chart/env/prod.yaml

image:
  repository: huggingface
  name: chat-ui

nodeSelector:
  role-huggingchat: "true"

tolerations:
  - key: "huggingface.co/huggingchat"
    operator: "Equal"
    value: "true"
    effect: "NoSchedule"

serviceAccount:
  enabled: true
  create: true
  name: huggingchat-prod

ingress:
  path: "/chat"
  annotations:
    alb.ingress.kubernetes.io/healthcheck-path: "/healthcheck"
    alb.ingress.kubernetes.io/listen-ports: "[{\"HTTP\": 80}, {\"HTTPS\": 443}]"
    alb.ingress.kubernetes.io/group.name: "hub-prod"
    alb.ingress.kubernetes.io/scheme: "internet-facing"
    alb.ingress.kubernetes.io/ssl-redirect: "443"
    alb.ingress.kubernetes.io/tags: "Env=prod,Project=hub,Terraform=true"
    alb.ingress.kubernetes.io/target-node-labels: "role-hub-utils=true"
    kubernetes.io/ingress.class: "alb"

ingressInternal:
  enabled: true
  path: "/chat"
  annotations:
    alb.ingress.kubernetes.io/group.name: hub-prod-internal-public
    alb.ingress.kubernetes.io/healthcheck-path: "/healthcheck"
    alb.ingress.kubernetes.io/listen-ports: "[{\"HTTP\": 80}, {\"HTTPS\": 443}]"
    alb.ingress.kubernetes.io/load-balancer-name: hub-prod-internal-public
    alb.ingress.kubernetes.io/target-group-attributes: deregistration_delay.timeout_seconds=30
    alb.ingress.kubernetes.io/target-node-labels: role-hub-lb=true
    alb.ingress.kubernetes.io/target-type: ip
    kubernetes.io/ingress.class: "alb"

