← Zendesk + 302.AI integrations

Chat using Functions with 302.AI API on New Ticket Added to View (Instant) from Zendesk API

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New Ticket Added to View (Instant) from the Zendesk API
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Chat using Functions with the 302.AI API
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Getting Started

This integration creates a workflow with a Zendesk trigger and 302.AI action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

  1. Select this integration
  2. Configure the New Ticket Added to View (Instant) trigger
    1. Connect your Zendesk account
    2. Optional- Select a Trigger Category ID
    3. Optional- Configure Custom Subdomain
    4. Optional- Select one or more Fields
    5. Optional- Configure JSON Body
    6. Select a View ID
  3. Configure the Chat using Functions action
    1. Connect your 302.AI account
    2. Select a Model
    3. Configure Chat Input
    4. Configure Functions
    5. Optional- Configure Instructions
    6. Optional- Select a Tool Choice
    7. Optional- Select a Parallel Function Calling
    8. Optional- Configure Max Tokens
    9. Optional- Configure Temperature
    10. Optional- Select a Response Format
  4. Deploy the workflow
  5. Send a test event to validate your setup
  6. Turn on the trigger

Details

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger

Description:Emit new event when a ticket is added to the specified view
Version:0.0.9
Key:zendesk-ticket-added-to-view

Zendesk Overview

The Zendesk API enables seamless integration of Zendesk's customer service platform with your existing business processes and third-party applications. By leveraging this API with Pipedream, you can automate ticket tracking, sync customer data, escalate issues, and streamline communication across multiple channels. This can significantly increase efficiency, accelerate response times, and enhance the overall customer experience. Automations can range from simple notifications to complex workflows involving data transformation and multi-step actions across various services.

Trigger Code

import common from "../common/ticket.mjs";

export default {
  ...common,
  key: "zendesk-ticket-added-to-view",
  name: "New Ticket Added to View (Instant)",
  description: "Emit new event when a ticket is added to the specified view",
  version: "0.0.9",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    viewId: {
      propDefinition: [
        common.props.app,
        "viewId",
      ],
    },
  },
  methods: {
    ...common.methods,
    getWebhookName() {
      return "Ticket Added To View Webhook";
    },
    getTriggerTitle() {
      return "Ticket Added To View Trigger";
    },
    getTriggerConditions() {
      return {
        any: [
          {
            field: "update_type",
            value: "Change",
          },
        ],
      };
    },
    async isRelevant(payload) {
      const { tickets } = await this.app.listTicketsInView({
        viewId: this.viewId,
      });
      const foundTicket = tickets.find(({ id }) => id == payload.ticketId);
      return foundTicket;
    },
  },
};

Trigger Configuration

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.
LabelPropTypeDescription
ZendeskappappThis component uses the Zendesk app.
N/Adb$.service.dbThis component uses $.service.db to maintain state between executions.
N/Ahttp$.interface.httpThis component uses $.interface.http to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the run() method of the component.
Trigger Category IDcategoryIdstringSelect a value from the drop down menu.
Custom SubdomaincustomSubdomainstring

For Enterprise Zendesk accounts: optionally specify the subdomain to use. This will override the subdomain that was provided when connecting your Zendesk account to Pipedream. For example, if you Zendesk URL is https://examplehelp.zendesk.com, your subdomain is examplehelp

Fieldsfieldsstring[]Select a value from the drop down menu.
JSON BodyjsonBodystring

Custom JSON Body of the incoming payload. Setting jsonBody will overwrite the fields prop

View IDviewIdstringSelect a value from the drop down menu.

Trigger Authentication

Zendesk uses OAuth authentication. When you connect your Zendesk account, Pipedream will open a popup window where you can sign into Zendesk and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Zendesk API.

Pipedream requests the following authorization scopes when you connect your account:

triggers:writewebhooks:writereadtickets:writetickets:readusers:writeorganizations:readorganizations:write

About Zendesk

Zendesk is award-winning customer service software trusted by 200K+ customers. Make customers happy via text, mobile, phone, email, live chat, social media.

Action

Description:Enable your 302.AI model to invoke user-defined functions. Useful for conditional logic, workflow orchestration, and tool invocation within conversations. [See documentation](https://doc.302.ai/211560247e0)
Version:0.0.1
Key:_302_ai-chat-using-functions

