variables.tf

variable "name" {
  description = "Name for the agent."
  type        = string
}

variable "alias_name" {
  description = "Name for the agent alias."
  type        = string
}

variable "alias_description" {
  description = "Description for the agent alias."
  type        = string
  default     = null
}

variable "agent_model_id" {
  description = "Model identifier for agent."
  type        = string
  default     = "anthropic.claude-v2"
}

variable "agent_instructions" {
  description = "Model identifier for agent."
  type        = string
}

variable "knowledgebase_name" {
  description = "Name for the knowledgebase."
  type        = string
}

variable "knowledgebase_description" {
  description = "Description for the knowledgebase."
  type        = string
}

variable "knowledgebase_model_id" {
  description = "Model identifier for Knowledgebase."
  type        = string
  default     = "amazon.titan-embed-text-v1"
}

variable "knowledgebase_data_deletion_policy" {
  description = "Data deletion policy for a data source. Valid values: `RETAIN`, `DELETE`"
  type        = string
  default     = "RETAIN"
}

variable "s3_configuration" {
  description = "ARN of S3 bucket with data"
  type = object({
    bucket_arn              = string
    bucket_owner_account_id = optional(string)
    inclusion_prefixes      = optional(set(string))
  })
  validation {
    condition     = var.s3_configuration.inclusion_prefixes == null ? true : length(var.s3_configuration.inclusion_prefixes) == 1
    error_message = "For now s3 data source support only one prefix."
  }
}

variable "vector_ingestion_configuration" {
  type = object({
    chunking_configuration = object({
      chunking_strategy = string
      fixed_size_chunking_configuration = optional(object({
        max_tokens         = number
        overlap_percentage = optional(number)
      }))
      hierarchical_chunking_configuration = optional(object({
        overlap_tokens = number
        level_1        = object({ max_tokens = number })
        level_2        = object({ max_tokens = number })
      }))
      semantic_chunking_configuration = optional(object({
        breakpoint_percentile_threshold = number
        buffer_size                     = number
        max_token                       = number
      }))
    })
    custom_transformation_configuration = optional(object({
      intermediate_storage    = string
      transformation_function = string
    }))
  })
  default = {
    chunking_configuration = {
      chunking_strategy = "FIXED_SIZE"
      fixed_size_chunking_configuration = {
        max_tokens         = 300
        overlap_percentage = 20
      }
      hierarchical_chunking_configuration = null
      semantic_chunking_configuration     = null
    }
  }
}

variable "oss_collection_name" {
  description = "Name of OpenSearch Serverless Collection."
  type        = string
}

variable "oss_additional_roles_arns" {
  description = "Additional ARNs of roles to access OpenSearch"
  type        = list(string)
  default     = []
}

# KNOWLEDGE_BASE_RESPONSE_GENERATION Configuration
variable "knowledge_base_response_generation_prompt_template" {
  description = "Prompt template for pre-processing."
  type        = string
  default     = <<EOF
        You are a helpful assistant. Answer the following question using the context provided:
        Question: {question}
        Context: {context}
        Your response should be thoughtful, detailed, and relevant to the provided context.
        EOF
}

variable "knowledge_base_response_generation_parser_mode" {
  description = "Parser mode for pre-processing."
  type        = string
  default     = "DEFAULT"
}

variable "knowledge_base_response_generation_prompt_creation_mode" {
  description = "Prompt creation mode for pre-processing."
  type        = string
  default     = "OVERRIDDEN"
}

variable "knowledge_base_response_generation_prompt_state" {
  description = "Prompt state for pre-processing."
  type        = string
  default     = "ENABLED"
}

variable "knowledge_base_response_generation_max_length" {
  description = "Maximum number of tokens to allow in the generated response."
  type        = number
  default     = 512
}

variable "knowledge_base_response_generation_stop_sequences" {
  description = "List of stop sequences that will stop generation."
  type        = list(string)
  default     = ["END"]
}

variable "knowledge_base_response_generation_temperature" {
  description = "Likelihood of the model selecting higher-probability options while generating a response."
  type        = number
  default     = 0.7
}

variable "knowledge_base_response_generation_top_k" {
  description = "Number of top most-likely candidates from which the model chooses the next token."
  type        = number
  default     = 50
}

variable "knowledge_base_response_generation_top_p" {
  description = "Top percentage of the probability distribution of next tokens, from which the model chooses the next token."
  type        = number
  default     = 0.9
}

