Create Chat Completion with Cerebras API on New Item in Feed from RSS API

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Trigger workflow on

New Item in Feed from the RSS API

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Create Chat Completion with the Cerebras API

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Getting Started#

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

Select this integration
Configure the New Item in Feed trigger
1. Connect your RSS account
2. Configure timer
3. Configure Feed URL
4. Configure Published After
Configure the Create Chat Completion action
1. Connect your Cerebras account
2. Select a Model
3. Configure Message
4. Optional- Configure Max Completion Tokens
5. Optional- Configure Stream
6. Optional- Configure Seed
7. Optional- Configure Stop
8. Optional- Configure Temperature
9. Optional- Configure Top P
10. Optional- Select a Tool Choice
11. Optional- Configure Tools
12. Optional- Configure User
13. Optional- Configure Log Probabilities
14. Optional- Configure Top Log Probabilities
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 Item in Feed on RSS

Description:Emit new items from an RSS feed

Version:1.2.7

Key:rss-new-item-in-feed

View on GitHub

RSS Overview#

The RSS app allows users to automatically fetch and parse updates from web feeds. This functionality is pivotal for staying abreast of content changes or updates from websites, blogs, and news outlets that offer RSS feeds. With Pipedream, you can harness the RSS API to trigger workflows that enable a broad range of automations, like content aggregation, monitoring for specific keywords, notifications, and data synchronization across platforms.

Trigger Code#

import { defineSource } from "@pipedream/types";
import rss from "../../app/rss.app.mjs";
import rssCommon from "../common/common.mjs";
export default defineSource({
    ...rssCommon,
    key: "rss-new-item-in-feed",
    name: "New Item in Feed",
    description: "Emit new items from an RSS feed",
    version: "1.2.7",
    type: "source",
    dedupe: "unique",
    props: {
        ...rssCommon.props,
        url: {
            propDefinition: [
                rss,
                "url",
            ],
        },
        publishedAfter: {
            type: "string",
            label: "Published After",
            description: "Emit items published after the specified date in ISO 8601 format .e.g `2022-12-07T12:57:10+07:00`",
            default: new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString(), // default to one day ago
        },
    },
    hooks: {
        async activate() {
            // Try to parse the feed one time to confirm we can fetch and parse.
            // The code will throw any errors to the user.
            await this.rss.fetchAndParseFeed(this.url);
        },
    },
    methods: {
        ...rssCommon.methods,
        generateMeta: function (item) {
            return {
                id: this.rss.itemKey(item),
                summary: item.title,
                ts: Date.now(),
            };
        },
    },
    async run() {
        const items = await this.rss.fetchAndParseFeed(this.url);
        for (const item of items.reverse()) {
            const publishedAfter = +new Date(this.publishedAfter);
            const ts = this.rss.itemTs(item);
            if (Number.isNaN(publishedAfter) || publishedAfter > ts) {
                continue;
            }
            const meta = this.generateMeta(item);
            this.$emit(item, meta);
        }
    },
});

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
RSS	`rss`	`app`	This component uses the RSS app.
	`timer`	`$.interface.timer`	How often you want to poll the feed for new items
Feed URL	`url`	`string`	Enter the URL for any public RSS feed
Published After	`publishedAfter`	`string`	Emit items published after the specified date in ISO 8601 format .e.g `2022-12-07T12:57:10+07:00`

Trigger Authentication#

The RSS API does not require authentication.

