Converse

bedrockruntime_converse

R Documentation

Sends messages to the specified Amazon Bedrock model

Description

Sends messages to the specified Amazon Bedrock model. converse provides a consistent interface that works with all models that support messages. This allows you to write code once and use it with different models. If a model has unique inference parameters, you can also pass those unique parameters to the model.

Amazon Bedrock doesn't store any text, images, or documents that you provide as content. The data is only used to generate the response.

For information about the Converse API, see Use the Converse API in the Amazon Bedrock User Guide. To use a guardrail, see Use a guardrail with the Converse API in the Amazon Bedrock User Guide. To use a tool with a model, see Tool use (Function calling) in the Amazon Bedrock User Guide

For example code, see Converse API examples in the Amazon Bedrock User Guide.

This operation requires permission for the bedrock:InvokeModel action.

Usage

bedrockruntime_converse(modelId, messages, system, inferenceConfig,
  toolConfig, guardrailConfig, additionalModelRequestFields,
  additionalModelResponseFieldPaths)

Arguments

modelId

[required] The identifier for the model that you want to call.

The modelId to provide depends on the type of model or throughput that you use:

• If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.

• If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.

• If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.

• If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.

The Converse API doesn't support imported models.

messages

[required] The messages that you want to send to the model.

system

A system prompt to pass to the model.

inferenceConfig

Inference parameters to pass to the model. converse supports a base set of inference parameters. If you need to pass additional parameters that the model supports, use the additionalModelRequestFields request field.

toolConfig

Configuration information for the tools that the model can use when generating a response.

This field is only supported by Anthropic Claude 3, Cohere Command R, Cohere Command R+, and Mistral Large models.

guardrailConfig

Configuration information for a guardrail that you want to use in the request.

additionalModelRequestFields

Additional inference parameters that the model supports, beyond the base set of inference parameters that converse supports in the inferenceConfig field. For more information, see Model parameters.

additionalModelResponseFieldPaths

Additional model parameters field paths to return in the response. converse returns the requested fields as a JSON Pointer object in the additionalModelResponseFields field. The following is example JSON for additionalModelResponseFieldPaths.

⁠[ "/stop_sequence" ]⁠

For information about the JSON Pointer syntax, see the Internet Engineering Task Force (IETF) documentation.

converse rejects an empty JSON Pointer or incorrectly structured JSON Pointer with a 400 error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored by converse.

Value

A list with the following syntax:

