Add yamlfmt precommit hook (#2584)
Co-authored-by: Shuchang Zheng <wintonzheng0325@gmail.com>
This commit is contained in:
Asher Foa
@@ -17,7 +17,6 @@ services:
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interval: 5s
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timeout: 5s
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retries: 5
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skyvern:
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image: public.ecr.aws/skyvern/skyvern:latest
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restart: on-failure
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@@ -45,82 +44,80 @@ services:
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# "C:\Program Files\Google\Chrome\Application\chrome.exe" --remote-debugging-port=9222 --user-data-dir="C:\chrome-cdp-profile" --no-first-run --no-default-browser-check
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# /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome --remote-debugging-port=9222 --user-data-dir="/Users/yourusername/chrome-cdp-profile" --no-first-run --no-default-browser-check
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# - BROWSER_REMOTE_DEBUGGING_URL=http://host.docker.internal:9222/
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# =========================
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# LLM Settings - Recommended to use skyvern CLI, `skyvern init llm` to setup your LLM's
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# =========================
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# OpenAI Support:
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# If you want to use OpenAI as your LLM provider, uncomment the following lines and fill in your OpenAI API key.
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# - ENABLE_OPENAI=true
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# - LLM_KEY=OPENAI_GPT4O
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# - OPENAI_API_KEY=<your_openai_key>
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# Gemini Support:
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# Gemini is a new LLM provider that is currently in beta. You can use it by uncommenting the following lines and filling in your Gemini API key.
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# - LLM_KEY=GEMINI
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# - ENABLE_GEMINI=true
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# - GEMINI_API_KEY=YOUR_GEMINI_KEY
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# - LLM_KEY=GEMINI_2.5_PRO_PREVIEW_03_25
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# If you want to use other LLM provider, like azure and anthropic:
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# - ENABLE_ANTHROPIC=true
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# - LLM_KEY=ANTHROPIC_CLAUDE3.5_SONNET
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# - ANTHROPIC_API_KEY=<your_anthropic_key>
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# Microsoft Azure OpenAI support:
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# If you'd like to use Microsoft Azure OpenAI as your managed LLM service integration with Skyvern, use the environment variables below.
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# In your Microsoft Azure subscription, you will need to provision the OpenAI service and deploy a model, in order to utilize it.
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# 1. Login to the Azure Portal
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# 2. Create an Azure Resource Group
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# 3. Create an OpenAI resource in the Resource Group (choose a region and pricing tier)
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# 4. From the OpenAI resource's Overview page, open the "Azure AI Foundry" portal (click the "Explore Azure AI Foundry Portal" button)
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# 5. In Azure AI Foundry, click "Shared Resources" --> "Deployments"
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# 6. Click "Deploy Model" --> "Deploy Base Model" --> select a model (specify this model "Deployment Name" value for the AZURE_DEPLOYMENT variable below)
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# - ENABLE_AZURE=true
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# - LLM_KEY=AZURE_OPENAI # Leave this value static, don't change it
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# - AZURE_DEPLOYMENT=<your_azure_deployment> # Use the OpenAI model "Deployment Name" that you deployed, using the steps above
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# - AZURE_API_KEY=<your_azure_api_key> # Copy and paste Key1 or Key2 from the OpenAI resource in Azure Portal
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# - AZURE_API_BASE=<your_azure_api_base> # Copy and paste the "Endpoint" from the OpenAI resource in Azure Portal (eg. https://xyzxyzxyz.openai.azure.com/)
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# - AZURE_API_VERSION=<your_azure_api_version> # Specify a valid Azure OpenAI data-plane API version (eg. 2024-08-01-preview) Docs: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference
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# Amazon Bedrock Support:
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# Amazon Bedrock is a managed service that enables you to invoke LLMs and bill them through your AWS account.
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# To use Amazon Bedrock as the LLM provider for Skyvern, specify the following environment variables.