envVars:
  ADDRESS_HEADER: 'X-Forwarded-For'
  ADMIN_CLI_LOGIN: "false"
  ALTERNATIVE_REDIRECT_URLS: '["huggingchat://login/callback"]'
  APP_BASE: "/chat"
  ALLOW_IFRAME: "false"
  COMMUNITY_TOOLS: "true"
  COOKIE_SAMESITE: "lax"
  COOKIE_SECURE: "true"
  ENABLE_ASSISTANTS: "true"
  ENABLE_ASSISTANTS_RAG: "true"
  ENABLE_CONFIG_MANAGER: "false"
  METRICS_PORT: 5565
  LOG_LEVEL: "debug"
  METRICS_ENABLED: "true"
  MODELS: >
    [
      {
        "name": "meta-llama/Llama-3.3-70B-Instruct",
        "id": "meta-llama/Llama-3.3-70B-Instruct",
        "description": "Ideal for everyday use. A fast and extremely capable model matching closed source models' capabilities. Now with the latest Llama 3.3 weights!",
        "modelUrl": "https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct",
        "websiteUrl": "https://llama.meta.com/",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/meta-logo.png",
        "tools": true,
        "preprompt": "",
        "parameters": {
          "stop": ["<|endoftext|>", "<|eot_id|>"],
          "temperature": 0.6,
          "max_new_tokens": 1024,
          "truncate": 7167
        },
        "endpoints": [{"type" : "inference-client"}],
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ]
      },
      {
        "name": "Qwen/Qwen3-235B-A22B",
        "description": "Qwen's flagship model featuring optional reasoning. Exceptional performance with benchmarks rivaling R1 and o1.",
        "modelUrl": "https://huggingface.co/Qwen/Qwen3-235B-A22B",
        "websiteUrl": "https://qwenlm.github.io/blog/qwen3/",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png",
        "preprompt": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
        "reasoning": {
          "type": "tokens",
          "beginToken": "<think>",
          "endToken": "</think>"
        },
        "parameters": {
          "stop": ["<|endoftext|>", "<|im_end|>"],
          "temperature": 0.6,
        },
        "tools": true,
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12) /nothink"
          },
          {
            "title": "Build a website",
            "prompt": "Generate a snazzy static landing page for a local coffee shop using HTML and CSS. You can use tailwind using <script src='https://cdn.tailwindcss.com'></script>."
          },
          {
            "title": "Larger number",
            "prompt": "9.11 or 9.9 which number is larger?"
          },
        ],
        "endpoints": [
          {
            "type": "inference-client",
            "baseURL": "https://api-inference.endpoints.huggingface.tech/models/Qwen/Qwen3-235B-A22B/v1"
          }
        ]
      },
      {
        "name": "Qwen/Qwen2.5-72B-Instruct",
        "description": "The latest Qwen open model with improved role-playing, long text generation and structured data understanding.",
        "modelUrl": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct",
        "websiteUrl": "https://qwenlm.github.io/blog/qwen2.5/",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png",
        "preprompt": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
        "parameters": {
          "stop": ["<|endoftext|>", "<|im_end|>"],
          "temperature": 0.6,
          "truncate": 28672,
          "max_new_tokens": 3072
        },
        "tools": true,
        "endpoints": [{"type" : "inference-client"}],
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ]
      },
      {
        "name": "CohereLabs/c4ai-command-r-plus-08-2024",
        "description": "Cohere's largest language model, optimized for conversational interaction and tool use. Now with the 2024 update!",
        "modelUrl": "https://huggingface.co/CohereLabs/c4ai-command-r-plus-08-2024",
        "websiteUrl": "https://docs.cohere.com/docs/command-r-plus",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/cohere-logo.png",
        "tools": true,
        "systemRoleSupported": false,
        "parameters": {
          "stop": ["<|END_OF_TURN_TOKEN|>", "<|im_end|>"],
          "truncate": 28672,
          "max_new_tokens": 2048,
          "temperature": 0.3
        },
        "endpoints": [{"type" : "inference-client"}],
        "promptExamples": [
          {
            "title": "Generate image",
            "prompt": "Generate the portrait of a scientific mouse in its laboratory."
          },
          {
            "title": "Review code",
            "prompt": "Review this pull request: https://github.com/huggingface/chat-ui/pull/1131/files"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          }
        ]
      },
      {
        "name": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
        "modelUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
        "websiteUrl": "https://deepseek.com/",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/deepseek-logo.png",
        "description": "The first reasoning model from DeepSeek, distilled into a 32B dense model. Outperforms o1-mini on multiple benchmarks.",
        "reasoning": {
          "type": "tokens",
          "beginToken": "",
          "endToken": "</think>"
        },
        "tools": true,
        "promptExamples": [
          {
            "title": "Rs in strawberry",
            "prompt": "how many R in strawberry?"
          },
          {
            "title": "Larger number",
            "prompt": "9.11 or 9.9 which number is larger?"
          },
          {
            "title": "Measuring 6 liters",
            "prompt": "I have a 6- and a 12-liter jug. I want to measure exactly 6 liters."
          }
        ],
        "endpoints": [
          {
            "type": "inference-client",
            "baseURL": "https://b8xf586h164t4vk7.us-east-1.aws.endpoints.huggingface.cloud/v1"
          }
        ]
      },
      {
        "name": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF",
        "modelUrl": "https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF",
        "websiteUrl": "https://www.nvidia.com/",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/nvidia-logo.png",
        "description": "Nvidia's latest Llama fine-tune, topping alignment benchmarks and optimized for instruction following.",
        "parameters": {
          "stop": ["<|eot_id|>", "<|im_end|>"],
          "temperature": 0.5,
          "truncate": 28672,
          "max_new_tokens": 2048
        },
        "promptExamples": [
          {
            "title": "Rs in strawberry",
            "prompt": "how many R in strawberry?"
          },
          {
            "title": "Larger number",
            "prompt": "9.11 or 9.9 which number is larger?"
          },
          {
            "title": "Measuring 6 liters",
            "prompt": "I have a 6- and a 12-liter jug. I want to measure exactly 6 liters."
          }
        ],
        "endpoints": [
          {
            "type": "inference-client",
            "baseURL": "https://api-inference.endpoints.huggingface.tech/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF/v1"
          }
        ]
      },
      {
        "name": "Qwen/QwQ-32B",
        "tools": true,
        "preprompt": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step.",
        "modelUrl": "https://huggingface.co/Qwen/QwQ-32B",
        "websiteUrl": "https://qwenlm.github.io/blog/qwq-32b/",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png",
        "description": "QwQ is the latest reasoning model released by the Qwen team, approaching the capabilities of R1 in benchmarks.",
        "reasoning": {
          "type": "tokens",
          "beginToken": "",
          "endToken": "</think>"
        },
        "promptExamples": [
          {
            "title": "Rs in strawberry",
            "prompt": "how many R in strawberry?"
          },
          {
            "title": "Larger number",
            "prompt": "9.11 or 9.9 which number is larger?"
          },
          {
            "title": "Measuring 6 liters",
            "prompt": "I have a 6- and a 12-liter jug. I want to measure exactly 6 liters."
          }
        ],
        "endpoints": [
          {
            "type": "inference-client",
          }
        ]
      },
      {
        "name": "google/gemma-3-27b-it",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/google-logo.png",
        "multimodal": true,
        "tools": true,
        "systemRoleSupported" : false,
        "description": "Google's latest open model with great multilingual performance, supports image inputs natively.",
        "websiteUrl": "https://blog.google/technology/developers/gemma-3/",
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ],
        "endpoints": [
          {
            "type": "inference-client",
            "baseURL": "https://wp0d3hn6s3k8jk22.us-east-1.aws.endpoints.huggingface.cloud/v1",
            "multimodal": {
              "image": {
                "maxSizeInMB": 10,
                "maxWidth": 560,
                "maxHeight": 560,
                "supportedMimeTypes": ["image/jpeg", "image/png", "image/webp"],
                "preferredMimeType": "image/webp"
              }
            }
          }
        ]
      },
      {
        "name": "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
        "tools": true,
        "displayName": "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
        "description": "A small model with good capabilities in language understanding and commonsense reasoning.",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/mistral-logo.png",
        "websiteUrl": "https://mistral.ai/news/mistral-nemo/",
        "modelUrl": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503",
        "preprompt": "",
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ],

