode 100644 .changeset/tough-swans-behave.md diff --git a/.changeset/brave-countries-build.md b/.changeset/brave-countries-build.md new file mode 100644 index 00000000..04b72212 --- /dev/null +++ b/.changeset/brave-countries-build.md @@ -0,0 +1,5 @@ +--- +"@plutolang/pluto-infra": patch +--- + +enhance(sdk): remove the fuzzyArn, instead use the lazy value from pulumi. diff --git a/.changeset/breezy-cats-mate.md b/.changeset/breezy-cats-mate.md new file mode 100644 index 00000000..a5af5cc8 --- /dev/null +++ b/.changeset/breezy-cats-mate.md @@ -0,0 +1,5 @@ +--- +"@plutolang/pluto": patch +--- + +feat(sdk): export the table name and partition key of the created dynamodb instance to users diff --git a/.changeset/config.json b/.changeset/config.json index 4d1fa8c6..0eb6f376 100644 --- a/.changeset/config.json +++ b/.changeset/config.json @@ -15,6 +15,7 @@ "quickstart", "gpt2-hf-sagemaker", "langchain-llama2-sagemaker", - "quickstart-python" + "quickstart-python", + "langchain-llama2-chatbot-sagemaker" ] } diff --git a/.changeset/tough-swans-behave.md b/.changeset/tough-swans-behave.md new file mode 100644 index 00000000..9aadac30 --- /dev/null +++ b/.changeset/tough-swans-behave.md @@ -0,0 +1,5 @@ +--- +"@plutolang/static-deducer": patch +--- + +fix(deducer): incorrectly generate duplicate client statements when one code section accesses a single resource multiple times From d41136ec69c273789ba0b27a31f8d6c1c05ee43a Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Fri, 22 Mar 2024 17:35:13 +0800 Subject: [PATCH 6/8] fix(ci): missing python environment --- .github/workflows/release.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index dd7cb3fe..05ef487d 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -29,6 +29,10 @@ jobs: with: node-version: 20.x + - uses: actions/setup-python@v5 + with: + python-version: "3.10" + - name: Setup PNPM uses: pnpm/action-setup@v2 with: @@ -38,7 +42,7 @@ jobs: run: bash scripts/prepare.sh - name: Install Dependencies - run: pnpm install + run: pnpm install && pip install -r requirements.txt - name: Creating .npmrc run: | From 80591c8f339073208e8cb606aa65485b60f4c631 Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Mon, 25 Mar 2024 17:40:17 +0800 Subject: [PATCH 7/8] chore: include more comparisons in the langchain-llama2-chatbot-sagemaker example Compare with Pulumi Serverless, Winglang, Lepton, and Serverless. --- .../README.md | 23 +- .../README_zh.md | 24 +- .../comparison/application/main.ts | 33 +- .../comparison/lepton/photon.py | 156 + .../comparison/pulumi-app/Pulumi.dev.yaml | 5 + .../comparison/pulumi-app/Pulumi.yaml | 2 + .../comparison/pulumi-app/app.ts | 96 + .../comparison/pulumi-app/package.json | 17 + .../comparison/pulumi-app/pulumi.ts | 241 + .../comparison/pulumi-app/tsconfig.json | 27 + .../comparison/pulumi/pulumi.ts | 5 +- .../comparison/serverless/.gitignore | 2 + .../comparison/serverless/README.md | 139 + .../comparison/serverless/index.ts | 98 + .../comparison/serverless/package-lock.json | 746 + .../comparison/serverless/package.json | 14 + .../comparison/serverless/serverless.yml | 137 + .../comparison/serverless/tsconfig.json | 27 + .../comparison/terraform/terraform.tf | 3 +- .../comparison/wing-ts/main.ts | 124 + .../comparison/wing-ts/package-lock.json | 14200 ++++++++++++++ .../comparison/wing-ts/package.json | 22 + .../comparison/wing-wing/.gitignore | 3 + .../comparison/wing-wing/langchain.ts | 87 + .../comparison/wing-wing/main.w | 58 + .../comparison/wing-wing/package-lock.json | 15329 ++++++++++++++++ .../comparison/wing-wing/package.json | 14 + pnpm-lock.yaml | 23 +- pnpm-workspace.yaml | 1 + 29 files changed, 31617 insertions(+), 39 deletions(-) create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/lepton/photon.py create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.dev.yaml create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.yaml create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/app.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/package.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/pulumi.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/tsconfig.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/.gitignore create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/README.md create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/index.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/package-lock.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/package.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/serverless.yml create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/tsconfig.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/main.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/package-lock.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/package.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/.gitignore create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/langchain.ts create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/main.w create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package-lock.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package.json diff --git a/examples/langchain-llama2-chatbot-sagemaker/README.md b/examples/langchain-llama2-chatbot-sagemaker/README.md index 08731639..6edad7fe 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/README.md +++ b/examples/langchain-llama2-chatbot-sagemaker/README.md @@ -21,14 +21,21 @@ As illustrated above, our example application relies on the following AWS servic The table below shows a comparison between Pluto and other tools regarding the amount and type of code: -| | Code Type | Lines of Code | -| ----------- | ---------------------------------- | ------------- | -| Application | Business Logic | 79 | -| Pulumi | Infrastructure Configuration | 159 | -| Terraform | Infrastructure Configuration | 201 | -| **Pluto** | **Business Logic + Configuration** | **87** | - -Using Pluto, you can integrate business logic and infrastructure configuration with only an 8-line increase compared to pure business logic code, significantly reducing the amount of code. When building with Pulumi or Terraform, the total lines of code for business logic plus infrastructure configuration reach 238 and 280 lines, respectively, with Pluto's code amounting to only about a third of that. +| | Code Type | Lines of Code | Code Location | +| ------------------------------------------------------- | ---------------------------------- | -------------------- | -------------------------------- | +| Application | Business Logic | 83(.ts) | [Code](./comparison/application) | +| [Terraform](https://www.terraform.io/) | Infrastructure Configuration | 201(.tf) | [Code](./comparison/terraform) | +| [Pulumi](https://www.pulumi.com/) | Infrastructure Configuration | 157(.ts) | [Code](./comparison/pulumi) | +| [Pulumi Serverless](https://www.pulumi.com/serverless/) | Business Logic + Configuration | 256(.ts) | [Code](./comparison/pulumi-app) | +| [Winglang - TS](https://github.com/winglang/wing) | Business Logic + Configuration | 100(.ts) | [Code](./comparison/wing-ts) | +| [Winglang - Wing](https://github.com/winglang/wing) | Business Logic + Configuration | 71(.w) + 47(.ts) | [Code](./comparison/wing-wing) | +| [Lepton](https://www.lepton.ai/) | Business Logic + Configuration | 116(.py) | [Code](./comparison/lepton) | +| [Serverless](https://github.com/serverless/serverless) | Business Logic + Configuration | 79(.ts) + 120(.yaml) | [Code](./comparison/serverless) | +| **Pluto** | **Business Logic + Configuration** | **100(.ts)** | [Code](./src) | + +By examining the table data, it's evident that Pluto adds 17 lines of code in contrast to pure business logic code. This includes code related to cloud resource creation and permission configuration. When infrastructure setup is handled using provisioning tools like Pulumi or Terraform, the combined code for business logic and infrastructure configuration exceeds twice the size of Pluto's code. Compared to tools such as Pulumi's Serverless feature, Winglang, Lepton, and Serverless, which streamline cloud usage, Pluto demands lower cloud and AI background knowledge and entails less code. + +While using other tools, we faced several issues that ultimately hindered the deployment of this code. For instance, Winglang lacks support for SageMaker Model and EndpointConfig resource types, Pulumi runtime errors, and intricate resource configuration. If you're interested, feel free to lend a hand with the fixes. ## Deployment Steps diff --git a/examples/langchain-llama2-chatbot-sagemaker/README_zh.md b/examples/langchain-llama2-chatbot-sagemaker/README_zh.md index ea370496..3c7c772a 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/README_zh.md +++ b/examples/langchain-llama2-chatbot-sagemaker/README_zh.md @@ -19,16 +19,23 @@ ## 比较 -以下表格展示了 Pluto 与其他工具在代码量和类型上的对比： +我们尝试使用其他工具构建相同的示例应用，以下表格展示了 Pluto 与其他工具在代码量和类型上的对比： -| | 代码类型 | 代码量/行 | -| --------- | ------------------- | --------- | -| 应用程序 | 业务逻辑 | 79 | -| Pulumi | 基础设施配置 | 159 | -| Terraform | 基础设施配置 | 201 | -| **Pluto** | **业务逻辑 + 配置** | **87** | +| | 代码类型 | 代码量/行 | 代码地址 | +| ------------------------------------------------------- | ------------------- | -------------------- | -------------------------------- | +| 应用程序 | 业务逻辑 | 83(.ts) | [源码](./comparison/application) | +| [Terraform](https://www.terraform.io/) | 基础设施配置 | 201(.tf) | [源码](./comparison/terraform) | +| [Pulumi](https://www.pulumi.com/) | 基础设施配置 | 157(.ts) | [源码](./comparison/pulumi) | +| [Pulumi Serverless](https://www.pulumi.com/serverless/) | 业务逻辑 + 配置 | 256(.ts) | [源码](./comparison/pulumi-app) | +| [Winglang - TS](https://github.com/winglang/wing) | 业务逻辑 + 配置 | 100(.ts) | [源码](./comparison/wing-ts) | +| [Winglang - Wing](https://github.com/winglang/wing) | 业务逻辑 + 配置 | 71(.w) + 47(.ts) | [源码](./comparison/wing-wing) | +| [Lepton](https://www.lepton.ai/) | 业务逻辑 + 配置 | 116(.py) | [源码](./comparison/lepton) | +| [Serverless](https://github.com/serverless/serverless) | 业务逻辑 + 配置 | 79(.ts) + 120(.yaml) | [源码](./comparison/serverless) | +| **Pluto** | **业务逻辑 + 配置** | **100(.ts)** | [源码](./src) | -使用 Pluto 可以整合业务逻辑和基础设施配置，与纯业务逻辑代码相比，仅增加了 8 行代码，显著减少代码量。