# Dialogflow API . projects . generators

## Instance Methods

Close httplib2 connections.

Creates a generator.

Lists generators.

Retrieves the next page of results.

### Method Details

`close()`

    Close httplib2 connections.

`create(parent, body=None, generatorId=None, x__xgafv=None)`

    Creates a generator.

    Args:
      parent: string, Required. The project/location to create generator for. Format: `projects//locations/` (required)
      body: object, The request body.
        The object takes the form of:

    { # LLM generator.
      "agentCoachingContext": { # Agent Coaching context that customer can configure. # Input of Agent Coaching feature.
        "instructions": [ # Optional. Customized instructions for agent coaching.
          { # Agent Coaching instructions that customer can configure.
            "agentAction": "A String", # Optional. The action that human agent should take. For example, "apologize for the slow shipping". If the users only want to use agent coaching for intent detection, agent_action can be empty
            "condition": "A String", # Optional. The condition of the instruction. For example, "the customer wants to cancel an order". If the users want the instruction to be triggered unconditionally, the condition can be empty.
            "displayDetails": "A String", # Optional. The detailed description of this instruction.
            "displayName": "A String", # Optional. Display name for the instruction.
            "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplication check for the AgentCoachingInstruction.
              "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                { # The duplicate suggestion details.
                  "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                  "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                  "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                },
              ],
            },
            "systemAction": "A String", # Optional. The action that system should take. For example, "call GetOrderTime with order_number={order number provided by the customer}". If the users don't have plugins or don't want to trigger plugins, the system_action can be empty
          },
        ],
        "outputLanguageCode": "A String", # Optional. Output language code.
        "overarchingGuidance": "A String", # Optional. The overarching guidance for the agent coaching. This should be set only for v1.5 and later versions.
        "version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["2.5"].
      },
      "createTime": "A String", # Output only. Creation time of this generator.
      "description": "A String", # Optional. Human readable description of the generator.
      "freeFormContext": { # Free form generator context that customer can configure. # Input of free from generator to LLM.
        "text": "A String", # Optional. Free form text input to LLM.
      },
      "inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
        "maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
        "temperature": 3.14, # Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
        "topK": 42, # Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
        "topP": 3.14, # Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.
      },
      "name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
      "publishedModel": "A String", # Optional. The published Large Language Model name. * To use the latest model version, specify the model name without version number. Example: `text-bison` * To use a stable model version, specify the version number as well. Example: `text-bison@002`.
      "suggestionDedupingConfig": { # Config for suggestion deduping. NEXT_ID: 3 # Optional. Configuration for suggestion deduping. This is only applicable to AI Coach feature.
        "enableDeduping": True or False, # Optional. Whether to enable suggestion deduping.
        "similarityThreshold": 3.14, # Optional. The threshold for similarity between two suggestions. Acceptable value is [0.0, 1.0], default to 0.8
      },
      "summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
        "fewShotExamples": [ # Optional. List of few shot examples.
          { # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response.
            "conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
              "messageEntries": [ # Optional. List of message transcripts in the conversation.
                { # Represents a message entry of a conversation.
                  "createTime": "A String", # Optional. Create time of the message entry.
                  "languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
                  "role": "A String", # Optional. Participant role of the message.
                  "text": "A String", # Optional. Transcript content of the message.
                },
              ],
            },
            "extraInfo": { # Optional. Key is the placeholder field name in input, value is the value of the placeholder. E.g. instruction contains "@price", and ingested data has <"price", "10">
              "a_key": "A String",
            },
            "output": { # Suggestion generated using a Generator. # Required. Example output of the model.
              "agentCoachingSuggestion": { # Suggestion for coaching agents. # Optional. Suggestion to coach the agent.
                "agentActionSuggestions": [ # Optional. Suggested actions for the agent to take.
                  { # Actions suggested for the agent. This is based on applicable instructions.
                    "agentAction": "A String", # Optional. The suggested action for the agent.
                    "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplicate check result for the agent action suggestion.
                      "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                        { # The duplicate suggestion details. Keeping answer_record and sources together as they are identifiers for duplicate suggestions.
                          "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                          "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                          "sources": { # Sources for the suggestion. # Output only. Sources for the suggestion.
                            "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                              42,
                            ],
                          },
                          "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                        },
                      ],
                    },
                    "sources": { # Sources for the suggestion. # Output only. Sources for the agent action suggestion.
                      "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                        42,
                      ],
                    },
                  },
                ],
                "applicableInstructions": [ # Optional. Instructions applicable based on the current context.
                  { # Agent Coaching instructions that customer can configure.
                    "agentAction": "A String", # Optional. The action that human agent should take. For example, "apologize for the slow shipping". If the users only want to use agent coaching for intent detection, agent_action can be empty
