From 4b4956f429e61757978452ae363b306a1a39a883 Mon Sep 17 00:00:00 2001 From: bedanley Date: Fri, 1 Nov 2024 14:27:05 -0600 Subject: [PATCH] Create LISA Docs Deployment (#166) * Adding LISA documentation deployment stack --- .../workflows/docs.deploy.github-pages.yml | 57 + README.md | 2 + lib/docs/.gitignore | 2 + lib/docs/.vitepress/config.mts | 86 + lib/docs/admin/api-tokens.md | 77 + lib/docs/admin/api.md | 364 +++ lib/docs/admin/architecture.md | 41 + lib/docs/admin/components.md | 55 + lib/docs/admin/deploy.md | 70 + lib/docs/admin/error.md | 69 + lib/docs/admin/getting-started.md | 303 +++ lib/docs/admin/idp.md | 1 + lib/docs/admin/lite-llm.md | 1 + lib/docs/admin/model-management.md | 1 + lib/docs/admin/security.md | 1 + lib/docs/assets/LisaArchitecture.png | Bin 0 -> 232928 bytes lib/docs/assets/LisaChat.png | Bin 0 -> 99793 bytes lib/docs/assets/LisaModelManagement.png | Bin 0 -> 172795 bytes lib/docs/assets/LisaServe.png | Bin 0 -> 139509 bytes lib/docs/config/api-tokens.md | 77 + lib/docs/config/branding.md | 1 + lib/docs/config/features.md | 1 + lib/docs/config/hiding-chat-components.md | 1 + lib/docs/config/model-compatibility.md | 26 + lib/docs/config/model-management-api.md | 1 + lib/docs/config/model-management-ui.md | 1 + lib/docs/config/vector-stores.md | 1 + lib/docs/index.md | 30 + lib/docs/index.ts | 178 ++ lib/docs/package-lock.json | 2255 +++++++++++++++++ lib/docs/package.json | 17 + lib/docs/public/favicon.ico | Bin 0 -> 1150 bytes lib/docs/public/logo.png | Bin 0 -> 2278 bytes lib/docs/user/chat.md | 172 ++ lib/docs/user/context-windows.md | 1 + lib/docs/user/history.md | 1 + lib/docs/user/model-kwargs.md | 1 + lib/docs/user/models.md | 1 + lib/docs/user/nonrag-management.md | 1 + lib/docs/user/prompt-engineering.md | 1 + lib/docs/user/rag.md | 1 + lib/schema.ts | 10 + lib/stages.ts | 96 +- lib/user-interface/react/package-lock.json | 48 +- package-lock.json | 1 + package.json | 6 +- test/cdk/stacks/docs.test.ts | 104 + tsconfig.json | 5 +- 48 files changed, 4098 insertions(+), 70 deletions(-) create mode 100644 .github/workflows/docs.deploy.github-pages.yml create mode 100644 lib/docs/.gitignore create mode 100644 lib/docs/.vitepress/config.mts create mode 100644 lib/docs/admin/api-tokens.md create mode 100644 lib/docs/admin/api.md create mode 100644 lib/docs/admin/architecture.md create mode 100644 lib/docs/admin/components.md create mode 100644 lib/docs/admin/deploy.md create mode 100644 lib/docs/admin/error.md create mode 100644 lib/docs/admin/getting-started.md create mode 100644 lib/docs/admin/idp.md create mode 100644 lib/docs/admin/lite-llm.md create mode 100644 lib/docs/admin/model-management.md create mode 100644 lib/docs/admin/security.md create mode 100644 lib/docs/assets/LisaArchitecture.png create mode 100644 lib/docs/assets/LisaChat.png create mode 100644 lib/docs/assets/LisaModelManagement.png create mode 100644 lib/docs/assets/LisaServe.png create mode 100644 lib/docs/config/api-tokens.md create mode 100644 lib/docs/config/branding.md create mode 100644 lib/docs/config/features.md create mode 100644 lib/docs/config/hiding-chat-components.md create mode 100644 lib/docs/config/model-compatibility.md create mode 100644 lib/docs/config/model-management-api.md create mode 100644 lib/docs/config/model-management-ui.md create mode 100644 lib/docs/config/vector-stores.md create mode 100644 lib/docs/index.md create mode 100644 lib/docs/index.ts create mode 100644 lib/docs/package-lock.json create mode 100644 lib/docs/package.json create mode 100644 lib/docs/public/favicon.ico create mode 100644 lib/docs/public/logo.png create mode 100644 lib/docs/user/chat.md create mode 100644 lib/docs/user/context-windows.md create mode 100644 lib/docs/user/history.md create mode 100644 lib/docs/user/model-kwargs.md create mode 100644 lib/docs/user/models.md create mode 100644 lib/docs/user/nonrag-management.md create mode 100644 lib/docs/user/prompt-engineering.md create mode 100644 lib/docs/user/rag.md create mode 100644 test/cdk/stacks/docs.test.ts diff --git a/.github/workflows/docs.deploy.github-pages.yml b/.github/workflows/docs.deploy.github-pages.yml new file mode 100644 index 00000000..a6162e43 --- /dev/null +++ b/.github/workflows/docs.deploy.github-pages.yml @@ -0,0 +1,57 @@ + +name: Deploy VitePress site to Github Pages +on: + push: + branches: [main] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Setup Node + uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + - name: Setup Pages + uses: actions/configure-pages@v4 + - name: Install dependencies + working-directory: ./lib/docs + run: npm install + env: + CI: "" + - name: Build with VitePress + working-directory: ./lib/docs + run: npm run docs:build + env: + CI: "" + DOCS_BASE_PATH: '/lisa/' + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: ./lib/docs/dist + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + needs: build + runs-on: ubuntu-latest + name: Deploy + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/README.md b/README.md index e8dec549..2651ce0e 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +[![Full Documentation](https://img.shields.io/badge/Full%20Documentation-blue?style=for-the-badge&logo=Vite&logoColor=white)](https://awslabs.github.io/lisa/) + # LLM Inference Solution for Amazon Dedicated Cloud (LISA) ![LISA Architecture](./assets/LisaArchitecture.png) LISA is an infrastructure-as-code solution that supports model hosting and inference. Customers deploy LISA directly diff --git a/lib/docs/.gitignore b/lib/docs/.gitignore new file mode 100644 index 00000000..80ac0588 --- /dev/null +++ b/lib/docs/.gitignore @@ -0,0 +1,2 @@ +dist/ +.vitepress/cache/ diff --git a/lib/docs/.vitepress/config.mts b/lib/docs/.vitepress/config.mts new file mode 100644 index 00000000..9b996c9b --- /dev/null +++ b/lib/docs/.vitepress/config.mts @@ -0,0 +1,86 @@ +/** + Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"). + You may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + */ + +import { defineConfig } from 'vitepress'; + +const navLinks = [ + { + text: 'System Administrator Guide', + items: [ + { text: 'Architecture Overview', link: '/admin/architecture' }, + { text: 'LISA Components', link: '/admin/components' }, + { text: 'Getting Started', link: '/admin/getting-started' }, + { text: 'Configure IdP: Cognito & Keycloak Examples', link: '/admin/idp' }, + { text: 'Deployment', link: '/admin/deploy' }, + { text: 'Setting Model Management Admin Group', link: '/admin/model-management' }, + { text: 'LiteLLM', link: '/admin/lite-llm' }, + { text: 'API Overview', link: '/admin/api' }, + { text: 'API Request Error Handling', link: '/admin/error' }, + { text: 'Security', link: '/admin/security' }, + ], + }, + { + text: 'Advanced Configuration', + items: [ + { text: 'Programmatic API Tokens', link: '/config/api-tokens' }, + { text: 'Model Compatibility', link: '/config/model-compatibility' }, + { text: 'Model Management API', link: '/config/model-management-api' }, + { text: 'Model Management UI', link: '/config/model-management-ui' }, + { text: 'Rag Vector Stores', link: '/config/vector-stores' }, + { text: 'Usage & Features', link: '/config/features' }, + { text: 'Branding', link: '/config/branding' }, + { text: 'Hiding Advanced Chat UI Components', link: '/config/hiding-chat-components' }, + ], + }, + { + text: 'User Guide', + items: [ + { text: 'LISA Chat UI', link: '/user/chat' }, + { text: 'RAG', link: '/user/rag' }, + { text: 'Context Windows', link: '/user/context-windows' }, + { text: 'Model KWARGS', link: '/user/model-kwargs' }, + { text: 'Non-RAG in Context File Management', link: '/user/nonrag-management' }, + { text: 'Prompt Engineering', link: '/user/prompt-engineering' }, + { text: 'Session History', link: '/user/history' }, + ], + }]; + +// https://vitepress.dev/reference/site-config +export default defineConfig({ + lang: 'en-US', + title: 'LISA Documentation', + description: 'LLM Inference Solution for Amazon Dedicated Cloud (LISA)', + outDir: 'dist', + base: '/lisa/', + head: [['link', { rel: 'icon', href: '/lisa/favicon.ico' }]], + // https://vitepress.dev/reference/default-theme-config + themeConfig: { + logo: '/logo.png', + nav: [ + { text: 'Home', link: '/' }, + ...navLinks, + ], + + sidebar: navLinks, + + socialLinks: [ + { icon: 'github', link: 'https://github.com/awslabs/lisa' }, + ], + search: { + provider: 'local', + }, + }, +}); diff --git a/lib/docs/admin/api-tokens.md b/lib/docs/admin/api-tokens.md new file mode 100644 index 00000000..1a0afa75 --- /dev/null +++ b/lib/docs/admin/api-tokens.md @@ -0,0 +1,77 @@ +## Programmatic API Tokens + +The LISA Serve ALB can be used for programmatic access outside the example Chat application. +An example use case would be for allowing LISA to serve LLM requests that originate from the [Continue VSCode Plugin](https://www.continue.dev/). +To facilitate communication directly with the LISA Serve ALB, a user with sufficient DynamoDB PutItem permissions may add +API keys to the APITokenTable, and once created, a user may make requests by including the `Authorization: Bearer ${token}` +header or the `Api-Key: ${token}` header with that token. If using any OpenAI-compatible library, the `api_key` fields +will use the `Authorization: Bearer ${token}` format automatically, so there is no need to include additional headers +when using those libraries. + +### Adding a Token + +An account owner may create a long-lived API Token using the following AWS CLI command. + +```bash +AWS_REGION="us-east-1" # change to your deployment region +token_string="YOUR_STRING_HERE" # change to a unique string for a user +aws --region $AWS_REGION dynamodb put-item --table-name $DEPLOYMENT_NAME-LISAApiTokenTable \ + --item '{"token": {"S": "'${token_string}'"}}' +``` + +If an account owner wants the API Token to be temporary and expire after a specific date, LISA will allow for this too. +In addition to the `token` field, the owner may specify the `tokenExpiration` field, which accepts a UNIX timestamp, +in seconds. The following command shows an example of how to do this. + +```bash +AWS_REGION="us-east-1" # change to your deployment region +token_string="YOUR_STRING_HERE" +token_expiration=$(echo $(date +%s) + 3600 | bc) # token that expires in one hour, 3600 seconds +aws --region $AWS_REGION dynamodb put-item --table-name $DEPLOYMENT_NAME-LISAApiTokenTable \ + --item '{ + "token": {"S": "'${token_string}'"}, + "tokenExpiration": {"N": "'${token_expiration}'"} + }' +``` + +Once the token is inserted into the DynamoDB Table, a user may use the token in the `Authorization` request header like +in the following snippet. + +```bash +lisa_serve_rest_url="https://" +token_string="YOUR_STRING_HERE" +curl ${lisa_serve_rest_url}/v2/serve/models \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -H "Authorization: Bearer ${token_string}" +``` + +### Updating a Token + +In the case that an owner wishes to change an existing expiration time or add one to a key that did not previously have +an expiration, this can be accomplished by editing the existing item. The following commands can be used as an example +for updating an existing token. Setting the expiration time to a time in the past will effectively remove access for +that key. + +```bash +AWS_REGION="us-east-1" # change to your deployment region +token_string="YOUR_STRING_HERE" +token_expiration=$(echo $(date +%s) + 600 | bc) # token that expires in 10 minutes