Configuration

Ways to provide configuration

There are multiple ways to provide configuration to Erato.

All sources are configuration are merged together into one, so configuration can be specified via e.g. erato.toml, as well as via environment variables.

As of now, there is no specified precedence order when it comes to values provided for the same configuration key that are provided in different sources.

`erato.toml` files

The erato.toml file is the preferred way to provide configuration for Erato. The file must be placed in the current working directory of the Erato process.

In the Helm chart, a secret from where the erato.toml file should be mounted can be specified via backend.configFile.

`*.auto.erato.toml` files

In addition to the main erato.toml file, Erato will also auto-discover all files matching the pattern *.auto.erato.toml in the current working directory.

This is useful if you e.g. want to split out all the secret values (LLM API keys, Database credentials) into a different file that is not checked into source control.

Environment variables

Configuration can also be provided via environment variables.

Though it is not recommended, values for nested configuration can also be provided via environment variables. In that case, each nesting level is separated by double underscores (__). E.g. CHAT_PROVIDER__BASE_URL is equivalent to chat_provider.base_url.

Configuration reference

🚧Work in progress; Covers ~80% of available configuration options 🚧

`server`

`server.encryption_key`

Optional encryption key used by the backend for encrypting application data before storing it in the database.

The key must be base64-encoded and must decode to exactly 32 bytes. This matches the AES-256-GCM-SIV key size used by the backend.

To generate a suitable key:


openssl rand -base64 32

When providing this value via an environment variable, use SERVER__ENCRYPTION_KEY.

Default value: None

Type: string | None

Example


[server]
encryption_key = "replace-with-openssl-output"

`frontend`

`frontend.theme`

The name of the theme to use for the frontend.

When provided, the theme must be part of the frontend bundle (usually located in the public directory), and placed in the custom-theme directory, under the name provided here. E.g. if frontend.theme is set to my-theme, the theme must be placed in public/custom-theme/my-theme.

See Theming for more information about themes and theme directory structure.

If not provided, the default bundled theme will be used.

Default value: None

Type: string | None

Example


[frontend]
theme = "my-theme"

`frontend.disable_upload`

Whether to disable file upload functionality in the UI. This is useful for embedded scenarios where file uploads should not be available.

Default value: false

Type: boolean

Example


[frontend]
disable_upload = true

`frontend.disable_chat_input_autofocus`

Whether to disable automatic focusing of the chat input field. This prevents unwanted scrolling behavior when navigating to pages with embedded chat widgets.

When enabled, the chat input field will not automatically receive focus on page load/navigation.

Default value: false

Type: boolean

Example


[frontend]
disable_chat_input_autofocus = true

`frontend.disable_logout`

Whether to hide logout functionality from the UI. This is useful for embedded scenarios where logout should be handled by the parent application.

When enabled, the logout button/option will be hidden from the user interface.

Default value: false

Type: boolean

Example


[frontend]
disable_logout = true

`frontend.enable_message_feedback`

Whether to enable message feedback functionality in the UI. When enabled, users can submit thumbs up/down ratings with optional comments for individual messages.

This feature allows collecting user feedback on message quality, which can be useful for improving model performance and understanding user satisfaction.

Default value: false

Type: boolean

Example


[frontend]
enable_message_feedback = true

`frontend.enable_message_feedback_comments`

Whether to enable the comment text field in message feedback. When enabled, users can add optional text comments along with their thumbs up/down ratings.

This setting requires enable_message_feedback to be true to have any effect.

Default value: false

Type: boolean

Example


[frontend]
enable_message_feedback = true
enable_message_feedback_comments = true

`frontend.message_feedback_edit_time_limit_seconds`

Time limit in seconds for editing message feedback after creation. When set, feedback can only be edited within this time window after being submitted. When not set (default), feedback can be edited at any time.

This setting only affects editing existing feedback - initial feedback submission is always allowed regardless of this setting. Attempts to edit feedback after the time limit will return a 403 Forbidden error.

Default value: None (unlimited editing)

Type: integer (seconds)

Example


[frontend]
enable_message_feedback = true
message_feedback_edit_time_limit_seconds = 300  # 5 minutes

`frontend.sidebar_collapsed_mode`

Controls the behavior of the collapsed sidebar state.

When set to "slim", the collapsed sidebar shows icon-only navigation elements. When set to "hidden" (default), the sidebar is completely hidden when collapsed.

Default value: "hidden"

Type: string

Supported values:

"hidden" - Sidebar is completely hidden when collapsed (default behavior)
"slim" - Sidebar shows icon-only navigation when collapsed

Example:


[frontend]
sidebar_collapsed_mode = "slim"

`frontend.sidebar_logo_path`

Optional path to a sidebar-specific logo file. When provided, this logo is displayed in the sidebar header instead of the theme logo. Useful for showing a compact logo variant in the sidebar.

Default value: None (uses theme logo)

Type: string | None

Example:


[frontend]
sidebar_logo_path = "/custom-theme/my-company/sidebar-logo.svg"

`frontend.sidebar_logo_dark_path`

Optional path to a sidebar-specific logo file for dark mode. When provided along with sidebar_logo_path, this logo is displayed in the sidebar when dark mode is active.

Default value: None (uses sidebar_logo_path or theme logo)

Type: string | None

Example:


[frontend]
sidebar_logo_path = "/custom-theme/my-company/sidebar-logo.svg"
sidebar_logo_dark_path = "/custom-theme/my-company/sidebar-logo-dark.svg"

`frontend.additional_environment`

Additional values to inject into the frontend environment as global variables. These will be made available to the frontend Javascript, and added to the window object.

This is a dictionary where each value can be a string or a map (string key, string value).

This may be useful if you are using a forked version of the frontend, which you need to pass some configuration to.

Default value: None

Type: object<string, any>

Example


[frontend]
additional_environment = { "FOO": "bar" }

This will be inejcted into the frontend as:


window.FOO = "bar";

`i18n`

Internationalization-related settings used by the backend when determining user language preferences.

`i18n.language`

Configuration for language detection and defaulting.

`i18n.language.language_detection_priority`

Ordered list of language sources to evaluate when resolving a user’s preferred language.

id_token_any expands to checking id_token_xms_pl then id_token_xms_tpl in that order.

Supported values:

id_token_any
id_token_xms_pl
id_token_xms_tpl
browser_accept_language

Type: array<string>

Default value: ["id_token_any", "browser_accept_language"]

Any value omitted in the order is treated as unsupported for that source and detection continues to the next value.

`i18n.language.default_language`

Fallback language used after no configured sources match.

Type: string

Default value: "en"

Example:


[i18n]
[i18n.language]
language_detection_priority = ["id_token_xms_tpl", "browser_accept_language"]
default_language = "de"

`chat_provider` (deprecated)

⚠️ Deprecated: Please use chat_providers instead for multiple provider support and better flexibility.

Configuration for a single chat provider (LLM) that Erato will use for generating responses. This is maintained for backward compatibility but will be automatically migrated to the new chat_providers format.

`chat_provider.provider_kind`

The type of chat provider to use.

Type: string

Supported values: "openai", "azure_openai"

Example: "openai"

`chat_provider.model_name`

The name of the model to use with the chat provider.

Type: string

Example: "gpt-4o"

`chat_provider.model_display_name`

The display name for the model shown to users. If not provided, falls back to model_name.

Type: string | None

Example: "GPT-4 Omni (Production)"

`chat_provider.model_description`

An optional description shown below the model display name in the frontend model selector.

The frontend resolves this through a dynamic Lingui translation key using the chat provider id: chat_models.<chat-provider-id>.description. If no translation exists, the configured value is used as the fallback text.

Type: string | None

Example: "Fast, lower-cost model for everyday questions"

`chat_provider.model_icon`

An optional icon identifier shown next to the model in the frontend model selector.

Supported formats:

iconoir-* for icons resolved from the iconoir-react library
simpleicons-* for icons resolved from the simple-icons library, for example simpleicons-anthropic
builtin-chatgpt for the built-in ChatGPT/OpenAI mark
/path/to/icon.svg for a custom SVG asset served by the frontend

Type: string | None

Example: "builtin-chatgpt"

`chat_provider.model_name_langfuse`

The model name to report to Langfuse for observability and tracing. If not provided, falls back to model_name.

This is useful when the provider’s model name differs from the standardized name used in Langfuse. For example, Azure OpenAI uses deployment names that may differ from the underlying model name, or you may want to use a consistent naming convention across different providers for better analytics in Langfuse.