                    "condition": "A String", # Optional. The condition of the instruction. For example, "the customer wants to cancel an order". If the users want the instruction to be triggered unconditionally, the condition can be empty.
                    "displayDetails": "A String", # Optional. The detailed description of this instruction.
                    "displayName": "A String", # Optional. Display name for the instruction.
                    "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplication check for the AgentCoachingInstruction.
                      "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                        { # The duplicate suggestion details.
                          "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                          "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                          "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                        },
                      ],
                    },
                    "systemAction": "A String", # Optional. The action that system should take. For example, "call GetOrderTime with order_number={order number provided by the customer}". If the users don't have plugins or don't want to trigger plugins, the system_action can be empty
                  },
                ],
                "sampleResponses": [ # Optional. Sample response for the Agent.
                  { # Sample response that the agent can use. This could be based on applicable instructions and ingested data from other systems.
                    "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplicate check result for the sample response.
                      "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                        { # The duplicate suggestion details. Keeping answer_record and sources together as they are identifiers for duplicate suggestions.
                          "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                          "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                          "sources": { # Sources for the suggestion. # Output only. Sources for the suggestion.
                            "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                              42,
                            ],
                          },
                          "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                        },
                      ],
                    },
                    "responseText": "A String", # Optional. Sample response for Agent in text.
                    "sources": { # Sources for the suggestion. # Output only. Sources for the Sample Response.
                      "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                        42,
                      ],
                    },
                  },
                ],
              },
              "freeFormSuggestion": { # Suggestion generated using free form generator. # Optional. Free form suggestion.
                "response": "A String", # Required. Free form suggestion.
              },
              "summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
                "summarySections": [ # Required. All the parts of generated summary.
                  { # A component of the generated summary.
                    "section": "A String", # Required. Name of the section.
                    "summary": "A String", # Required. Summary text for the section.
                  },
                ],
              },
              "toolCallInfo": [ # Optional. List of request and response for tool calls executed.
                { # Request and response for a tool call.
                  "toolCall": { # Represents a call of a specific tool's action with the specified inputs. # Required. Request for a tool call.
                    "action": "A String", # Optional. The name of the tool's action associated with this call.
                    "answerRecord": "A String", # Optional. The answer record associated with this tool call.
                    "createTime": "A String", # Output only. Create time of the tool call.
                    "inputParameters": { # Optional. The action's input parameters.
                      "a_key": "", # Properties of the object.
                    },
                    "state": "A String", # Output only. State of the tool call
                    "tool": "A String", # Optional. The tool associated with this call. Format: `projects//locations//tools/`.
                    "toolDisplayDetails": "A String", # Optional. A human readable description of the tool.
                    "toolDisplayName": "A String", # Optional. A human readable short name of the tool, to be shown on the UI.
                  },
                  "toolCallResult": { # The result of calling a tool's action. # Required. Response for a tool call.
                    "action": "A String", # Optional. The name of the tool's action associated with this call.
                    "answerRecord": "A String", # Optional. The answer record associated with this tool call result.
                    "content": "A String", # Only populated if the response content is utf-8 encoded.
                    "createTime": "A String", # Output only. Create time of the tool call result.
                    "error": { # An error produced by the tool call. # The tool call's error.
                      "message": "A String", # Optional. The error message of the function.
                    },
                    "rawContent": "A String", # Only populated if the response content is not utf-8 encoded. (by definition byte fields are base64 encoded).
                    "tool": "A String", # Optional. The tool associated with this call. Format: `projects//locations//tools/`.
                  },
                },
              ],
            },
            "summarizationSectionList": { # List of summarization sections. # Summarization sections.
              "summarizationSections": [ # Optional. Summarization sections.
                { # Represents the section of summarization.
                  "definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
                  "key": "A String", # Optional. Name of the section, for example, "situation".
                  "type": "A String", # Optional. Type of the summarization section.
                },
              ],
            },
          },
        ],
        "outputLanguageCode": "A String", # Optional. The target language of the generated summary. The language code for conversation will be used if this field is empty. Supported 2.0 and later versions.
        "summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
          { # Represents the section of summarization.
            "definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
            "key": "A String", # Optional. Name of the section, for example, "situation".
            "type": "A String", # Optional. Type of the summarization section.
          },
        ],
        "version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
      },
      "tools": [ # Optional. Resource names of the tools that the generator can choose from. Format: `projects//locations//tools/`.
        "A String",
      ],
      "triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
      "updateTime": "A String", # Output only. Update time of this generator.
    }