from now +aws --region $AWS_REGION dynamodb update-item --table-name $DEPLOYMENT_NAME-LISAApiTokenTable \ + --key '{"token": {"S": "'${token_string}'"}}' \ + --update-expression 'SET tokenExpiration=:t' \ + --expression-attribute-values '{":t": {"N": "'${token_expiration}'"}}' +``` + +### Removing a Token + +Tokens will not be automatically removed even if they are no longer valid. An owner may remove an key, expired or not, +from the database to fully revoke the key, by deleting the item. As an example, the following commands can be used to +remove a token. + +```bash +AWS_REGION="us-east-1" # change to your deployment region +token_string="YOUR_STRING_HERE" # change to the token to remove +aws --region $AWS_REGION dynamodb delete-item --table-name $DEPLOYMENT_NAME-LISAApiTokenTable \ + --key '{"token": {"S": "'${token_string}'"}}' +``` diff --git a/lib/docs/admin/api.md b/lib/docs/admin/api.md new file mode 100644 index 00000000..ad9e63e6 --- /dev/null +++ b/lib/docs/admin/api.md @@ -0,0 +1,364 @@ + +# API Usage Overview + +LISA provides robust API endpoints for managing models, both for users and administrators. These endpoints allow for operations such as listing, creating, updating, and deleting models. + +## API Gateway and ALB Endpoints + +LISA uses two primary APIs for model management: + +1. **User-facing OpenAI-Compatible API**: Available to all users for inference tasks and accessible through the LISA Serve ALB. This API provides an interface for querying and interacting with models deployed on Amazon ECS, Amazon Bedrock, or through LiteLLM. +2. **Admin-level Model Management API**: Available only to administrators through the API Gateway (APIGW). This API allows for full control of model lifecycle management, including creating, updating, and deleting models. + +### LiteLLM Routing in All Models + +Every model request is routed through LiteLLM, regardless of whether infrastructure (like ECS) is created for it. Whether deployed on ECS, external models via Bedrock, or managed through LiteLLM, all models are added to LiteLLM for traffic routing. The distinction is whether infrastructure is created (determined by request payloads), but LiteLLM integration is consistent for all models. The model management APIs will handle adding or removing model configurations from LiteLLM, and the LISA Serve endpoint will handle the inference requests against models available in LiteLLM. + +## User-facing OpenAI-Compatible API + +The OpenAI-compatible API is accessible through the LISA Serve ALB and allows users to list models available for inference tasks. Although not specifically part of the model management APIs, any model that is added or removed from LiteLLM via the model management API Gateway APIs will be reflected immediately upon queries to LiteLLM through the LISA Serve ALB. + +### Listing Models + +The `/v2/serve/models` endpoint on the LISA Serve ALB allows users to list all models available for inference in the LISA system. + +#### Request Example: + +```bash +curl -s -H 'Authorization: Bearer ' -X GET https:///v2/serve/models +``` + +#### Response Example: + +```json +{ + "data": [ + { + "id": "bedrock-embed-text-v2", + "object": "model", + "created": 1677610602, + "owned_by": "openai" + }, + { + "id": "titan-express-v1", + "object": "model", + "created": 1677610602, + "owned_by": "openai" + }, + { + "id": "sagemaker-amazon-mistrallite", + "object": "model", + "created": 1677610602, + "owned_by": "openai" + } + ], + "object": "list" +} +``` + +#### Explanation of Response Fields: + +These fields are all defined by the OpenAI API specification, which is documented [here](https://platform.openai.com/docs/api-reference/models/list). + +- `id`: A unique identifier for the model. +- `object`: The type of object, which is "model" in this case. +- `created`: A Unix timestamp representing when the model was created. +- `owned_by`: The entity responsible for the model, such as "openai." + +## Admin-level Model Management API + +This API is only accessible by administrators via the API Gateway and is used to create, update, and delete models. It supports full model lifecycle management. + +### Listing Models (Admin API) + +The `/models` route allows admins to list all models managed by the system. This includes models that are either creating, deleting, already active, or in a failed state. Models can be deployed via ECS or managed externally through a LiteLLM configuration. + +#### Request Example: + +```bash +curl -s -H "Authorization: Bearer " -X GET https:///models +``` + +#### Response Example: + +```json +{ + "models": [ + { + "autoScalingConfig": { + "minCapacity": 1, + "maxCapacity": 1, + "cooldown": 420, + "defaultInstanceWarmup": 180, + "metricConfig": { + "albMetricName": "RequestCountPerTarget", + "targetValue": 30, + "duration": 60, + "estimatedInstanceWarmup": 330 + } + }, + "containerConfig": { + "image": { + "baseImage": "vllm/vllm-openai:v0.5.0", + "type": "asset" + }, + "sharedMemorySize": 2048, + "healthCheckConfig": { + "command": [ + "CMD-SHELL", + "exit 0" + ], + "interval": 10, + "startPeriod": 30, + "timeout": 5, + "retries": 3 + }, + "environment": { + "MAX_TOTAL_TOKENS": "2048", + "MAX_CONCURRENT_REQUESTS": "128", + "MAX_INPUT_LENGTH": "1024" + } + }, + "loadBalancerConfig": { + "healthCheckConfig": { + "path": "/health", + "interval": 60, + "timeout": 30, + "healthyThresholdCount": 2, + "unhealthyThresholdCount": 10 + } + }, + "instanceType": "g5.xlarge", + "modelId": "mistral-vllm", + "modelName": "mistralai/Mistral-7B-Instruct-v0.2", + "modelType": "textgen", + "modelUrl": null, + "status": "Creating", + "streaming": true + }, + { + "autoScalingConfig": null, + "containerConfig": null, + "loadBalancerConfig": null, + "instanceType": null, + "modelId": "titan-express-v1", + "modelName": "bedrock/amazon.titan-text-express-v1", + "modelType": "textgen", + "modelUrl": null, + "status": "InService", + "streaming": true + } + ] +} +``` + +#### Explanation of Response Fields: + +- `modelId`: A unique identifier for the model. +- `modelName`: The name of the model, typically referencing the underlying service (Bedrock, SageMaker, etc.). +- `status`: The current state of the model, e.g., "Creating," "Active," or "Failed." +- `streaming`: Whether the model supports streaming inference. +- `instanceType` (optional): The instance type if the model is deployed via ECS. + +### Creating a Model (Admin API) + +LISA provides the `/models` endpoint for creating both ECS and LiteLLM-hosted models. Depending on the request payload, infrastructure will be created or bypassed (e.g., for LiteLLM-only models). + +This API accepts the same model definition parameters that were accepted in the V2 model definitions within the config.yaml file with one notable difference: the `containerConfig.image.path` field is +now omitted because it corresponded with the `inferenceContainer` selection. As a convenience, this path is no longer required. + +#### Request Example: + +``` +POST https:///models +``` + +#### Example Payload for ECS Model: + +```json +{ + "modelId": "mistral-vllm", + "modelName": "mistralai/Mistral-7B-Instruct-v0.2", + "modelType": "textgen", + "inferenceContainer": "vllm", + "instanceType": "g5.xlarge", + "streaming": true, + "containerConfig": { + "image": { + "baseImage": "vllm/vllm-openai:v0.5.0", + "type": "asset" + }, + "sharedMemorySize": 2048, + "environment": { + "MAX_CONCURRENT_REQUESTS": "128", + "MAX_INPUT_LENGTH": "1024", + "MAX_TOTAL_TOKENS": "2048" + }, + "healthCheckConfig": { + "command": ["CMD-SHELL", "exit 0"], + "interval": 10, + "startPeriod": 30, + "timeout": 5, + "retries": 3 + } + }, + "autoScalingConfig": { + "minCapacity": 1, + "maxCapacity": 1, + "cooldown": 420, + "defaultInstanceWarmup": 180, + "metricConfig": { + "albMetricName": "RequestCountPerTarget", + "targetValue": 30, + "duration": 60, + "estimatedInstanceWarmup": 330 + } + }, + "loadBalancerConfig": { + "healthCheckConfig": { + "path": "/health", + "interval": 60, + "timeout": 30, + "healthyThresholdCount": 2, + "unhealthyThresholdCount": 10 + } + } +} +``` + +#### Creating a LiteLLM-Only Model: + +```json +{ + "modelId": "titan-express-v1", + "modelName": "bedrock/amazon.titan-text-express-v1", + "modelType": "textgen", + "streaming": true +} +``` + +#### Explanation of Key Fields for Creation Payload: + +- `modelId`: The unique identifier for the model. This is any name you would like it to be. +- `modelName`: The name of the model as it appears in the system. For LISA-hosted models, this must be the S3 Key to your model artifacts, otherwise + this is the LiteLLM-compatible reference to a SageMaker Endpoint or Bedrock Foundation Model. Note: Bedrock and SageMaker resources must exist in the + same region as your LISA deployment. If your LISA installation is in us-east-1, then all SageMaker and Bedrock calls will also happen in us-east-1. + Configuration examples: + - LISA hosting: If your model artifacts are in `s3://${lisa_models_bucket}/path/to/model/weights`, then the `modelName` value here should be `path/to/model/weights` + - LiteLLM-only, Bedrock: If you want to use `amazon.titan-text-lite-v1`, your `modelName` value should be `bedrock/amazon.titan-text-lite-v1` + - LiteLLM-only, SageMaker: If you want to use a SageMaker Endpoint named `my-sm-endpoint`, then the `modelName` value should be `sagemaker/my-sm-endpoint`. +- `modelType`: The type of model, such as text generation (textgen). +- `streaming`: Whether the model supports streaming inference. +- `instanceType`: The type of EC2 instance to be used (only applicable for ECS models). +- `containerConfig`: Details about the Docker container, memory allocation, and environment variables. +- `autoScalingConfig`: Configuration related to ECS autoscaling. +- `loadBalancerConfig`: Health check configuration for load balancers. + +### Deleting a Model (Admin API) + +Admins can delete a model using the following endpoint. Deleting a model removes the infrastructure (ECS) or disconnects from LiteLLM. + +#### Request Example: + +``` +DELETE https:///models/{modelId} +``` + +#### Response Example: + +```json +{ + "status": "success", + "message": "Model mistral-vllm has been deleted successfully." +} +``` + +### Updating a Model + +LISA offers basic updating functionality for both LISA-hosted and LiteLLM-only models. For both types, the model type and streaming support can be updated +in the cases that the models were originally created with the wrong parameters. For example, if an embedding model was accidentally created as a `textgen` +model, the UpdateModel API can be used to set it to the intended `embedding` value. Additionally, for LISA-hosted models, users may update the AutoScaling +configuration to increase or decrease capacity usage for each model. Users may use this API to completely shut down all instances behind a model until +they want to add capacity back to the model for usage later. This feature can help users to effectively manage costs so that instances do not have to stay +running in time periods of little or no expected usage. + +The UpdateModel API has mutually exclusive payload fields to avoid conflicting requests. The API does not allow for shutting off a model at the same time +as updating its AutoScaling configuration, as these would introduce ambiguous intents. The API does not allow for setting AutoScaling limits to 0 and instead +requires the usage of the enable/disable