Type: string | None

Example:


[chat_providers.providers.azure-gpt4]
provider_kind = "azure_openai"
model_name = "gpt-4-deployment-prod-v1"  # Azure deployment name
model_name_langfuse = "gpt-4"            # Standardized name for Langfuse

`chat_provider.base_url`

The base URL for the chat provider API. If not provided, will use the default for the provider.

Type: string | None

Example: "https://api.openai.com/v1/", "http://localhost:11434/v1/"

`chat_provider.api_key`

The API key for the chat provider.

Type: string | None

Example: "sk-..."

`chat_provider.system_prompt`

A system prompt specification to use with the chat provider. This sets the behavior and personality of the AI assistant.

Type: string | object | None

Note: This option is mutually exclusive with system_prompt_langfuse (deprecated). Requires the Langfuse integration to be enabled when using source = "langfuse".

Example:


[chat_provider]
provider_kind = "openai"
model_name = "gpt-4"
system_prompt = "You are a helpful assistant that provides concise and accurate answers."

Using Langfuse:


[chat_provider]
provider_kind = "openai"
model_name = "gpt-4"
system_prompt = { source = "langfuse", prompt_name = "assistant-prompt-v1", label = "production", fallback = "You are a helpful assistant." }

Using a static object:


[chat_provider]
provider_kind = "openai"
model_name = "gpt-4"
system_prompt = { source = "static", prompt = "You are a helpful assistant that provides concise and accurate answers." }

`chat_provider.system_prompt_langfuse`

Configuration for using a system prompt from Langfuse prompt management instead of a static prompt.

Deprecated: Use chat_provider.system_prompt with a prompt source specification instead.

Note: This option is mutually exclusive with system_prompt. Requires the Langfuse integration to be enabled.

`chat_providers`

Configuration for multiple chat providers with priority ordering. This is the recommended way to configure chat providers as it supports multiple providers, fallback mechanisms, and better flexibility.

`chat_providers.priority_order`

An ordered list of provider IDs that defines the priority for provider selection. The first provider in the list has the highest priority.

Type: array of strings

Required: Yes

Example: ["primary", "backup", "local"]

`chat_providers.providers`

A map of provider configurations, where each key is a unique provider ID and the value is a provider configuration object.

Type: object

Required: Yes

Example:


[chat_providers]
priority_order = ["primary", "backup"]
 
[chat_providers.providers.primary]
provider_kind = "openai"
model_name = "gpt-4"
model_display_name = "GPT-4 (Primary)"
model_description = "Best quality for complex tasks"
model_icon = "builtin-chatgpt"
api_key = "sk-primary-key"
 
[chat_providers.providers.primary.model_capabilities]
context_size_tokens = 8192
supports_image_understanding = false
cost_input_tokens_per_1m = 30.0
cost_output_tokens_per_1m = 60.0
 
[chat_providers.providers.backup]
provider_kind = "openai"
model_name = "gpt-3.5-turbo"
model_display_name = "GPT-3.5 Turbo (Backup)"
model_description = "Faster and cheaper fallback model"
model_icon = "simpleicons-anthropic"
api_key = "sk-backup-key"
base_url = "https://api.backup-provider.com/v1/"
 
[chat_providers.providers.backup.model_capabilities]
context_size_tokens = 16385
supports_image_understanding = false
cost_input_tokens_per_1m = 1.0
cost_output_tokens_per_1m = 2.0

Provider Configuration Fields

Each provider in chat_providers.providers supports the following fields:

provider_kind - The type of chat provider ("openai", "azure_openai", "ollama")
model_name - The model identifier for the provider
model_display_name - Optional display name for the model (falls back to model_name)
model_description - Optional frontend description shown below the model name; translatable via chat_models.<chat-provider-id>.description
model_icon - Optional frontend icon id, for example iconoir-*, simpleicons-*, builtin-chatgpt, or /path/to/icon.svg
model_name_langfuse - Optional model name for Langfuse reporting (falls back to model_name)
api_key - Optional API key for authentication
base_url - Optional custom base URL for the provider API
system_prompt - Optional prompt source specification (string or object)
system_prompt_langfuse - Optional Langfuse prompt configuration (deprecated, mutually exclusive with system_prompt)
additional_request_parameters - Optional array of additional request parameters
additional_request_headers - Optional array of additional request headers
model_capabilities - Optional configuration for model capabilities and limitations
model_settings - Optional configuration for model behavior and generation settings

`model_capabilities`

The model_capabilities field allows you to configure the capabilities and limitations of each chat provider’s model. This information is used for token usage estimation, cost calculation, and determining which features are available.

Type: object | None

Example:


[chat_providers.providers.main.model_capabilities]
context_size_tokens = 128000
supports_image_understanding = true
supports_reasoning = false
supports_verbosity = false
cost_input_tokens_per_1m = 5.0
cost_output_tokens_per_1m = 15.0

Fields:

context_size_tokens (default: 1000000) - Maximum number of tokens that may be provided to the model including input messages, system prompt, and files
supports_image_understanding (default: false) - Whether the model supports being provided with images for understanding
supports_reasoning (default: false) - Whether the model supports reasoning mode (e.g., OpenAI o1 family)
supports_verbosity (default: false) - Whether the model supports providing a verbosity parameter (for future support of advanced models)
cost_input_tokens_per_1m (default: 0.0) - Price per 1 million input tokens (unit-less, for cost estimation)
cost_output_tokens_per_1m (default: 0.0) - Price per 1 million output tokens (unit-less, for cost estimation)

If not explicitly configured, all capabilities default to conservative values (large context window, no special features, no cost tracking).

`model_settings`

The model_settings field allows you to configure how the model should behave during generation. While model_capabilities describes what a model can do, model_settings controls what the model should do.

Type: object | None

Example:


[chat_providers.providers.image-gen.model_settings]
generate_images = true
temperature = 0.2
top_p = 0.9
reasoning_effort = "minimal"
verbosity = "high"

Fields:

generate_images (default: false) - When set to true, the model will generate images instead of text. This is useful for image generation models like DALL-E. The user’s message text will be used as the prompt for image generation. The generated image is automatically downloaded, stored as a file upload, and added to the chat history as an image file pointer.
temperature (default: None) - Optional sampling temperature for generation (higher values increase randomness).
top_p (default: None) - Optional nucleus sampling parameter to control diversity.
reasoning_effort (default: None) - Optional reasoning effort for supported models. Values: "none", "minimal", "low", "medium", "high".
verbosity (default: None) - Optional verbosity for supported models. Values: "low", "medium", "high".

Note: Image generation requires a model that supports the image generation API (currently only OpenAI’s DALL-E models are supported). The chat history is not sent when generating images - only the most recent user message text is used as the prompt.

Complete Image Generation Example:


[chat_providers]
priority_order = ["text-model", "image-gen"]
 
# Standard text generation model
[chat_providers.providers.text-model]
provider_kind = "openai"
model_name = "gpt-4"
api_key = "sk-..."
 
# Image generation model
[chat_providers.providers.image-gen]
provider_kind = "openai"
model_name = "dall-e-3"
model_display_name = "DALL-E 3 (Image Generator)"
api_key = "sk-..."
 
[chat_providers.providers.image-gen.model_settings]
generate_images = true
 
# Allow users to access the image generation model
[model_permissions.rules.allow-image-gen]
rule_type = "allow-all"
chat_provider_ids = ["image-gen"]

`chat_providers.summary`

Configuration for chat summary generation. This allows you to specify which provider and settings to use when generating chat summaries.

Type: object | None

Example:


[chat_providers.summary]
summary_chat_provider_id = "summarizer"
max_tokens = 150

`chat_providers.summary.summary_chat_provider_id`

The ID of the chat provider to use specifically for generating chat summaries. This provider must exist in the chat_providers.providers map.

If not specified, Erato will use the highest priority provider from priority_order for summary generation.

Type: string | None

Default value: None (uses highest priority provider)

Example: "summarizer"

`chat_providers.summary.max_tokens`

The maximum number of output tokens to allow when generating chat summaries. This helps control the length and cost of summary generation.