而采用 Pulumi 或 Terraform 构建时，业务逻辑加基础设施配置的整体代码量分别达到 238 行和 280 行，Pluto 的代码量仅为其约三分之一。 +通过对比表格数据可以看到，Pluto 与纯业务逻辑代码相比，增加了 17 行代码，包含了云资源创建与权限配置相关代码。如果采用 Pulumi 或 Terraform 等 IaC 工具进行基础设施配置，业务逻辑加基础设施配置的整体代码量均在 Pluto 代码量的两倍以上。与 Pulumi 的 Serverless 功能、Winglang、Lepton、Serverless 等简化云使用的工具相比，Pluto 对云、AI 背景知识的要求更低，所需代码量也更少。 + +我们在使用其他工具构建的过程中，遇到了许多问题，导致这些代码最终并没有部署成功，例如，Winglang 没有提供 SageMaker Model、EndpointConfig 资源类型，Pulumi 运行时报错，繁琐的资源项配置等，如果你有兴趣欢迎进行修复。 ## 部署步骤 @@ -70,5 +77,4 @@ time curl --get "$CHATBOT_API_URL/chat" \ - **增加自定义业务逻辑**：根据特定的业务需求，你可以编写更多的 Lambda 函数，实现更复杂的业务逻辑。 - **整合更多 AWS 服务**：你可以利用 AWS 提供的丰富服务，例如 Amazon SNS 作为消息队列，实现异步消息处理。 - - **增强用户体验**：你可以开发一个前端用户界面，让用户能够通过网页或移动应用与聊天机器人互动，而不仅仅是通过 API 请求。 diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts index ad02bec4..02ec1677 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/application/main.ts @@ -59,19 +59,7 @@ const model = new SageMakerEndpoint({ }, }); -async function handler(event, context) { - const queries = event.queryStringParameters ?? {}; - const sessionId = Array.isArray(queries["sessionid"]) - ? queries["sessionid"][0] - : queries["sessionid"]; - const query = Array.isArray(queries["query"]) ? queries["query"][0] : queries["query"]; - if (!sessionId || !query) { - return { - statusCode: 400, - body: "Both sessionid and query are required.", - }; - } - +async function chat(sessionId: string, query: string) { const memory = new BufferMemory({ chatHistory: new DynamoDBChatMessageHistory({ tableName: DYNAMODB_TABLE_NAME, @@ -92,8 +80,25 @@ Context: const llmChain = new ConversationChain({ llm: model, memory: memory, prompt: promptTemplate }); const result = await llmChain.invoke({ query }); + return result["response"]; +} + +async function handler(event, context) { + const queries = event.queryStringParameters ?? {}; + const sessionId = Array.isArray(queries["sessionid"]) + ? queries["sessionid"][0] + : queries["sessionid"]; + const query = Array.isArray(queries["query"]) ? queries["query"][0] : queries["query"]; + if (!sessionId || !query) { + return { + statusCode: 400, + body: "Both sessionid and query are required.", + }; + } + + const result = await chat(sessionId, query); return { statusCode: 200, - body: result["response"], + body: result, }; } diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/lepton/photon.py b/examples/langchain-llama2-chatbot-sagemaker/comparison/lepton/photon.py new file mode 100644 index 00000000..148efe37 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/lepton/photon.py @@ -0,0 +1,156 @@ +# Not working. Related issue: https://github.com/leptonai/leptonai/issues/354 +import os +import json +from typing import List, Optional, Any + +import torch +from transformers import pipeline, Pipeline + +from leptonai.kv import KV +from leptonai.photon import Photon +from langchain_core.chat_history import BaseChatMessageHistory + +from langchain_core.messages import ( + BaseMessage, + message_to_dict, + messages_from_dict, + messages_to_dict, +) +from langchain.prompts import PromptTemplate +from langchain_core.language_models import LLM +from langchain.memory.buffer import ConversationBufferMemory +from langchain_core.callbacks import CallbackManagerForLLMRun +from langchain_community.llms.utils import enforce_stop_tokens +from langchain.chains.conversation.base import ConversationChain + + +_KV_NAMESPACE = "lepton-chatbot" + + +class LeptonMessageHistory(BaseChatMessageHistory): + def __init__(self, namespace: str, session_id: str): + self.kv = KV(namespace, create_if_not_exists=True, wait_for_creation=True) + self.session_id = session_id + + def add_message(self, message: BaseMessage) -> None: + messages = messages_to_dict(self.messages) + _message = message_to_dict(message) + messages.append(_message) + self.kv.put(self.session_id, json.dumps(messages)) + + @property + def messages(self) -> List[BaseMessage]: # type: ignore + messages = [] + try: + data = self.kv.get(self.session_id) + messages = json.loads(data) + except: + messages = [] + return messages_from_dict(messages) + + def clear(self): + self.kv.delete(self.session_id) + + +class LeptonLLM(LLM): + + pipeline: Pipeline + + @property + def _llm_type(self): + return "lepton" + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + res = self.pipeline(prompt) + + text = str(self._get_generated_text(res)) + + if stop is not None: + text = enforce_stop_tokens(text, stop) + + return text + + def _get_generated_text(self, res): + if isinstance(res, str): + return res + elif isinstance(res, dict): + return res["generated_text"] + elif isinstance(res, list): + if len(res) == 1: + return self._get_generated_text(res[0]) + else: + return [self._get_generated_text(r) for r in res] + else: + raise ValueError( + f"Unsupported result type in _get_generated_text: {type(res)}" + ) + + +class Llama2(Photon): + requirement_dependency = [ + "git+https://github.com/huggingface/transformers.git@015f8e1", + "accelerate", + "langchain", + ] + + def init(self): + if torch.cuda.is_available(): + device = 0 + else: + device = -1 + + self.pipeline = pipeline( + "text-generation", + model=os.environ.get("MODEL", "meta-llama/Llama-2-7b-chat-hf"), + torch_dtype=torch.float16, + device=device, + ) + + print(self.pipeline is None) + + @Photon.handler( + "chat", + example={ + "session_id": "session_id", + "query": "What is the capital of France?", + }, + ) + def chat(self, session_id: str, query: str): + llm = LeptonLLM(pipeline=self.pipeline) + + memory = ConversationBufferMemory( + chat_memory=LeptonMessageHistory( + namespace=_KV_NAMESPACE, session_id=session_id + ) + ) + + promptTemplate = PromptTemplate.from_template( + """<|system|> + You are a cool and aloof robot, answering questions very briefly and directly. + + Context: + {history} + <|user|> + {query} + <|assistant|>""" + ) + + chain = ConversationChain( + llm=llm, + memory=memory, + prompt=promptTemplate, + ) + + result = chain({"query": query}) + print(result) + + +if __name__ == "__main__": + p = Llama2() + p.launch() diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.dev.yaml b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.dev.yaml new file mode 100644 index 00000000..f818e902 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.dev.yaml @@ -0,0 +1,5 @@ +project: pulumi-chatbot-app +config: + aws:region: us-east-1 + stageName: dev + huggingFaceToken: hf_EmXPwpnyHoNrxxxxxxxxx diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.yaml b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.yaml new file mode 100644 index 00000000..81b08e07 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/Pulumi.yaml @@ -0,0 +1,2 @@ +runtime: nodejs +name: pulumi-chatbot-app diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/app.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/app.ts new file mode 100644 index 00000000..64438f64 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/app.ts @@ -0,0 +1,96 @@ +import * as pulumi from "@pulumi/pulumi"; + +import { BufferMemory } from "langchain/memory"; +import { PromptTemplate } from "langchain/prompts"; +import { ConversationChain } from "langchain/chains"; +import { + SageMakerEndpoint, + SageMakerLLMContentHandler, +} from "@langchain/community/llms/sagemaker_endpoint"; +import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb"; + +const awsConfig = new pulumi.Config("aws"); +const awsRegion = awsConfig.require("region"); + +class LLama27BHandler implements SageMakerLLMContentHandler { + contentType = "application/json"; + + accepts = "application/json"; + + async transformInput(prompt: string, modelKwargs: Record): Promise { + const payload = { + inputs: prompt, + parameters: modelKwargs, + }; + const stringifiedPayload = JSON.stringify(payload); + return new TextEncoder().encode(stringifiedPayload); + } + + async transformOutput(output: any): Promise { + const response_json = JSON.parse(new TextDecoder("utf-8").decode(output)); + const content: string = response_json[0]["generated_text"] ?? ""; + const answer = content + .substring(content.indexOf("<|assistant|>") + "<|assistant|>".length) + .trim(); + return answer; + } +} + +function createModel(endpointName: string) { + return new SageMakerEndpoint({ + endpointName: endpointName, + modelKwargs: { + temperature: 0.5, + max_new_tokens: 700, + top_p: 0.9, + }, + endpointKwargs: { + CustomAttributes: "accept_eula=true", + }, + contentHandler: new LLama27BHandler(), + clientOptions: { + // In theory, there's no need to supply the following details as the code will be executed within + // the AWS Lambda environment. However, due to the way SageMakerEndpoint is implemented, it's + // required to specify a region. + region: awsRegion, + // credentials: { + // accessKeyId: "YOUR AWS ACCESS ID", + // secretAccessKey: "YOUR AWS SECRET ACCESS KEY", + // }, + }, + }); +} + +export async function chat( + endpointName: string, + tableName: string, + partitionKey: string, + sessionId: string, + query: string +) { + const memory = new BufferMemory({ + chatHistory: new DynamoDBChatMessageHistory({ + tableName: tableName, + partitionKey: partitionKey, + sessionId: sessionId, + }), + }); + + const promptTemplate = PromptTemplate.fromTemplate(`<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>`); + + const llmChain = new ConversationChain({ + llm: createModel(endpointName), + memory: memory, + prompt: promptTemplate, + }); + + const result = await llmChain.invoke({ query }); + return result["response"]; +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/package.json b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/package.json new file mode 100644 index 00000000..9568941d --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/package.json @@ -0,0 +1,17 @@ +{ + "name": "pulumi-chatbot-app", + "version": "0.0.1", + "description": "", + "main": "pulumi.ts", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "author": "", + "license": "ISC", + "dependencies": { + "@langchain/community": "^0.0.33", + "@pulumi/aws": "^6.4.1", + "@pulumi/pulumi": "^3.88.0", + "langchain": "^0.1.23" + } +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/pulumi.