functionality to allow models to fully scale down or turn back on. Metadata updates, such as changing the model type +or streaming compatibility, can happen in either type of update or simply by themselves. + +#### Request Example + +``` +PUT https:///models/{modelId} +``` + +#### Example Payloads + +##### Update Model Metadata + +This payload will simply update the model metadata, which will complete within seconds of invoking. If setting a model as an `embedding` model, then the +`streaming` option must be set to `false` or omitted as LISA does not support streaming with embedding models. Both the `streaming` and `modelType` options +may be included in any other update request. + +```json +{ + "streaming": true, + "modelType": "textgen" +} +``` + +##### Update AutoScaling Configuration + +This payload will update the AutoScaling configuration for minimum, maximum, and desired number of instances. The desired number must be between the +minimum or maximum numbers, inclusive, and all the numbers must be strictly greater than 0. If the model currently has less than the minimum number, then +the desired count will automatically raise to the minimum if a desired count is not specified. Despite setting a desired capacity, the model will scale down +to the minimum number over time if you are not hitting the scaling thresholds set when creating the model in the first place. + +The AutoScaling configuration **can** be updated while the model is in the Stopped state, but it won't be applied immediately. Instead, the configuration will +be saved until the model is started again, in which it will use the most recently updated AutoScaling configuration. + +The request will fail if the `autoScalingInstanceConfig` is defined at the same time as the `enabled` field. These options are mutually exclusive and must be +handled as separate operations. Any or all of the options within the `autoScalingInstanceConfig` may be set as needed, so if you only wish to change the `desiredCapacity`, +then that is the only option that you need to specify in the request object within the `autoScalingInstanceConfig`. + +```json +{ + "autoScalingInstanceConfig": { + "minCapacity": 2, + "maxCapacity": 4, + "desiredCapacity": 3 + } +} +``` + +##### Stop Model - Scale Down to 0 Instances + +This payload will stop all model EC2 instances and remove the model reference from LiteLLM so that users are unable to make inference requests against a model +with no capacity. This option is useful for users who wish to manage costs and turn off instances when the model is not currently needed but will be used again +in the future. + +The request will fail if the `enabled` field is defined at the same time as the `autoScalingInstanceConfig` field. These options are mutually exclusive and must be +handled as separate operations. + +```json +{ + "enabled": false +} +``` + +##### Start Model - Restore Previous AutoScaling Configuration + +After stopping a model, this payload will turn the model back on by spinning up instances, waiting for the expected spin-up time to allow models to initialize, and then +adding the reference back to LiteLLM so that users may query the model again. This is expected to be a much faster operation than creating the model through the CreateModel +API, so as long as the model details don't have to change, this in combination with the Stop payload will help to manage costs while still providing model availability as +quickly as the system can spin it up again. + +The request will fail if the `enabled` field is defined at the same time as the `autoScalingInstanceConfig` field. These options are mutually exclusive and must be +handled as separate operations. + +```json +{ + "enabled": true +} +``` diff --git a/lib/docs/admin/architecture.md b/lib/docs/admin/architecture.md new file mode 100644 index 00000000..e0570bea --- /dev/null +++ b/lib/docs/admin/architecture.md @@ -0,0 +1,41 @@ +# LLM Inference Solution for Amazon Dedicated Cloud (LISA) +![LISA Architecture](../assets/LisaArchitecture.png) +LISA is an infrastructure-as-code solution that supports model hosting and inference. Customers deploy LISA directly +into an AWS account and provision their own infrastructure. Customers bring their own models to LISA for hosting and +inference through Amazon ECS. LISA accelerates the use of Generative AI (GenAI) applications by providing scalable, +low latency access to customers’ generative LLMs and embedding language models. Customers can then focus on +experimenting with LLMs and developing GenAI applications. + +LISA’s chatbot user interface can be used for experiment with features and for production use cases. LISA enhances model +output by integrating retrieval-augmented generation (RAG) with Amazon OpenSearch or PostgreSQL’s PGVector extension, +incorporating external knowledge sources into model responses. This helps reduce the need for fine-tuning and delivers +more contextually relevant outputs. + +LISA supports OpenAI’s API Spec via the LiteLLM proxy. This means that LISA is compatible for customers to configure +with models hosted externally by supported model providers. LiteLLM also allows customers to use LISA to standardize +model orchestration and communication across model providers instead of managing each individually. With OpenAI API spec +support, LISA can also be used as a stand-in replacement for any application that already utilizes OpenAI-centric +tooling (ex: OpenAI’s Python library, LangChain). + +## Background + +LISA is a robust, AWS-native platform designed to simplify the deployment and management of Large Language Models (LLMs) in scalable, secure, and highly available environments. Drawing inspiration from the AWS open-source project [aws-genai-llm-chatbot](https://github.com/aws-samples/aws-genai-llm-chatbot), LISA builds on this foundation by offering more specialized functionality, particularly in the areas of security, modularity, and flexibility. + +One of the key differentiators of LISA is its ability to leverage the [text-generation-inference](https://github.com/huggingface/text-generation-inference/tree/main) text-generation-inference container from HuggingFace, allowing users to deploy cutting-edge LLMs. LISA also introduces several innovations that extend beyond its inspiration: + +1. **Support for Amazon Dedicated Cloud (ADC):** LISA is designed to operate in highly controlled environments like Amazon Dedicated Cloud (ADC) partitions, making it ideal for industries with stringent regulatory and security requirements. This focus on secure, isolated deployments differentiates LISA from other open-source platforms. +1. **Modular Design for Composability:** LISA's architecture is designed to be composable, splitting its components into distinct services. The core components, LISA Serve (for LLM serving and inference) and LISA Chat (for the chat interface), can be deployed as independent stacks. This modularity allows users to deploy only the parts they need, enhancing flexibility and scalability across different deployment environments. +1. **OpenAI API Specification Support:** LISA is built to support the OpenAI API specification, allowing users to replace OpenAI’s API with LISA without needing to change existing application code. This makes LISA a drop-in replacement for any workflow or application that already leverages OpenAI’s tooling, such as the OpenAI Python library or LangChain. + +## System Overview + +LISA is designed using a modular, microservices-based architecture, where each service performs a distinct function. It is composed of three core components: LISA Model Management, LISA Serve, and LISA Chat. Each of these components is responsible for specific functionality and interacts via well-defined API endpoints to ensure scalability, security, and fault tolerance across the system. + +**Key System Functionalities:** + +* **Authentication and Authorization** via AWS Cognito or OpenID Connect (OIDC) providers, ensuring secure access to both the REST API and Chat UI through token-based authentication and role-based access control. +* **Model Hosting** on AWS ECS with autoscaling and efficient traffic management using Application Load Balancers (ALBs), providing scalable and high-performance model inference. +* **Model Management** using AWS Step Functions to orchestrate complex workflows for creating, updating, and deleting models, automatically managing underlying ECS infrastructure. +* **Inference Requests** served via both the REST API and the Chat UI, dynamically routing user inputs to the appropriate ECS-hosted models for real-time inference. +* **Chat Interface** enabling users to interact with LISA through a user-friendly web interface, offering seamless real-time model interaction and session continuity. +* **Retrieval-Augmented Generation (RAG) Operations**, leveraging either OpenSearch or PGVector for efficient retrieval of relevant external data to enhance model responses. diff --git a/lib/docs/admin/components.md b/lib/docs/admin/components.md new file mode 100644 index 00000000..3df5fda1 --- /dev/null +++ b/lib/docs/admin/components.md @@ -0,0 +1,55 @@ +## LISA Components + +### LISA Model Management +![LISA Model Management Architecture](../assets/LisaModelManagement.png) +The Model Management component is responsible for managing the entire lifecycle of models in LISA. This includes creation, updating, deletion, and scaling of models deployed on ECS. The system automates and scales these operations, ensuring that the underlying infrastructure is managed efficiently. + +* **Model Hosting**: Models are containerized and deployed on AWS ECS, with each model hosted in its own isolated ECS task. This design allows models to be independently scaled based on demand. Traffic to the models is balanced using Application Load Balancers (ALBs), ensuring that the autoscaling mechanism reacts to load fluctuations in real time, optimizing both performance and availability. +* **External Model Routing**: LISA utilizes the LiteLLM proxy to route traffic to different model providers, no matter their API and payload format. Users may add models from external providers, such as SageMaker or Bedrock, to their system to allow requests to models hosted in those systems and services. LISA will simply add the configuration to LiteLLM without creating any additional supporting infrastructure. +* **Model Lifecycle Management**: AWS Step Functions are used to orchestrate the lifecycle of models, handling the creation, update, and deletion workflows. Each workflow provisions the required resources using CloudFormation templates, which manage infrastructure components like EC2 instances, security groups, and ECS services. The system ensures that the necessary security, networking, and infrastructure components are automatically deployed and configured. + * The CloudFormation stacks define essential resources using the LISA core VPC configuration, ensuring best practices for security and access across all resources in the environment. + * DynamoDB stores model metadata, while Amazon S3 securely manages model weights, enabling ECS instances to retrieve the weights dynamically during deployment. + +#### Technical Implementation + +* **Model Lifecycle**: Lifecycle operations such as creation, update, and deletion are executed by Step Functions and backed by AWS Lambda in ```lambda/models/lambda_functions.py```. +* **CloudFormation**: Infrastructure