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# 1. In the AWS IAM console, create a new AWS IAM User (name it whatever you want)
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# 2. Assign the "AmazonBedrockFullAccess" policy to the user
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# 3. Generate an IAM Access Key under the IAM User's Security Credentials tab
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# 4. In the Amazon Bedrock console, go to "Model Access"
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# 5. Click Modify Model Access button
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# 6. Enable "Claude 3.5 Sonnet v2" and save changes
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# - ENABLE_BEDROCK=true
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# - LLM_KEY=BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET # This is the Claude 3.5 Sonnet "V2" model. Change to BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET_V1 for the non-v2 version.
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# - AWS_REGION=us-west-2 # Replace this with a different AWS region, if you desire
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# - AWS_ACCESS_KEY_ID=FILL_ME_IN_PLEASE
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# - AWS_SECRET_ACCESS_KEY=FILL_ME_IN_PLEASE
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# Ollama Support:
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# Ollama is a local LLM provider that can be used to run models locally on your machine.
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# - LLM_KEY=OLLAMA
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# - ENABLE_OLLAMA=true
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# - OLLAMA_MODEL=qwen2.5:7b-instruct
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# - OLLAMA_SERVER_URL=http://host.docker.internal:11434
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# Open Router Support:
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# - ENABLE_OPENROUTER=true
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# - LLM_KEY=OPENROUTER
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# - OPENROUTER_API_KEY=<your_openrouter_api_key>
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# - OPENROUTER_MODEL=mistralai/mistral-small-3.1-24b-instruct
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# Groq Support:
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# - ENABLE_GROQ=true
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# - LLM_KEY=GROQ
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# - GROQ_API_KEY=<your_groq_api_key>
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# - GROQ_MODEL=llama-3.1-8b-instant
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# =========================
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# LLM Settings - Recommended to use skyvern CLI, `skyvern init llm` to setup your LLM's
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# =========================
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# OpenAI Support:
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# If you want to use OpenAI as your LLM provider, uncomment the following lines and fill in your OpenAI API key.
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# - ENABLE_OPENAI=true
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# - LLM_KEY=OPENAI_GPT4O
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# - OPENAI_API_KEY=<your_openai_key>
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# Gemini Support:
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# Gemini is a new LLM provider that is currently in beta. You can use it by uncommenting the following lines and filling in your Gemini API key.
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# - LLM_KEY=GEMINI
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# - ENABLE_GEMINI=true
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# - GEMINI_API_KEY=YOUR_GEMINI_KEY
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# - LLM_KEY=GEMINI_2.5_PRO_PREVIEW_03_25
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# If you want to use other LLM provider, like azure and anthropic:
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# - ENABLE_ANTHROPIC=true
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# - LLM_KEY=ANTHROPIC_CLAUDE3.5_SONNET
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# - ANTHROPIC_API_KEY=<your_anthropic_key>
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# Microsoft Azure OpenAI support:
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# If you'd like to use Microsoft Azure OpenAI as your managed LLM service integration with Skyvern, use the environment variables below.
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# In your Microsoft Azure subscription, you will need to provision the OpenAI service and deploy a model, in order to utilize it.
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# 1. Login to the Azure Portal
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# 2. Create an Azure Resource Group
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# 3. Create an OpenAI resource in the Resource Group (choose a region and pricing tier)
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# 4. From the OpenAI resource's Overview page, open the "Azure AI Foundry" portal (click the "Explore Azure AI Foundry Portal" button)
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# 5. In Azure AI Foundry, click "Shared Resources" --> "Deployments"
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# 6. Click "Deploy Model" --> "Deploy Base Model" --> select a model (specify this model "Deployment Name" value for the AZURE_DEPLOYMENT variable below)
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# - ENABLE_AZURE=true
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# - LLM_KEY=AZURE_OPENAI # Leave this value static, don't change it
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# - AZURE_DEPLOYMENT=<your_azure_deployment> # Use the OpenAI model "Deployment Name" that you deployed, using the steps above
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# - AZURE_API_KEY=<your_azure_api_key> # Copy and paste Key1 or Key2 from the OpenAI resource in Azure Portal
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# - AZURE_API_BASE=<your_azure_api_base> # Copy and paste the "Endpoint" from the OpenAI resource in Azure Portal (eg. https://xyzxyzxyz.openai.azure.com/)
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# - AZURE_API_VERSION=<your_azure_api_version> # Specify a valid Azure OpenAI data-plane API version (eg. 2024-08-01-preview) Docs: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference
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# Amazon Bedrock Support:
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# Amazon Bedrock is a managed service that enables you to invoke LLMs and bill them through your AWS account.