list(
  output = list(
    message = list(
      role = "user"|"assistant",
      content = list(
        list(
          text = "string",
          image = list(
            format = "png"|"jpeg"|"gif"|"webp",
            source = list(
              bytes = raw
            )
          ),
          document = list(
            format = "pdf"|"csv"|"doc"|"docx"|"xls"|"xlsx"|"html"|"txt"|"md",
            name = "string",
            source = list(
              bytes = raw
            )
          ),
          toolUse = list(
            toolUseId = "string",
            name = "string",
            input = list()
          ),
          toolResult = list(
            toolUseId = "string",
            content = list(
              list(
                json = list(),
                text = "string",
                image = list(
                  format = "png"|"jpeg"|"gif"|"webp",
                  source = list(
                    bytes = raw
                  )
                ),
                document = list(
                  format = "pdf"|"csv"|"doc"|"docx"|"xls"|"xlsx"|"html"|"txt"|"md",
                  name = "string",
                  source = list(
                    bytes = raw
                  )
                )
              )
            ),
            status = "success"|"error"
          ),
          guardContent = list(
            text = list(
              text = "string",
              qualifiers = list(
                "grounding_source"|"query"|"guard_content"
              )
            )
          )
        )
      )
    )
  ),
  stopReason = "end_turn"|"tool_use"|"max_tokens"|"stop_sequence"|"guardrail_intervened"|"content_filtered",
  usage = list(
    inputTokens = 123,
    outputTokens = 123,
    totalTokens = 123
  ),
  metrics = list(
    latencyMs = 123
  ),
  additionalModelResponseFields = list(),
  trace = list(
    guardrail = list(
      modelOutput = list(
        "string"
      ),
      inputAssessment = list(
        list(
          topicPolicy = list(
            topics = list(
              list(
                name = "string",
                type = "DENY",
                action = "BLOCKED"
              )
            )
          ),
          contentPolicy = list(
            filters = list(
              list(
                type = "INSULTS"|"HATE"|"SEXUAL"|"VIOLENCE"|"MISCONDUCT"|"PROMPT_ATTACK",
                confidence = "NONE"|"LOW"|"MEDIUM"|"HIGH",
                action = "BLOCKED"
              )
            )
          ),
          wordPolicy = list(
            customWords = list(
              list(
                match = "string",
                action = "BLOCKED"
              )
            ),
            managedWordLists = list(
              list(
                match = "string",
                type = "PROFANITY",
                action = "BLOCKED"
              )
            )
          ),
          sensitiveInformationPolicy = list(
            piiEntities = list(
              list(
                match = "string",
                type = "ADDRESS"|"AGE"|"AWS_ACCESS_KEY"|"AWS_SECRET_KEY"|"CA_HEALTH_NUMBER"|"CA_SOCIAL_INSURANCE_NUMBER"|"CREDIT_DEBIT_CARD_CVV"|"CREDIT_DEBIT_CARD_EXPIRY"|"CREDIT_DEBIT_CARD_NUMBER"|"DRIVER_ID"|"EMAIL"|"INTERNATIONAL_BANK_ACCOUNT_NUMBER"|"IP_ADDRESS"|"LICENSE_PLATE"|"MAC_ADDRESS"|"NAME"|"PASSWORD"|"PHONE"|"PIN"|"SWIFT_CODE"|"UK_NATIONAL_HEALTH_SERVICE_NUMBER"|"UK_NATIONAL_INSURANCE_NUMBER"|"UK_UNIQUE_TAXPAYER_REFERENCE_NUMBER"|"URL"|"USERNAME"|"US_BANK_ACCOUNT_NUMBER"|"US_BANK_ROUTING_NUMBER"|"US_INDIVIDUAL_TAX_IDENTIFICATION_NUMBER"|"US_PASSPORT_NUMBER"|"US_SOCIAL_SECURITY_NUMBER"|"VEHICLE_IDENTIFICATION_NUMBER",
                action = "ANONYMIZED"|"BLOCKED"
              )
            ),
            regexes = list(
              list(
                name = "string",
                match = "string",
                regex = "string",
                action = "ANONYMIZED"|"BLOCKED"
              )
            )
          ),
          contextualGroundingPolicy = list(
            filters = list(
              list(
                type = "GROUNDING"|"RELEVANCE",
                threshold = 123.0,
                score = 123.0,
                action = "BLOCKED"|"NONE"
              )
            )
          )
        )
      ),
      outputAssessments = list(
        list(
          list(
            topicPolicy = list(
              topics = list(
                list(
                  name = "string",
                  type = "DENY",
                  action = "BLOCKED"
                )
              )
            ),
            contentPolicy = list(
              filters = list(
                list(
                  type = "INSULTS"|"HATE"|"SEXUAL"|"VIOLENCE"|"MISCONDUCT"|"PROMPT_ATTACK",
                  confidence = "NONE"|"LOW"|"MEDIUM"|"HIGH",
                  action = "BLOCKED"
                )
              )
            ),
            wordPolicy = list(
              customWords = list(
                list(
                  match = "string",
                  action = "BLOCKED"
                )
              ),
              managedWordLists = list(
                list(
                  match = "string",
                  type = "PROFANITY",
                  action = "BLOCKED"
                )
              )
            ),
            sensitiveInformationPolicy = list(
              piiEntities = list(
                list(
                  match = "string",
                  type = "ADDRESS"|"AGE"|"AWS_ACCESS_KEY"|"AWS_SECRET_KEY"|"CA_HEALTH_NUMBER"|"CA_SOCIAL_INSURANCE_NUMBER"|"CREDIT_DEBIT_CARD_CVV"|"CREDIT_DEBIT_CARD_EXPIRY"|"CREDIT_DEBIT_CARD_NUMBER"|"DRIVER_ID"|"EMAIL"|"INTERNATIONAL_BANK_ACCOUNT_NUMBER"|"IP_ADDRESS"|"LICENSE_PLATE"|"MAC_ADDRESS"|"NAME"|"PASSWORD"|"PHONE"|"PIN"|"SWIFT_CODE"|"UK_NATIONAL_HEALTH_SERVICE_NUMBER"|"UK_NATIONAL_INSURANCE_NUMBER"|"UK_UNIQUE_TAXPAYER_REFERENCE_NUMBER"|"URL"|"USERNAME"|"US_BANK_ACCOUNT_NUMBER"|"US_BANK_ROUTING_NUMBER"|"US_INDIVIDUAL_TAX_IDENTIFICATION_NUMBER"|"US_PASSPORT_NUMBER"|"US_SOCIAL_SECURITY_NUMBER"|"VEHICLE_IDENTIFICATION_NUMBER",
                  action = "ANONYMIZED"|"BLOCKED"
                )
              ),
              regexes = list(
                list(
                  name = "string",
                  match = "string",
                  regex = "string",
                  action = "ANONYMIZED"|"BLOCKED"
                )
              )
            ),
            contextualGroundingPolicy = list(
              filters = list(
                list(
                  type = "GROUNDING"|"RELEVANCE",
                  threshold = 123.0,
                  score = 123.0,
                  action = "BLOCKED"|"NONE"
                )
              )
            )
          )
        )
      )
    )
  )
)