      generatorId: string, Optional. The ID to use for the generator, which will become the final component of the generator's resource name. The generator ID must be compliant with the regression formula `a-zA-Z*` with the characters length in range of [3,64]. If the field is not provided, an Id will be auto-generated. If the field is provided, the caller is responsible for 1. the uniqueness of the ID, otherwise the request will be rejected. 2. the consistency for whether to use custom ID or not under a project to better ensure uniqueness.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # LLM generator.
      "agentCoachingContext": { # Agent Coaching context that customer can configure. # Input of Agent Coaching feature.
        "instructions": [ # Optional. Customized instructions for agent coaching.
          { # Agent Coaching instructions that customer can configure.
            "agentAction": "A String", # Optional. The action that human agent should take. For example, "apologize for the slow shipping". If the users only want to use agent coaching for intent detection, agent_action can be empty
            "condition": "A String", # Optional. The condition of the instruction. For example, "the customer wants to cancel an order". If the users want the instruction to be triggered unconditionally, the condition can be empty.
            "displayDetails": "A String", # Optional. The detailed description of this instruction.
            "displayName": "A String", # Optional. Display name for the instruction.
            "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplication check for the AgentCoachingInstruction.
              "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                { # The duplicate suggestion details.
                  "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                  "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                  "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                },
              ],
            },
            "systemAction": "A String", # Optional. The action that system should take. For example, "call GetOrderTime with order_number={order number provided by the customer}". If the users don't have plugins or don't want to trigger plugins, the system_action can be empty
          },
        ],
        "outputLanguageCode": "A String", # Optional. Output language code.
        "overarchingGuidance": "A String", # Optional. The overarching guidance for the agent coaching. This should be set only for v1.5 and later versions.
        "version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["2.5"].
      },
      "createTime": "A String", # Output only. Creation time of this generator.
      "description": "A String", # Optional. Human readable description of the generator.
      "freeFormContext": { # Free form generator context that customer can configure. # Input of free from generator to LLM.
        "text": "A String", # Optional. Free form text input to LLM.
      },
      "inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
        "maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
        "temperature": 3.14, # Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
        "topK": 42, # Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
        "topP": 3.14, # Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.
      },
      "name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
      "publishedModel": "A String", # Optional. The published Large Language Model name. * To use the latest model version, specify the model name without version number. Example: `text-bison` * To use a stable model version, specify the version number as well. Example: `text-bison@002`.
      "suggestionDedupingConfig": { # Config for suggestion deduping. NEXT_ID: 3 # Optional. Configuration for suggestion deduping. This is only applicable to AI Coach feature.
        "enableDeduping": True or False, # Optional. Whether to enable suggestion deduping.
        "similarityThreshold": 3.14, # Optional. The threshold for similarity between two suggestions. Acceptable value is [0.0, 1.0], default to 0.8
      },
      "summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
        "fewShotExamples": [ # Optional. List of few shot examples.
          { # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response.
            "conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
              "messageEntries": [ # Optional. List of message transcripts in the conversation.
                { # Represents a message entry of a conversation.
                  "createTime": "A String", # Optional. Create time of the message entry.
                  "languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
                  "role": "A String", # Optional. Participant role of the message.
                  "text": "A String", # Optional. Transcript content of the message.
                },
              ],
            },
            "extraInfo": { # Optional. Key is the placeholder field name in input, value is the value of the placeholder. E.g. instruction contains "@price", and ingested data has <"price", "10">
              "a_key": "A String",
            },
            "output": { # Suggestion generated using a Generator. # Required. Example output of the model.
              "agentCoachingSuggestion": { # Suggestion for coaching agents. # Optional. Suggestion to coach the agent.
                "agentActionSuggestions": [ # Optional. Suggested actions for the agent to take.
                  { # Actions suggested for the agent. This is based on applicable instructions.
                    "agentAction": "A String", # Optional. The suggested action for the agent.
                    "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplicate check result for the agent action suggestion.
                      "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                        { # The duplicate suggestion details. Keeping answer_record and sources together as they are identifiers for duplicate suggestions.
                          "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                          "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                          "sources": { # Sources for the suggestion. # Output only. Sources for the suggestion.
                            "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                              42,
                            ],
                          },
                          "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                        },
                      ],
                    },
                    "sources": { # Sources for the suggestion. # Output only. Sources for the agent action suggestion.
                      "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                        42,
                      ],
                    },
                  },
                ],
                "applicableInstructions": [ # Optional. Instructions applicable based on the current context.
                  { # Agent Coaching instructions that customer can configure.
                    "agentAction": "A String", # Optional. The action that human agent should take. For example, "apologize for the slow shipping". If the users only want to use agent coaching for intent detection, agent_action can be empty