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# To use Amazon Bedrock as the LLM provider for Skyvern, specify the following environment variables.
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# 1. In the AWS IAM console, create a new AWS IAM User (name it whatever you want)
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# 2. Assign the "AmazonBedrockFullAccess" policy to the user
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# 3. Generate an IAM Access Key under the IAM User's Security Credentials tab
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# 4. In the Amazon Bedrock console, go to "Model Access"
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# 5. Click Modify Model Access button
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# 6. Enable "Claude 3.5 Sonnet v2" and save changes
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# - ENABLE_BEDROCK=true
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# - LLM_KEY=BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET # This is the Claude 3.5 Sonnet "V2" model. Change to BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET_V1 for the non-v2 version.
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# - AWS_REGION=us-west-2 # Replace this with a different AWS region, if you desire
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# - AWS_ACCESS_KEY_ID=FILL_ME_IN_PLEASE
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# - AWS_SECRET_ACCESS_KEY=FILL_ME_IN_PLEASE
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# Ollama Support:
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# Ollama is a local LLM provider that can be used to run models locally on your machine.
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# - LLM_KEY=OLLAMA
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# - ENABLE_OLLAMA=true
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# - OLLAMA_MODEL=qwen2.5:7b-instruct
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# - OLLAMA_SERVER_URL=http://host.docker.internal:11434
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# Open Router Support:
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# - ENABLE_OPENROUTER=true
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# - LLM_KEY=OPENROUTER
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# - OPENROUTER_API_KEY=<your_openrouter_api_key>
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# - OPENROUTER_MODEL=mistralai/mistral-small-3.1-24b-instruct
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# Groq Support:
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# - ENABLE_GROQ=true
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# - LLM_KEY=GROQ
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# - GROQ_API_KEY=<your_groq_api_key>
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# - GROQ_MODEL=llama-3.1-8b-instant
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# Maximum tokens to use: (only set for OpenRouter aand Ollama)
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# - LLM_CONFIG_MAX_TOKENS=128000
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# Maximum tokens to use: (only set for OpenRouter aand Ollama)
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# - LLM_CONFIG_MAX_TOKENS=128000
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# Bitwarden Settings
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# If you are looking to integrate Skyvern with a password manager (eg Bitwarden), you can use the following environment variables.
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# - BITWARDEN_SERVER=http://localhost # OPTIONAL IF YOU ARE SELF HOSTING BITWARDEN
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# - BITWARDEN_SERVER_PORT=8002 # OPTIONAL IF YOU ARE SELF HOSTING BITWARDEN
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# - BITWARDEN_CLIENT_ID=FILL_ME_IN_PLEASE
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# - BITWARDEN_CLIENT_SECRET=FILL_ME_IN_PLEASE
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# - BITWARDEN_MASTER_PASSWORD=FILL_ME_IN_PLEASE
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# Bitwarden Settings
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# If you are looking to integrate Skyvern with a password manager (eg Bitwarden), you can use the following environment variables.
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# - BITWARDEN_SERVER=http://localhost # OPTIONAL IF YOU ARE SELF HOSTING BITWARDEN
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# - BITWARDEN_SERVER_PORT=8002 # OPTIONAL IF YOU ARE SELF HOSTING BITWARDEN
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# - BITWARDEN_CLIENT_ID=FILL_ME_IN_PLEASE
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# - BITWARDEN_CLIENT_SECRET=FILL_ME_IN_PLEASE
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# - BITWARDEN_MASTER_PASSWORD=FILL_ME_IN_PLEASE
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depends_on:
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postgres:
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condition: service_healthy
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@@ -129,7 +126,6 @@ services:
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interval: 5s
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timeout: 5s
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retries: 5
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skyvern-ui:
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image: public.ecr.aws/skyvern/skyvern-ui:latest
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restart: on-failure
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