        "endpoints": [
          {
            "type": "inference-client",
              "baseURL": "https://hkjfqcryevvq9cie.us-east-1.aws.endpoints.huggingface.cloud/v1"
          }
        ]
      },
      {
        "name": "Qwen/Qwen2.5-VL-32B-Instruct",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/qwen-logo.png",
        "description": "The latest multimodal model from Qwen! Supports image inputs natively.",
        "websiteUrl": "https://qwenlm.github.io/blog/qwen2.5-vl/",
        "modelUrl": "https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct",
        "multimodal": true,
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ],
        "endpoints": [
          {
            "type": "inference-client",
            "multimodal": {
              "image": {
                "maxSizeInMB": 10,
                "maxWidth": 1024,
                "maxHeight": 1024,
                "supportedMimeTypes": ["image/png", "image/jpeg", "image/webp"],
                "preferredMimeType": "image/webp"
              }
            }
          }
        ]
      },
      {
        "name": "microsoft/Phi-4",
        "tools": true,
        "systemRoleSupported": false,
        "description": "One of the best small models, super fast for simple tasks.",
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/microsoft-logo.png",
        "modelUrl": "https://huggingface.co/microsoft/Phi-4",
        "websiteUrl": "https://techcommunity.microsoft.com/blog/aiplatformblog/introducing-phi-4-microsoft%E2%80%99s-newest-small-language-model-specializing-in-comple/4357090",
        "preprompt": "",
        "parameters": {
          "stop": ["<|end|>", "<|endoftext|>", "<|assistant|>"],
          "temperature": 0.6,
          "truncate": 28672,
          "max_new_tokens": 3072
        },
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ],
        "endpoints": [
          {
            "type": "inference-client",
            "baseURL": "https://up5ijetg6a2e9zlb.us-east-1.aws.endpoints.huggingface.cloud/v1"
          }
        ]
      },
      {
        "name": "NousResearch/Hermes-3-Llama-3.1-8B",
        "description": "Nous Research's latest Hermes 3 release in 8B size. Follows instruction closely.",
        "tools": true,
        "logoUrl": "https://huggingface.co/datasets/huggingchat/models-logo/resolve/main/nous-logo.png",
        "websiteUrl": "https://nousresearch.com/",
        "modelUrl": "https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B",
        "promptExamples": [
          {
            "title": "Write an email",
            "prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
          },
          {
            "title": "Code a game",
            "prompt": "Code a basic snake game in python, give explanations for each step."
          },
          {
            "title": "Recipe help",
            "prompt": "How do I make a delicious lemon cheesecake?"
          }
        ],
        "parameters": {
          "stop": ["<|im_end|>"],
          "temperature": 0.6,
          "truncate": 14336,
          "max_new_tokens": 1536
        },
        "endpoints": [{"type" : "inference-client"}]
      }
    ]