Type: number | None

Default value: 300

Example: 150

Complete Example:


[chat_providers]
priority_order = ["main"]
 
[chat_providers.summary]
summary_chat_provider_id = "summarizer"
max_tokens = 150
 
[chat_providers.providers.main]
provider_kind = "openai"
model_name = "gpt-4"
api_key = "sk-main-key"
 
[chat_providers.providers.summarizer]
provider_kind = "openai"
model_name = "gpt-4o-mini"
model_display_name = "GPT-4o Mini (Summarizer)"
api_key = "sk-summarizer-key"

In this example:

Regular chat interactions use the “main” provider (GPT-4)
Chat summaries are generated using the “summarizer” provider (GPT-4o Mini)
Summary generation is limited to 150 output tokens
This allows using a faster, cheaper model for summaries while keeping a more powerful model for main interactions

Migration from `chat_provider`

If you’re using the old chat_provider configuration, it will be automatically migrated to the new format:

The old provider will be given the ID "default"
The priority order will be set to ["default"]
A deprecation warning will be logged

Before (deprecated):


[chat_provider]
provider_kind = "openai"
model_name = "gpt-4"
api_key = "sk-key"

After (recommended):


[chat_providers]
priority_order = ["main"]
 
[chat_providers.providers.main]
provider_kind = "openai"
model_name = "gpt-4"
api_key = "sk-key"

`file_storage_providers`

Configuration for file storage providers that handle uploaded files and documents in Erato. You can configure multiple storage providers with different backends (S3-compatible or Azure Blob Storage) and designate one as the default.

Type: object<string, FileStorageProviderConfig>

Required: At least one file storage provider must be configured.

Example:


[file_storage_providers.primary]
provider_kind = "s3"
display_name = "Primary Storage"
max_upload_size_kb = 102400  # 100 MB
config = { bucket = "erato-files", region = "us-east-1", access_key_id = "AKIAIOSFODNN7EXAMPLE", secret_access_key = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY" }
 
# Set which provider to use as default
default_file_storage_provider = "primary"

`file_storage_providers.<provider_id>.provider_kind`

The type of file storage backend to use.

Type: string

Required: Yes

Supported values:

"s3" - Amazon S3 or services that expose an S3-compatible API (e.g., MinIO, DigitalOcean Spaces, Cloudflare R2)
"azblob" - Azure Blob Storage

Example: "s3" or "azblob"

`file_storage_providers.<provider_id>.display_name`

A human-readable name to display in the UI for this storage provider.

Type: string | None

Default: Falls back to the provider ID if not specified

Example: "Primary Storage" or "Azure Backup Storage"

`file_storage_providers.<provider_id>.max_upload_size_kb`

The maximum file size that may be uploaded in kilobytes. This limit is enforced by the backend.

Type: number | None

Default: No limit (system default applies)

Example: 102400 (100 MB), 10240 (10 MB)

`file_storage_providers.<provider_id>.config`

Provider-specific configuration object. The available fields depend on the provider_kind.

Type: object

Required: Yes

S3 Provider Configuration

When provider_kind = "s3", the following configuration fields are available:

bucket (required)

The name of the S3 bucket to store files in.

Type: string

Example: "erato-files", "my-company-documents"

endpoint (optional)

The S3-compatible endpoint URL. Use this when connecting to S3-compatible services other than AWS S3 (e.g., MinIO, DigitalOcean Spaces).

If not specified, defaults to AWS S3.

Type: string | None

Example: "https://nyc3.digitaloceanspaces.com", "http://localhost:9000"

region (optional)

The AWS region where the bucket is located.

Type: string | None

Example: "us-east-1", "eu-central-1"

root (optional)

A prefix path within the bucket where files should be stored. Useful for organizing files or sharing buckets across multiple applications.

Type: string | None

Example: "erato-uploads/", "production/files/"

access_key_id (optional)

The AWS access key ID for authentication. If not provided, the SDK will attempt to use environment variables or instance credentials.

Type: string | None

Example: "AKIAIOSFODNN7EXAMPLE"

secret_access_key (optional)

The AWS secret access key for authentication. If not provided, the SDK will attempt to use environment variables or instance credentials.

Type: string | None

Example: "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"

Complete S3 Example:


[file_storage_providers.s3_primary]
provider_kind = "s3"
display_name = "AWS S3 Primary"
max_upload_size_kb = 204800  # 200 MB
config = { bucket = "erato-production-files", region = "us-west-2", root = "uploads/", access_key_id = "AKIAIOSFODNN7EXAMPLE", secret_access_key = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY" }
 
default_file_storage_provider = "s3_primary"

MinIO Example:


[file_storage_providers.minio]
provider_kind = "s3"
display_name = "MinIO Local Storage"
max_upload_size_kb = 102400  # 100 MB
config = { bucket = "erato-storage", endpoint = "http://localhost:9000", access_key_id = "minioadmin", secret_access_key = "minioadmin" }
 
default_file_storage_provider = "minio"

Azure Blob Storage Configuration

When provider_kind = "azblob", the following configuration fields are available:

container (required)

The name of the Azure Blob Storage container to store files in.

Type: string

Example: "erato-files", "documents"

endpoint (required)

The Azure Blob Storage endpoint URL for your storage account.

Type: string

Example: "https://mystorageaccount.blob.core.windows.net"

root (optional)

A prefix path within the container where files should be stored. Useful for organizing files or sharing containers across multiple applications.

Type: string | None

Example: "erato-uploads/", "production/files/"

account_name (optional)

The Azure storage account name for authentication. If not provided, the SDK will attempt to use environment variables or managed identity.

Type: string | None

Example: "mystorageaccount"

account_key (optional)

The Azure storage account key for authentication. If not provided, the SDK will attempt to use environment variables or managed identity.

Type: string | None

Example: "Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw=="

Complete Azure Blob Storage Example:


[file_storage_providers.azure]
provider_kind = "azblob"
display_name = "Azure Blob Storage"
max_upload_size_kb = 153600  # 150 MB
config = { endpoint = "https://mycompany.blob.core.windows.net", container = "erato-files", root = "uploads/", account_name = "mycompany", account_key = "Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==" }
 
default_file_storage_provider = "azure"

Azurite (Azure Blob Storage Emulator) Example:


[file_storage_providers.azurite]
provider_kind = "azblob"
display_name = "Azurite Local Storage"
max_upload_size_kb = 102400  # 100 MB
config = { endpoint = "http://127.0.0.1:10000/devstoreaccount1", container = "erato-storage", account_name = "devstoreaccount1", account_key = "Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==" }
 
default_file_storage_provider = "azurite"

`default_file_storage_provider`

Specifies which file storage provider to use as the default for new file uploads. This must match one of the keys in the file_storage_providers map.

When multiple file storage providers are configured, this setting is required and determines where new files will be written. All configured providers remain available for reading existing files, which is useful for migration scenarios.

Type: string | None

Required when: Multiple file storage providers are configured

Example: "primary", "s3_main", "azure"

Note: This is a top-level configuration key, not nested under file_storage_providers.

Configuration Example:


# Define multiple providers
[file_storage_providers.primary]
provider_kind = "s3"
display_name = "Primary Storage"
max_upload_size_kb = 102400
config = { bucket = "erato-files", region = "us-east-1" }
 
[file_storage_providers.backup]
provider_kind = "azblob"
display_name = "Backup Storage"
max_upload_size_kb = 102400
config = { endpoint = "https://backup.blob.core.windows.net", container = "erato-backup" }
 
# Specify which one is default (top-level key)
default_file_storage_provider = "primary"

In this example:

New files will be uploaded to the “primary” S3 provider
Existing files can still be read from both “primary” and “backup” providers
This allows migrating from one storage provider to another without data loss

Multiple Providers Example

You can configure multiple file storage providers. This is currently mainly useful for migration scenarios, where new file uploads should be read + written from a new file storage provider, but past uploaded files should still be available to be read from an older file storage provider.


# Primary S3 storage
[file_storage_providers.s3_primary]
provider_kind = "s3"
display_name = "S3 Primary (US)"
max_upload_size_kb = 204800  # 200 MB
config = { bucket = "erato-us-files", region = "us-east-1", access_key_id = "AKIAIOSFODNN7EXAMPLE", secret_access_key = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY" }
 
# Backup Azure storage
[file_storage_providers.azure_backup]
provider_kind = "azblob"
display_name = "Azure Backup (EU)"
max_upload_size_kb = 204800  # 200 MB
config = { endpoint = "https://eratoeu.blob.core.windows.net", container = "erato-eu-files", account_name = "eratoeu", account_key = "..." }
 
# Set default provider
default_file_storage_provider = "s3_primary"

`file_processor`

Configuration for the file processing engine that extracts text content from uploaded documents. Erato supports multiple file processor backends with different capabilities.