Request syntax

svc$converse(
  modelId = "string",
  messages = list(
    list(
      role = "user"|"assistant",
      content = list(
        list(
          text = "string",
          image = list(
            format = "png"|"jpeg"|"gif"|"webp",
            source = list(
              bytes = raw
            )
          ),
          document = list(
            format = "pdf"|"csv"|"doc"|"docx"|"xls"|"xlsx"|"html"|"txt"|"md",
            name = "string",
            source = list(
              bytes = raw
            )
          ),
          toolUse = list(
            toolUseId = "string",
            name = "string",
            input = list()
          ),
          toolResult = list(
            toolUseId = "string",
            content = list(
              list(
                json = list(),
                text = "string",
                image = list(
                  format = "png"|"jpeg"|"gif"|"webp",
                  source = list(
                    bytes = raw
                  )
                ),
                document = list(
                  format = "pdf"|"csv"|"doc"|"docx"|"xls"|"xlsx"|"html"|"txt"|"md",
                  name = "string",
                  source = list(
                    bytes = raw
                  )
                )
              )
            ),
            status = "success"|"error"
          ),
          guardContent = list(
            text = list(
              text = "string",
              qualifiers = list(
                "grounding_source"|"query"|"guard_content"
              )
            )
          )
        )
      )
    )
  ),
  system = list(
    list(
      text = "string",
      guardContent = list(
        text = list(
          text = "string",
          qualifiers = list(
            "grounding_source"|"query"|"guard_content"
          )
        )
      )
    )
  ),
  inferenceConfig = list(
    maxTokens = 123,
    temperature = 123.0,
    topP = 123.0,
    stopSequences = list(
      "string"
    )
  ),
  toolConfig = list(
    tools = list(
      list(
        toolSpec = list(
          name = "string",
          description = "string",
          inputSchema = list(
            json = list()
          )
        )
      )
    ),
    toolChoice = list(
      auto = list(),
      any = list(),
      tool = list(
        name = "string"
      )
    )
  ),
  guardrailConfig = list(
    guardrailIdentifier = "string",
    guardrailVersion = "string",
    trace = "enabled"|"disabled"
  ),
  additionalModelRequestFields = list(),
  additionalModelResponseFieldPaths = list(
    "string"
  )
)