                    "condition": "A String", # Optional. The condition of the instruction. For example, "the customer wants to cancel an order". If the users want the instruction to be triggered unconditionally, the condition can be empty.
                    "displayDetails": "A String", # Optional. The detailed description of this instruction.
                    "displayName": "A String", # Optional. Display name for the instruction.
                    "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplication check for the AgentCoachingInstruction.
                      "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                        { # The duplicate suggestion details.
                          "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                          "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                          "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                        },
                      ],
                    },
                    "systemAction": "A String", # Optional. The action that system should take. For example, "call GetOrderTime with order_number={order number provided by the customer}". If the users don't have plugins or don't want to trigger plugins, the system_action can be empty
                  },
                ],
                "sampleResponses": [ # Optional. Sample response for the Agent.
                  { # Sample response that the agent can use. This could be based on applicable instructions and ingested data from other systems.
                    "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplicate check result for the sample response.
                      "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                        { # The duplicate suggestion details. Keeping answer_record and sources together as they are identifiers for duplicate suggestions.
                          "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                          "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                          "sources": { # Sources for the suggestion. # Output only. Sources for the suggestion.
                            "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                              42,
                            ],
                          },
                          "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                        },
                      ],
                    },
                    "responseText": "A String", # Optional. Sample response for Agent in text.
                    "sources": { # Sources for the suggestion. # Output only. Sources for the Sample Response.
                      "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                        42,
                      ],
                    },
                  },
                ],
              },
              "freeFormSuggestion": { # Suggestion generated using free form generator. # Optional. Free form suggestion.
                "response": "A String", # Required. Free form suggestion.
              },
              "summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
                "summarySections": [ # Required. All the parts of generated summary.
                  { # A component of the generated summary.
                    "section": "A String", # Required. Name of the section.
                    "summary": "A String", # Required. Summary text for the section.
                  },
                ],
              },
              "toolCallInfo": [ # Optional. List of request and response for tool calls executed.
                { # Request and response for a tool call.
                  "toolCall": { # Represents a call of a specific tool's action with the specified inputs. # Required. Request for a tool call.
                    "action": "A String", # Optional. The name of the tool's action associated with this call.
                    "answerRecord": "A String", # Optional. The answer record associated with this tool call.
                    "createTime": "A String", # Output only. Create time of the tool call.
                    "inputParameters": { # Optional. The action's input parameters.
                      "a_key": "", # Properties of the object.
                    },
                    "state": "A String", # Output only. State of the tool call
                    "tool": "A String", # Optional. The tool associated with this call. Format: `projects//locations//tools/`.
                    "toolDisplayDetails": "A String", # Optional. A human readable description of the tool.
                    "toolDisplayName": "A String", # Optional. A human readable short name of the tool, to be shown on the UI.
                  },
                  "toolCallResult": { # The result of calling a tool's action. # Required. Response for a tool call.
                    "action": "A String", # Optional. The name of the tool's action associated with this call.
                    "answerRecord": "A String", # Optional. The answer record associated with this tool call result.
                    "content": "A String", # Only populated if the response content is utf-8 encoded.
                    "createTime": "A String", # Output only. Create time of the tool call result.
                    "error": { # An error produced by the tool call. # The tool call's error.
                      "message": "A String", # Optional. The error message of the function.
                    },
                    "rawContent": "A String", # Only populated if the response content is not utf-8 encoded. (by definition byte fields are base64 encoded).
                    "tool": "A String", # Optional. The tool associated with this call. Format: `projects//locations//tools/`.
                  },
                },
              ],
            },
            "summarizationSectionList": { # List of summarization sections. # Summarization sections.
              "summarizationSections": [ # Optional. Summarization sections.
                { # Represents the section of summarization.
                  "definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
                  "key": "A String", # Optional. Name of the section, for example, "situation".
                  "type": "A String", # Optional. Type of the summarization section.
                },
              ],
            },
          },
        ],
        "outputLanguageCode": "A String", # Optional. The target language of the generated summary. The language code for conversation will be used if this field is empty. Supported 2.0 and later versions.
        "summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
          { # Represents the section of summarization.
            "definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
            "key": "A String", # Optional. Name of the section, for example, "situation".
            "type": "A String", # Optional. Type of the summarization section.
          },
        ],
        "version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
      },
      "tools": [ # Optional. Resource names of the tools that the generator can choose from. Format: `projects//locations//tools/`.
        "A String",
      ],
      "triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
      "updateTime": "A String", # Output only. Update time of this generator.
    }

`list(parent, pageSize=None, pageToken=None, x__xgafv=None)`

    Lists generators.

    Args:
      parent: string, Required. The project/location to list generators for. Format: `projects//locations/` (required)
      pageSize: integer, Optional. Maximum number of conversation models to return in a single page. Default to 10.
      pageToken: string, Optional. The next_page_token value returned from a previous list request.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # Response of ListGenerators.
      "generators": [ # List of generators retrieved.
        { # LLM generator.
          "agentCoachingContext": { # Agent Coaching context that customer can configure. # Input of Agent Coaching feature.
            "instructions": [ # Optional. Customized instructions for agent coaching.
              { # Agent Coaching instructions that customer can configure.
                "agentAction": "A String", # Optional. The action that human agent should take. For example, "apologize for the slow shipping". If the users only want to use agent coaching for intent detection, agent_action can be empty
                "condition": "A String", # Optional. The condition of the instruction. For example, "the customer wants to cancel an order". If the users want the instruction to be triggered unconditionally, the condition can be empty.
                "displayDetails": "A String", # Optional. The detailed description of this instruction.
                "displayName": "A String", # Optional. Display name for the instruction.
                "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplication check for the AgentCoachingInstruction.
                  "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                    { # The duplicate suggestion details.
                      "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                      "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                      "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                    },
                  ],
                },
                "systemAction": "A String", # Optional. The action that system should take. For example, "call GetOrderTime with order_number={order number provided by the customer}". If the users don't have plugins or don't want to trigger plugins, the system_action can be empty
              },
            ],
            "outputLanguageCode": "A String", # Optional. Output language code.
            "overarchingGuidance": "A String", # Optional. The overarching guidance for the agent coaching. This should be set only for v1.5 and later versions.
            "version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["2.5"].
          },
          "createTime": "A String", # Output only. Creation time of this generator.
          "description": "A String", # Optional. Human readable description of the generator.
          "freeFormContext": { # Free form generator context that customer can configure. # Input of free from generator to LLM.
            "text": "A String", # Optional. Free form text input to LLM.
          },
          "inferenceParameter": { # The parameters of inference. # Optional. Inference parameters for this generator.
            "maxOutputTokens": 42, # Optional. Maximum number of the output tokens for the generator.
            "temperature": 3.14, # Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
            "topK": 42, # Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
            "topP": 3.14, # Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.
          },
          "name": "A String", # Output only. Identifier. The resource name of the generator. Format: `projects//locations//generators/`
          "publishedModel": "A String", # Optional. The published Large Language Model name. * To use the latest model version, specify the model name without version number. Example: `text-bison` * To use a stable model version, specify the version number as well. Example: `text-bison@002`.
          "suggestionDedupingConfig": { # Config for suggestion deduping. NEXT_ID: 3 # Optional. Configuration for suggestion deduping. This is only applicable to AI Coach feature.
            "enableDeduping": True or False, # Optional. Whether to enable suggestion deduping.
            "similarityThreshold": 3.14, # Optional. The threshold for similarity between two suggestions. Acceptable value is [0.0, 1.0], default to 0.8
          },
          "summarizationContext": { # Summarization context that customer can configure. # Input of Summarization feature.
            "fewShotExamples": [ # Optional. List of few shot examples.
              { # Providing examples in the generator (i.e. building a few-shot generator) helps convey the desired format of the LLM response.
                "conversationContext": { # Context of the conversation, including transcripts. # Optional. Conversation transcripts.
                  "messageEntries": [ # Optional. List of message transcripts in the conversation.
                    { # Represents a message entry of a conversation.
                      "createTime": "A String", # Optional. Create time of the message entry.
                      "languageCode": "A String", # Optional. The language of the text. See [Language Support](https://cloud.google.com/dialogflow/docs/reference/language) for a list of the currently supported language codes.
                      "role": "A String", # Optional. Participant role of the message.
                      "text": "A String", # Optional. Transcript content of the message.
                    },
                  ],
                },
                "extraInfo": { # Optional. Key is the placeholder field name in input, value is the value of the placeholder. E.g. instruction contains "@price", and ingested data has <"price", "10">
                  "a_key": "A String",
                },
                "output": { # Suggestion generated using a Generator. # Required. Example output of the model.
                  "agentCoachingSuggestion": { # Suggestion for coaching agents. # Optional. Suggestion to coach the agent.
                    "agentActionSuggestions": [ # Optional. Suggested actions for the agent to take.
                      { # Actions suggested for the agent. This is based on applicable instructions.
                        "agentAction": "A String", # Optional. The suggested action for the agent.
                        "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplicate check result for the agent action suggestion.
                          "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                            { # The duplicate suggestion details. Keeping answer_record and sources together as they are identifiers for duplicate suggestions.
                              "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                              "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                              "sources": { # Sources for the suggestion. # Output only. Sources for the suggestion.
                                "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                                  42,
                                ],
                              },
                              "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                            },
                          ],
                        },
                        "sources": { # Sources for the suggestion. # Output only. Sources for the agent action suggestion.
                          "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                            42,
                          ],
                        },
                      },
                    ],
                    "applicableInstructions": [ # Optional. Instructions applicable based on the current context.
                      { # Agent Coaching instructions that customer can configure.
                        "agentAction": "A String", # Optional. The action that human agent should take. For example, "apologize for the slow shipping". If the users only want to use agent coaching for intent detection, agent_action can be empty
                        "condition": "A String", # Optional. The condition of the instruction. For example, "the customer wants to cancel an order". If the users want the instruction to be triggered unconditionally, the condition can be empty.
                        "displayDetails": "A String", # Optional. The detailed description of this instruction.
                        "displayName": "A String", # Optional. Display name for the instruction.
                        "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplication check for the AgentCoachingInstruction.
                          "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                            { # The duplicate suggestion details.
                              "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                              "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                              "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                            },
                          ],
                        },
                        "systemAction": "A String", # Optional. The action that system should take. For example, "call GetOrderTime with order_number={order number provided by the customer}". If the users don't have plugins or don't want to trigger plugins, the system_action can be empty
                      },
                    ],
                    "sampleResponses": [ # Optional. Sample response for the Agent.
                      { # Sample response that the agent can use. This could be based on applicable instructions and ingested data from other systems.
                        "duplicateCheckResult": { # Duplication check for the suggestion. # Output only. Duplicate check result for the sample response.
                          "duplicateSuggestions": [ # Output only. The duplicate suggestions.
                            { # The duplicate suggestion details. Keeping answer_record and sources together as they are identifiers for duplicate suggestions.
                              "answerRecord": "A String", # Output only. The answer record id of the past duplicate suggestion.
                              "similarityScore": 3.14, # Output only. The similarity score of between the past and current suggestion.
                              "sources": { # Sources for the suggestion. # Output only. Sources for the suggestion.
                                "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                                  42,
                                ],
                              },
                              "suggestionIndex": 42, # Output only. The index of the duplicate suggestion in the past suggestion list.
                            },
                          ],
                        },
                        "responseText": "A String", # Optional. Sample response for Agent in text.
                        "sources": { # Sources for the suggestion. # Output only. Sources for the Sample Response.
                          "instructionIndexes": [ # Output only. Source instruction indexes for the suggestion. This is the index of the applicable_instructions field.
                            42,
                          ],
                        },