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/pulumi.ts new file mode 100644 index 00000000..ac82d388 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/pulumi.ts @@ -0,0 +1,241 @@ +/** + * Not working. The following error is thrown during runtime: + * ``` + * { + * "errorType": "Runtime.UserCodeSyntaxError", + * "errorMessage": "SyntaxError: Identifier 'exports' has already been declared", + * "stack": [ + * "Runtime.UserCodeSyntaxError: SyntaxError: Identifier 'exports' has already been declared", + * " at _loadUserApp (file:///var/runtime/index.mjs:1084:17)", + * " at async UserFunction.js.module.exports.load (file:///var/runtime/index.mjs:1119:21)", + * " at async start (file:///var/runtime/index.mjs:1282:23)", + * " at async file:///var/runtime/index.mjs:1288:1" + * ] + * } + * ``` + */ +import * as aws from "@pulumi/aws"; +import * as pulumi from "@pulumi/pulumi"; + +// Initialize Pulumi configuration +const config = new pulumi.Config(); +const stageName = config.require("stageName"); +const huggingFaceToken = config.requireSecret("huggingFaceToken"); // Use Pulumi's secrets for sensitive data + +const PARTITION_KEY = "Id"; + +// Lambda IAM role policy document +const lambdaPolicyDoc = aws.iam.getPolicyDocumentOutput({ + statements: [ + { + actions: ["sts:AssumeRole"], + effect: "Allow", + principals: [ + { + identifiers: ["lambda.amazonaws.com"], + type: "Service", + }, + ], + }, + ], +}); + +// Lambda IAM role +const lambdaRole = new aws.iam.Role("lambda-role", { + assumeRolePolicy: lambdaPolicyDoc.json, +}); + +const lambdaFunction = new aws.lambda.CallbackFunction("lambda-function", { + callback: async (event: any) => { + const queries = event.queryStringParameters ?? {}; + const sessionId = Array.isArray(queries["sessionid"]) + ? queries["sessionid"][0] + : queries["sessionid"]; + const query = Array.isArray(queries["query"]) ? queries["query"][0] : queries["query"]; + if (!sessionId || !query) { + return { + statusCode: 400, + body: "Both sessionid and query are required.", + }; + } + + const combinedArgs = pulumi.all([sagemakerEndpoint.name, dynamoDbTable.name]); + return combinedArgs.apply(async ([endpointName, tableName]) => { + const chat = (await import("./app")).chat; + const result = chat(endpointName, tableName, PARTITION_KEY, sessionId, query); + return { + statusCode: 200, + body: result, + }; + }); + }, + role: lambdaRole.arn, + runtime: aws.lambda.Runtime.NodeJS18dX, + timeout: 600, +}); + +// API Gateway +const api = new aws.apigatewayv2.Api("api", { + protocolType: "HTTP", +}); + +// API Gateway integration +const integration = new aws.apigatewayv2.Integration("integration", { + apiId: api.id, + integrationType: "AWS_PROXY", + integrationMethod: "POST", + integrationUri: lambdaFunction.invokeArn, + timeoutMilliseconds: 30000, +}); + +// API Gateway route +const route = new aws.apigatewayv2.Route("route", { + apiId: api.id, + routeKey: "GET /chat", + target: pulumi.interpolate`integrations/${integration.id}`, +}); + +// API Gateway permissions for Lambda +new aws.lambda.Permission("api-lambda-permission", { + action: "lambda:InvokeFunction", + function: lambdaFunction.name, + principal: "apigateway.amazonaws.com", + sourceArn: pulumi.interpolate`${api.executionArn}/*`, +}); + +// API Gateway deployment +const deployment = new aws.apigatewayv2.Deployment( + "deployment", + { + apiId: api.id, + }, + { dependsOn: [route] } +); + +// API Gateway stage +new aws.apigatewayv2.Stage( + "api-stage", + { + apiId: api.id, + deploymentId: deployment.id, + name: stageName, + autoDeploy: true, + accessLogSettings: { + destinationArn: pulumi.interpolate`${ + new aws.cloudwatch.LogGroup("api-loggroup", { + retentionInDays: 7, + }).arn + }`, + format: `$context.identity.sourceIp - - [$context.requestTime] "$context.httpMethod $context.routeKey $context.protocol" $context.status $context.responseLength $context.requestId $context.integrationErrorMessage $context.error.messageString $context.error.responseType`, + }, + }, // Depends on deployment to ensure the API stage is created after deployment + { dependsOn: [deployment] } +); + +// SageMaker execution role +const sageMakerExecutionRole = new aws.iam.Role("sagemaker-role", { + assumeRolePolicy: JSON.stringify({ + Version: "2012-10-17", + Statement: [ + { + Action: "sts:AssumeRole", + Effect: "Allow", + Principal: { + Service: "sagemaker.amazonaws.com", + }, + }, + ], + }), +}); + +// SageMaker policy attachment +new aws.iam.RolePolicyAttachment("sagemaker-policy-attachment", { + role: sageMakerExecutionRole.name, + policyArn: "arn:aws:iam::aws:policy/AmazonSageMakerFullAccess", +}); + +// SageMaker model +const sagemakerModel = new aws.sagemaker.Model("sagemaker-model", { + executionRoleArn: sageMakerExecutionRole.arn, + primaryContainer: { + image: + "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.1.1-tgi1.4.0-gpu-py310-cu121-ubuntu20.04", + environment: { + HF_MODEL_ID: "meta-llama/Llama-2-7b-chat-hf", + HF_TASK: "text-classification", + HUGGING_FACE_HUB_TOKEN: huggingFaceToken, + }, + }, +}); + +// SageMaker endpoint configuration +const endpointConfig = new aws.sagemaker.EndpointConfiguration("sagemaker-endpoint-config", { + productionVariants: [ + { + instanceType: "ml.g5.2xlarge", + modelName: sagemakerModel.name, + initialInstanceCount: 1, + variantName: "AllTraffic", + }, + ], +}); + +// SageMaker endpoint +const sagemakerEndpoint = new aws.sagemaker.Endpoint("sagemaker-endpoint", { + endpointConfigName: endpointConfig.name, +}); + +// DynamoDB table +const dynamoDbTable = new aws.dynamodb.Table("dynamodb-table", { + attributes: [ + { + name: PARTITION_KEY, + type: "S", + }, + ], + hashKey: "Id", + billingMode: "PAY_PER_REQUEST", +}); + +// IAM policy for Lambda function to access DynamoDB and SageMaker +const policyDocument = aws.iam.getPolicyDocumentOutput({ + statements: [ + { + actions: ["dynamodb:*"], + effect: "Allow", + resources: [dynamoDbTable.arn], + }, + { + actions: ["sagemaker:InvokeEndpoint"], + effect: "Allow", + resources: [sagemakerEndpoint.arn], + }, + ], +}); + +// IAM policy +const lambdaAccessPolicy = new aws.iam.Policy("lambda-access-policy", { + path: "/", + description: "Policy for Lambda Function to access resources", + policy: policyDocument.json, +}); + +// Attach the IAM policy to Lambda role +new aws.iam.RolePolicyAttachment("lambda-policy-attachment", { + role: lambdaRole.name, + policyArn: lambdaAccessPolicy.arn, +}); + +new aws.iam.RolePolicyAttachment("cloudwatch-policy-attachment", { + role: lambdaRole.name, + policyArn: "arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole", +}); + +// Export the API endpoint URL +export const apiEndpointUrl = api.apiEndpoint; + +// Export the SageMaker endpoint name +export const sagemakerEndpointName = sagemakerEndpoint.name; + +// Export the DynamoDB table name +export const dynamoDbTableName = dynamoDbTable.name; diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/tsconfig.json b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/tsconfig.json new file mode 100644 index 00000000..597c7c96 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi-app/tsconfig.json @@ -0,0 +1,27 @@ +{ + "compilerOptions": { + "outDir": "./dist", + "alwaysStrict": true, + "declaration": true, + "esModuleInterop": true, + "experimentalDecorators": true, + "inlineSourceMap": true, + "inlineSources": true, + "module": "CommonJS", + "noEmitOnError": false, + "noFallthroughCasesInSwitch": true, + "noImplicitAny": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "resolveJsonModule": true, + "strict": true, + "strictNullChecks": true, + "strictPropertyInitialization": true, + "stripInternal": true, + "target": "ES2019" + }, + "include": ["*.ts"], + "exclude": ["node_modules"] +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts index 4801269c..15738f53 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/pulumi/pulumi.ts @@ -1,3 +1,4 @@ +// Not verified if it'll work. import * as aws from "@pulumi/aws"; import * as pulumi from "@pulumi/pulumi"; @@ -124,7 +125,7 @@ const sagemakerModel = new aws.sagemaker.Model("sagemaker-model", { executionRoleArn: sageMakerExecutionRole.arn, primaryContainer: { image: - "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:pytorch-1.7.1-transformers-4.6.1-gpu-py36-cu110-ubuntu18.04", + "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.1.1-tgi1.4.0-gpu-py310-cu121-ubuntu20.04", environment: { HF_MODEL_ID: "meta-llama/Llama-2-7b-chat-hf", HF_TASK: "text-classification", @@ -160,8 +161,6 @@ const dynamoDbTable = new aws.dynamodb.Table("dynamodb-table", { ], hashKey: "Id", billingMode: "PAY_PER_REQUEST", - readCapacity: 10, - writeCapacity: 10, }); // IAM policy for Lambda function to access DynamoDB and SageMaker diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/.gitignore b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/.gitignore new file mode 100644 index 00000000..2c444806 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/.gitignore @@ -0,0 +1,2 @@ +node_modules +.serverless diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/README.md b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/README.md new file mode 100644 index 00000000..0c3e4513 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/README.md @@ -0,0 +1,139 @@ + + +# Serverless Framework Node Express API on AWS + +This template demonstrates how to develop and deploy a simple Node Express API service, backed by DynamoDB database, running on AWS Lambda using the traditional Serverless Framework. + +## Anatomy of the template + +This template configures a single function, `api`, which is responsible for handling all incoming requests thanks to the `httpApi` event. To learn more about `httpApi` event configuration options, please refer to [httpApi event docs](https://www.serverless.com/framework/docs/providers/aws/events/http-api/). As the