Type: object | None

Default behavior: If not configured, Erato uses the kreuzberg processor by default.

`file_processor.processor`

The file processor backend to use for extracting text from uploaded files.

Type: string

Default: "kreuzberg"

Supported values:

Since the removal of the parser-core file processor, there is only a single option kreuzberg

"kreuzberg" - Advanced file processor with page-aware markdown extraction. Extracts content with page boundary markers, making it easy to identify which page content comes from.

Example:


[file_processor]
processor = "kreuzberg"

Processor Comparison

Feature	kreuzberg
Text extraction	✅
Page awareness	✅
Page markers	✅ (`<page number="N">`)
Output format	Markdown
Supported formats	Word, PDF, Excel, PowerPoint, text files

Page-Aware Extraction

When using kreuzberg, extracted content includes XML-style page markers that clearly indicate page boundaries:


<page number="1">
First page content here...
 
<page number="2">
Second page content here...

This is particularly useful when working with:

Multi-page legal documents where page references matter
Academic papers with citations to specific pages
Technical documentation with page-specific diagrams
Any scenario where knowing the source page of content is important

Example configuration:


[file_processor]
# Use kreuzberg for page-aware extraction
processor = "kreuzberg"

Testing page-aware extraction:

You can generate test PDFs with distinct content per page using the provided script:


cd e2e-tests
./scripts/generate_multipage_test_pdf.py test.pdf 20

This creates a PDF with 20 pages, each containing unique identifiers like PAGE-001, PAGE-002, etc.

`model_permissions`

Configuration for controlling user access to chat providers based on user attributes like group membership. This allows you to implement fine-grained access control, such as restricting premium models to certain user groups or providing different models to different teams.

Type: object | None

Default behavior: If no model permissions are configured, all users have access to all configured chat providers.

`model_permissions.rules`

A map of permission rules, where each key is a unique rule name and the value is a rule configuration object. Rules are evaluated independently, and if any rule grants access to a chat provider, the user is allowed to use it.

Type: object<string, ModelPermissionRule>

Example:


[model_permissions.rules.basic_access]
rule_type = "allow-all"
chat_provider_ids = ["gpt-4o-mini"]
 
[model_permissions.rules.premium_access]
rule_type = "allow-for-group-members"
chat_provider_ids = ["gpt-4", "claude-3-opus"]
groups = ["premium-users", "administrators"]
 
[model_permissions.rules.admin_only]
rule_type = "allow-for-group-members"
chat_provider_ids = ["gpt-4-turbo", "experimental-model"]
groups = ["administrators"]

Rule Types

There are two types of permission rules:

`allow-all`

Grants access to specified chat providers for all users, regardless of their group membership.

Fields:

rule_type - Must be "allow-all"
chat_provider_ids - Array of chat provider IDs that this rule grants access to

Example:


[model_permissions.rules.public_models]
rule_type = "allow-all"
chat_provider_ids = ["gpt-4o-mini", "claude-3-haiku"]

`allow-for-group-members`

Grants access to specified chat providers only for users who belong to at least one of the specified groups. User groups are derived from the groups claim in the user’s ID token (see SSO/OIDC configuration).

Fields:

rule_type - Must be "allow-for-group-members"
chat_provider_ids - Array of chat provider IDs that this rule grants access to
groups - Array of group names/identifiers that are allowed access

Example:


[model_permissions.rules.premium_models]
rule_type = "allow-for-group-members"
chat_provider_ids = ["gpt-4", "claude-3-opus"]
groups = ["premium-subscribers", "enterprise-users"]

How Permission Evaluation Works

No rules configured: All users have access to all chat providers
Rules configured: For each chat provider, check if any rule grants access:
- allow-all rules always grant access
- allow-for-group-members rules grant access only if the user belongs to at least one specified group
Access granted: If any rule grants access to a chat provider, the user can use it
Access denied: If no rules grant access to a chat provider, it’s hidden from the user

Complete Example


# Chat provider configuration
[chat_providers]
priority_order = ["basic", "premium", "experimental"]
 
[chat_providers.providers.basic]
provider_kind = "openai"
model_name = "gpt-4o-mini"
model_display_name = "GPT-4o Mini (Basic)"
api_key = "sk-basic-key"
 
[chat_providers.providers.premium]
provider_kind = "openai"
model_name = "gpt-4"
model_display_name = "GPT-4 (Premium)"
api_key = "sk-premium-key"
 
[chat_providers.providers.experimental]
provider_kind = "openai"
model_name = "gpt-4-turbo"
model_display_name = "GPT-4 Turbo (Experimental)"
api_key = "sk-experimental-key"
 
# Model permissions configuration
[model_permissions.rules.basic_access]
rule_type = "allow-all"
chat_provider_ids = ["basic"]
 
[model_permissions.rules.premium_access]
rule_type = "allow-for-group-members"
chat_provider_ids = ["premium"]
groups = ["premium-users", "enterprise"]
 
[model_permissions.rules.experimental_access]
rule_type = "allow-for-group-members"
chat_provider_ids = ["experimental"]
groups = ["beta-testers", "administrators"]

In this example:

All users can access the “basic” model (GPT-4o Mini)
Premium users and enterprise users can access both “basic” and “premium” models
Beta testers and administrators can access “basic”, “premium” (if they’re also in premium groups), and “experimental” models
Regular users (not in any special groups) can only access the “basic” model

Integration with SSO/OIDC

Model permissions rely on group information from the user’s ID token. The groups claim in the JWT token is parsed and used for permission evaluation. See the SSO/OIDC documentation for information on how to configure your identity provider to include group information in ID tokens.

Common group claim formats:

Array of strings: ["group1", "group2", "group3"]
Single string: "group1" (automatically converted to array)

If no groups claim is present in the ID token, the user is treated as having no group memberships, and only allow-all rules will grant them access.

`mcp_server_permissions`

Configuration for controlling user access to configured MCP servers based on group membership. This is evaluated through the same policy engine as model_permissions, and affects which MCP servers can be attached to assistants or used during generation.

Type: object | None

Default behavior: If no MCP server permissions are configured, all users have access to all configured MCP servers.

`mcp_server_permissions.rules`

A map of permission rules, where each key is a unique rule name and the value is a rule configuration object. Rules are evaluated independently, and if any rule grants access to an MCP server, the user is allowed to use it.

Type: object<string, McpServerPermissionRule>

Example:


[mcp_server_permissions.rules.public_servers]
rule_type = "allow-all"
mcp_server_ids = ["filesystem", "company-search"]
 
[mcp_server_permissions.rules.premium_servers]
rule_type = "allow-for-group-members"
mcp_server_ids = ["browser-automation", "code-execution"]
groups = ["premium-users", "administrators"]

Rule Types

mcp_server_permissions supports the same rule types as model_permissions:

allow-all
allow-for-group-members

The only difference is the resource field name:

mcp_server_ids instead of chat_provider_ids

Example:


[mcp_server_permissions.rules.restricted_server]
rule_type = "allow-for-group-members"
mcp_server_ids = ["browser-automation"]
groups = ["security-reviewed-users"]

`facet_permissions`

Configuration for controlling which facets are visible and usable for different users. This is evaluated through the same policy engine as model_permissions, and affects facet visibility in /me/facets, starter prompts, and message generation.

Type: object | None

Default behavior: If no facet permissions are configured, all users have access to all configured facets.

`facet_permissions.rules`

A map of permission rules, where each key is a unique rule name and the value is a rule configuration object. Rules are evaluated independently, and if any rule grants access to a facet, the user is allowed to use it.

Type: object<string, FacetPermissionRule>

Example:


[facet_permissions.rules.default_facets]
rule_type = "allow-all"
facet_ids = ["extended_thinking"]
 
[facet_permissions.rules.web_search]
rule_type = "allow-for-group-members"
facet_ids = ["web_search"]
groups = ["premium-users", "research-team"]

Rule Types

facet_permissions supports the same rule types as model_permissions:

allow-all
allow-for-group-members

The only difference is the resource field name:

facet_ids instead of chat_provider_ids

Example:


[facet_permissions.rules.image_generation]
rule_type = "allow-for-group-members"
facet_ids = ["image_generation"]
groups = ["design-team"]

Integration with SSO/OIDC

Like model_permissions, both mcp_server_permissions and facet_permissions use the groups claim from the user’s ID token for allow-for-group-members rules. If no groups claim is present, only allow-all rules grant access.