Action Code

import _302_ai from "../../_302_ai.app.mjs";
import constants from "../common/constants.mjs";

export default {
  name: "Chat using Functions",
  version: "0.0.1",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  key: "_302_ai-chat-using-functions",
  description: "Enable your 302.AI model to invoke user-defined functions. Useful for conditional logic, workflow orchestration, and tool invocation within conversations. [See documentation](https://doc.302.ai/211560247e0)",
  type: "action",
  props: {
    _302_ai,
    modelId: {
      propDefinition: [
        _302_ai,
        "chatCompletionModelId",
      ],
    },
    input: {
      type: "string",
      label: "Chat Input",
      description: "Text input to the model used to generate a response",
    },
    functions: {
      type: "string",
      label: "Functions",
      description: "A valid JSON array of tools/functions using the OpenAI function schema definition. Each tool must have a `type` property set to \"function\" and a `function` object with `name`, `description`, and `parameters`.",
      default:
`[
  {
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          },
          "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"]
          }
        },
        "required": ["location"]
      }
    }
  }
]`,
    },
    instructions: {
      type: "string",
      label: "Instructions",
      description: "System instructions for the model",
      optional: true,
    },
    toolChoice: {
      type: "string",
      label: "Tool Choice",
      description: "- **auto**: The model decides whether and how many functions to call.\n- **required**: The model must call one or more functions.\n- **function_name**: Enter a custom function name to force the model to call this specific function.",
      optional: true,
      default: "auto",
      options: [
        "auto",
        "required",
      ],
    },
    parallelToolCalls: {
      type: "string",
      label: "Parallel Function Calling",
      description: "Allow or prevent the model to call multiple functions in a single turn",
      optional: true,
      default: "1",
      options: [
        {
          label: "Enabled",
          value: "1",
        },
        {
          label: "Disabled",
          value: "0",
        },
      ],
    },
    maxTokens: {
      label: "Max Tokens",
      description: "The maximum number of tokens to generate in the completion.",
      type: "string",
      optional: true,
    },
    temperature: {
      label: "Temperature",
      description: "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.",
      type: "string",
      optional: true,
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "- **Text**: Returns unstructured text output.\n- **JSON Schema**: Enables you to define a specific structure for the model's output using a JSON schema.",
      options: [
        constants.CHAT_RESPONSE_FORMAT.TEXT.value,
        constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value,
      ],
      default: constants.CHAT_RESPONSE_FORMAT.TEXT.value,
      optional: true,
      reloadProps: true,
    },
  },
  additionalProps() {
    const props = {};

    if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      props.jsonSchema = {
        type: "string",
        label: "JSON Schema",
        description: "Define the schema that the model's output must adhere to.",
      };
    }

    return props;
  },
  async run({ $ }) {
    const messages = [];

    if (this.instructions) {
      messages.push({
        role: "system",
        content: this.instructions,
      });
    }

    messages.push({
      role: "user",
      content: this.input,
    });

    const data = {
      model: this.modelId,
      messages,
      parallel_tool_calls: parseInt(this.parallelToolCalls) === 1,
      tools: [],
    };

    if (this.maxTokens) {
      data.max_tokens = parseInt(this.maxTokens);
    }

    if (this.temperature) {
      data.temperature = parseFloat(this.temperature);
    }

    let functions = this.functions;
    if (typeof functions === "string") {
      try {
        functions = JSON.parse(functions);
      } catch (error) {
        throw new Error("Invalid JSON format in the provided Functions Schema");
      }
    }

    if (Array.isArray(functions)) {
      data.tools.push(...functions);
    } else {
      data.tools.push(functions);
    }

    if (this.toolChoice) {
      if (this.toolChoice === "auto" || this.toolChoice === "required") {
        data.tool_choice = this.toolChoice;
      } else {
        data.tool_choice = {
          type: "function",
          name: this.toolChoice,
        };
      }
    }

    if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value
      && this.jsonSchema) {
      try {
        data.response_format = {
          type: this.responseFormat,
          json_schema: typeof this.jsonSchema === "string"
            ? JSON.parse(this.jsonSchema)
            : this.jsonSchema,
        };
      } catch (error) {
        throw new Error("Invalid JSON format in the provided JSON Schema");
      }
    }

    const response = await this._302_ai.createChatCompletion({
      $,
      data,
    });

    if (response) {
      $.export("$summary", `Successfully sent chat with id ${response.id}`);
    }

    return response;
  },
};

Action Configuration

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

LabelPropTypeDescription
302.AI_302_aiappThis component uses the 302.AI app.
ModelmodelIdstringSelect a value from the drop down menu.
Chat Inputinputstring

Text input to the model used to generate a response

Functionsfunctionsstring

A valid JSON array of tools/functions using the OpenAI function schema definition. Each tool must have a type property set to "function" and a function object with name, description, and parameters.

Instructionsinstructionsstring

System instructions for the model

Tool ChoicetoolChoicestringSelect a value from the drop down menu:autorequired
Parallel Function CallingparallelToolCallsstringSelect a value from the drop down menu:{ "label": "Enabled", "value": "1" }{ "label": "Disabled", "value": "0" }
Max TokensmaxTokensstring

The maximum number of tokens to generate in the completion.

Temperaturetemperaturestring

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

Response FormatresponseFormatstringSelect a value from the drop down menu:textjson_schema

Action Authentication

302.AI uses API keys for authentication. When you connect your 302.AI account, Pipedream securely stores the keys so you can easily authenticate to 302.AI APIs in both code and no-code steps.

About 302.AI

302.AI is a pay-as-you-go enterprise AI resource hub that offers the latest and most comprehensive AI models and APIs on the market, along with a variety of ready-to-use online AI applications.

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