  NODE_ENV: "prod"
  NODE_LOG_STRUCTURED_DATA: true
  OLD_MODELS: >
    [
      { "name": "bigcode/starcoder" },
      { "name": "OpenAssistant/oasst-sft-6-llama-30b-xor" },
      { "name": "HuggingFaceH4/zephyr-7b-alpha" },
      { "name": "openchat/openchat_3.5" },
      { "name": "openchat/openchat-3.5-1210" },
      { "name": "tiiuae/falcon-180B-chat" },
      { "name": "codellama/CodeLlama-34b-Instruct-hf" },
      { "name": "google/gemma-7b-it" },
      { "name": "meta-llama/Llama-2-70b-chat-hf" },
      { "name": "codellama/CodeLlama-70b-Instruct-hf" },
      { "name": "openchat/openchat-3.5-0106" },
      { "name": "meta-llama/Meta-Llama-3-70B-Instruct" },
      { "name": "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8" },
      {
        "name": "CohereForAI/c4ai-command-r-plus-08-2024",
        "transferTo": "CohereLabs/c4ai-command-r-plus-08-2024"
      },
      {
        "name": "CohereForAI/c4ai-command-r-plus",
        "transferTo": "CohereLabs/c4ai-command-r-plus-08-2024"
      },
      {
        "name": "01-ai/Yi-1.5-34B-Chat",
        "transferTo": "CohereLabs/c4ai-command-r-plus-08-2024"
      },
      {
        "name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
        "transferTo": "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
      },
      {
        "name": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
        "transferTo": "NousResearch/Hermes-3-Llama-3.1-8B"
      },
      {
        "name": "mistralai/Mistral-7B-Instruct-v0.3",
        "transferTo": "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
      },
      {
        "name": "microsoft/Phi-3-mini-4k-instruct",
        "transferTo": "microsoft/Phi-4"
      },
      {
        "name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
        "transferTo": "meta-llama/Llama-3.3-70B-Instruct"
      },
      {
        "name": "Qwen/QwQ-32B-Preview",
        "transferTo": "Qwen/QwQ-32B"
      },
      {
        "name": "mistralai/Mistral-Nemo-Instruct-2407",
        "transferTo": "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
      },
      {
        "name": "microsoft/Phi-3.5-mini-instruct",
        "transferTo": "microsoft/Phi-4"
      },
      {
        "name": "Qwen/Qwen2.5-Coder-32B-Instruct",
        "transferTo": "Qwen/QwQ-32B"
      },
      {
        "name": "meta-llama/Llama-3.2-11B-Vision-Instruct",
        "transferTo" : "Qwen/Qwen2.5-VL-32B-Instruct"
      }
    ]
  PUBLIC_ORIGIN: "https://huggingface.co"
  PUBLIC_SHARE_PREFIX: "https://hf.co/chat"
  PUBLIC_ANNOUNCEMENT_BANNERS: >
    [
      {
        "title": "Qwen 3 235B is available!",
        "linkTitle": "Try it out!",
        "linkHref": "https://huggingface.co/chat/models/Qwen/Qwen3-235B-A22B"
      }
    ]
  PUBLIC_APP_NAME: "HuggingChat"
  PUBLIC_APP_ASSETS: "huggingchat"
  PUBLIC_APP_COLOR: "yellow"
  PUBLIC_APP_DESCRIPTION: "Making the community's best AI chat models available to everyone."
  PUBLIC_APP_DISCLAIMER_MESSAGE: "Disclaimer: AI is an area of active research with known problems such as biased generation and misinformation. Do not use this application for high-stakes decisions or advice."
  PUBLIC_APP_GUEST_MESSAGE: "Sign in with a free Hugging Face account to continue using HuggingChat."
  PUBLIC_APP_DATA_SHARING: 0
  PUBLIC_APP_DISCLAIMER: 1
  PUBLIC_PLAUSIBLE_SCRIPT_URL: "/js/script.js"
  REQUIRE_FEATURED_ASSISTANTS: "true"
  TASK_MODEL: >
        {
        "name": "NousResearch/Hermes-3-Llama-3.1-8B",
        "unlisted": true,
        "endpoints": [{"type" : "inference-client"}],
        "parameters": {
          "temperature": 0.1,
          "max_new_tokens": 256
        }
        }