`integrations`

Configuration for external service integrations.

`integrations.langfuse`

Configuration for the Langfuse observability and prompt management integration.

`integrations.langfuse.enabled`

Whether the Langfuse integration is enabled.

Default value: false

Type: boolean

`integrations.langfuse.base_url`

The base URL for your Langfuse instance. Use https://cloud.langfuse.com for Langfuse Cloud or your self-hosted URL.

Required when enabled: Yes

Type: string

Example: "https://cloud.langfuse.com" or "https://langfuse.yourcompany.com"

`integrations.langfuse.public_key`

Your Langfuse project’s public key. You can find this in your Langfuse project settings.

Required when enabled: Yes

Type: string

Example: "pk-lf-1234567890abcdef"

`integrations.langfuse.secret_key`

Your Langfuse project’s secret key. You can find this in your Langfuse project settings.

Required when enabled: Yes

Type: string

Example: "sk-lf-abcdef1234567890"

`integrations.langfuse.tracing_enabled`

Whether to enable detailed tracing of LLM interactions. When enabled, all chat completions will be logged to Langfuse.

Default value: false

Type: boolean

`integrations.langfuse.enable_feedback`

Whether to forward user feedback (thumbs up/down with optional comments) to Langfuse as scores. When enabled, feedback submitted by users will be sent to Langfuse and associated with the corresponding message trace for observability and quality tracking.

Default value: false

Type: boolean

Note: This requires frontend.enable_message_feedback to be enabled for users to submit feedback through the UI.

See the Langfuse Integration documentation for detailed usage instructions and feature descriptions.

`integrations.sentry`

Configuration for the Sentry error reporting and performance monitoring integration.

`integrations.sentry.sentry_dsn`

The Sentry DSN (Data Source Name) for your Sentry project. This enables error reporting and performance monitoring.

Default value: None

Type: string | None

Example:


[integrations.sentry]
sentry_dsn = "https://public@sentry.example.com/1"

See the Sentry Integration documentation for detailed setup instructions.

`integrations.otel`

Configuration for OpenTelemetry tracing.

`integrations.otel.enabled`

Whether OpenTelemetry tracing is enabled.

Default value: false

Type: boolean

`integrations.otel.endpoint`

The endpoint URL of the OpenTelemetry collector.

Default value: "http://localhost:4318"

Type: string

Example: "http://localhost:4318" or "http://otel-collector:4317"

`integrations.otel.protocol`

The protocol to use for exporting traces.

Default value: "http"

Type: string

Supported values: "http", "grpc"

`integrations.otel.service_name`

The service name to identify this application in traces.

Default value: "erato-backend"

Type: string

`integrations.prometheus`

Configuration for Prometheus metrics export.

`integrations.prometheus.enabled`

Whether the Prometheus metrics endpoint is enabled.

Default value: false

Type: boolean

`integrations.prometheus.host`

Host interface for the Prometheus metrics listener.

Default value: "127.0.0.1"

Type: string

`integrations.prometheus.port`

Port for the Prometheus metrics listener. This must be different from http_port.

Default value: 3131

Type: integer

`integrations.experimental_sharepoint`

Configuration for the experimental SharePoint/OneDrive integration. This allows users to attach files from their Microsoft cloud storage directly to chats.

Note: This is an experimental feature. The configuration key will be renamed to integrations.sharepoint once the feature has stabilized.

`integrations.experimental_sharepoint.enabled`

Whether the SharePoint/OneDrive integration is enabled.

Default value: false

Type: boolean

`integrations.experimental_sharepoint.file_upload_enabled`

Whether file linking from SharePoint is enabled. When disabled, users can browse their drives but cannot link files to chats.

Default value: true

Type: boolean

`integrations.experimental_sharepoint.auth_via_access_token`

Whether to use the user’s forwarded access token for Microsoft Graph API authentication. Currently, this is the only supported authentication method.

Default value: true

Type: boolean

`integrations.experimental_sharepoint.all_drives_sources`

Which Microsoft Graph discovery surfaces Erato should query for /integrations/sharepoint/all-drives.

If omitted or set to an empty list, Erato queries all supported sources.

Supported values:

me_drive
me_drives
joined_teams
group_drives
site_search
site_drives
shared_with_me
shared_drive_details

Default value: [] (interpreted as all sources)

Type: array[string]

Example:


[integrations.experimental_sharepoint]
enabled = true
file_upload_enabled = true
auth_via_access_token = true
all_drives_sources = ["me_drive", "me_drives", "shared_with_me", "shared_drive_details"]

Prerequisites:

Your OIDC provider (e.g., Microsoft Entra ID) must issue access tokens with Microsoft Graph API permissions (Files.Read or Files.ReadWrite)
Your authentication proxy (e.g., oauth2-proxy) must forward the access token via the X-Forwarded-Access-Token header

See the SharePoint/OneDrive Integration documentation for detailed setup instructions.

`integrations.experimental_entra_id`

Configuration for the experimental Entra ID integration. This allows exposing organization users and groups to the frontend for sharing features.

Note: This is an experimental feature. The configuration key will be renamed to integrations.entra_id once the feature has stabilized.

`integrations.experimental_entra_id.enabled`

Whether the Entra ID integration is enabled. When enabled, the /me/organization/users and /me/organization/groups endpoints will query the Microsoft Graph API to list organization users and groups. When disabled, these endpoints will return empty lists.

Default value: false

Type: boolean

`integrations.experimental_entra_id.auth_via_access_token`

Whether to use the user’s forwarded access token for Microsoft Graph API authentication. Currently, this is the only supported authentication method.

Default value: true

Type: boolean

Example:


[integrations.experimental_entra_id]
enabled = true
auth_via_access_token = true

Prerequisites:

Your OIDC provider (e.g., Microsoft Entra ID) must issue access tokens with Microsoft Graph API permissions (User.Read.All and Group.Read.All)
Your authentication proxy (e.g., oauth2-proxy) must forward the access token via the X-Forwarded-Access-Token header
The ID token must include the oid claim for organization user IDs and the groups claim for organization group memberships

Related Features:

When the Entra ID integration is enabled, the user profile will include additional fields:

organization_user_id - Extracted from the oid claim (Entra ID specific)
organization_group_ids - Extracted from the groups claim

These fields enable sharing assistants with organization groups via share grants with subject_type = "organization_group" and subject_id_type = "organization_group_id".

`logging`

Configuration for backend log output formatting.

`logging.format`

Controls how application logs are formatted on stdout/stderr.

Use "plain" for human-readable local development logs, or "json" for structured logs that are easier to ingest in production log pipelines.

When "json" is enabled, startup messages that are emitted before the tracing subscriber is initialized are also re-emitted as structured log events once logging is ready.

Default value: "plain"

Type: string

Supported values: "plain", "json"

Example:


[logging]
format = "json"

`mcp_servers`

Configuration for Model Context Protocol (MCP) servers that extend Erato’s capabilities through custom tools and integrations.

Type: object<string, McpServerConfig>

`mcp_servers_global`

Global configuration for MCP server behavior across all configured servers.

Type: McpServersGlobalConfig

`mcp_servers_global.max_session_idle_seconds`

Global default maximum idle time for MCP sessions, in seconds. If a session is idle longer than this threshold, it is evicted by the backend cleanup task.

Server-specific settings can override this value via mcp_servers.<server_id>.max_session_idle_seconds.

Type: integer | None

Default value: 3600 (1 hour)

Example: 900

Operational note: For MCP servers where each session allocates substantial resources (e.g. memory-heavy tools, expensive backend handles), use a lower idle timeout to reclaim server capacity faster.

`mcp_servers_global.show_frontend_tab`

Whether the MCP servers tab should be shown in the frontend preferences dialog.

This only controls frontend visibility of the tab. It does not change backend MCP server availability, authorization, or permissions.

Type: boolean

Default value: false

Example:


[mcp_servers.file_provider]
transport_type = "sse"
url = "http://127.0.0.1:63490/sse"
 
[mcp_servers.filesystem]
transport_type = "sse"
url = "https://my-mcp-server.example.com/sse"
[mcp_servers.filesystem.authentication]
mode = "fixed"
 
[mcp_servers.filesystem.authentication.fixed]
api_key = "token123"
header_name = "Authorization"
prefix = "Bearer "
 
max_session_idle_seconds = 1800

`mcp_servers.<server_id>.transport_type`

The type of transport protocol used to communicate with the MCP server.