                      },
                    ],
                  },
                  "freeFormSuggestion": { # Suggestion generated using free form generator. # Optional. Free form suggestion.
                    "response": "A String", # Required. Free form suggestion.
                  },
                  "summarySuggestion": { # Suggested summary of the conversation. # Optional. Suggested summary.
                    "summarySections": [ # Required. All the parts of generated summary.
                      { # A component of the generated summary.
                        "section": "A String", # Required. Name of the section.
                        "summary": "A String", # Required. Summary text for the section.
                      },
                    ],
                  },
                  "toolCallInfo": [ # Optional. List of request and response for tool calls executed.
                    { # Request and response for a tool call.
                      "toolCall": { # Represents a call of a specific tool's action with the specified inputs. # Required. Request for a tool call.
                        "action": "A String", # Optional. The name of the tool's action associated with this call.
                        "answerRecord": "A String", # Optional. The answer record associated with this tool call.
                        "createTime": "A String", # Output only. Create time of the tool call.
                        "inputParameters": { # Optional. The action's input parameters.
                          "a_key": "", # Properties of the object.
                        },
                        "state": "A String", # Output only. State of the tool call
                        "tool": "A String", # Optional. The tool associated with this call. Format: `projects//locations//tools/`.
                        "toolDisplayDetails": "A String", # Optional. A human readable description of the tool.
                        "toolDisplayName": "A String", # Optional. A human readable short name of the tool, to be shown on the UI.
                      },
                      "toolCallResult": { # The result of calling a tool's action. # Required. Response for a tool call.
                        "action": "A String", # Optional. The name of the tool's action associated with this call.
                        "answerRecord": "A String", # Optional. The answer record associated with this tool call result.
                        "content": "A String", # Only populated if the response content is utf-8 encoded.
                        "createTime": "A String", # Output only. Create time of the tool call result.
                        "error": { # An error produced by the tool call. # The tool call's error.
                          "message": "A String", # Optional. The error message of the function.
                        },
                        "rawContent": "A String", # Only populated if the response content is not utf-8 encoded. (by definition byte fields are base64 encoded).
                        "tool": "A String", # Optional. The tool associated with this call. Format: `projects//locations//tools/`.
                      },
                    },
                  ],
                },
                "summarizationSectionList": { # List of summarization sections. # Summarization sections.
                  "summarizationSections": [ # Optional. Summarization sections.
                    { # Represents the section of summarization.
                      "definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
                      "key": "A String", # Optional. Name of the section, for example, "situation".
                      "type": "A String", # Optional. Type of the summarization section.
                    },
                  ],
                },
              },
            ],
            "outputLanguageCode": "A String", # Optional. The target language of the generated summary. The language code for conversation will be used if this field is empty. Supported 2.0 and later versions.
            "summarizationSections": [ # Optional. List of sections. Note it contains both predefined section sand customer defined sections.
              { # Represents the section of summarization.
                "definition": "A String", # Optional. Definition of the section, for example, "what the customer needs help with or has question about."
                "key": "A String", # Optional. Name of the section, for example, "situation".
                "type": "A String", # Optional. Type of the summarization section.
              },
            ],
            "version": "A String", # Optional. Version of the feature. If not set, default to latest version. Current candidates are ["1.0"].
          },
          "tools": [ # Optional. Resource names of the tools that the generator can choose from. Format: `projects//locations//tools/`.
            "A String",
          ],
          "triggerEvent": "A String", # Optional. The trigger event of the generator. It defines when the generator is triggered in a conversation.
          "updateTime": "A String", # Output only. Update time of this generator.
        },
      ],
      "nextPageToken": "A String", # Token to retrieve the next page of results, or empty if there are no more results in the list.
    }

`list_next()`

    Retrieves the next page of results.

            Args:
              previous_request: The request for the previous page. (required)
              previous_response: The response from the request for the previous page. (required)

            Returns:
              A request object that you can call 'execute()' on to request the next
              page. Returns None if there are no more items in the collection.