  TEXT_EMBEDDING_MODELS: >
    [{
      "name": "bge-base-en-v1-5-sxa",
      "displayName": "bge-base-en-v1-5-sxa",
      "chunkCharLength": 512,
      "endpoints": [{
        "type": "tei",
        "url": "https://huggingchat-tei.hf.space/"
      }]
    }]
  WEBSEARCH_BLOCKLIST: '["youtube.com", "twitter.com"]'
  XFF_DEPTH: '2'
  TOOLS: >
    [
      {
        "_id": "000000000000000000000001",
        "displayName": "Image Generation",
        "description": "Use this tool to generate images based on a prompt.",
        "color": "yellow",
        "icon": "camera",
        "baseUrl": "black-forest-labs/FLUX.1-schnell",
        "name": "image_generation",
        "endpoint": "/infer",
        "inputs": [
          {
            "name": "prompt",
            "description": "A prompt to generate an image from",
            "paramType": "required",
            "type": "str"
          },
          { "name": "seed", "paramType": "fixed", "value": "0", "type": "float" },
          {
            "name": "randomize_seed",
            "paramType": "fixed",
            "value": "true",
            "type": "bool"
          },
          {
            "name": "width",
            "description": "numeric value between 256 and 2048",
            "paramType": "optional",
            "default": 1024,
            "type": "float"
          },
          {
            "name": "height",
            "description": "numeric value between 256 and 2048",
            "paramType": "optional",
            "default": 1024,
            "type": "float"
          },
          {
            "name": "num_inference_steps",
            "paramType": "fixed",
            "value": "4",
            "type": "float"
          }
        ],
        "outputComponent": "image",
        "outputComponentIdx": 0,
        "showOutput": true
      },
      {
        "_id": "000000000000000000000002",
        "displayName": "Document Parser",
        "description": "Use this tool to parse any document and get its content in markdown format.",
        "color": "yellow",
        "icon": "cloud",
        "baseUrl": "huggingchat/document-parser",
        "name": "document_parser",
        "endpoint": "/predict",
        "inputs": [
          {
            "name": "document",
            "description": "Filename of the document to parse",
            "paramType": "required",
            "type": "file",
            "mimeTypes": 'application/*'
          },
          {
            "name": "filename",
            "paramType": "fixed",
            "value": "document.pdf",
            "type": "str"
          }
        ],
        "outputComponent": "textbox",
        "outputComponentIdx": 0,
        "showOutput": false,
        "isHidden": true
      },
      {
        "_id": "000000000000000000000003",
        "name": "edit_image",
        "baseUrl": "multimodalart/cosxl",
        "endpoint": "/run_edit",
        "inputs": [
          {
            "name": "image",
            "description": "The image path to be edited",
            "paramType": "required",
            "type": "file",
            "mimeTypes": 'image/*'
          },
          {
            "name": "prompt",
            "description": "The prompt with which to edit the image",
            "paramType": "required",
            "type": "str"
          },
          {
            "name": "negative_prompt",
            "paramType": "fixed",
            "value": "",
            "type": "str"
          },
          {
            "name": "guidance_scale",
            "paramType": "fixed",
            "value": 6.5,
            "type": "float"
          },
          {
            "name": "steps",
            "paramType": "fixed",
            "value": 30,
            "type": "float"
          }
        ],
        "outputComponent": "image",
        "showOutput": true,
        "displayName": "Image Editor",
        "color": "green",
        "icon": "camera",
        "description": "This tool lets you edit images",
        "outputComponentIdx": 0
      }
    ]
  HF_ORG_ADMIN: '644171cfbd0c97265298aa99'
  HF_ORG_EARLY_ACCESS: '5e67bd5b1009063689407478'
  HF_API_ROOT: 'https://api-inference.endpoints.huggingface.tech/models'
infisical:
  enabled: true
  env: "prod-us-east-1"

autoscaling:
  enabled: true
  minReplicas: 12
  maxReplicas: 30
  targetMemoryUtilizationPercentage: "50"
  targetCPUUtilizationPercentage: "50"

resources:
  requests:
    cpu: 2
    memory: 4Gi
  limits:
    cpu: 4
    memory: 8Gi

monitoring:
  enabled: true