About RSS#

Real Simple Syndication

Action#

Create Chat Completion on Cerebras

Description:Create a chat completion with Cerebras AI. [See the documentation](https://inference-docs.cerebras.ai/api-reference/chat-completions)

Version:0.0.1

Key:cerebras-create-chat-completion

View on GitHub

Action Code#

import cerebras from "../../cerebras.app.mjs";

export default {
  name: "Create Chat Completion",
  key: "cerebras-create-chat-completion",
  description: "Create a chat completion with Cerebras AI. [See the documentation](https://inference-docs.cerebras.ai/api-reference/chat-completions)",
  version: "0.0.1",
  type: "action",
  props: {
    cerebras,
    model: {
      propDefinition: [
        cerebras,
        "model",
      ],
    },
    message: {
      type: "string",
      label: "Message",
      description: "The message to send to the model",
    },
    maxCompletionTokens: {
      type: "integer",
      label: "Max Completion Tokens",
      description: "The maximum number of tokens that can be generated in the completion. The total length of input tokens and generated tokens is limited by the model's context length.",
      optional: true,
    },
    stream: {
      propDefinition: [
        cerebras,
        "stream",
      ],
    },
    seed: {
      propDefinition: [
        cerebras,
        "seed",
      ],
    },
    stop: {
      propDefinition: [
        cerebras,
        "stop",
      ],
    },
    temperature: {
      propDefinition: [
        cerebras,
        "temperature",
      ],
    },
    topP: {
      propDefinition: [
        cerebras,
        "topP",
      ],
    },
    toolChoice: {
      type: "string",
      label: "Tool Choice",
      description: "Controls which (if any) tool is called by the model",
      optional: true,
      options: [
        "none",
        "auto",
        "required",
      ],
    },
    tools: {
      type: "object",
      label: "Tools",
      description: "A list of tools the model may call. [See the documentation](https://inference-docs.cerebras.ai/api-reference/chat-completions#tool-choice) for more information",
      optional: true,
    },
    user: {
      propDefinition: [
        cerebras,
        "user",
      ],
    },
    logprobs: {
      type: "boolean",
      label: "Log Probabilities",
      description: "Whether to return log probabilities of the output tokens or not",
      optional: true,
      default: false,
    },
    topLogprobs: {
      type: "integer",
      label: "Top Log Probabilities",
      description: "An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability",
      optional: true,
    },
  },
  async run({ $ }) {
    const {
      model,
      message,
      maxCompletionTokens,
      stream,
      seed,
      stop,
      temperature,
      topP,
      toolChoice,
      tools,
      user,
      logprobs,
      topLogprobs,
    } = this;

    const response = await this.cerebras.chatCompletion({
      $,
      data: {
        model,
        messages: [
          {
            role: "user",
            content: message,
          },
        ],
        max_completion_tokens: maxCompletionTokens,
        stream,
        seed,
        stop,
        temperature,
        top_p: topP,
        tool_choice: toolChoice,
        tools,
        user,
        logprobs,
        top_logprobs: topLogprobs,
      },
    });

    $.export("$summary", "Successfully created chat completion");
    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.

Label	Prop	Type	Description
Cerebras	`cerebras`	`app`	This component uses the Cerebras app.
Model	`model`	`string`	Select a value from the drop down menu.
Message	`message`	`string`	The message to send to the model
Max Completion Tokens	`maxCompletionTokens`	`integer`	The maximum number of tokens that can be generated in the completion. The total length of input tokens and generated tokens is limited by the model's context length.
Stream	`stream`	`boolean`	If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events as they become available
Seed	`seed`	`integer`	If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result
Stop	`stop`	`string`	Up to 4 sequences, separated by commas, where the API will stop generating further tokens. The returned text will not contain the stop sequence
Temperature	`temperature`	`string`	What sampling temperature to use, between 0 and 1.5. Higher values (e.g., 0.8) will make the output more random, while lower values (e.g., 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 tokens with top_p probability mass
Tool Choice	`toolChoice`	`string`	Select a value from the drop down menu:`noneautorequired`
Tools	`tools`	`object`	A list of tools the model may call. See the documentation for more information
User	`user`	`string`	A unique identifier representing your end-user, which can help Cerebras to monitor and detect abuse
Log Probabilities	`logprobs`	`boolean`	Whether to return log probabilities of the output tokens or not
Top Log Probabilities	`topLogprobs`	`integer`	An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability