Chat with 302.AI with 302.AI API on New Scheduled Tasks from Pipedream API

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New Scheduled Tasks from the Pipedream API

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Chat with 302.AI with the 302.AI API

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Getting Started#

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

Select this integration
Configure the New Scheduled Tasks trigger
1. Connect your Pipedream account
2. Optional- Configure Secret
Configure the Chat with 302.AI action
1. Connect your 302.AI account
2. Select a Model
3. Configure User Message
4. Optional- Configure Max Tokens
5. Optional- Configure Temperature
6. Optional- Configure Top P
7. Optional- Configure N
8. Optional- Configure Stop
9. Optional- Configure Presence Penalty
10. Optional- Configure Frequency Penalty
11. Optional- Configure User
12. Optional- Configure System Instructions
13. Optional- Configure Prior Message History
14. Optional- Select a Response Format
Deploy the workflow
Send a test event to validate your setup
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#

New Scheduled Tasks on Pipedream

Description:Exposes an HTTP API for scheduling messages to be emitted at a future time

Version:0.3.1

Key:pipedream-new-scheduled-tasks

View on GitHub

Pipedream Overview#

Pipedream is an API that allows you to build applications that can connect to
various data sources and processes them in real-time. You can use Pipedream to
create applications that can perform ETL (Extract, Transform, and Load) tasks,
as well as to create data-driven workflows.

Some examples of applications you can build using the Pipedream API include:

An application that can extract data from a database, transform it, and then
load it into another database.
An application that can monitor a data source for changes, and then trigger a
workflow in response to those changes.
An application that can poll an API for new data, and then process that data
in real-time.

Trigger Code#

import pipedream from "../../pipedream.app.mjs";
import sampleEmit from "./test-event.mjs";
import { uuid } from "uuidv4";

export default {
  key: "pipedream-new-scheduled-tasks",
  name: "New Scheduled Tasks",
  type: "source",
  description:
    "Exposes an HTTP API for scheduling messages to be emitted at a future time",
  version: "0.3.1",
  dedupe: "unique", // Dedupe on a UUID generated for every scheduled task
  props: {
    pipedream,
    secret: {
      type: "string",
      secret: true,
      label: "Secret",
      optional: true,
      description:
        "**Optional but recommended**: if you enter a secret here, you must pass this value in the `x-pd-secret` HTTP header when making requests",
    },
    http: {
      label: "Endpoint",
      description: "The endpoint where you'll send task scheduler requests",
      type: "$.interface.http",
      customResponse: true,
    },
    db: "$.service.db",
  },
  methods: {
    // To schedule future emits, we emit to the selfChannel of the component
    selfChannel() {
      return "self";
    },
    // Queue for future emits that haven't yet been delivered
    queuedEventsChannel() {
      return "$in";
    },
    httpRespond({
      status, body,
    }) {
      this.http.respond({
        headers: {
          "content-type": "application/json",
        },
        status,
        body,
      });
    },
    async selfSubscribe() {
      // Subscribe the component to itself. We do this here because even in
      // the activate hook, the component isn't available to take subscriptions.
      // Scheduled tasks are sent to the self channel, which emits the message at
      // the specified delivery_ts to this component.
      const isSubscribedToSelf = this.db.get("isSubscribedToSelf");
      if (!isSubscribedToSelf) {
        const componentId = process.env.PD_COMPONENT;
        const selfChannel = this.selfChannel();
        console.log(`Subscribing to ${selfChannel} channel for event source`);
        console.log(
          await this.pipedream.subscribe(componentId, componentId, selfChannel),
        );
        this.db.set("isSubscribedToSelf", true);
      }
    },
    validateEventBody(event, operation) {
      const errors = [];

      // Secrets are optional, so we first check if the user configured
      // a secret, then check its value against the prop (validation below)
      if (this.secret && event.headers["x-pd-secret"] !== this.secret) {
        errors.push(
          "Secret on incoming request doesn't match the configured secret",
        );
      }

      if (operation === "schedule") {
        const {
          timestamp,
          message,
        } = event.body;
        // timestamp should be an ISO 8601 string. Parse and check for validity below.
        const epoch = Date.parse(timestamp);

        if (!timestamp) {
          errors.push(
            "No timestamp included in payload. Please provide an ISO8601 timestamp in the 'timestamp' field",
          );
        }
        if (timestamp && !epoch) {
          errors.push("Timestamp isn't a valid ISO 8601 string");
        }
        if (!message) {
          errors.push("No message passed in payload");
        }
      }

      return errors;
    },
    scheduleTask(event) {
      const errors = this.validateEventBody(event, "schedule");
      let status, body;

      if (errors.length) {
        console.log(errors);
        status = 400;
        body = {
          errors,
        };
      } else {
        const id = this.emitScheduleEvent(event.body, event.body.timestamp);
        status = 200;
        body = {
          msg: "Successfully scheduled task",
          id,
        };
      }

      this.httpRespond({
        status,
        body,
      });
    },
    emitScheduleEvent(event, timestamp) {
      const selfChannel = this.selfChannel();
      const epoch = Date.parse(timestamp);
      const $id = uuid();

      console.log(`Scheduled event to emit on: ${new Date(epoch)}`);

      this.$emit(
        {
          ...event,
          $channel: selfChannel,
          $id,
        },
        {
          name: selfChannel,
          id: $id,
          delivery_ts: epoch,
        },
      );

      return $id;
    },
    async cancelTask(event) {
      const errors = this.validateEventBody(event, "cancel");
      let status, msg;

      if (errors.length) {
        console.log(errors);
        status = 400;
        msg = "Secret on incoming request doesn't match the configured secret";
      } else {
        try {
          const id = event.body.id;
          const isCanceled = await this.deleteEvent(event);
          if (isCanceled) {
            status = 200;
            msg = `Cancelled scheduled task for event ${id}`;
          } else {
            status = 404;
            msg = `No event with ${id} found`;
          }
        } catch (error) {
          console.log(error);
          status = 500;
          msg = "Failed to schedule task. Please see the logs";
        }
      }

      this.httpRespond({
        status,
        body: {
          msg,
        },
      });
    },
    async deleteEvent(event) {
      const componentId = process.env.PD_COMPONENT;
      const inChannel = this.queuedEventsChannel();

      // The user must pass a scheduled event UUID they'd like to cancel
      // We lookup the event by ID and delete it
      const { id } = event.body;

      // List events in the $in channel - the queue of scheduled events, to be emitted in the future
      const events = await this.pipedream.listEvents(
        componentId,
        inChannel,
      );
      console.log("Events: ", events);

      // Find the event in the list by id
      const eventToCancel = events.data.find((e) => {
        const { metadata } = e;
        return metadata.id === id;
      });

      console.log("Event to cancel: ", eventToCancel);

      if (!eventToCancel) {
        console.log(`No event with ${id} found`);
        return false;
      }

      // Delete the event
      await this.pipedream.deleteEvent(
        componentId,
        eventToCancel.id,
        inChannel,
      );
      return true;
    },
    emitEvent(event, summary) {
      // Delete the channel name and id from the incoming event, which were used only as metadata
      const id = event.$id;
      delete event.$channel;
      delete event.$id;

      this.$emit(event, {
        summary: summary ?? JSON.stringify(event),
        id,
        ts: +new Date(),
      });
    },
  },
  async run(event) {
    await this.selfSubscribe();

    const { path } = event;
    if (path === "/schedule") {
      this.scheduleTask(event);
    } else if (path === "/cancel") {
      await this.cancelTask(event);
    } else if (event.$channel === this.selfChannel()) {
      this.emitEvent(event);
    }
  },
  sampleEmit,
};

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.

Label	Prop	Type	Description
Pipedream	`pipedream`	`app`	This component uses the Pipedream app.
Secret	`secret`	`string`	Optional but recommended: if you enter a secret here, you must pass this value in the `x-pd-secret` HTTP header when making requests
N/A	`http`	`$.interface.http`	This component uses `$.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.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.

Trigger Authentication#

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

About Pipedream#

Integration platform for developers

Action#

Chat with 302.AI on 302.AI

Description:Send a message to the 302.AI Chat API. Ideal for dynamic conversations, contextual assistance, and creative generation. [See documentation](https://doc.302.ai/147522039e0)