Type: string

Supported values:

"sse" (Server-Sent Events)
"streamable_http" (Streamable HTTP)

Example: "sse" or "streamable_http"

`mcp_servers.<server_id>.url`

The URL endpoint for the MCP server.

For transport_type = "sse", this conventionally ends with /sse
For transport_type = "streamable_http", this should be the base HTTP endpoint

Type: string

Example: "http://127.0.0.1:63490/sse", "https://my-mcp-server.example.com/sse", "https://api.example.com/mcp"

`mcp_servers.<server_id>.http_headers`

Optional static HTTP headers to include with every request to the MCP server. Use this for non-authentication headers. Prefer mcp_servers.<server_id>.authentication for credentials.

Type: object<string, string> | None

Example:


[mcp_servers.custom_headers_server]
transport_type = "sse"
url = "https://mcp-server.example.com/sse"
http_headers = { "X-Tenant-ID" = "tenant-123", "X-Environment" = "production" }

`mcp_servers.<server_id>.authentication`

Authentication settings for the MCP server.

Type: McpServerAuthenticationConfig

Supported values:

mode = "none": No credentials are attached to MCP requests.
mode = "forwarded": Forward credentials from the current user session.
mode = "fixed": Attach a configured fixed API key using the configured header and prefix.
mode = "oauth2": Use an OAuth 2.0 authorization flow managed by Erato and persist credentials per user.

Examples:


[mcp_servers.public_tools.authentication]
mode = "none"
 
[mcp_servers.intranet.authentication]
mode = "forwarded"
 
[mcp_servers.intranet.authentication.forwarded]
credential = "oidc_id_token"
 
[mcp_servers.partner_api.authentication]
mode = "fixed"
 
[mcp_servers.partner_api.authentication.fixed]
api_key = "your-api-key"
header_name = "Authorization"
prefix = "Bearer "
 
[mcp_servers.jira.authentication]
mode = "oauth2"
 
[mcp_servers.jira.authentication.oauth2]
client_name = "Erato Jira MCP"
scopes = ["read:jira-work"]

`mcp_servers.<server_id>.authentication.forwarded.credential`

Selects which user credential to forward when authentication.mode = "forwarded".

Type: string

Supported values:

"access_token"
"oidc_id_token"

`mcp_servers.<server_id>.authentication.fixed.api_key`

The fixed API key to attach when authentication.mode = "fixed".

Type: string

`mcp_servers.<server_id>.authentication.fixed.header_name`

The HTTP header to use when authentication.mode = "fixed".

Type: string

Default value: "Authorization"

`mcp_servers.<server_id>.authentication.fixed.prefix`

The prefix prepended to authentication.fixed.api_key when authentication.mode = "fixed".

This defaults to bearer-token formatting, so the default fixed auth header is Authorization: Bearer <api_key>.

Type: string

Default value: "Bearer "

`mcp_servers.<server_id>.authentication.oauth2.client_id`

Optional statically configured OAuth client ID for authentication.mode = "oauth2".

If omitted, Erato attempts dynamic client registration and persists the returned client identity for later reuse.

Type: string | None

`mcp_servers.<server_id>.authentication.oauth2.client_secret`

Optional statically configured OAuth client secret for authentication.mode = "oauth2".

Use this for confidential clients that are pre-registered with the MCP server’s authorization server.

Type: string | None

`mcp_servers.<server_id>.authentication.oauth2.client_name`

Optional display name used during dynamic client registration when authentication.mode = "oauth2" and no static client_id is configured.

Type: string | None

Default behavior: Falls back to "Erato MCP OAuth Client"

`mcp_servers.<server_id>.authentication.oauth2.scopes`

Optional list of OAuth scopes requested during authorization for authentication.mode = "oauth2".

If omitted, Erato relies on the scope guidance advertised by the MCP server and its authorization metadata.

Type: array<string>

Example:


[mcp_servers.jira.authentication]
mode = "oauth2"
 
[mcp_servers.jira.authentication.oauth2]
client_id = "erato-jira"
client_secret = "super-secret"
scopes = ["read:jira-work", "offline_access"]

Operational note: oauth2 authentication requires server.encryption_key to be configured because Erato stores registered client secrets, authorization state, and user credentials encrypted at rest.

`mcp_servers.<server_id>.max_session_idle_seconds`

Optional per-server override for maximum MCP session idle time, in seconds.

If set, this value takes precedence over mcp_servers_global.max_session_idle_seconds for sessions created against this server.

Type: integer | None

Default behavior: Uses mcp_servers_global.max_session_idle_seconds (or 3600 when not set)

Example: 300

Operational note: Prefer low values for resource-intensive MCP servers where long-lived idle sessions can consume significant server resources.

See the MCP Servers documentation for more information about Model Context Protocol integration.

`prompt_optimizer`

Configuration for the prompt optimizer. When enabled, the backend exposes the POST /prompt-optimizer endpoint to optimize user prompts using a dedicated chat provider and system prompt.

`prompt_optimizer.enabled`

Whether to enable the prompt optimizer feature.

Default value: false

Type: boolean

`prompt_optimizer.chat_provider_id`

The chat provider ID to use for prompt optimization. Must match a provider configured in chat_providers.

Type: string

`prompt_optimizer.prompt`

System prompt used by the prompt optimizer. If omitted, a built-in non-interactive default prompt is used.

Type: string | object | None

Example:


[prompt_optimizer]
enabled = true
chat_provider_id = "gpt-4o-mini"
# Optional override:
# prompt = "Rewrite the user's prompt to be clearer, more specific, and action-oriented. Return only the improved prompt."

Using Langfuse:


[prompt_optimizer]
enabled = true
chat_provider_id = "gpt-4o-mini"
prompt = { source = "langfuse", prompt_name = "prompt-optimizer", label = "production", fallback = "Rewrite the user's prompt to be clearer, more specific, and action-oriented. Return only the improved prompt." }

Using a static object:


[prompt_optimizer]
enabled = true
chat_provider_id = "gpt-4o-mini"
prompt = { source = "static", prompt = "Rewrite the user's prompt to be clearer, more specific, and action-oriented. Return only the improved prompt." }

`user_preferences`

Configuration for the user preferences feature.

`user_preferences.enabled`

Whether to enable the user preferences feature.

When disabled, the frontend hides the Preferences item in the user profile dropdown.

Default value: true

Type: boolean

Example:


[user_preferences]
enabled = false

`experimental_assistants`

Configuration for the experimental assistants feature. This allows enabling the assistants functionality in the frontend, which provides users with pre-configured chat assistants tuned for specific use-cases.

Note: This is an experimental feature that will be replaced with a stable [assistants] configuration section once the feature has stabilized.

`experimental_assistants.enabled`

Whether to enable the experimental assistants feature in the frontend. When enabled, the frontend will expose assistant-related functionality and UI components to users.

Default value: false

Type: boolean

Example:


[experimental_assistants]
enabled = true

`experimental_assistants.show_recent_items`

Whether to show recent assistant items in the chat sidebar.

Default value: false

Type: boolean

Example:


[experimental_assistants]
show_recent_items = true

`experimental_assistants.context_warning_threshold`

Threshold between 0.0 and 1.0 at or above which the assistant editor shows the overall context warning block.

Default value: 0.5

Type: number

Example:


[experimental_assistants]
context_warning_threshold = 0.6

`experimental_assistants.context_file_contributor_threshold`

Threshold between 0.0 and 1.0 at or above which a file is included in the assistant editor’s “largest file context contributors” list.

Default value: 0.05

Type: number

Example:


[experimental_assistants]
context_file_contributor_threshold = 0.01

Future Migration Plan:

Once the assistants feature has stabilized, this configuration will be replaced by:


[assistants]
enabled = true  # Will default to true once stable

`starter_prompts`

Configuration for welcome-screen starter prompts. Starter prompts prefill the chat input, optional facet selections, and an optional chat provider when clicked, but do not submit the message automatically.

Starter prompt titles and subtitles can be translated in the frontend with Lingui message IDs derived from the starter prompt id:

starter_prompts.<starter-prompt-id>.title
starter_prompts.<starter-prompt-id>.subtitle

If no translation exists for a given starter prompt id, the frontend falls back to the configured title and subtitle.

`starter_prompts.enabled`

Whether to enable starter prompts globally.

Default value: false

Type: boolean

Example:


[starter_prompts]
enabled = true

`starter_prompts.prompts`

Map of starter prompt configurations, keyed by starter prompt id.

Type: object

Example:


[starter_prompts.prompts.research_topic]
title = "Research a topic"
subtitle = "Prefill a research request with web search selected"
icon = "iconoir-globe"
prompt = "Research this topic and summarize the most important findings."
localized_prompts = { de = { source = "static", prompt = "Recherchiere dieses Thema und fasse die wichtigsten Erkenntnisse zusammen." } }
selected_facets = ["web_search"]
chat_provider = "mock-llm"
 
[starter_prompts.prompts.draft_email]
title = "Draft an email"
subtitle = "Write a concise and professional reply"
icon = "iconoir-mail"
prompt = { source = "static", prompt = "Draft a concise and professional reply to this email." }

Using Langfuse:


[starter_prompts.prompts.summarize_notes]
title = "Summarize notes"
subtitle = "Turn rough notes into a structured summary"
icon = "iconoir-page"
prompt = { source = "langfuse", prompt_name = "starter-summarize-notes", label = "production", fallback = "Summarize these notes into clear action items and decisions." }

Each starter prompt supports these fields:

title - Fallback title shown when no frontend translation exists
subtitle - Fallback subtitle shown when no frontend translation exists
icon - Optional icon id
prompt - Default prompt source specification
localized_prompts - Optional map of locale code to prompt source specification
selected_facets - Optional list of facet ids to preselect
chat_provider - Optional chat provider id to preselect

localized_prompts is resolved in the backend against the authenticated user’s profile locale. Resolution order is:

Exact locale match, for example de-DE
Base language match, for example de
Fallback to prompt

`starter_prompts.priority_order`

List of starter prompt IDs in the order they should be displayed.

Type: list<string>

Default value: []

Example:


[starter_prompts]
priority_order = ["research_topic", "draft_email", "summarize_notes"]

`experimental_facets`

Configuration for the experimental facets feature. Facets allow users to select tool-focused modes (e.g. web search) with optional tool allowlists, model setting overrides, and system prompt additions.

Note: This is an experimental feature. The configuration key may change once the feature has stabilized.

`experimental_facets.facets`

Map of facet configurations, keyed by facet id. Each facet defines display properties and optional behavior.

Type: object

Example:


[experimental_facets.facets.extended_thinking]
display_name = "Extended thinking"
icon = "iconoir-lightbulb"
disable_facet_prompt_template = true
model_settings = { reasoning_effort = "high" }
 
[experimental_facets.facets.web_search]
display_name = "Web search"
icon = "iconoir-globe"
tool_call_allowlist = ["web-search-mcp/*", "web-access-mcp/*"]
additional_system_prompt = "Please execute one or multiple web searches to answer the user's question."

Using Langfuse:


[experimental_facets.facets.web_search]
display_name = "Web search"
icon = "iconoir-globe"
tool_call_allowlist = ["web-search-mcp/*", "web-access-mcp/*"]
additional_system_prompt = { source = "langfuse", prompt_name = "facet-web-search", label = "production", fallback = "Please execute one or multiple web searches to answer the user's question." }

Using a static object:


[experimental_facets.facets.web_search]
display_name = "Web search"
icon = "iconoir-globe"
tool_call_allowlist = ["web-search-mcp/*", "web-access-mcp/*"]
additional_system_prompt = { source = "static", prompt = "Please execute one or multiple web searches to answer the user's question." }

`experimental_facets.priority_order`

List of facet IDs in the order they should be displayed.

Type: list<string>

Default value: []

Example:


[experimental_facets]
priority_order = ["extended_thinking", "web_search"]

`experimental_facets.tool_call_allowlist`

Global tool allowlist that applies regardless of selected facets.

Type: list<string>

Default value: []

Example:


[experimental_facets]
tool_call_allowlist = ["web-search-mcp/*"]

`experimental_facets.facet_prompt_template`

Optional global prompt template injected when facets are selected. Set to an empty string to disable.

Type: string | object | None

Default value: The user has requested the use of the "{{facet_display_name}}" feature.\n\nPrioritize the use of the following tools:\n{{facet_tools_list}}

Example:


[experimental_facets]
facet_prompt_template = ""

Using Langfuse:


[experimental_facets]
facet_prompt_template = { source = "langfuse", prompt_name = "facet-template", label = "production", fallback = "The user has requested the use of the \"{{facet_display_name}}\" feature.\n\nPrioritize the use of the following tools:\n{{facet_tools_list}}" }

Using a static object:


[experimental_facets]
facet_prompt_template = { source = "static", prompt = "The user has requested the use of the \"{{facet_display_name}}\" feature.\n\nPrioritize the use of the following tools:\n{{facet_tools_list}}" }

`experimental_facets.only_single_facet`

Whether only a single facet can be selected at the same time.

Type: boolean

Default value: false

`experimental_facets.show_facet_indicator_with_display_name`

Whether the chat box indicator should include the facet display name.

Type: boolean

Default value: false

`experimental_facets.default_selected_facets`

Facets that should be selected by default in the frontend (no backend logic is applied).

Type: list<string>

Default value: []

Example:


[experimental_facets]
default_selected_facets = ["web_search"]

`action_facets`

Configuration for action facets. Action facets are non-user-visible, parameterized extensions of the facets system that inject context-aware additional system prompts based on client-supplied action ID and arguments. Unlike regular facets, action facets are not selectable by the user — they are resolved from client context at request time.

Clients send an action_facet object (containing id and args) alongside the user’s message on submit, edit, and regenerate requests. The backend resolves the configured prompt template, validates the arguments against the allowed list, and injects the rendered prompt as an additional system prompt before generation.

Note: Action facet prompts are per-request and ephemeral. They do not replace or suppress any other prompt in the composition pipeline — the base system prompt is always preserved regardless of whether an action facet is active.

`action_facets.enable_builtin_ms_office_addin`

Whether to inject the builtin Outlook action facets used by the Microsoft Office add-in.

Type: boolean

Default value: false

`action_facets.facets.<id>`

Map of action facet configurations, keyed by action facet id. Each action facet defines a prompt template with declared argument variables.

Type: object

Example:


[action_facets.facets.excel_rewrite_selection]
display_name = "Excel Rewrite Selection"
platform = "excel"
template = """
The user is working in Excel on sheet "{{sheet_name}}".
The current selection type is "{{selection_type}}".
Rewrite the following selected content:
{{selected_text}}
"""
allowed_args = ["sheet_name", "selection_type", "selected_text"]

When action_facets.enable_builtin_ms_office_addin = true, the backend also injects these builtin Outlook action facets unless the same IDs are explicitly configured under action_facets.facets:

outlook_rewrite_selection
outlook_review_draft

Each action facet supports these fields:

display_name - Human-readable name for the action facet
platform - Optional platform identifier (e.g. "excel", "outlook")
template - Prompt template with {{variable}} placeholders
allowed_args - List of allowed argument names for the template

Template Rendering

Templates use {{variable}} placeholders that are replaced with argument values supplied in the request. Rendering uses single-pass left-to-right scanning — substituted values are never re-scanned for further {{…}} patterns, preventing injection via argument values.

Validation

The backend validates each request against the action facet configuration:

The action_facet.id must match a configured action facet — unknown IDs are rejected
All keys in args must be present in allowed_args — unexpected keys are rejected
Argument values are subject to a maximum size limit (10 KB per value)

Request Shape

The action_facet field is accepted on submit, edit, and regenerate endpoints. It is not accepted on resume, since the prompt was already composed when the generation started.


{
  "action_facet": {
    "id": "excel_rewrite_selection",
    "args": {
      "selection_type": "cell_text",
      "sheet_name": "Q1 Plan",
      "selected_text": "badly phrased content here"
    }
  }
}

Differences from Regular Facets

Aspect	Regular Facets	Action Facets
User-visible	Yes, selectable in UI	No, resolved from client context
Parameterized	No, behavior is config-time only	Yes, runtime args from request
Request field	`selected_facet_ids`	`action_facet`
Prompt lifecycle	Toggle-state based, persistent across messages	Per-request, ephemeral
Base system prompt	May suppress historical replay on newly-enabled turn	Never suppresses — always additive

`budget`

Configuration for budget tracking and display functionality. This feature allows you to track and display per-user spending based on token usage and model pricing.

Type: object

Default behavior: Budget tracking is disabled by default.

Example:


[budget]
enabled = true
max_budget = 100.0
budget_currency = "USD"
warn_threshold = 0.8
budget_period_days = 30

`budget.enabled`

Whether budget tracking and display is enabled. When disabled, no budget information will be calculated or displayed to users.

Type: boolean

Default value: false

Example: true

`budget.max_budget`

The maximum budget amount per budget period (unit-less). This value is used for calculating budget usage percentages and determining when warning thresholds are exceeded.

The budget amount is treated as unit-less for flexibility - the actual monetary value depends on your model pricing configuration and the configured display currency.

Type: number | None

Required when: budget.enabled = true

Example: 100.0

`budget.budget_currency`

The currency to use for display purposes in the frontend. This is purely cosmetic and doesn’t affect calculations - all budget amounts and costs are treated as unit-less values.

Type: string

Supported values: "USD", "EUR"

Default value: "USD"

Example: "EUR"

`budget.warn_threshold`

The threshold (between 0.0 and 1.0) at which to warn users about budget usage. When a user’s spending exceeds this percentage of their budget limit, they will receive warnings in the UI.

For example, a value of 0.7 means users will be warned when they’ve used 70% of their budget.

Type: number

Default value: 0.7

Valid range: 0.0 to 1.0

Example: 0.8

`budget.budget_period_days`

Number of days that counts as one budget period. Budget usage is calculated and reset based on this period length.

Type: number

Default value: 30

Example: 7 (for weekly budgets), 30 (for monthly budgets)

Complete Budget Configuration Example


[budget]
enabled = true
max_budget = 50.0
budget_currency = "EUR"
warn_threshold = 0.75
budget_period_days = 7
 
# You also need to configure model pricing for accurate cost calculations
[chat_providers]
priority_order = ["main"]
 
[chat_providers.providers.main]
provider_kind = "openai"
model_name = "gpt-4o"
api_key = "sk-your-key"
 
[chat_providers.providers.main.model_capabilities]
cost_input_tokens_per_1m = 5.0
cost_output_tokens_per_1m = 15.0

Note: Budget tracking requires that you configure accurate pricing information in your chat provider’s model_capabilities section. The budget calculations are based on actual token usage multiplied by the configured token prices.

`caches`

Configuration for in-memory caches that improve performance by reducing redundant file processing and token counting operations. Caches use memory-based size limits (in megabytes) rather than entry counts.

Type: object

Default behavior: Both caches default to 100MB each.

Example:


[caches]
file_contents_cache_mb = 100
token_count_cache_mb = 100

`caches.file_contents_cache_mb`

Maximum memory size in megabytes for the file contents cache. This cache stores parsed file contents indexed by file ID to avoid repeatedly reading and parsing the same files.

When the cache reaches this size limit, the least recently used entries will be automatically evicted to make room for new entries.

Type: number

Default value: 100

Example: 200 (for 200MB cache), 50 (for 50MB cache)

`caches.token_count_cache_mb`

Maximum memory size in megabytes for the token count cache. This cache stores token counts indexed by file content to avoid repeatedly tokenizing the same content.

When the cache reaches this size limit, the least recently used entries will be automatically evicted to make room for new entries.

Type: number

Default value: 100

Example: 200 (for 200MB cache), 50 (for 50MB cache)

Complete Cache Configuration Example


# Configure caches for optimal performance
[caches]
# Allocate 200MB for file contents caching
file_contents_cache_mb = 200
# Allocate 100MB for token count caching
token_count_cache_mb = 100

Performance Considerations:

Increase cache sizes if you have many users frequently accessing the same files
Decrease cache sizes in memory-constrained environments
The file contents cache is typically more beneficial than the token count cache
Cache efficiency depends on file reuse patterns in your workflow

Configuration

Ways to provide configuration

erato.toml files

*.auto.erato.toml files

Environment variables

Configuration reference

server

server.encryption_key

frontend

frontend.theme

frontend.disable_upload

frontend.disable_chat_input_autofocus

frontend.disable_logout

frontend.enable_message_feedback

frontend.enable_message_feedback_comments

frontend.message_feedback_edit_time_limit_seconds

frontend.sidebar_collapsed_mode

frontend.sidebar_logo_path

frontend.sidebar_logo_dark_path

frontend.additional_environment

i18n

i18n.language

i18n.language.language_detection_priority

i18n.language.default_language

chat_provider (deprecated)

chat_provider.provider_kind

chat_provider.model_name

chat_provider.model_display_name

chat_provider.model_description

chat_provider.model_icon

chat_provider.model_name_langfuse

chat_provider.base_url

chat_provider.api_key

chat_provider.system_prompt

chat_provider.system_prompt_langfuse

chat_providers

chat_providers.priority_order

chat_providers.providers

Provider Configuration Fields

model_capabilities

model_settings

chat_providers.summary

chat_providers.summary.summary_chat_provider_id

chat_providers.summary.max_tokens

Migration from chat_provider

file_storage_providers

file_storage_providers.<provider_id>.provider_kind

file_storage_providers.<provider_id>.display_name

file_storage_providers.<provider_id>.max_upload_size_kb

file_storage_providers.<provider_id>.config

S3 Provider Configuration

Azure Blob Storage Configuration

default_file_storage_provider

Multiple Providers Example

file_processor

file_processor.processor

Processor Comparison

Page-Aware Extraction

model_permissions

model_permissions.rules

Rule Types

allow-all

allow-for-group-members

How Permission Evaluation Works

Complete Example

Integration with SSO/OIDC

mcp_server_permissions

mcp_server_permissions.rules

Rule Types

facet_permissions

facet_permissions.rules

Rule Types

Integration with SSO/OIDC

integrations

integrations.langfuse

integrations.langfuse.enabled

integrations.langfuse.base_url

integrations.langfuse.public_key

integrations.langfuse.secret_key

integrations.langfuse.tracing_enabled

`erato.toml` files

`*.auto.erato.toml` files

`server`

`server.encryption_key`

`frontend`

`frontend.theme`

`frontend.disable_upload`

`frontend.disable_chat_input_autofocus`

`frontend.disable_logout`

`frontend.enable_message_feedback`

`frontend.enable_message_feedback_comments`

`frontend.message_feedback_edit_time_limit_seconds`

`frontend.sidebar_collapsed_mode`

`frontend.sidebar_logo_path`

`frontend.sidebar_logo_dark_path`

`frontend.additional_environment`

`i18n`

`i18n.language`

`i18n.language.language_detection_priority`

`i18n.language.default_language`

`chat_provider` (deprecated)

`chat_provider.provider_kind`

`chat_provider.model_name`

`chat_provider.model_display_name`

`chat_provider.model_description`

`chat_provider.model_icon`

`chat_provider.model_name_langfuse`

`chat_provider.base_url`

`chat_provider.api_key`

`chat_provider.system_prompt`

`chat_provider.system_prompt_langfuse`

`chat_providers`

`chat_providers.priority_order`

`chat_providers.providers`

`model_capabilities`

`model_settings`

`chat_providers.summary`

`chat_providers.summary.summary_chat_provider_id`

`chat_providers.summary.max_tokens`

Migration from `chat_provider`

`file_storage_providers`

`file_storage_providers.<provider_id>.provider_kind`

`file_storage_providers.<provider_id>.display_name`

`file_storage_providers.<provider_id>.max_upload_size_kb`

`file_storage_providers.<provider_id>.config`

`default_file_storage_provider`

`file_processor`

`file_processor.processor`

`model_permissions`

`model_permissions.rules`

`allow-all`

`allow-for-group-members`

`mcp_server_permissions`

`mcp_server_permissions.rules`

`facet_permissions`

`facet_permissions.rules`

`integrations`

`integrations.langfuse`

`integrations.langfuse.enabled`

`integrations.langfuse.base_url`

`integrations.langfuse.public_key`

`integrations.langfuse.secret_key`

`integrations.langfuse.tracing_enabled`

`integrations.langfuse.enable_feedback`

`integrations.sentry`

`integrations.sentry.sentry_dsn`

`integrations.otel`

`integrations.otel.enabled`

`integrations.otel.endpoint`

`integrations.otel.protocol`

`integrations.otel.service_name`

`integrations.prometheus`

`integrations.prometheus.enabled`

`integrations.prometheus.host`

`integrations.prometheus.port`

`integrations.experimental_sharepoint`

`integrations.experimental_sharepoint.enabled`

`integrations.experimental_sharepoint.file_upload_enabled`

`integrations.experimental_sharepoint.auth_via_access_token`

`integrations.experimental_sharepoint.all_drives_sources`