event is configured in a way to accept all incoming requests, `express` framework is responsible for routing and handling requests internally. Implementation takes advantage of `serverless-http` package, which allows you to wrap existing `express` applications. To learn more about `serverless-http`, please refer to corresponding [GitHub repository](https://github.com/dougmoscrop/serverless-http). Additionally, it also handles provisioning of a DynamoDB database that is used for storing data about users. The `express` application exposes two endpoints, `POST /users` and `GET /user/{userId}`, which allow to create and retrieve users. + +## Usage + +### Deployment + +Install dependencies with: + +``` +npm install +``` + +and then deploy with: + +``` +serverless deploy +``` + +After running deploy, you should see output similar to: + +```bash +Deploying aws-node-express-dynamodb-api-project to stage dev (us-east-1) + +✔ Service deployed to stack aws-node-express-dynamodb-api-project-dev (196s) + +endpoint: ANY - https://xxxxxxxxxx.execute-api.us-east-1.amazonaws.com +functions: + api: aws-node-express-dynamodb-api-project-dev-api (766 kB) +``` + +_Note_: In current form, after deployment, your API is public and can be invoked by anyone. For production deployments, you might want to configure an authorizer. For details on how to do that, refer to [`httpApi` event docs](https://www.serverless.com/framework/docs/providers/aws/events/http-api/). Additionally, in current configuration, the DynamoDB table will be removed when running `serverless remove`. To retain the DynamoDB table even after removal of the stack, add `DeletionPolicy: Retain` to its resource definition. + +### Invocation + +After successful deployment, you can create a new user by calling the corresponding endpoint: + +```bash +curl --request POST 'https://xxxxxx.execute-api.us-east-1.amazonaws.com/users' --header 'Content-Type: application/json' --data-raw '{"name": "John", "userId": "someUserId"}' +``` + +Which should result in the following response: + +```bash +{"userId":"someUserId","name":"John"} +``` + +You can later retrieve the user by `userId` by calling the following endpoint: + +```bash +curl https://xxxxxxx.execute-api.us-east-1.amazonaws.com/users/someUserId +``` + +Which should result in the following response: + +```bash +{"userId":"someUserId","name":"John"} +``` + +If you try to retrieve user that does not exist, you should receive the following response: + +```bash +{"error":"Could not find user with provided \"userId\""} +``` + +### Local development + +It is also possible to emulate DynamoDB, API Gateway and Lambda locally using the `serverless-dynamodb-local` and `serverless-offline` plugins. In order to do that, run: + +```bash +serverless plugin install -n serverless-dynamodb-local +serverless plugin install -n serverless-offline +``` + +It will add both plugins to `devDependencies` in `package.json` file as well as will add it to `plugins` in `serverless.yml`. Make sure that `serverless-offline` is listed as last plugin in `plugins` section: + +``` +plugins: + - serverless-dynamodb-local + - serverless-offline +``` + +You should also add the following config to `custom` section in `serverless.yml`: + +``` +custom: + (...) + dynamodb: + start: + migrate: true + stages: + - dev +``` + +Additionally, we need to reconfigure `AWS.DynamoDB.DocumentClient` to connect to our local instance of DynamoDB. We can take advantage of `IS_OFFLINE` environment variable set by `serverless-offline` plugin and replace: + +```javascript +const dynamoDbClient = new AWS.DynamoDB.DocumentClient(); +``` + +with the following: + +```javascript +const dynamoDbClientParams = {}; +if (process.env.IS_OFFLINE) { + dynamoDbClientParams.region = "localhost"; + dynamoDbClientParams.endpoint = "http://localhost:8000"; +} +const dynamoDbClient = new AWS.DynamoDB.DocumentClient(dynamoDbClientParams); +``` + +After that, running the following command with start both local API Gateway emulator as well as local instance of emulated DynamoDB: + +```bash +serverless offline start +``` + +To learn more about the capabilities of `serverless-offline` and `serverless-dynamodb-local`, please refer to their corresponding GitHub repositories: + +- https://github.com/dherault/serverless-offline +- https://github.com/99x/serverless-dynamodb-local diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/index.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/index.ts new file mode 100644 index 00000000..a1e50c1b --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/index.ts @@ -0,0 +1,98 @@ +import { BufferMemory } from "langchain/memory"; +import { PromptTemplate } from "langchain/prompts"; +import { ConversationChain } from "langchain/chains"; +import { + SageMakerEndpoint, + SageMakerLLMContentHandler, +} from "@langchain/community/llms/sagemaker_endpoint"; +import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb"; + +const DYNAMODB_PARTITION_KEY = "Id"; +const DYNAMODB_TABLE_NAME = process.env.USERS_TABLE!; +const SAGEMAKER_ENDPONIT_NAME = process.env.ENDPOINT_NAME!; + +class LLama27BHandler implements SageMakerLLMContentHandler { + contentType = "application/json"; + + accepts = "application/json"; + + async transformInput(prompt: string, modelKwargs: Record): Promise { + const payload = { + inputs: prompt, + parameters: modelKwargs, + }; + const stringifiedPayload = JSON.stringify(payload); + return new TextEncoder().encode(stringifiedPayload); + } + + async transformOutput(output: any): Promise { + const response_json = JSON.parse(new TextDecoder("utf-8").decode(output)); + const content: string = response_json[0]["generated_text"] ?? ""; + const answer = content + .substring(content.indexOf("<|assistant|>") + "<|assistant|>".length) + .trim(); + return answer; + } +} + +const model = new SageMakerEndpoint({ + endpointName: SAGEMAKER_ENDPONIT_NAME, + modelKwargs: { + temperature: 0.5, + max_new_tokens: 700, + top_p: 0.9, + }, + endpointKwargs: { + CustomAttributes: "accept_eula=true", + }, + contentHandler: new LLama27BHandler(), + clientOptions: { + // In theory, there's no need to supply the following details as the code will be executed within + // the AWS Lambda environment. However, due to the way SageMakerEndpoint is implemented, it's + // required to specify a region. + region: process.env["AWS_REGION"], + // credentials: { + // accessKeyId: "YOUR AWS ACCESS ID", + // secretAccessKey: "YOUR AWS SECRET ACCESS KEY", + // }, + }, +}); + +export async function handler(event: any) { + const queries = event.queryStringParameters ?? {}; + const sessionId = Array.isArray(queries["sessionid"]) + ? queries["sessionid"][0] + : queries["sessionid"]; + const query = Array.isArray(queries["query"]) ? queries["query"][0] : queries["query"]; + if (!sessionId || !query) { + return { + statusCode: 400, + body: "Both sessionid and query are required.", + }; + } + + const memory = new BufferMemory({ + chatHistory: new DynamoDBChatMessageHistory({ + tableName: DYNAMODB_TABLE_NAME, + partitionKey: DYNAMODB_PARTITION_KEY, + sessionId: sessionId, + }), + }); + + const promptTemplate = PromptTemplate.fromTemplate(`<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>`); + + const llmChain = new ConversationChain({ llm: model, memory: memory, prompt: promptTemplate }); + + const result = await llmChain.invoke({ query }); + return { + statusCode: 200, + body: result["response"], + }; +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/package-lock.json b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/package-lock.json new file mode 100644 index 00000000..48ee842c --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/package-lock.json @@ -0,0 +1,746 @@ +{ + "name": "serverless-chatbot", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "serverless-chatbot", + "version": "1.0.0", + "dependencies": { + "express": "^4.18.2", + "serverless-http": "^3.1.1" + }, + "devDependencies": { + "serverless-iam-roles-per-function": "^3.2.0", + 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b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/package.json @@ -0,0 +1,14 @@ +{ + "name": "serverless-chatbot", + "version": "1.0.0", + "description": "", + "devDependencies": { + "serverless-iam-roles-per-function": "^3.2.0", + "serverless-plugin-resource-tagging": "^1.2.0", + "typescript": "^5.2.2" + }, + "dependencies": { + "@langchain/community": "^0.0.33", + "langchain": "^0.1.23" + } +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/serverless.yml b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/serverless.yml new file mode 100644 index 00000000..f99502bd --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/serverless.yml @@ -0,0 +1,137 @@ +# Not working. The following error is thrown during deployment: +# CREATE_FAILED: SageMakerEndpoint (AWS::SageMaker::Endpoint) +# The role 'arn:aws:iam:::role/serverless-chatbot-dev-SageMakerRole-z6bsYWR34Qax' does not have BatchGetImage permission for the image: '763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference@sha256:2c57392f2788a6d21d2fbdae920826d1354eda7e89e2016b4409ef003a6a14af'. + +service: serverless-chatbot +frameworkVersion: "3" + +custom: + tableName: "conversations" + endpointName: "llama2-chatbot" + sagemakerInstanceType: "ml.g5.2xlarge" + huggingFaceModelId: "meta-llama/Llama-2-7b-chat-hf" + huggingFaceTask: "text-classification" + huggingFaceToken: "hf_EmXPwpnyHoNrxxxxxxxxx" + +provider: + name: aws + runtime: nodejs18.x + region: us-east-1 + stage: ${opt:stage, 'dev'} + environment: + HUGGING_FACE_TOKEN: ${self:custom.huggingFaceToken} + TABLE_NAME: ${self:custom.tableName} + ENDPOINT_NAME: ${self:custom.endpointName} + iam: + role: + statements: + - Effect: "Allow" + Action: + - "dynamodb:GetItem" + - "dynamodb:PutItem" + - "dynamodb:UpdateItem" + - "sagemaker:InvokeEndpoint" + Resource: "*" + +functions: + api: + handler: dist/index.handler + events: + - httpApi: + path: /chat + method: get + +resources: + Resources: + DynamoDBTable: + Type: "AWS::DynamoDB::Table" + Properties: + TableName: ${self:custom.tableName} + BillingMode: "PAY_PER_REQUEST" + AttributeDefinitions: + - AttributeName: "Id" + AttributeType: "S" + KeySchema: + - AttributeName: "Id" + KeyType: "HASH" + + SageMakerRole: + Type: "AWS::IAM::Role" + Properties: + AssumeRolePolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: "Allow" + Principal: + Service: + - "sagemaker.amazonaws.com" + Action: "sts:AssumeRole" + Policies: + - PolicyName: "AmazonSageMakerFullAccess" + PolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: "Allow" + Action: "sagemaker:*" + Resource: "*" + - PolicyName: "AmazonEC2ContainerRegistryReadOnly" + PolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: "Allow" + Action: + - "ecr:BatchCheckLayerAvailability" + - "ecr:GetDownloadUrlForLayer" + - "ecr:BatchGetImage" + Resource: "*" + + SageMakerModel: + Type: "AWS::SageMaker::Model" + Properties: + ModelName: ${self:custom.endpointName} + ExecutionRoleArn: !GetAtt SageMakerRole.Arn + PrimaryContainer: + Image: "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.1.1-tgi1.4.0-gpu-py310-cu121-ubuntu20.04" + Environment: + HF_MODEL_ID: ${self:custom.huggingFaceModelId} + HF_TASK: ${self:custom.huggingFaceTask} + HUGGING_FACE_HUB_TOKEN: ${self:custom.huggingFaceToken} + + SageMakerEndpointConfig: + Type: "AWS::SageMaker::EndpointConfig" + Properties: + EndpointConfigName: ${self:custom.endpointName} + ProductionVariants: + - InitialInstanceCount: 1 + InstanceType: ${self:custom.sagemakerInstanceType} + ModelName: !GetAtt SageMakerModel.ModelName + VariantName: "AllTraffic" + + SageMakerEndpoint: + Type: "AWS::SageMaker::Endpoint" + Properties: + EndpointName: ${self:custom.endpointName} + EndpointConfigName: !GetAtt SageMakerEndpointConfig.EndpointConfigName + + ApiLogGroup: + Type: "AWS::Logs::LogGroup" + Properties: + LogGroupName: "/aws/vendedlogs/apis/api-loggroup" + RetentionInDays: 7 + +plugins: + - serverless-iam-roles-per-function + - serverless-plugin-resource-tagging + +outputs: + ApiUrl: + Description: "The URL of the API Gateway endpoint." + Value: !Sub "https://${ApiGatewayRestApi}.execute-api.${AWS::Region}.amazonaws.com/${self:provider.stage}/chat" + + DynamoDBTableName: + Description: "The name of the DynamoDB table." + Value: !GetAtt DynamoDBTable.TableName + + SagemakerEndpointName: + Description: "The name of the SageMaker endpoint." + Value: !GetAtt SageMakerEndpoint.EndpointName diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/tsconfig.json b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/tsconfig.json new file mode 100644 index 00000000..45869971 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/serverless/tsconfig.json @@ -0,0 +1,27 @@ +{ + "compilerOptions": { + "outDir": "./dist", + "alwaysStrict": true, + "declaration": true, + "esModuleInterop": true, + "experimentalDecorators": true, + "inlineSourceMap": true, + "inlineSources": true, + "module": "CommonJS", + "noEmitOnError": false, + "noFallthroughCasesInSwitch": true, + "noImplicitAny": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "resolveJsonModule": true, + "strict": true, + "strictNullChecks": true, + "strictPropertyInitialization": true, + "stripInternal": true, + "target": "ES2020" + }, + "include": ["*.ts"], + "exclude": ["node_modules"] +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf b/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf index 114d1eec..7cbee0d6 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/terraform/terraform.tf @@ -1,3 +1,4 @@ +# Not verified if it'll work. terraform { required_version = ">= 0.12" @@ -168,7 +169,7 @@ resource "aws_sagemaker_model" "sagemaker_model" { execution_role_arn = aws_iam_role.sagemaker_role.arn primary_container { - image = "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:pytorch-1.7.1-transformers-4.6.1-gpu-py36-cu110-ubuntu18.04" + image = "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.1.1-tgi1.4.0-gpu-py310-cu121-ubuntu20.04" environment = { HF_MODEL_ID = "meta-llama/Llama-2-7b-chat-hf" HF_TASK = "text-classification" diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/main.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/main.ts new file mode 100644 index 00000000..7b8589d6 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/main.ts @@ -0,0 +1,124 @@ +import { cloud, inflight, main } from "@wingcloud/framework"; +import { Table } from "@winglibs/dynamodb"; +import { Endpoint } from "@winglibs/sagemaker"; + +import { LLM } from "langchain/llms/base"; +import { BufferMemory } from "langchain/memory"; +import { PromptTemplate } from "langchain/prompts"; +import { ConversationChain } from "langchain/chains"; +import { + SageMakerEndpoint, + SageMakerLLMContentHandler, +} from "@langchain/community/llms/sagemaker_endpoint"; +import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb"; + +const TABLE_NAME = "conversations"; +const PARTITION_KEY = "Id"; + +class LLama27BHandler implements SageMakerLLMContentHandler { + contentType = "application/json"; + + accepts = "application/json"; + + async transformInput(prompt: string, modelKwargs: Record): Promise { + const payload = { + inputs: prompt, + parameters: modelKwargs, + }; + const stringifiedPayload = JSON.stringify(payload); + return new TextEncoder().encode(stringifiedPayload); + } + + async transformOutput(output: any): Promise { + const response_json = JSON.parse(new TextDecoder("utf-8").decode(output)); + const content: string = response_json[0]["generated_text"] ?? ""; + const answer = content + .substring(content.indexOf("<|assistant|>") + "<|assistant|>".length) + .trim(); + return answer; + } +} + +async function chat(model: LLM, sessionId: string, query: string) { + const memory = new BufferMemory({ + chatHistory: new DynamoDBChatMessageHistory({ + tableName: TABLE_NAME, + partitionKey: PARTITION_KEY, + sessionId: sessionId, + }), + }); + + const promptTemplate = PromptTemplate.fromTemplate(`<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>`); + + const llmChain = new ConversationChain({ llm: model, memory: memory, prompt: promptTemplate }); + + const result = await llmChain.invoke({ query }); + return result["response"]; +} + +main((root) => { + const sagemaker = new Endpoint(root, "chatbot-wing", "chatbot-wing", "inferenceComponentName"); + + new Table(root, TABLE_NAME, { + name: TABLE_NAME, + attributes: [ + { + name: PARTITION_KEY, + type: "S", + }, + ], + hashKey: PARTITION_KEY, + }); + + const model = new SageMakerEndpoint({ + endpointName: sagemaker.endpointName, + modelKwargs: { + temperature: 0.5, + max_new_tokens: 700, + top_p: 0.9, + }, + endpointKwargs: { + CustomAttributes: "accept_eula=true", + }, + contentHandler: new LLama27BHandler(), + clientOptions: { + // In theory, there's no need to supply the following details as the code will be executed within + // the AWS Lambda environment. However, due to the way SageMakerEndpoint is implemented, it's + // required to specify a region. + region: process.env["AWS_REGION"], + // credentials: { + // accessKeyId: "YOUR AWS ACCESS ID", + // secretAccessKey: "YOUR AWS SECRET ACCESS KEY", + // }, + }, + }); + + const api = new cloud.Api(root, "chatbot-api"); + api.get( + "/chatbot", + inflight(async (ctx, req) => { + const sessionId = req.query.sessionId; + const query = req.query.query; + + if (!sessionId || !query) { + return { + statusCode: 400, + body: "Both sessionid and query are required.", + }; + } + + const result = await chat(model, sessionId, query); + return { + statusCode: 200, + body: result, + }; + }) + ); +}); diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/package-lock.json b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-ts/package-lock.json new file mode 100644 index 00000000..acf9ea64 --- /dev/null +++ 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"^0.1.28" + }, + "devDependencies": { + "@types/node": "^20", + "winglang": "^0.63.2" + } +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/.gitignore b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/.gitignore new file mode 100644 index 00000000..5ce3cf05 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/.gitignore @@ -0,0 +1,3 @@ +*.d.ts +target/ +node_modules/ \ No newline at end of file diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/langchain.ts b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/langchain.ts new file mode 100644 index 00000000..7719c92f --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/langchain.ts @@ -0,0 +1,87 @@ +import { LLM } from "langchain/llms/base"; +import { BufferMemory } from "langchain/memory"; +import { PromptTemplate } from "langchain/prompts"; +import { ConversationChain } from "langchain/chains"; +import { + SageMakerEndpoint, + SageMakerLLMContentHandler, +} from "@langchain/community/llms/sagemaker_endpoint"; +import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/dynamodb"; + +class LLama27BHandler implements SageMakerLLMContentHandler { + contentType = "application/json"; + + accepts = "application/json"; + + async transformInput(prompt: string, modelKwargs: Record): Promise { + const payload = { + inputs: prompt, + parameters: modelKwargs, + }; + const stringifiedPayload = JSON.stringify(payload); + return new TextEncoder().encode(stringifiedPayload); + } + + async transformOutput(output: any): Promise { + const response_json = JSON.parse(new TextDecoder("utf-8").decode(output)); + const content: string = response_json[0]["generated_text"] ?? ""; + const answer = content + .substring(content.indexOf("<|assistant|>") + "<|assistant|>".length) + .trim(); + return answer; + } +} + +export function createModel(endpointName: string) { + return new SageMakerEndpoint({ + endpointName: endpointName, + modelKwargs: { + temperature: 0.5, + max_new_tokens: 700, + top_p: 0.9, + }, + endpointKwargs: { + CustomAttributes: "accept_eula=true", + }, + contentHandler: new LLama27BHandler(), + clientOptions: { + // In theory, there's no need to supply the following details as the code will be executed within + // the AWS Lambda environment. However, due to the way SageMakerEndpoint is implemented, it's + // required to specify a region. + region: process.env["AWS_REGION"], + // credentials: { + // accessKeyId: "YOUR AWS ACCESS ID", + // secretAccessKey: "YOUR AWS SECRET ACCESS KEY", + // }, + }, + }); +} + +export function createMemory(tableName: string, partitionKey: string, sessionId: string) { + return new BufferMemory({ + chatHistory: new DynamoDBChatMessageHistory({ + tableName: tableName, + partitionKey: partitionKey, + sessionId: sessionId, + }), + }); +} + +export function createPromptTemplate() { + return PromptTemplate.fromTemplate(`<|system|> + You are a cool and aloof robot, answering questions very briefly and directly. + + Context: + {history} + <|user|> + {query} + <|assistant|>`); +} + +export function createConversationChain( + model: LLM, + memory: BufferMemory, + promptTemplate: PromptTemplate +) { + return new ConversationChain({ llm: model, memory: memory, prompt: promptTemplate }); +} diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/main.w b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/main.w new file mode 100644 index 00000000..b9359529 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/main.w @@ -0,0 +1,58 @@ +// This is an example of how to use the LangChain library in Winglang, but it's not working. +bring cloud; +bring dynamodb; +bring sagemaker; +bring expect; + +class LangChainFactory { + pub extern "./langchain.ts" static inflight createModel(endpointName: str); + pub extern "./langchain.ts" static inflight createMemory(tableName: str, partitionKey: str, sessionId: str); + pub extern "./langchain.ts" static inflight createPromptTemplate(); + pub extern "./langchain.ts" static inflight createConversationChain(model: any, memory: any, promptTemplate: any); +} + +let endpont = new sagemaker.Endpoint("chatbot-endpoint", ""); + +let tableName = "conversations"; +let partitionKey = "Id"; +let table = new dynamodb.Table( + name: tableName, + attributes: [ + { + name: partitionKey, + type: "S", + }, + ], + hashKey: partitionKey, +); + +let api = new cloud.Api(); + +let chat = inflight(sessionId: str, query: str) => { + let memory = LangChainFactory.createMemory(tableName, partitionKey, sessionId); + let model = LangChainFactory.createModel(endpont.endpointName); + let promptTemplate = LangChainFactory.createPromptTemplate(); + let llmChain = LangChainFactory.createConversationChain(model, memory, promptTemplate); + + let result = llmChain.invoke({ query: query }); + return result.response; +}; + +api.get("/chat", inflight (request: cloud.ApiRequest): cloud.ApiResponse => { + let sessionId = request.query.get("session_id"); + let query = request.query.get("query"); + + if (sessionId == "" || query == "") { + return { + status: 400, + body: "Both sessionid and query are required.", + }; + } + + let result = chat(sessionId, query); + return { + status: 200, + body: result, + }; +}); + diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package-lock.json b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package-lock.json new file mode 100644 index 00000000..32424111 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package-lock.json @@ -0,0 +1,15329 @@ +{ + "name": "wing-chatbot", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "wing-chatbot", + "version": "0.0.0", + "license": "MIT", + "dependencies": { + "@langchain/community": "^0.0.42", + "@winglibs/dynamodb": "^0.0.3", + "@winglibs/sagemaker": "^0.0.3", + "langchain": "^0.1.28" + } + }, + "node_modules/@anthropic-ai/sdk": { + "version": "0.9.1", + "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.9.1.tgz", + "integrity": "sha512-wa1meQ2WSfoY8Uor3EdrJq0jTiZJoKoSii2ZVWRY1oN4Tlr5s59pADg9T79FTbPe1/se5c3pBeZgJL63wmuoBA==", + "dependencies": { + "@types/node": 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b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package.json new file mode 100644 index 00000000..b477c3f9 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/wing-wing/package.json @@ -0,0 +1,14 @@ +{ + "name": "wing-chatbot", + "version": "0.0.0", + "description": "A description of my Wing application", + "author": "Your Name", + "license": "MIT", + "wing": true, + "dependencies": { + "@langchain/community": "^0.0.42", + "@winglibs/dynamodb": "^0.0.3", + "@winglibs/sagemaker": "^0.0.3", + "langchain": "^0.1.28" + } +} diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index c883b54a..c7b0ad95 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -779,7 +779,7 @@ packages: resolution: {integrity: sha512-IzSgsrxUcsrejQbPVilIKy16kAT52EwB6zSaI+M3xxIhKh5+aldEyvI+z6erM7TCLB2BJsFrtHjp6/4/sr+3dA==} dependencies: '@aws-crypto/util': 3.0.0 - '@aws-sdk/types': 3.460.0 + '@aws-sdk/types': 3.535.0 tslib: 1.14.1 dev: false @@ -796,7 +796,7 @@ packages: '@aws-crypto/sha256-js': 3.0.0 '@aws-crypto/supports-web-crypto': 3.0.0 '@aws-crypto/util': 3.0.0 - '@aws-sdk/types': 3.521.0 + '@aws-sdk/types': 3.535.0 '@aws-sdk/util-locate-window': 3.465.0 '@aws-sdk/util-utf8-browser': 3.259.0 tslib: 1.14.1 @@ -806,7 +806,7 @@ packages: resolution: {integrity: sha512-PnNN7os0+yd1XvXAy23CFOmTbMaDxgxXtTKHybrJ39Y8kGzBATgBFibWJKH6BhytLI/Zyszs87xCOBNyBig6vQ==} dependencies: '@aws-crypto/util': 3.0.0 - '@aws-sdk/types': 3.521.0 + '@aws-sdk/types': 3.535.0 tslib: 1.14.1 dev: false @@ -819,7 +819,7 @@ packages: /@aws-crypto/util@3.0.0: resolution: {integrity: sha512-2OJlpeJpCR48CC8r+uKVChzs9Iungj9wkZrl8Z041DWEWvyIHILYKCPNzJghKsivj+S3mLo6BVc7mBNzdxA46w==} dependencies: - '@aws-sdk/types': 3.460.0 + '@aws-sdk/types': 3.535.0 '@aws-sdk/util-utf8-browser': 3.259.0 tslib: 1.14.1 dev: false @@ -2414,6 +2414,14 @@ packages: tslib: 2.6.2 dev: false + /@aws-sdk/types@3.535.0: + resolution: {integrity: sha512-aY4MYfduNj+sRR37U7XxYR8wemfbKP6lx00ze2M2uubn7mZotuVrWYAafbMSXrdEMSToE5JDhr28vArSOoLcSg==} + engines: {node: '>=14.0.0'} + dependencies: + '@smithy/types': 2.12.0 + tslib: 2.6.2 + dev: false + /@aws-sdk/util-dynamodb@3.431.0(@aws-sdk/client-dynamodb@3.427.0): resolution: {integrity: sha512-eH40URobQ57HloQmOxDlt9e0Q6JFd9QnMsNmVLrth/tFSy1J9/shRDbbKDBuLD3pmr5/UJECOPdtqPXYJK+t7A==} engines: {node: '>=14.0.0'} @@ -5355,6 +5363,13 @@ packages: tslib: 2.6.2 dev: false + /@smithy/types@2.12.0: + resolution: {integrity: sha512-QwYgloJ0sVNBeBuBs65cIkTbfzV/Q6ZNPCJ99EICFEdJYG50nGIY/uYXp+TbsdJReIuPr0a0kXmCvren3MbRRw==} + engines: {node: '>=14.0.0'} + dependencies: + tslib: 2.6.2 + dev: false + /@smithy/types@2.8.0: resolution: {integrity: sha512-h9sz24cFgt/W1Re22OlhQKmUZkNh244ApgRsUDYinqF8R+QgcsBIX344u2j61TPshsTz3CvL6HYU1DnQdsSrHA==} engines: {node: '>=14.0.0'} diff --git a/pnpm-workspace.yaml b/pnpm-workspace.yaml index d6242c3d..798aaafd 100644 --- a/pnpm-workspace.yaml +++ b/pnpm-workspace.yaml @@ -5,4 +5,5 @@ packages: # - "devapps/*" - "testapps/*" - "examples/*" + - "!examples/langchain-llama2-chatbot-sagemaker/comparison/*" - "!components/deducers/python-pyright/libs/*" From 56d2d265ada26223db00cfd89ae732a380a33fb0 Mon Sep 17 00:00:00 2001 From: Shoujian Zheng Date: Mon, 25 Mar 2024 20:20:16 +0800 Subject: [PATCH 8/8] chore: add a python version of the langchain-llama2-chatbot-sagemaker example --- .changeset/config.json | 3 +- .../.gitignore | 38 ++++++ .../.pluto/pluto.yml | 4 +- .../README.md | 6 + .../README_zh.md | 6 + .../app/main.py | 127 ++++++++++++++++++ .../package.json | 20 +++ .../requirements.txt | 1 + .../README.md | 30 +++-- .../README_zh.md | 30 +++-- .../comparison/application-python/main.py | 85 ++++++++++++ .../src/index.ts | 5 +- pnpm-lock.yaml | 24 ++-- 13 files changed, 333 insertions(+), 46 deletions(-) create mode 100644 examples/langchain-llama2-chatbot-sagemaker-python/.gitignore rename examples/{langchain-py => langchain-llama2-chatbot-sagemaker-python}/.pluto/pluto.yml (81%) create mode 100644 examples/langchain-llama2-chatbot-sagemaker-python/README.md create mode 100644 examples/langchain-llama2-chatbot-sagemaker-python/README_zh.md create mode 100644 examples/langchain-llama2-chatbot-sagemaker-python/app/main.py create mode 100644 examples/langchain-llama2-chatbot-sagemaker-python/package.json create mode 100644 examples/langchain-llama2-chatbot-sagemaker-python/requirements.txt create mode 100644 examples/langchain-llama2-chatbot-sagemaker/comparison/application-python/main.py diff --git a/.changeset/config.json b/.changeset/config.json index 0eb6f376..3e7edfb8 100644 --- a/.changeset/config.json +++ b/.changeset/config.json @@ -16,6 +16,7 @@ "gpt2-hf-sagemaker", "langchain-llama2-sagemaker", "quickstart-python", - "langchain-llama2-chatbot-sagemaker" + "langchain-llama2-chatbot-sagemaker", + "langchain-llama2-chatbot-sagemaker-python" ] } diff --git a/examples/langchain-llama2-chatbot-sagemaker-python/.gitignore b/examples/langchain-llama2-chatbot-sagemaker-python/.gitignore new file mode 100644 index 00000000..9e6a4d69 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker-python/.gitignore @@ -0,0 +1,38 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +node_modules +.pnp +.pnp.js + +# testing +coverage + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# local env files +.env +.env.local +.env.development.local +.env.test.local +.env.production.local + +# turbo +.turbo + +# vercel +.vercel + +# compilation result +**/dist/ + +# pluto +.pluto/**/state/ +.pluto/*.test.*/ \ No newline at end of file diff --git a/examples/langchain-py/.pluto/pluto.yml b/examples/langchain-llama2-chatbot-sagemaker-python/.pluto/pluto.yml similarity index 81% rename from examples/langchain-py/.pluto/pluto.yml rename to examples/langchain-llama2-chatbot-sagemaker-python/.pluto/pluto.yml index d8b0878b..33bfaa6c 100644 --- a/examples/langchain-py/.pluto/pluto.yml +++ b/examples/langchain-llama2-chatbot-sagemaker-python/.pluto/pluto.yml @@ -2,6 +2,6 @@ current: aws language: python stacks: - configs: {} - provisionType: Pulumi name: aws - platformType: AWS \ No newline at end of file + platformType: AWS + provisionType: Pulumi diff --git a/examples/langchain-llama2-chatbot-sagemaker-python/README.md b/examples/langchain-llama2-chatbot-sagemaker-python/README.md new file mode 100644 index 00000000..08fb0072 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker-python/README.md @@ -0,0 +1,6 @@ +# Building a Llama2 Conversational Chatbot with AWS and LangChain + +The difference between this and the [TypeScript version of the Pluto example](../langchain-llama2-chatbot-sagemaker/) lies in its implementation in Python, which has not yet been successfully deployed. The issues encountered include: + +1. When deploying across platforms, dependencies such as Numpy and Pydantic cannot be correctly installed on the target platform. +2. The size of the compressed package exceeds the AWS Lambda limit of 50MB. diff --git a/examples/langchain-llama2-chatbot-sagemaker-python/README_zh.md b/examples/langchain-llama2-chatbot-sagemaker-python/README_zh.md new file mode 100644 index 00000000..5c619a37 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker-python/README_zh.md @@ -0,0 +1,6 @@ +# 基于 AWS 和 LangChain 的 Llama2 会话聊天机器人 + +与 [TypeScript 版本的 Pluto 示例](../langchain-llama2-chatbot-sagemaker/)应用区别在于使用 Python 实现，尚未成功部署，存在的问题包括： + +1. 跨平台部署时，Numpy、Pydantic 等依赖包无法在目标平台正确安装。 +2. 压缩包大小超过 AWS Lambda 限制，50MB。 diff --git a/examples/langchain-llama2-chatbot-sagemaker-python/app/main.py b/examples/langchain-llama2-chatbot-sagemaker-python/app/main.py new file mode 100644 index 00000000..68b31973 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker-python/app/main.py @@ -0,0 +1,127 @@ +import os +import json +from typing import Dict + +from pluto_client.sagemaker import SageMaker, SageMakerOptions +from pluto_client import Router, HttpRequest, HttpResponse, KVStore + +from langchain.prompts import PromptTemplate +from langchain.memory.buffer import ConversationBufferMemory +from langchain.chains.conversation.base import ConversationChain +from langchain_community.chat_message_histories.dynamodb import ( + DynamoDBChatMessageHistory, +) +from langchain_community.llms.sagemaker_endpoint import ( + SagemakerEndpoint, + LLMContentHandler, +) + +""" +Deploy the Llama2 model on AWS SageMaker using the Hugging Face Text Generation Inference (TGI) +container. If you're unable to deploy the model because of the instance type, consider using the +TinyLlama-1.1B-Chat-v1.0 model, which is compatible with the ml.m5.xlarge instance. + +Below is a set up minimum requirements for each model size of Llama2 model: +``` +Model Instance Type Quantization # of GPUs per replica +Llama 7B ml.g5.2xlarge - 1 +Llama 13B ml.g5.12xlarge - 4 +Llama 70B ml.g5.48xlarge bitsandbytes 8 +Llama 70B ml.p4d.24xlarge - 8 +``` + +The initial limit set for these instances is zero. If you need more, you can request an increase +in quota via the [AWS Management Console](https://console.aws.amazon.com/servicequotas/home). +""" +sagemaker = SageMaker( + "llama2", + "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.1.1-tgi1.4.0-gpu-py310-cu121-ubuntu20.04", + SageMakerOptions( + instanceType="ml.g5.2xlarge", + envs={ + "HF_MODEL_ID": "meta-llama/Llama-2-7b-chat-hf", + "HF_TASK": "text-generation", + # If you want to deploy the Meta Llama2 model, you need to request a permission and + # prepare the token. You can get the token from https://huggingface.co/settings/tokens + "HUGGING_FACE_HUB_TOKEN": "hf_EmXPwpnyHoNrxxxxxxxxx", + }, + ), +) + +router = Router("chatbot") +conversations = KVStore("conversations") + + +class ContentHandler(LLMContentHandler): + content_type = "application/json" + accepts = "application/json" + + def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes: + input_str = json.dumps({"inputs": prompt, "parameters": model_kwargs}) + return input_str.encode("utf-8") + + def transform_output(self, output: bytes) -> str: + response_json = json.loads(output.decode("utf-8")) + return response_json[0]["generated_text"] + + +def get_aws_region() -> str: + aws_region = os.environ.get("AWS_REGION") + if aws_region is None: + raise ValueError("AWS_REGION environment variable must be set") + return aws_region + + +llm = SagemakerEndpoint( + endpoint_name=sagemaker.endpoint_name, # SageMaker endpoint name + region_name=get_aws_region(), + content_handler=ContentHandler(), +) + + +def chat_handler(req: HttpRequest) -> HttpResponse: + query = req.query["query"] + sessionid = req.query["sessionid"] + if isinstance(sessionid, list): + sessionid = sessionid[0] + + memory = ConversationBufferMemory( + chat_memory=DynamoDBChatMessageHistory( + table_name=conversations.aws_table_name, # DynamoDB table name + primary_key_name=conversations.aws_partition_key, # DynamoDB partition key + session_id=sessionid, + ) + ) + + promptTemplate = PromptTemplate.from_template( + """<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>""" + ) + + chain = ConversationChain( + llm=llm, + memory=memory, + prompt=promptTemplate, + ) + + result = chain({"query": query}) + print(result) + + return HttpResponse(status_code=200, body="hello world") + + # TODO: Use the following statement to help the deducer identify the right relationship between + # Lambda and other resources. This will be used to grant permission for the Lambda instance to + # access the SageMaker endpoint and the DynamoDB. This code should be removed after the deducer + # supports the analysis of libraries. + conversations.get("") + conversations.set("", "") + sagemaker.invoke("") + + +router.get("/chatbot", chat_handler) diff --git a/examples/langchain-llama2-chatbot-sagemaker-python/package.json b/examples/langchain-llama2-chatbot-sagemaker-python/package.json new file mode 100644 index 00000000..1cc61cc3 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker-python/package.json @@ -0,0 +1,20 @@ +{ + "name": "langchain-llama2-chatbot-sagemaker-python", + "private": true, + "version": "0.0.1", + "scripts": { + "test:dev": "pluto test --sim", + "test:prod": "pluto test", + "deploy": "pluto deploy", + "destroy": "pluto destroy" + }, + "dependencies": {}, + "devDependencies": { + "@types/node": "^20", + "typescript": "^5.2.2", + "@plutolang/base": "latest", + "@plutolang/pluto-infra": "latest", + "@pulumi/pulumi": "^3.88.0" + }, + "main": "dist/index.js" +} diff --git a/examples/langchain-llama2-chatbot-sagemaker-python/requirements.txt b/examples/langchain-llama2-chatbot-sagemaker-python/requirements.txt new file mode 100644 index 00000000..14a946e4 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker-python/requirements.txt @@ -0,0 +1 @@ +pluto_client \ No newline at end of file diff --git a/examples/langchain-llama2-chatbot-sagemaker/README.md b/examples/langchain-llama2-chatbot-sagemaker/README.md index 6edad7fe..be92eb5d 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/README.md +++ b/examples/langchain-llama2-chatbot-sagemaker/README.md @@ -21,19 +21,21 @@ As illustrated above, our example application relies on the following AWS servic The table below shows a comparison between Pluto and other tools regarding the amount and type of code: -| | Code Type | Lines of Code | Code Location | -| ------------------------------------------------------- | ---------------------------------- | -------------------- | -------------------------------- | -| Application | Business Logic | 83(.ts) | [Code](./comparison/application) | -| [Terraform](https://www.terraform.io/) | Infrastructure Configuration | 201(.tf) | [Code](./comparison/terraform) | -| [Pulumi](https://www.pulumi.com/) | Infrastructure Configuration | 157(.ts) | [Code](./comparison/pulumi) | -| [Pulumi Serverless](https://www.pulumi.com/serverless/) | Business Logic + Configuration | 256(.ts) | [Code](./comparison/pulumi-app) | -| [Winglang - TS](https://github.com/winglang/wing) | Business Logic + Configuration | 100(.ts) | [Code](./comparison/wing-ts) | -| [Winglang - Wing](https://github.com/winglang/wing) | Business Logic + Configuration | 71(.w) + 47(.ts) | [Code](./comparison/wing-wing) | -| [Lepton](https://www.lepton.ai/) | Business Logic + Configuration | 116(.py) | [Code](./comparison/lepton) | -| [Serverless](https://github.com/serverless/serverless) | Business Logic + Configuration | 79(.ts) + 120(.yaml) | [Code](./comparison/serverless) | -| **Pluto** | **Business Logic + Configuration** | **100(.ts)** | [Code](./src) | - -By examining the table data, it's evident that Pluto adds 17 lines of code in contrast to pure business logic code. This includes code related to cloud resource creation and permission configuration. When infrastructure setup is handled using provisioning tools like Pulumi or Terraform, the combined code for business logic and infrastructure configuration exceeds twice the size of Pluto's code. Compared to tools such as Pulumi's Serverless feature, Winglang, Lepton, and Serverless, which streamline cloud usage, Pluto demands lower cloud and AI background knowledge and entails less code. +| | Code Type | Lines of Code | Code Location | +| ------------------------------------------------------- | ---------------------------------- | -------------------- | ---------------------------------------------------- | +| Application - TypeScript | Business Logic | 83(.ts) | [Code](./comparison/application) | +| [Terraform](https://www.terraform.io/) | Infrastructure Configuration | 201(.tf) | [Code](./comparison/terraform) | +| [Pulumi](https://www.pulumi.com/) | Infrastructure Configuration | 157(.ts) | [Code](./comparison/pulumi) | +| [Pulumi Serverless](https://www.pulumi.com/serverless/) | Business Logic + Configuration | 256(.ts) | [Code](./comparison/pulumi-app) | +| [Winglang - TS](https://github.com/winglang/wing) | Business Logic + Configuration | 100(.ts) | [Code](./comparison/wing-ts) | +| [Winglang - Wing](https://github.com/winglang/wing) | Business Logic + Configuration | 71(.w) + 47(.ts) | [Code](./comparison/wing-wing) | +| [Serverless](https://github.com/serverless/serverless) | Business Logic + Configuration | 79(.ts) + 120(.yaml) | [Code](./comparison/serverless) | +| **Pluto - TypeScript** | **Business Logic + Configuration** | **100(.ts)** | [Code](./src) | +| Application - Python | Business Logic | 60(.py) | [Code](./comparison/application-python) | +| [Lepton](https://www.lepton.ai/) | Business Logic + Configuration | 116(.py) | [Code](./comparison/lepton) | +| **Pluto - Python** | **Business Logic + Configuration** | **74(.py) ** | [Code](../langchain-llama2-chatbot-sagemaker-python) | + +By examining the table data, the TypeScript and Python versions of the Pluto application add 17 and 14 lines of code, respectively, in contrast to pure business logic code. This includes code related to cloud resource creation and permission configuration. When infrastructure setup is handled using provisioning tools like Pulumi or Terraform, the combined code for business logic and infrastructure configuration exceeds twice the size of Pluto's code. Compared to tools such as Pulumi's Serverless feature, Winglang, Lepton, and Serverless, which streamline cloud usage, Pluto demands lower cloud and AI background knowledge and entails less code. While using other tools, we faced several issues that ultimately hindered the deployment of this code. For instance, Winglang lacks support for SageMaker Model and EndpointConfig resource types, Pulumi runtime errors, and intricate resource configuration. If you're interested, feel free to lend a hand with the fixes. @@ -58,7 +60,7 @@ To deploy this chatbot, please follow the steps below: pluto deploy ``` -Be patient, as deploying the model in SageMaker can take some time. Once the deployment is complete, the console will display the URL for ApiGateway, which you can access via a browser or a curl command. Here is a simple test command: +**Be patient, as deploying the model in SageMaker can take some time.** Once the deployment is complete, the console will display the URL for ApiGateway, which you can access via a browser or a curl command. Here is a simple test command: ```bash CHATBOT_API_URL=your ApiGateway URL diff --git a/examples/langchain-llama2-chatbot-sagemaker/README_zh.md b/examples/langchain-llama2-chatbot-sagemaker/README_zh.md index 3c7c772a..6c87f8ff 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/README_zh.md +++ b/examples/langchain-llama2-chatbot-sagemaker/README_zh.md @@ -21,19 +21,21 @@ 我们尝试使用其他工具构建相同的示例应用，以下表格展示了 Pluto 与其他工具在代码量和类型上的对比： -| | 代码类型 | 代码量/行 | 代码地址 | -| ------------------------------------------------------- | ------------------- | -------------------- | -------------------------------- | -| 应用程序 | 业务逻辑 | 83(.ts) | [源码](./comparison/application) | -| [Terraform](https://www.terraform.io/) | 基础设施配置 | 201(.tf) | [源码](./comparison/terraform) | -| [Pulumi](https://www.pulumi.com/) | 基础设施配置 | 157(.ts) | [源码](./comparison/pulumi) | -| [Pulumi Serverless](https://www.pulumi.com/serverless/) | 业务逻辑 + 配置 | 256(.ts) | [源码](./comparison/pulumi-app) | -| [Winglang - TS](https://github.com/winglang/wing) | 业务逻辑 + 配置 | 100(.ts) | [源码](./comparison/wing-ts) | -| [Winglang - Wing](https://github.com/winglang/wing) | 业务逻辑 + 配置 | 71(.w) + 47(.ts) | [源码](./comparison/wing-wing) | -| [Lepton](https://www.lepton.ai/) | 业务逻辑 + 配置 | 116(.py) | [源码](./comparison/lepton) | -| [Serverless](https://github.com/serverless/serverless) | 业务逻辑 + 配置 | 79(.ts) + 120(.yaml) | [源码](./comparison/serverless) | -| **Pluto** | **业务逻辑 + 配置** | **100(.ts)** | [源码](./src) | - -通过对比表格数据可以看到，Pluto 与纯业务逻辑代码相比，增加了 17 行代码，包含了云资源创建与权限配置相关代码。如果采用 Pulumi 或 Terraform 等 IaC 工具进行基础设施配置，业务逻辑加基础设施配置的整体代码量均在 Pluto 代码量的两倍以上。与 Pulumi 的 Serverless 功能、Winglang、Lepton、Serverless 等简化云使用的工具相比，Pluto 对云、AI 背景知识的要求更低，所需代码量也更少。 +| | 代码类型 | 代码量/行 | 代码地址 | +| --------------------------------------------------------- | ------------------- | -------------------- | ---------------------------------------------------- | +| 应用程序 - TypeScript | 业务逻辑 | 83(.ts) | [源码](./comparison/application) | +| [Terraform](https://www.terraform.io/) | 基础设施配置 | 201(.tf) | [源码](./comparison/terraform) | +| [Pulumi](https://www.pulumi.com/) | 基础设施配置 | 157(.ts) | [源码](./comparison/pulumi) | +| [Pulumi Serverless](https://www.pulumi.com/serverless/) | 业务逻辑 + 配置 | 256(.ts) | [源码](./comparison/pulumi-app) | +| [Winglang - TypeScript](https://github.com/winglang/wing) | 业务逻辑 + 配置 | 100(.ts) | [源码](./comparison/wing-ts) | +| [Winglang - Wing](https://github.com/winglang/wing) | 业务逻辑 + 配置 | 71(.w) + 47(.ts) | [源码](./comparison/wing-wing) | +| [Serverless](https://github.com/serverless/serverless) | 业务逻辑 + 配置 | 79(.ts) + 120(.yaml) | [源码](./comparison/serverless) | +| **Pluto - TypeScript** | **业务逻辑 + 配置** | **100(.ts) ** | [源码](./src) | +| 应用程序 - Python | 业务逻辑 | 60(.py) | [源码](./comparison/application-python) | +| [Lepton](https://www.lepton.ai/) | 业务逻辑 + 配置 | 116(.py) | [源码](./comparison/lepton) | +| **Pluto - Python** | **业务逻辑 + 配置** | **74(.py) ** | [源码](../langchain-llama2-chatbot-sagemaker-python) | + +通过对比表格数据可以看到，TypeScript 版本与 Python 版本的 Pluto 应用与纯业务逻辑代码相比，增加了 17 行、14 行代码，包含了云资源创建与权限配置相关代码。如果采用 Pulumi 或 Terraform 等 IaC 工具进行基础设施配置，业务逻辑加基础设施配置的整体代码量均在 Pluto 代码量的两倍以上。与 Pulumi 的 Serverless 功能、Winglang、Lepton、Serverless 等简化云使用的工具相比，Pluto 对云、AI 背景知识的要求更低，所需代码量也更少。 我们在使用其他工具构建的过程中，遇到了许多问题，导致这些代码最终并没有部署成功，例如，Winglang 没有提供 SageMaker Model、EndpointConfig 资源类型，Pulumi 运行时报错，繁琐的资源项配置等，如果你有兴趣欢迎进行修复。 @@ -58,7 +60,7 @@ pluto deploy ``` -这个过程需要耐心等待一会，因为 SageMaker 部署模型需要一定时间。一旦部署完成，控制台会显示出 ApiGateway 的 URL，你可以通过浏览器或 curl 命令来访问它。以下是一个简单的测试命令： +**这个过程需要耐心等待一会，因为 SageMaker 部署模型需要一定时间。** 一旦部署完成，控制台会显示出 ApiGateway 的 URL，你可以通过浏览器或 curl 命令来访问它。以下是一个简单的测试命令： ```bash CHATBOT_API_URL=你的 ApiGateway URL diff --git a/examples/langchain-llama2-chatbot-sagemaker/comparison/application-python/main.py b/examples/langchain-llama2-chatbot-sagemaker/comparison/application-python/main.py new file mode 100644 index 00000000..8a008ea3 --- /dev/null +++ b/examples/langchain-llama2-chatbot-sagemaker/comparison/application-python/main.py @@ -0,0 +1,85 @@ +import os +import json +from typing import Dict + +from langchain.prompts import PromptTemplate +from langchain.memory.buffer import ConversationBufferMemory +from langchain.chains.conversation.base import ConversationChain +from langchain_community.chat_message_histories.dynamodb import ( + DynamoDBChatMessageHistory, +) +from langchain_community.llms.sagemaker_endpoint import ( + SagemakerEndpoint, + LLMContentHandler, +) + +ENDPOINT_NAME = "llama2" +TABLE_NAME = "conversations" +PARTITION_KEY = "Id" + + +class ContentHandler(LLMContentHandler): + content_type = "application/json" + accepts = "application/json" + + def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes: + input_str = json.dumps({"inputs": prompt, "parameters": model_kwargs}) + return input_str.encode("utf-8") + + def transform_output(self, output: bytes) -> str: + response_json = json.loads(output.decode("utf-8")) + return response_json[0]["generated_text"] + + +def get_aws_region() -> str: + aws_region = os.environ.get("AWS_REGION") + if aws_region is None: + raise ValueError("AWS_REGION environment variable must be set") + return aws_region + + +llm = SagemakerEndpoint( + endpoint_name=ENDPOINT_NAME, + region_name=get_aws_region(), + content_handler=ContentHandler(), +) + + +def handler(event): + query = event.query["query"] + sessionid = event.query["sessionid"] + if isinstance(sessionid, list): + sessionid = sessionid[0] + + memory = ConversationBufferMemory( + chat_memory=DynamoDBChatMessageHistory( + table_name=TABLE_NAME, + session_id=sessionid, + primary_key_name=PARTITION_KEY, + ) + ) + + promptTemplate = PromptTemplate.from_template( + """<|system|> +You are a cool and aloof robot, answering questions very briefly and directly. + +Context: +{history} +<|user|> +{query} +<|assistant|>""" + ) + + chain = ConversationChain( + llm=llm, + memory=memory, + prompt=promptTemplate, + ) + + result = chain.invoke({"query": query}) + print(result) + + return { + "statusCode": 200, + "body": json.dumps(result["generated_text"]), + } diff --git a/examples/langchain-llama2-chatbot-sagemaker/src/index.ts b/examples/langchain-llama2-chatbot-sagemaker/src/index.ts index 1f882868..2f05aa7f 100644 --- a/examples/langchain-llama2-chatbot-sagemaker/src/index.ts +++ b/examples/langchain-llama2-chatbot-sagemaker/src/index.ts @@ -10,8 +10,8 @@ import { DynamoDBChatMessageHistory } from "@langchain/community/stores/message/ /** * Deploy the Llama2 model on AWS SageMaker using the Hugging Face Text Generation Inference (TGI) - * container. Here will deploy the TinyLlama-1.1B-Chat-v1.0 model, which can be run on the - * ml.m5.xlarge instance. + * container. If you're unable to deploy the model because of the instance type, consider using the + * TinyLlama-1.1B-Chat-v1.0 model, which is compatible with the ml.m5.xlarge instance. * * Below is a set up minimum requirements for each model size of Llama2 model: * ``` @@ -32,7 +32,6 @@ const sagemaker = new SageMaker( instanceType: "ml.g5.2xlarge", envs: { HF_MODEL_ID: "meta-llama/Llama-2-7b-chat-hf", - // HF_MODEL_ID: "TinyLlama/TinyLlama-1.1B-Chat-v1.0", HF_TASK: "text-generation", // If you want to deploy the Meta Llama2 model, you need to request a permission and prepare the // token. You can get the token from https://huggingface.co/settings/tokens diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index c7b0ad95..db9fe100 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -389,17 +389,7 @@ importers: specifier: ^5.2.2 version: 5.2.2 - examples/langchain-llama2-sagemaker: - dependencies: - '@langchain/community': - specifier: ^0.0.33 - version: 0.0.33 - '@plutolang/pluto': - specifier: latest - version: link:../../packages/pluto - langchain: - specifier: ^0.1.23 - version: 0.1.23 + examples/langchain-llama2-chatbot-sagemaker-python: devDependencies: '@plutolang/base': specifier: latest @@ -417,7 +407,17 @@ importers: specifier: ^5.2.2 version: 5.2.2 - examples/langchain-py: + examples/langchain-llama2-sagemaker: + dependencies: + '@langchain/community': + specifier: ^0.0.33 + version: 0.0.33 + '@plutolang/pluto': + specifier: latest + version: link:../../packages/pluto + langchain: + specifier: ^0.1.23 + version: 0.1.23 devDependencies: '@plutolang/base': specifier: latest

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