components are provisioned using CloudFormation templates, as defined in ```ecs_model_deployer/src/lib/lisa_model_stack.ts```. +* **ECS Cluster**: ECS cluster and task definitions are located in ```ecs_model_deployer/src/lib/ecsCluster.ts```, with model containers specified in ```ecs_model_deployer/src/lib/ecs-model.ts```. + + +### LISA Serve +![LISA Serve Architecture](../assets/LisaServe.png) +LISA Serve is responsible for processing inference requests and serving model predictions. This component manages user requests to interact with LLMs and ensures that the models deliver low-latency responses. + +* **Inference Requests**: Requests are routed via ALB, which serves as the main entry point to LISA’s backend infrastructure. The ALB forwards requests to the appropriate ECS-hosted model or externally-hosted model based on the request parameters. For models hosted within LISA, traffic to the models is managed with model-specific ALBs, which enable autoscaling if the models are under heavy load. LISA supports both direct REST API-based interaction and interaction through the Chat UI, enabling programmatic access or a user-friendly chat experience. +* **RAG (Retrieval-Augmented Generation)**: RAG operations enhance model responses by integrating external data sources. LISA leverages OpenSearch or PGVector (PostgreSQL) as vector stores, enabling vector-based search and retrieval of relevant knowledge to augment LLM outputs dynamically. + +#### Technical Implementation + +* RAG operations are managed through ```lambda/rag/lambda_functions.py```, which handles embedding generation and document retrieval via OpenSearch and PostgreSQL. +* Direct requests to the LISA Serve ALB entrypoint must utilize the OpenAI API spec, which we support through the use of the LiteLLM proxy. + + +### LISA Chat +![LISA Chatbot Architecture](../assets/LisaChat.png) +LISA Chat provides a customizable chat interface that enables users to interact with models in real-time. This component ensures that users have a seamless experience for submitting queries and maintaining session continuity. + +* **Chat Interface**: The Chat UI is hosted as a static website on Amazon S3 and is served via API Gateway. Users can interact with models directly through the web-based frontend, sending queries and viewing real-time responses from the models. The interface is integrated with LISA's backend services for model inference, retrieval augmented generation, and session management. +* **Session History Management**: LISA maintains session histories using DynamoDB, allowing users to retrieve and continue previous conversations seamlessly. This feature is crucial for maintaining continuity in multi-turn conversations with the models. + +#### Technical Implementation + +* The Chat UI is implemented in the ```lib/user-interface/react/``` folder and is deployed using the scripts in the ```scripts/``` folder. +* Session management logic is handled in ```lambda/session/lambda_functions.py```, where session data is stored and retrieved from DynamoDB. +* RAG operations are defined in lambda/repository/lambda_functions.py + + +## Interaction Flow + +1. **User Interaction with Chat UI or API:** Users can interact with LISA through the Chat UI or REST API. Each interaction is authenticated using AWS Cognito or OIDC, ensuring secure access. +1. **Request Routing:** The API Gateway securely routes user requests to the appropriate backend services, whether for fetching the chat UI, performing RAG operations, or managing models. +1. **Model Management:** Administrators can deploy, update, or delete models via the Model Management API, which triggers ECS deployment and scaling workflows. +1. **Model Inference:** Inference requests are routed to ECS-hosted models or external models via the LiteLLM proxy. Responses are served back to users through the ALB. +1. **RAG Integration:** When RAG is enabled, LISA retrieves relevant documents from OpenSearch or PGVector, augmenting the model's response with external knowledge. +1. **Session Continuity:** User session data is stored in DynamoDB, ensuring that users can retrieve and continue previous conversations across multiple interactions. +1. **Autoscaling:** ECS tasks automatically scale based on system load, with ALBs distributing traffic across available instances to ensure performance. diff --git a/lib/docs/admin/deploy.md b/lib/docs/admin/deploy.md new file mode 100644 index 00000000..bf44b04e --- /dev/null +++ b/lib/docs/admin/deploy.md @@ -0,0 +1,70 @@ + +# Deployment +## Using pre-built resources + +A default configuration will build the necessary containers, lambda layers, and production optimized +web application at build time. In the event that you would like to use pre-built resources due to +network connectivity reasons or other concerns with the environment where you'll be deploying LISA +you can do so. + +- For ECS containers (Models, APIs, etc) you can modify the `containerConfig` block of + the corresponding entry in `config.yaml`. For container images you can provide a path to a directory + from which a docker container will be built (default), a path to a tarball, an ECR repository arn and + optional tag, or a public registry path. + - We provide immediate support for HuggingFace TGI and TEI containers and for vLLM containers. The `example_config.yaml` + file provides examples for TGI and TEI, and the only difference for using vLLM is to change the + `inferenceContainer`, `baseImage`, and `path` options, as indicated in the snippet below. All other options can + remain the same as the model definition examples we have for the TGI or TEI models. vLLM can also support embedding + models in this way, so all you need to do is refer to the embedding model artifacts and remove the `streaming` field + to deploy the embedding model. + - vLLM has support for the OpenAI Embeddings API, but model support for it is limited because the feature is new. Currently, + the only supported embedding model with vLLM is [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct), + but this list is expected to grow over time as vLLM updates. + ```yaml + ecsModels: + - modelName: your-model-name + inferenceContainer: tgi + baseImage: ghcr.io/huggingface/text-generation-inference:2.0.1 + ``` +- If you are deploying the LISA Chat User Interface you can optionally specify the path to the pre-built + website assets using the top level `webAppAssetsPath` parameter in `config.yaml`. Specifying this path + (typically `lib/user-interface/react/dist`) will avoid using a container to build and bundle the assets + at CDK build time. +- For the lambda layers you can specify the path to a local zip archive of the layer code by including + the optional `lambdaLayerAssets` block in `config.yaml` similar to the following: + +``` +lambdaLayerAssets: + authorizerLayerPath: lib/core/layers/authorizer_layer.zip + commonLayerPath: lib/core/layers/common_layer.zip + fastapiLayerPath: /path/to/fastapi_layer.zip + sdkLayerPath: lib/rag/layers/sdk_layer.zip +``` + +## Deploying + +Now that we have everything setup we are ready to deploy. + +```bash +make deploy +``` + +By default, all stacks will be deployed but a particular stack can be deployed by providing the `STACK` argument to the `deploy` target. + +```bash +make deploy STACK=LisaServe +``` + +Available stacks can be listed by running: + +```bash +make listStacks +``` + +After the `deploy` command is run, you should see many docker build outputs and eventually a CDK progress bar. The deployment should take about 10-15 minutes and will produce a single cloud formation output for the websocket URL. + +You can test the deployment with the integration test: + +```bash +pytest lisa-sdk/tests --url --verify | false +``` diff --git a/lib/docs/admin/error.md b/lib/docs/admin/error.md new file mode 100644 index 00000000..81b9fb78 --- /dev/null +++ b/lib/docs/admin/error.md @@ -0,0 +1,69 @@ + +# Error Handling for API Requests + +In the LISA model management API, error handling is designed to ensure robustness and consistent responses when errors occur during the execution of API requests. This section provides a detailed explanation of the error handling mechanisms in place, including the types of errors that are managed, how they are raised, and what kind of responses clients can expect when these errors occur. + +## Common Errors and Their HTTP Responses + +Below is a list of common errors that can occur in the system, along with the HTTP status codes and response structures that are returned to the client. + +### ModelNotFoundError + +* **Description**: Raised when a model that is requested for retrieval or deletion is not found in the system. +* **HTTP Status Code**: `404 Not Found` +* **Response Body**: + +```json +{ + "error": "ModelNotFoundError", + "message": "The requested model with ID could not be found." +} +``` + +* **Example Scenario**: When a client attempts to fetch details of a model that does not exist in the database, the `ModelNotFoundError` is raised. + +### ModelAlreadyExistsError + +* **Description:** Raised when a request to create a model is made, but the model already exists in the system. +* **HTTP Status Code**: `400` +* **Response Body**: + +```json +{ + "error": "ModelAlreadyExistsError", + "message": "A model with the given configuration already exists." +} +``` + +* **Example Scenario:** A client attempts to create a model with an ID or name that already exists in the database. The system detects the conflict and raises the `ModelAlreadyExistsError`. + +### InvalidInputError (Hypothetical Example) + +* **Description**: Raised when the input provided by the client for creating or updating a model is invalid or does not conform to expected formats. +* **HTTP Status Code**: `400 Bad Request` +* **Response Body**: + +```json +{ + "error": "InvalidInputError", + "message": "The input provided is invalid. Please check the required fields and formats." +} +``` + +* **Example Scenario**: The client submits a malformed JSON body or omits required fields in a model creation request, triggering an `InvalidInputError`. + +## Handling Validation Errors + +Validation errors are handled across the API via utility functions and model transformation logic. These errors typically occur when user inputs fail validation checks or when required data is missing from a request. + +### Example Response for Validation Error: + +* **HTTP Status Code**: `422 Unprocessable Entity` +* **Response Body**: + +```json +{ + "error": "ValidationError", + "message": "The input provided does not meet the required validation criteria." +} +``` diff --git a/lib/docs/admin/getting-started.md b/lib/docs/admin/getting-started.md new file mode 100644 index 00000000..7358be69 --- /dev/null +++ b/lib/docs/admin/getting-started.md @@ -0,0 +1,303 @@ + +# Getting Started with LISA + +LISA (LLM Inference Solution for Amazon Dedicated Cloud) is an advanced infrastructure solution for deploying and +managing Large Language Models (LLMs) on AWS. This guide will walk you through the setup process, from prerequisites +to deployment. + +## Prerequisites + +Before beginning, ensure you have: + +1. An AWS account with appropriate permissions. + 1. Because of all the resource creation that happens as part of CDK deployments, we expect Administrator or Administrator-like permissions with resource creation and mutation permissions. + Installation will not succeed if this profile does not have permissions to create and edit arbitrary resources for the system. + **Note**: This level of permissions is not required for the runtime of LISA, only its deployment and subsequent updates. +2. AWS CLI installed and configured +3. Familiarity with AWS Cloud Development Kit (CDK) and infrastructure-as-code principles +4. Python 3.9 or later +5. Node.js 14 or later +6. Docker installed and running +7. Sufficient disk space for model downloads and conversions + +If you're new to CDK, review the [AWS CDK Documentation](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html) and consult with your AWS support team. + +> [!TIP] +> To minimize version conflicts and ensure a consistent deployment environment, it is recommended to execute the following steps on a dedicated EC2 instance. However, LISA can be deployed from any machine that meets the prerequisites listed above. + +## Step 1: Clone the Repository + +Ensure you're working with the latest stable release of LISA: + +```bash +git clone -b main --single-branch +cd lisa +``` + +## Step 2: Set Up Environment Variables + +Create and configure your `config.yaml` file: + +```bash +cp example_config.yaml config.yaml +``` + +Set the following environment variables: + +```bash +export PROFILE=my-aws-profile # Optional, can be left blank +export DEPLOYMENT_NAME=my-deployment +export ENV=dev # Options: dev, test, or prod +``` + +## Step 3: Set Up Python and TypeScript Environments + +Install system dependencies and set up both Python and TypeScript environments: + +```bash +# Install system dependencies +sudo apt-get update +sudo apt-get install -y jq + +# Install Python packages +pip3 install --user --upgrade pip +pip3 install yq huggingface_hub s5cmd + +# Set up Python environment +make createPythonEnvironment + +# Activate your python environment +# The command is the output from the previous make command) + +# Install Python Requirements +make installPythonRequirements + +# Set up TypeScript environment +make createTypeScriptEnvironment +make installTypeScriptRequirements +``` + +## Step 4: Configure LISA + +Edit the `config.yaml` file to customize your LISA deployment. Key configurations include: + +- AWS account and region settings +- Model configurations +- Authentication settings +- Networking and infrastructure preferences + +## Step 5: Stage Model Weights + +LISA requires model weights to be staged in the S3 bucket specified in your `config.yaml` file, assuming the S3 bucket follows this structure: + +``` +s3:/// +s3://// +s3://// +... +s3:/// +``` + +**Example:** + +``` +s3:///mistralai/Mistral-7B-Instruct-v0.2 +s3:///mistralai/Mistral-7B-Instruct-v0.2/ +s3:///mistralai/Mistral-7B-Instruct-v0.2/ +... +``` + +To automatically download and stage the model weights defined by the `ecsModels` parameter in your `config.yaml`, use the following command: + +```bash +make modelCheck +``` + +This command verifies if the model's weights are already present in your S3 bucket. If not, it downloads the weights, converts them to the required format, and uploads them to your S3 bucket. Ensure adequate disk space is available for this process. + +> **WARNING** +> As of LISA 3.0, the `ecsModels` parameter in `config.yaml` is solely for staging model weights in your S3 bucket. Previously, before models could be managed through the [API](https://github.com/awslabs/LISA/blob/develop/README.md#creating-a-model-admin-api) or via the Model Management section of the [Chatbot](https://github.com/awslabs/LISA/blob/develop/README.md#chatbot-example), this parameter also dictated which models were deployed. + +> **NOTE** +> For air-gapped systems, before running `make modelCheck` you should manually download model artifacts and place them in a `models` directory at the project root, using the structure: `models/`. + +> **NOTE** +> This process is primarily designed and tested for HuggingFace models. For other model formats, you will need to manually create and upload safetensors. + +## Step 6: Configure Identity Provider + +In the `config.yaml` file, configure the `authConfig` block for authentication. LISA supports OpenID Connect (OIDC) providers such as AWS Cognito or Keycloak. Required fields include: + +- `authority`: URL of your identity provider +- `clientId`: Client ID for your application +- `adminGroup`: Group name for users with model management permissions +- `jwtGroupsProperty`: Path to the groups field in the JWT token +- `additionalScopes` (optional): Extra scopes for group membership information + +#### Cognito Configuration Example: +In Cognito, the `authority` will be the URL to your User Pool. As an example, if your User Pool ID, not the name, is `us-east-1_example`, and if it is +running in `us-east-1`, then the URL to put in the `authority` field would be `https://cognito-idp.us-east-1.amazonaws.com/us-east-1_example`. The `clientId` +can be found in your User Pool's "App integration" tab from within the AWS Management Console, and at the bottom of the page, you will see the list of clients +and their associated Client IDs. The ID here is what we will need for the `clientId` field. + + +```yaml +authConfig: + authority: https://cognito-idp.us-east-1.amazonaws.com/us-east-1_example + clientId: your-client-id + adminGroup: AdminGroup + jwtGroupsProperty: cognito:groups +``` + +#### Keycloak Configuration Example: +In Keycloak, the `authority` will be the URL to your Keycloak server. The `clientId` is likely not a random string like in the Cognito clients, and instead +will be a string configured by your Keycloak administrator. Your administrator will be able to give you a client name or create a client for you to use for +this application. Once you have this string, use that as the `clientId` within the `authConfig` block. + +```yaml +authConfig: + authority: https://your-keycloak-server.com + clientId: your-client-name + adminGroup: AdminGroup + jwtGroupsProperty: realm_access.roles +``` + +## Step 7: Configure LiteLLM +We utilize LiteLLM under the hood to allow LISA to respond to the [OpenAI specification](https://platform.openai.com/docs/api-reference). +For LiteLLM configuration, a key must be set up so that the system may communicate with a database for tracking all the models that are added or removed +using the [Model Management API](#admin-level-model-management-api). The key must start with `sk-` and then can be any arbitrary string. We recommend generating a new UUID and then using that as +the key. Configuration example is below. + + +```yaml +litellmConfig: + general_settings: + master_key: sk-00000000-0000-0000-0000-000000000000 # needed for db operations, create your own key # pragma: allowlist-secret + model_list: [] +``` + +**Note**: It is possible to add LiteLLM-only models to this configuration, but it is not recommended as the models in this configuration will not show in the +Chat or Model Management UIs. Instead, use the [Model Management UI](#admin-level-model-management-api) to add or remove LiteLLM-only model configurations. + +## Step 8: Set Up SSL Certificates (Development Only) + +**WARNING: THIS IS FOR DEV ONLY** +When deploying for dev and testing you can use a self-signed certificate for the REST API ALB. You can create this by using the script: `gen-cert.sh` and uploading it to `IAM`. + +```bash +export REGION= +./scripts/gen-certs.sh +aws iam upload-server-certificate --server-certificate-name --certificate-body file://scripts/server.pem --private-key file://scripts/server.key +``` + +Update your `config.yaml` with the certificate ARN: + +```yaml +restApiConfig: + loadBalancerConfig: + sslCertIamArn: arn:aws:iam:::server-certificate/ +``` + +## Step 9: Customize Model Deployment + +In the `ecsModels` section of `config.yaml`, allow our deployment process to pull the model weights for you. + +During the deployment process, LISA will optionally attempt to download your model weights if you specify an optional `ecsModels` +array, this will only work in non ADC regions. Specifically, see the `ecsModels` section of the [example_config.yaml](./example_config.yaml) file. +Here we define the model name, inference container, and baseImage: + +```yaml +ecsModels: + - modelName: your-model-name + inferenceContainer: tgi + baseImage: ghcr.io/huggingface/text-generation-inference:2.0.1 +``` + +## Step 10: Bootstrap CDK (If Not Already Done) + +If you haven't bootstrapped your AWS account for CDK: + +```bash +make bootstrap +``` + +## Recommended LiteLLM Configuration Options + +While LISA is designed to be flexible, configuring external models requires careful consideration. The following guide +provides a recommended minimal setup for integrating various model types with LISA using LiteLLM. + +### Configuration Overview + +This example configuration demonstrates how to set up: +1. A SageMaker Endpoint +2. An Amazon Bedrock Model +3. A self-hosted OpenAI-compatible text generation model +4. A self-hosted OpenAI-compatible embedding model + +**Note:** Ensure that all endpoints and models are in the same AWS region as your LISA installation. + +### SageMaker Endpoints and Bedrock Models + +LISA supports adding existing SageMaker Endpoints and Bedrock Models to the LiteLLM configuration. As long as these +services are in the same region as the LISA installation, LISA can use them alongside any other deployed models. + +**To use a SageMaker Endpoint:** +1. Install LISA without initially referencing the SageMaker Endpoint. +2. Create a SageMaker Model using the private subnets of the LISA deployment. +3. This setup allows the LISA REST API container to communicate with any Endpoint using that SageMaker Model. + +**SageMaker Endpoints and Bedrock Models can be configured:** +- Statically at LISA deployment time +- Dynamically using the LISA Model Management API + +**Important:** Endpoints or Models statically defined during LISA deployment cannot be removed or updated using the +LISA Model Management API, and they will not show in the Chat UI. These will only show as part of the OpenAI `/models` API. +Although there is support for it, we recommend using the [Model Management API](#admin-level-model-management-api) instead of the following static configuration. + +### Example Configuration + +```yaml +dev: + litellmConfig: + litellm_settings: + telemetry: false # Disable telemetry to LiteLLM servers (recommended for VPC deployments) + drop_params: true # Ignore unrecognized parameters instead of failing + + model_list: + # 1. SageMaker Endpoint Configuration + - model_name: test-endpoint # Human-readable name, can be anything and will be used for OpenAI API calls + litellm_params: + model: sagemaker/test-endpoint # Prefix required for SageMaker Endpoints and "test-endpoint" matches Endpoint name + api_key: ignored # Provide an ignorable placeholder key to avoid LiteLLM deployment failures + lisa_params: + model_type: textgen + streaming: true + + # 2. Amazon Bedrock Model Configuration + - model_name: bedrock-titan-express # Human-readable name for future OpenAI API calls + litellm_params: + model: bedrock/amazon.titan-text-express-v1 # Prefix required for Bedrock Models, and exact name of Model to use + api_key: ignored # Provide an ignorable placeholder key to avoid LiteLLM deployment failures + lisa_params: + model_type: textgen + streaming: true + + # 3. Custom OpenAI-compatible Text Generation Model + - model_name: custom-openai-model # Used in future OpenAI-compatible calls to LiteLLM + litellm_params: + model: openai/custom-provider/textgen-model # Format: openai// + api_base: https://your-domain-here:443/v1 # Your model's base URI + api_key: ignored # Provide an ignorable placeholder key to avoid LiteLLM deployment failures + lisa_params: + model_type: textgen + streaming: true + + # 4. Custom OpenAI-compatible Embedding Model + - model_name: custom-openai-embedding-model # Used in future OpenAI-compatible calls to LiteLLM + litellm_params: + model: openai/modelProvider/modelName # Prefix required for OpenAI-compatible models followed by model provider and name details + api_base: https://your-domain-here:443/v1 # Your model's base URI + api_key: ignored # Provide an ignorable placeholder key to avoid LiteLLM deployment failures + lisa_params: + model_type: embedding +``` diff --git a/lib/docs/admin/idp.md b/lib/docs/admin/idp.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/admin/idp.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/admin/lite-llm.md b/lib/docs/admin/lite-llm.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/admin/lite-llm.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/admin/model-management.md b/lib/docs/admin/model-management.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/admin/model-management.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/admin/security.md b/lib/docs/admin/security.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/admin/security.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/assets/LisaArchitecture.png b/lib/docs/assets/LisaArchitecture.png new file mode 100644 index 0000000000000000000000000000000000000000..884e275bfc68c3a977f51f491d8ee7e87c4a7ac7 GIT binary patch literal 232928 zcmZTwbzD^4);>eXF!YepF-QrhfRr@K0MaFmf*>IdjpR_`U=0n@NVl|fs5A%&NH-{{ zbn_kbz4yD`>p%WxoU_l~>#TU5XRRYfQ(cJ!Mh^o3fJ7Oos0{#k7y!V{gI)r^q2tF_ z2mgY&Xe-GBMZJta0Du)xR+Q8AG+Rp|h@%`j`Ydy=vUR|lpt)!SCrtZe;Nps2v6&>2 z0*@$+n?_bMjD(8n$IZ3+eHE>%7MrZ~f~q#Lnd|50$!i_zrSVn~@m5zx_ivH)MRezG zCI(B%Ol>BpaDsnYQo$EG)xo1D|_()n$~3MG0DlH^jg>8r_0CZ6f2uPDH14| zpDxXt0bh@qa}<#ur~l`wYdS!vEXlJR_@Bixif|4ZxDYCn$6e7{|1A8^UB+R$dAiDU zJ{HDz+<)H?3B!kCBIa&7Mf}Guz#lg;f!|N`C%x0YSYSdEjcq3x62J2IZjo?&I>#7< zXxL!J#UZ}wqGzpd;#;}=Im=%M`~4v!5C`>oKsgHL5n=zo|N6%UXcF+o4$s@N{@zb2 zJ9iVREDRO<;m*bCXlQFsHPQcAFCU;Za0&Xo5btXoV$poD{kMFX|7qY<94$VJOBfgt z(Zj>7MeQm_(_Ecw^{=N!k4yZ|{hCvF_44jLZt)V}#kL<;cx;;GntUidvdk_p`JlZr zl+P6(AMajs-!~T@F^HRKx#Es-= z^{^K&7DsJTqE!VxDC+g~^hDo&LDK;tV6@15`cdNP)2F4r$L^ywZuv8tH4`4wzRu&1 zt2*Ajk>(vSaZ&r|H<4peXa4z@O?MpI>CtXvIEPZ_a|(`=*s!oL1Q(Z5o?iZy@yf?9 z>^f=9ecronOc)l`?->oc&+c)!Cf<5ldUAHWnRohOIhhaKQZb|J7daT(?|8jxtMU9) zFi-qPXVtV{?b>*i#zy1$S=?9|1vN?m@+{#)nnc@gkNERQFMM1%PgYqOevm@1`ogeQ=KW;Dg#a_4koTDJh8b--U2WRp>`L&oT3vQYE5mI-w+&1!S zk*TqMu^B%Jy)gFa_EN|DHs;lDjKT<<=R)#f9B*!E`<)%y#0%SM-@6vU%_RTI@)9OX zBSpZ#+_z#(==@~+P2&&A%{q?dM3>iIuTH7e-s51p_c%5Tv(=Lp1~p{#Qh&sn`-RW8 zhITz8VW4940uQoV~CMZgh#C-g!mO30{>b{A9|6UevLGQDjVcXVd@OcxC`9 zfYzmB+5T4VU1j2TxCp!V(8>xKO3Gx_godm%oz1-vRN0)WA9AZsnX&k=-7C3>;Y!B# zNgv-;mp-)G^>cTx^f^D>@4PJHv0-96Qj|2|wORbgFr*KjaLBtG|?15Y4JRj^*S zr%E~Uik(^IHf>)kkzP*{z@GjLb|zlLw*` zsGu5_>_q3VuLXuWlTWJ4zbyBNP8X!ICR$dT0}p36Vx3IUij-XHy593(G-?WY{d%E=NKn@(uBVR}E_JvNY3BgQ6j0)C z197WU^|gs!E4igs9qjHU@TXYxCkb#7zwXb)xa%VC9B5TsJb63D_jYd0JCa0y6ti%1xgok-?! 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z!w#XGIRCl&UpK-}8v~qAAzUKtzUF^VWLyJ3POQGP+tR#Nl(mP^-*^4JyEzRsW7hZ4 zcR!c|Oul(|pyu3L845q(|G2H@weQHq(^+EaH z9ilQra}nA(x1J0AyOX;w4wHi3*3^0H|1SIAz1VGcvn^bXyUf(d-K@ nna1_T6*23wMQ}BI4W^5$I4(#OyM(MTfqxbzw#E+(@udF;+i, device_map="auto") +``` + +or + +```python +AutoModelForSeq2SeqLM.from_pretrained(, device_map="auto") +``` + +### HuggingFace Embedding Models + +Embedding models often utilize custom codebases and are not as uniform as generation models. For this reason you will likely need to create a new `inferenceContainer`. Follow the [example](https://github.com/awslabs/LISA/tree/develop/lib/serve/ecs-model/embedding/instructor) provided for the `instructor` model. + +### vLLM Models + +In addition to the support we have for the TGI and TEI containers, we support hosting models using the [vLLM container](https://docs.vllm.ai/en/latest/). vLLM abides by the OpenAI specification, and as such allows both text generation and embedding on the models that vLLM supports. +See the [deployment](#deployment) section for details on how to set up the vLLM container for your models. Similar to how the HuggingFace containers will serve safetensor weights downloaded from the +HuggingFace website, vLLM will do the same, and our configuration will allow you to serve these artifacts automatically. vLLM does not have many supported models for embeddings, but as they become available, +LISA will support them as long as the vLLM container version is updated in the config.yaml file and as long as the model's safetensors can be found in S3. diff --git a/lib/docs/config/model-management-api.md b/lib/docs/config/model-management-api.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/config/model-management-api.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/config/model-management-ui.md b/lib/docs/config/model-management-ui.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/config/model-management-ui.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/config/vector-stores.md b/lib/docs/config/vector-stores.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/config/vector-stores.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/index.md b/lib/docs/index.md new file mode 100644 index 00000000..d25cc398 --- /dev/null +++ b/lib/docs/index.md @@ -0,0 +1,30 @@ +# https://vitepress.dev/reference/default-theme-home-page +layout: home + +hero: +name: "LISA Documentation" +text: "LLM Inference Solution for Amazon Dedicated Cloud (LISA)" +actions: +- theme: brand +text: Getting Started +link: /admin/getting-started + +features: +- title: Authentication and Authorization + details: via AWS Cognito or OpenID Connect (OIDC) providers, ensuring secure access to both the REST API and Chat UI through token-based authentication and role-based access control. +- title: Model Hosting + details: on AWS ECS with autoscaling and efficient traffic management using Application Load Balancers (ALBs), providing scalable and high-performance model inference. +- title: Model Management + details: using AWS Step Functions to orchestrate complex workflows for creating, updating, and deleting models, automatically managing underlying ECS infrastructure. +- title: Inference Requests + details: served via both the REST API and the Chat UI, dynamically routing user inputs to the appropriate ECS-hosted models for real-time inference. +- title: Chat Interface + details: enabling users to interact with LISA through a user-friendly web interface, offering seamless real-time model interaction and session continuity. +- title: Retrieval-Augmented Generation (RAG) Operations + details: leveraging either OpenSearch or PGVector for efficient retrieval of relevant external data to enhance model responses. + + +### License Notice + +Although this repository is released under the Apache 2.0 license, when configured to use PGVector as a RAG store it uses +the third party `psycopg2-binary` library. The `psycopg2-binary` project's licensing includes the [LGPL with exceptions](https://github.com/psycopg/psycopg2/blob/master/LICENSE) license. diff --git a/lib/docs/index.ts b/lib/docs/index.ts new file mode 100644 index 00000000..74e7aa5f --- /dev/null +++ b/lib/docs/index.ts @@ -0,0 +1,178 @@ +/** + Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"). + You may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + */ + +import * as path from 'node:path'; +import * as fs from 'node:fs'; + +import { CfnOutput, RemovalPolicy, Stack, StackProps } from 'aws-cdk-lib'; +import { AwsIntegration, EndpointType, RestApi } from 'aws-cdk-lib/aws-apigateway'; +import { Role, ServicePrincipal } from 'aws-cdk-lib/aws-iam'; +import { BlockPublicAccess, Bucket, BucketEncryption } from 'aws-cdk-lib/aws-s3'; +import { BucketDeployment, Source } from 'aws-cdk-lib/aws-s3-deployment'; +import { Construct } from 'constructs'; +import { BaseProps } from '../schema'; + +/** + * Properties for DocsStack Construct. + */ +type DocsProps = {} & BaseProps & StackProps; + +/** + * User Interface Construct. + */ +export class LisaDocsStack extends Stack { + + /** + * @param {Construct} scope - The parent or owner of the construct. + * @param {string} id - The unique identifier for the construct within its scope. + * @param {DocsProps} props - The properties of the construct. + */ + constructor (scope: Construct, id: string, props: DocsProps) { + super(scope, id, props); + const { config } = props; + + // Create Docs S3 bucket + const docsBucket = new Bucket(this, 'DocsBucket', { + removalPolicy: RemovalPolicy.DESTROY, + autoDeleteObjects: true, + encryption: BucketEncryption.S3_MANAGED, + enforceSSL: true, + blockPublicAccess: BlockPublicAccess.BLOCK_ALL, + websiteIndexDocument: 'index.html', + websiteErrorDocument: '404.html', + }); + + // Ensure dist folder is created (for tests) + const docsPath = path.join(__dirname, 'dist'); + if (!fs.existsSync(docsPath)) { + fs.mkdirSync(docsPath); + } + // Deploy local folder to S3 + new BucketDeployment(this, 'DeployDocsWebsite', { + sources: [Source.asset(docsPath)], + destinationBucket: docsBucket, + }); + + // REST API GW S3 role + const apiGatewayRole = new Role(this, `${Stack.of(this).stackName}-s3-reader-role`, { + assumedBy: new ServicePrincipal('apigateway.amazonaws.com'), + description: 'Allows API gateway to proxy static website assets', + }); + docsBucket.grantRead(apiGatewayRole); + + // Create API Gateway + const api = new RestApi(this, 'DocsApi', { + description: 'API Gateway for S3 hosted website', + endpointConfiguration: { types: [EndpointType.REGIONAL] }, + deployOptions: { + stageName: 'lisa', + }, + binaryMediaTypes: ['*/*'], + }); + + const defaultIntegration = new AwsIntegration({ + service: 's3', + region: config.region, + integrationHttpMethod: 'GET', + path: `${docsBucket.bucketName}/index.html`, + options: { + credentialsRole: apiGatewayRole, + integrationResponses: [{ + statusCode: '200', + responseParameters: { + 'method.response.header.Content-Type': '\'text/html\'', + }, + }], + }, + }); + + // Create API Gateway integration with S3 + const s3Integration = new AwsIntegration({ + service: 's3', + region: config.region, + integrationHttpMethod: 'GET', + path: `${docsBucket.bucketName}/{key}`, + options: { + credentialsRole: apiGatewayRole, + requestParameters: { + 'integration.request.path.key': 'method.request.path.key', + }, + integrationResponses: [ + { + statusCode: '200', + responseParameters: { + 'method.response.header.Content-Type': 'integration.response.header.Content-Type', + 'method.response.header.Content-Disposition': 'integration.response.header.Content-Disposition', + 'method.response.header.Content-Length': 'integration.response.header.Content-Length', + }, + }, + { + selectionPattern: '403', + statusCode: '404', + responseParameters: { + 'method.response.header.Content-Type': '\'text/html\'', + }, + responseTemplates: { + 'text/html': `#set($context.responseOverride.header.Content-Type = 'text/html') + #set($context.responseOverride.status = 404) + #set($context.responseOverride.header.Location = "$context.domainName/404.html")`, + }, + }, + ], + }, + }); + + // Add GET method to API Gateway + api.root.addMethod('GET', defaultIntegration, { + methodResponses: [ + { + statusCode: '200', + responseParameters: { + 'method.response.header.Content-Type': true, + }, + }, + ], + }); + + api.root.addResource('{key+}').addMethod('GET', s3Integration, { + requestParameters: { + 'method.request.path.key': true, + }, + methodResponses: [ + { + statusCode: '200', + responseParameters: { + 'method.response.header.Content-Type': true, + 'method.response.header.Content-Disposition': true, + 'method.response.header.Content-Length': true, + }, + }, + { + statusCode: '404', + responseParameters: { + 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The react based web application can be optionally deployed to demonstrate the capabilities of LISA Serve. The chatbot consists of a static react based single page application hosted via API GW S3 proxy integration. The app connects to the LISA Serve REST API and an optional RAG API. The app integrates with an OIDC compatible IdP and allows users to interact directly with any of the textgen models hosted with LISA Serve. If the optional RAG stack is deployed then users can also leverage the embeddings models and AWS OpenSearch or PGVector to demonstrate chat with RAG. Chat sessions are maintained in dynamodb table and a number of parameters are exposed through the UI to allow experimentation with various parameters including prompt, temperature, top k, top p, max tokens, and more. + +## Local development + +### Configuring Pre-Commit Hooks + +To ensure code quality and consistency, this project uses pre-commit hooks. These hooks are configured to perform checks, such as linting and formatting, helping to catch potential issues early. These hooks are run automatically on each push to a remote branch but if you wish to run them locally before each commit, follow these steps: + +1. Install pre-commit: `pip install pre-commit` +2. Install the git hook scripts: `pre-commit install` + +The hooks will now run automatically on changed files but if you wish to test them against all files, run the following command: `pre-commit run --all-files`. + +### Run REST API locally + +``` +cd lib/serve/rest-api +pip install -r src/requirements.txt +export AWS_REGION= +export AUTHORITY= +export CLIENT_ID= +export REGISTERED_MODELS_PS_NAME= +export TOKEN_TABLE_NAME="/LISAApiTokenTable" +gunicorn -k uvicorn.workers.UvicornWorker -w 2 -b "0.0.0.0:8080" "src.main:app" +``` + +### Run example chatbot locally + +Create `lib/user-interface/react/public/env.js` file with the following contents: + +``` +window.env = { + AUTHORITY: '', + CLIENT_ID: '', + JWT_GROUPS_PROP: '', + ADMIN_GROUP: '', + CUSTOM_SCOPES:[], + // Alternatively you can set this to be your REST api elb endpoint + RESTAPI_URI: 'http://localhost:8080/', + API_BASE_URL: 'https://${deployment_id}.execute-api.${regional_domain}/${deployment_stage}', + RESTAPI_VERSION: 'v2', + "MODELS": [ + { + "model": "streaming-textgen-model", + "streaming": true, + "modelType": "textgen" + }, + { + "model": "non-streaming-textgen-model", + "streaming": false, + "modelType": "textgen" + }, + { + "model": "embedding-model", + "streaming": null, + "modelType": "embedding" + } + ] +} +``` + +Launch the Chat UI: + +``` +cd lib/user-interface/react/ +npm run dev +``` + +# Usage and Features + +The LISA Serve endpoint can be used independently of the Chat UI, and the following shows a few examples of how to do that. The Serve endpoint +will still validate user auth, so if you have a Bearer token from the IdP configured with LISA, we will honor it, or if you've set up an API +token using the [DynamoDB instructions](#programmatic-api-tokens), we will also accept that. This diagram shows the LISA Serve components that +would be utilized during direct REST API requests. + +## OpenAI Specification Compatibility + +We now provide greater support for the [OpenAI specification](https://platform.openai.com/docs/api-reference) for model inference and embeddings. +We utilize LiteLLM as a proxy for both models we spin up on behalf of the user and additional models configured through the config.yaml file, and because of that, the +LISA REST API endpoint allows for a central location for making text generation and embeddings requests. We support, and are not limited to, the following popular endpoint +routes as long as your underlying models can also respond to them. + +- /models +- /chat/completions +- /completions +- /embeddings + +By supporting the OpenAI spec, we can more easily allow users to integrate their collection of models into their LLM applications and workflows. In LISA, users can authenticate +using their OpenID Connect Identity Provider, or with an API token created through the DynamoDB token workflow as described [here](#programmatic-api-tokens). Once the token +is retrieved, users can use that in direct requests to the LISA Serve REST API. If using the IdP, users must set the 'Authorization' header, otherwise if using the API token, +either the 'Api-Key' header or the 'Authorization' header. After that, requests to `https://${lisa_serve_alb}/v2/serve` will handle the OpenAI API calls. As an example, the following call can list all +models that LISA is aware of, assuming usage of the API token. If you are using a self-signed cert, you must also provide the `--cacert $path` option to specify a CA bundle to trust for SSL verification. + +```shell +curl -s -H 'Api-Key: your-token' -X GET https://${lisa_serve_alb}/v2/serve/models +``` + +If using the IdP, the request would look like the following: + +```shell +curl -s -H 'Authorization: Bearer your-token' -X GET https://${lisa_serve_alb}/v2/serve/models +``` + +When using a library that requests an OpenAI-compatible base_url, you can provide `https://${lisa_serve_alb}/v2/serve` here. All of the OpenAI routes will +automatically be added to the base URL, just as we appended `/models` to the `/v2/serve` route for listing all models tracked by LISA. + + +## Continue JetBrains and VS Code Plugin + +For developers that desire an LLM assistant to help with programming tasks, we support adding LISA as an LLM provider for the [Continue plugin](https://www.continue.dev). +To add LISA as a provider, open up the Continue plugin's `config.json` file and locate the `models` list. In this list, add the following block, replacing the placeholder URL +with your own REST API domain or ALB. The `/v2/serve` is required at the end of the `apiBase`. This configuration requires an API token as created through the [DynamoDB workflow](#programmatic-api-tokens). + +```json +{ + "model": "AUTODETECT", + "title": "LISA", + "apiBase": "https:///v2/serve", + "provider": "openai", + "apiKey": "your-api-token" // pragma: allowlist-secret +} +``` + +Once you save the `config.json` file, the Continue plugin will call the `/models` API to get a list of models at your disposal. The ones provided by LISA will be prefaced +with "LISA" or with the string you place in the `title` field of the config above. Once the configuration is complete and a model is selected, you can use that model to +generate code and perform AI assistant tasks within your development environment. See the [Continue documentation](https://docs.continue.dev/how-to-use-continue) for more +information about its features, capabilities, and usage. + +### Usage in LLM Libraries + +If your workflow includes using libraries, such as [LangChain](https://python.langchain.com/v0.2/docs/introduction/) or [OpenAI](https://github.com/openai/openai-python), +then you can place LISA right in your application by changing only the endpoint and headers for the client objects. As an example, using the OpenAI library, the client would +normally be instantiated and invoked with the following block. + +```python +from openai import OpenAI + +client = OpenAI( + api_key="my_key" # pragma: allowlist-secret not a real key +) +client.models.list() +``` + +To use the models being served by LISA, the client needs only a few changes: + +1. Specify the `base_url` as the LISA Serve ALB, using the /v2/serve route at the end, similar to the apiBase in the [Continue example](#continue-jetbrains-and-vs-code-plugin) +2. Add the API key that you generated from the [token generation steps](#programmatic-api-tokens) as your `api_key` field. +3. If using a self-signed cert, you must provide a certificate path for validating SSL. If you're using an ACM or public cert, then this may be omitted. +1. We provide a convenience function in the `lisa-sdk` for generating a cert path from an IAM certificate ARN if one is provided in the `RESTAPI_SSL_CERT_ARN` environment variable. + +The Code block will now look like this and you can continue to use the library without any other modifications. + +```python +# for self-signed certificates +import boto3 +from lisapy.utils import get_cert_path +# main client library +from openai import DefaultHttpxClient, OpenAI + +iam_client = boto3.client("iam") +cert_path = get_cert_path(iam_client) + +client = OpenAI( + api_key="my_key", # pragma: allowlist-secret not a real key + base_url="https:///v2/serve", + http_client=DefaultHttpxClient(verify=cert_path), # needed for self-signed certs on your ALB, can be omitted otherwise +) +client.models.list() +``` diff --git a/lib/docs/user/context-windows.md b/lib/docs/user/context-windows.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/context-windows.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/user/history.md b/lib/docs/user/history.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/history.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/user/model-kwargs.md b/lib/docs/user/model-kwargs.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/model-kwargs.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/user/models.md b/lib/docs/user/models.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/models.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/user/nonrag-management.md b/lib/docs/user/nonrag-management.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/nonrag-management.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/user/prompt-engineering.md b/lib/docs/user/prompt-engineering.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/prompt-engineering.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/docs/user/rag.md b/lib/docs/user/rag.md new file mode 100644 index 00000000..46409041 --- /dev/null +++ b/lib/docs/user/rag.md @@ -0,0 +1 @@ +# TODO diff --git a/lib/schema.ts b/lib/schema.ts index 7e780ffe..bd34ccb0 100644 --- a/lib/schema.ts +++ b/lib/schema.ts @@ -679,6 +679,7 @@ const LiteLLMConfig = z.object({ * @property {string[]} [accountNumbersEcr=null] - List of AWS account numbers for ECR repositories. * @property {boolean} [deployRag=false] - Whether to deploy RAG stacks. * @property {boolean} [deployChat=true] - Whether to deploy chat stacks. + * @property {boolean} [deployDocs=true] - Whether to deploy docs stacks. * @property {boolean} [deployUi=true] - Whether to deploy UI stacks. * @property {string} logLevel - Log level for application. * @property {AuthConfigSchema} authConfig - Authorization configuration. @@ -729,6 +730,7 @@ const RawConfigSchema = z .optional(), deployRag: z.boolean().optional().default(true), deployChat: z.boolean().optional().default(true), + deployDocs: z.boolean().optional().default(true), deployUi: z.boolean().optional().default(true), logLevel: z.union([z.literal('DEBUG'), z.literal('INFO'), z.literal('WARNING'), z.literal('ERROR')]).default('DEBUG'), authConfig: AuthConfigSchema.optional(), @@ -792,6 +794,14 @@ const RawConfigSchema = z message: 'Chat stack is needed for UI stack. You must set deployChat to true if deployUi is true.', }, ) + .refine( + (config) => { + return !(config.deployRag && !config.deployUi); + }, + { + message: 'UI Stack is needed for Rag stack. You must set deployUI to true if deployRag is true.', + }, + ) .refine( (config) => { return ( diff --git a/lib/stages.ts b/lib/stages.ts index 247dc932..19ffce02 100644 --- a/lib/stages.ts +++ b/lib/stages.ts @@ -42,6 +42,7 @@ import { LisaRagStack } from './rag'; import { BaseProps, stackSynthesizerType } from './schema'; import { LisaServeApplicationStack } from './serve'; import { UserInterfaceStack } from './user-interface'; +import { LisaDocsStack } from './docs'; type CustomLisaServeApplicationStageProps = {} & BaseProps; type LisaServeApplicationStageProps = CustomLisaServeApplicationStageProps & StageProps; @@ -171,57 +172,68 @@ export class LisaServeApplicationStage extends Stage { apiDeploymentStack.addDependency(modelsApiDeploymentStack); stacks.push(modelsApiDeploymentStack); - const chatStack = new LisaChatApplicationStack(this, 'LisaChat', { - ...baseStackProps, - authorizer: apiBaseStack.authorizer, - stackName: createCdkId([config.deploymentName, config.appName, 'chat', config.deploymentStage]), - description: `LISA-chat: ${config.deploymentName}-${config.deploymentStage}`, - restApiId: apiBaseStack.restApiId, - rootResourceId: apiBaseStack.rootResourceId, - vpc: networkingStack.vpc, - }); - chatStack.addDependency(apiBaseStack); - chatStack.addDependency(coreStack); - apiDeploymentStack.addDependency(chatStack); - stacks.push(chatStack); - - const uiStack = new UserInterfaceStack(this, 'LisaUserInterface', { - ...baseStackProps, - architecture: ARCHITECTURE, - stackName: createCdkId([config.deploymentName, config.appName, 'ui', config.deploymentStage]), - description: `LISA-user-interface: ${config.deploymentName}-${config.deploymentStage}`, - restApiId: apiBaseStack.restApiId, - rootResourceId: apiBaseStack.rootResourceId, - }); - uiStack.addDependency(chatStack); - uiStack.addDependency(serveStack); - uiStack.addDependency(apiBaseStack); - apiDeploymentStack.addDependency(uiStack); - stacks.push(uiStack); - - if (config.deployRag) { - const ragStack = new LisaRagStack(this, 'LisaRAG', { + if (config.deployChat) { + const chatStack = new LisaChatApplicationStack(this, 'LisaChat', { ...baseStackProps, authorizer: apiBaseStack.authorizer, - description: `LISA-rag: ${config.deploymentName}-${config.deploymentStage}`, - endpointUrl: serveStack.endpointUrl, - modelsPs: serveStack.modelsPs, + stackName: createCdkId([config.deploymentName, config.appName, 'chat', config.deploymentStage]), + description: `LISA-chat: ${config.deploymentName}-${config.deploymentStage}`, restApiId: apiBaseStack.restApiId, rootResourceId: apiBaseStack.rootResourceId, - stackName: createCdkId([config.deploymentName, config.appName, 'rag', config.deploymentStage]), vpc: networkingStack.vpc, }); - ragStack.addDependency(coreStack); - ragStack.addDependency(iamStack); - ragStack.addDependency(apiBaseStack); - stacks.push(ragStack); - - if (config.deployRag) { - uiStack.addDependency(ragStack); - apiDeploymentStack.addDependency(ragStack); + chatStack.addDependency(apiBaseStack); + chatStack.addDependency(coreStack); + apiDeploymentStack.addDependency(chatStack); + stacks.push(chatStack); + + if (config.deployUi) { + const uiStack = new UserInterfaceStack(this, 'LisaUserInterface', { + ...baseStackProps, + architecture: ARCHITECTURE, + stackName: createCdkId([config.deploymentName, config.appName, 'ui', config.deploymentStage]), + description: `LISA-user-interface: ${config.deploymentName}-${config.deploymentStage}`, + restApiId: apiBaseStack.restApiId, + rootResourceId: apiBaseStack.rootResourceId, + }); + uiStack.addDependency(chatStack); + uiStack.addDependency(serveStack); + uiStack.addDependency(apiBaseStack); + apiDeploymentStack.addDependency(uiStack); + stacks.push(uiStack); + + if (config.deployRag) { + const ragStack = new LisaRagStack(this, 'LisaRAG', { + ...baseStackProps, + authorizer: apiBaseStack.authorizer, + description: `LISA-rag: ${config.deploymentName}-${config.deploymentStage}`, + endpointUrl: serveStack.endpointUrl, + modelsPs: serveStack.modelsPs, + restApiId: apiBaseStack.restApiId, + rootResourceId: apiBaseStack.rootResourceId, + stackName: createCdkId([config.deploymentName, config.appName, 'rag', config.deploymentStage]), + vpc: networkingStack.vpc, + }); + ragStack.addDependency(coreStack); + ragStack.addDependency(iamStack); + ragStack.addDependency(apiBaseStack); + stacks.push(ragStack); + + if (config.deployRag) { + uiStack.addDependency(ragStack); + apiDeploymentStack.addDependency(ragStack); + } + } } } + if (config.deployDocs) { + const docsStack = new LisaDocsStack(this, 'LisaDocs', { + ...baseStackProps + }); + stacks.push(docsStack); + } + stacks.push(apiDeploymentStack); // Set resource tags diff --git a/lib/user-interface/react/package-lock.json b/lib/user-interface/react/package-lock.json index 6003ea49..a5d4e3af 100644 --- a/lib/user-interface/react/package-lock.json +++ b/lib/user-interface/react/package-lock.json @@ -741,9 +741,9 @@ } }, "node_modules/@jridgewell/sourcemap-codec": { - 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"version": "1.0.0", - "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.0.0.tgz", - "integrity": "sha512-1fygroTLlHu66zi26VoTDv8yRgm0Fccecssto+MhsZ0D/DGW2sm8E8AjW7NU5VVTRt5GxbeZ5qBuJr+HyLYkjQ==" + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz", + "integrity": "sha512-xceH2snhtb5M9liqDsmEw56le376mTZkEX/jEb/RxNFyegNul7eNslCXP9FDj/Lcu0X8KEyMceP2ntpaHrDEVA==" }, "node_modules/picomatch": { "version": "2.3.1", @@ -5130,9 +5130,9 @@ } }, "node_modules/postcss": { - "version": "8.4.31", - "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.31.tgz", - "integrity": "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==", + "version": "8.4.47", + "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.4.47.tgz", + "integrity": "sha512-56rxCq7G/XfB4EkXq9Egn5GCqugWvDFjafDOThIdMBsI15iqPqR5r15TfSr1YPYeEI19YeaXMCbY6u88Y76GLQ==", "funding": [ { "type": "opencollective", @@ -5148,9 +5148,9 @@ } ], "dependencies": { - "nanoid": "^3.3.6", - "picocolors": "^1.0.0", - "source-map-js": "^1.0.2" + "nanoid": "^3.3.7", + "picocolors": "^1.1.0", + "source-map-js": "^1.2.1" }, "engines": { "node": "^10 || ^12 || >=14" @@ -5972,9 +5972,9 @@ } }, "node_modules/source-map-js": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.0.2.tgz", - "integrity": "sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==", + "version": "1.2.1", + "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz", + "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==", "engines": { "node": ">=0.10.0" } diff --git a/package-lock.json b/package-lock.json index b5881842..0664a897 100644 --- a/package-lock.json +++ b/package-lock.json @@ -7,6 +7,7 @@ "": { "name": "lisa", "version": "3.1.0", + "hasInstallScript": true, "license": "Apache-2.0", "dependencies": { "aws-cdk-lib": "2.125.0", diff --git a/package.json b/package.json index a06ca857..41e648f2 100644 --- a/package.json +++ b/package.json @@ -10,8 +10,10 @@ "test": "jest", "cdk": "cdk", "prepare": "husky install", - "migrate-properties": "node ./scripts/migrate-properties.mjs" - }, + "migrate-properties": "node ./scripts/migrate-properties.mjs", + "postinstall": "(cd lib/user-interface/react && npm install) && (cd lib/docs && npm install)", + "postbuild": "(cd lib/user-interface/react && npm build) && (cd lib/docs && npm build)" +}, "devDependencies": { "@aws-cdk/aws-lambda-python-alpha": "2.125.0-alpha.0", "@aws-sdk/client-iam": "^3.490.0", diff --git a/test/cdk/stacks/docs.test.ts b/test/cdk/stacks/docs.test.ts new file mode 100644 index 00000000..e5e2a924 --- /dev/null +++ b/test/cdk/stacks/docs.test.ts @@ -0,0 +1,104 @@ +/** + Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. + + Licensed under the Apache License, Version 2.0 (the "License"). + You may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +*/ + +import * as fs from 'fs'; +import * as path from 'path'; + +import { App, Aspects, Stack, StackProps } from 'aws-cdk-lib'; +import { Annotations, Match } from 'aws-cdk-lib/assertions'; +import { AwsSolutionsChecks, NIST80053R5Checks } from 'cdk-nag'; +import * as yaml from 'js-yaml'; + +import { LisaDocsStack } from '../../../lib/docs/index'; +import { BaseProps, Config, ConfigFile, ConfigSchema } from '../../../lib/schema'; + +const regions = ['us-east-1', 'us-gov-west-1', 'us-gov-east-1', 'us-isob-east-1', 'us-iso-east-1', 'us-iso-west-1']; + +describe.each(regions)('Docs Nag Pack Tests | Region Test: %s', (awsRegion) => { + let app: App; + let stack: Stack; + let config: Config; + let baseStackProps: BaseProps & StackProps; + + beforeAll(() => { + app = new App(); + + // Read configuration file + const configFilePath = path.join(__dirname, '../../../test/cdk/mocks/config.yaml'); + const configFile = yaml.load(fs.readFileSync(configFilePath, 'utf8')) as ConfigFile; + const configEnv = configFile.env || 'dev'; + const configData = configFile[configEnv]; + if (!configData) { + throw new Error(`Configuration for environment "${configEnv}" not found.`); + } + // Validate and parse configuration + try { + config = ConfigSchema.parse(configData); + } catch (error) { + if (error instanceof Error) { + console.error('Error parsing the configuration:', error.message); + } else { + console.error('An unexpected error occurred:', error); + } + process.exit(1); + } + + baseStackProps = { + env: { + account: '012345678901', + region: awsRegion, + }, + config, + }; + }); + + beforeEach(() => { + + stack = new LisaDocsStack(app, 'LisaDocs', { + ...baseStackProps, + }); + + // WHEN + Aspects.of(stack).add(new AwsSolutionsChecks({ verbose: true })); + Aspects.of(stack).add(new NIST80053R5Checks({ verbose: true })); + }); + + afterEach(() => { + app = new App(); + stack = new Stack(); + }); + + //TODO Update expect values to remediate CDK NAG findings and remove debug + test('AwsSolutions CDK NAG Warnings', () => { + const warnings = Annotations.fromStack(stack).findWarning('*', Match.stringLikeRegexp('AwsSolutions-.*')); + expect(warnings.length).toBe(1); + }); + + test('AwsSolutions CDK NAG Errors', () => { + const errors = Annotations.fromStack(stack).findError('*', Match.stringLikeRegexp('AwsSolutions-.*')); + expect(errors.length).toBe(23); + }); + + test('NIST800.53r5 CDK NAG Warnings', () => { + const warnings = Annotations.fromStack(stack).findWarning('*', Match.stringLikeRegexp('NIST.*')); + expect(warnings.length).toBe(0); + }); + + test('NIST800.53r5 CDK NAG Errors', () => { + const errors = Annotations.fromStack(stack).findError('*', Match.stringLikeRegexp('NIST.*')); + expect(errors.length).toBe(13); + }); +}); diff --git a/tsconfig.json b/tsconfig.json index 5473529e..747a2f76 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -19,7 +19,8 @@ "inlineSources": true, "experimentalDecorators": true, "strictPropertyInitialization": false, - "typeRoots": ["./node_modules/@types"] + "typeRoots": ["./node_modules/@types"], + "outDir": "dist/" }, - "exclude": ["node_modules", "cdk.out", ".git"] + "exclude": ["node_modules", "cdk.out", ".git", "dist"] }

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