Version:0.0.1

Key:_302_ai-chat-with-302-ai

View on GitHub

Action Code#

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

export default {
  ...common,
  name: "Chat with 302.AI",
  version: "0.0.1",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  key: "_302_ai-chat-with-302-ai",
  description: "Send a message to the 302.AI Chat API. Ideal for dynamic conversations, contextual assistance, and creative generation. [See documentation](https://doc.302.ai/147522039e0)",
  type: "action",
  props: {
    _302_ai,
    modelId: {
      propDefinition: [
        _302_ai,
        "chatCompletionModelId",
      ],
    },
    userMessage: {
      label: "User Message",
      type: "string",
      description: "The user message to send to the model",
    },
    ...common.props,
    systemInstructions: {
      label: "System Instructions",
      type: "string",
      description: "The system message helps set the behavior of the assistant. For example: \"You are a helpful assistant.\"",
      optional: true,
    },
    messages: {
      label: "Prior Message History",
      type: "string[]",
      description: "Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. **Formats supported:** 1) Plain strings with role prefix (e.g., `User: Hello` or `Assistant: Hi there`), 2) JSON strings (e.g., `{\"role\": \"user\", \"content\": \"Hello\"}`), 3) Plain strings without prefix (defaults to user role).",
      optional: true,
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "- **Text**: Returns unstructured text output.\n- **JSON Object**: Returns a JSON object.\n- **JSON Schema**: Enables you to define a specific structure for the model's output using a JSON schema.",
      options: Object.values(constants.CHAT_RESPONSE_FORMAT).map((format) => format.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. Must be a valid JSON schema object.",
      };
    }
    return props;
  },
  async run({ $ }) {
    let messages = [];

    if (this.messages && this.messages.length) {
      for (const message of this.messages) {
        let parsed;

        if (typeof message === "string") {
          // Try to parse as JSON first
          try {
            parsed = JSON.parse(message);
          } catch {
            // If not JSON, treat as a plain string and infer role from content
            // Check if message starts with "User:" or "Assistant:" or "System:"
            if (message.toLowerCase().startsWith("user:")) {
              parsed = {
                role: "user",
                content: message.replace(/^user:\s*/i, "").trim(),
              };
            } else if (message.toLowerCase().startsWith("assistant:")) {
              parsed = {
                role: "assistant",
                content: message.replace(/^assistant:\s*/i, "").trim(),
              };
            } else if (message.toLowerCase().startsWith("system:")) {
              parsed = {
                role: "system",
                content: message.replace(/^system:\s*/i, "").trim(),
              };
            } else {
              // Default to user role if no prefix
              parsed = {
                role: "user",
                content: message,
              };
            }
          }
        } else {
          parsed = message;
        }

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

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

    const responseFormat = this.responseFormat ===
      constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value && this.jsonSchema
      ? {
        response_format: {
          type: this.responseFormat,
          json_schema: typeof this.jsonSchema === "string"
            ? JSON.parse(this.jsonSchema)
            : this.jsonSchema,
        },
      }
      : this.responseFormat && this.responseFormat !== constants.CHAT_RESPONSE_FORMAT.TEXT.value
        ? {
          response_format: {
            type: this.responseFormat,
          },
        }
        : {};

    const data = {
      ...this._getCommonArgs(),
      ...responseFormat,
      messages,
    };

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

    if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      for (const choice of response.choices) {
        try {
          choice.message.content = JSON.parse(choice.message.content);
        } catch {
          console.log(`Unable to parse JSON: ${choice.message.content}`);
        }
      }
    }

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

    return {
      original_messages: messages,
      original_messages_with_assistant_response: messages.concat(response.choices[0]?.message),
      ...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.

Label	Prop	Type	Description
302.AI	`_302_ai`	`app`	This component uses the 302.AI app.
Model	`modelId`	`string`	Select a value from the drop down menu.
User Message	`userMessage`	`string`	The user message to send to the model
Max Tokens	`maxTokens`	`string`	The maximum number of tokens to generate in the completion.
Temperature	`temperature`	`string`	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.
Top P	`topP`	`string`	An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
N	`n`	`string`	How many completions to generate for each prompt
Stop	`stop`	`string[]`	Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
Presence Penalty	`presencePenalty`	`string`	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
Frequency Penalty	`frequencyPenalty`	`string`	Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
User	`user`	`string`	A unique identifier representing your end-user, which can help monitor and detect abuse.
System Instructions	`systemInstructions`	`string`	The system message helps set the behavior of the assistant. For example: "You are a helpful assistant."
Prior Message History	`messages`	`string[]`	Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. Formats supported: 1) Plain strings with role prefix (e.g., `User: Hello` or `Assistant: Hi there`), 2) JSON strings (e.g., `{"role": "user", "content": "Hello"}`), 3) Plain strings without prefix (defaults to user role).
Response Format	`responseFormat`	`string`	Select a value from the drop down menu:`textjson_objectjson_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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