# PRE_PROCESSING Configuration
variable "pre_processing_prompt_template" {
  description = "Prompt template for pre-processing."
  type        = string
  default     = <<EOF
        You are preparing the input. Extract relevant context and pre-process the following question:
        Question: {question}
        Context: {context}
        Pre-processing should focus on extracting the core information.
        EOF
}

variable "pre_processing_parser_mode" {
  description = "Parser mode for pre-processing."
  type        = string
  default     = "DEFAULT" # Change to OVERRIDDEN if necessary
}

variable "pre_processing_prompt_creation_mode" {
  description = "Prompt creation mode for pre-processing."
  type        = string
  default     = "OVERRIDDEN"
}

variable "pre_processing_prompt_state" {
  description = "Prompt state for pre-processing."
  type        = string
  default     = "ENABLED"
}

variable "pre_processing_max_length" {
  description = "Maximum number of tokens to allow in the generated response."
  type        = number
  default     = 512
}

variable "pre_processing_stop_sequences" {
  description = "List of stop sequences that will stop generation."
  type        = list(string)
  default     = ["END"]
}

variable "pre_processing_temperature" {
  description = "Likelihood of the model selecting higher-probability options while generating a response."
  type        = number
  default     = 0.7
}

variable "pre_processing_top_k" {
  description = "Number of top most-likely candidates from which the model chooses the next token."
  type        = number
  default     = 50
}

variable "pre_processing_top_p" {
  description = "Top percentage of the probability distribution of next tokens, from which the model chooses the next token."
  type        = number
  default     = 0.9
}

# ORCHESTRATION Configuration
variable "orchestration_prompt_template" {
  description = "Prompt template for orchestration."
  type        = string
  default     = <<EOF
        You are orchestrating the flow of the agent. Based on the question and context, determine the next steps in the process:
        Question: {question}
        Context: {context}
        Plan the next steps to follow the best strategy.
        EOF
}

variable "orchestration_parser_mode" {
  description = "Parser mode for orchestration."
  type        = string
  default     = "DEFAULT"
}

variable "orchestration_prompt_creation_mode" {
  description = "Prompt creation mode for orchestration."
  type        = string
  default     = "OVERRIDDEN"
}

variable "orchestration_prompt_state" {
  description = "Prompt state for orchestration."
  type        = string
  default     = "ENABLED"
}

variable "orchestration_max_length" {
  description = "Maximum number of tokens to allow in the generated response."
  type        = number
  default     = 512
}

variable "orchestration_stop_sequences" {
  description = "List of stop sequences that will stop generation."
  type        = list(string)
  default     = ["END"]
}

variable "orchestration_temperature" {
  description = "Likelihood of the model selecting higher-probability options while generating a response."
  type        = number
  default     = 0.7
}

variable "orchestration_top_k" {
  description = "Number of top most-likely candidates from which the model chooses the next token."
  type        = number
  default     = 50
}

variable "orchestration_top_p" {
  description = "Top percentage of the probability distribution of next tokens, from which the model chooses the next token."
  type        = number
  default     = 0.9
}

# POST_PROCESSING Configuration
variable "post_processing_prompt_template" {
  description = "Prompt template for post-processing."
  type        = string
  default     = <<EOF
You are performing post-processing. Review the agent's output and refine the response for clarity and relevance:
Response: {response}
Context: {context}
Ensure the output is polished and aligns with the context.
EOF
}

variable "post_processing_parser_mode" {
  description = "Parser mode for post-processing."
  type        = string
  default     = "DEFAULT"
}

variable "post_processing_prompt_creation_mode" {
  description = "Prompt creation mode for post-processing."
  type        = string
  default     = "OVERRIDDEN"
}

variable "post_processing_prompt_state" {
  description = "Prompt state for post-processing."
  type        = string
  default     = "DISABLED"
}

variable "post_processing_max_length" {
  description = "Maximum number of tokens to allow in the generated response."
  type        = number
  default     = 512
}

variable "post_processing_stop_sequences" {
  description = "List of stop sequences that will stop generation."
  type        = list(string)
  default     = ["END"]
}

variable "post_processing_temperature" {
  description = "Likelihood of the model selecting higher-probability options while generating a response."
  type        = number
  default     = 0.7
}

variable "post_processing_top_k" {
  description = "Number of top most-likely candidates from which the model chooses the next token."
  type        = number
  default     = 50
}

variable "post_processing_top_p" {
  description = "Top percentage of the probability distribution of next tokens, from which the model chooses the next token."
  type        = number
  default     = 0.9
}

variable "tags" {
  description = "A map of tags to assign to the customization job and custom model."
  type        = map(string)
  default     = {}
}