Generate Text with Cohere API on New Download Counts from npm API

Pipedream makes it easy to connect APIs for Cohere, npm and 2,200+ other apps remarkably fast.

Trigger workflow on

New Download Counts from the npm API

Next, do this

Generate Text with the Cohere API

No credit card required

▶

Watch us build a workflow

8 min

Watch now ➜

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

Getting Started#

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

Select this integration
Configure the New Download Counts trigger
1. Connect your npm account
2. Configure timer
3. Select a Period
4. Optional- Configure Package
Configure the Generate Text action
1. Connect your Cohere account
2. Configure Prompt
3. Optional- Select a Model
4. Optional- Configure Number of generations
5. Optional- Configure Max Tokens
6. Optional- Configure Preset
7. Optional- Configure Temperature
8. Optional- Configure K
9. Optional- Configure P
10. Optional- Configure Frequency Penalty
11. Optional- Configure End Sequences
12. Optional- Configure Stop Sequences
13. Optional- Select a Return Likelihoods
14. Optional- Configure Logit Bias
15. Optional- Select a Truncate
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 Download Counts on npm

Description:Emit new event with the latest count of downloads for an npm package. [See the documentation](https://github.com/npm/registry/blob/main/docs/download-counts.md).

Version:0.1.0

Key:npm-download-counts

View on GitHub

Trigger Code#

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

export default {
  key: "npm-download-counts",
  name: "New Download Counts",
  description: "Emit new event with the latest count of downloads for an npm package. [See the documentation](https://github.com/npm/registry/blob/main/docs/download-counts.md).",
  version: "0.1.0",
  type: "source",
  props: {
    app,
    db: "$.service.db",
    timer: {
      type: "$.interface.timer",
      description: "One day interval time is recommended because NPM only update metrics once a day. [See the documentation](https://github.com/npm/registry/blob/main/docs/download-counts.md#data-source).",
      default: {
        intervalSeconds: 60 * 60 * 24,
      },
    },
    period: {
      type: "string",
      label: "Period",
      description: "Select last-day, last-week or last-month.",
      optional: false,
      default: "last-day",
      options: [
        "last-day",
        "last-week",
        "last-month",
      ],
    },
    packageName: {
      type: "string",
      label: "Package",
      description: "Enter an npm package name. Leave blank for all",
      optional: true,
    },
  },
  methods: {
    getDownloadCounts({
      period, packageName, ...args
    } = {}) {
      const basePath = `/downloads/point/${encodeURIComponent(period)}`;
      return this.app.makeRequest({
        path: packageName
          ? `${basePath}/${encodeURIComponent(packageName)}`
          : basePath,
        ...args,
      });
    },
  },
  async run({ timestamp: ts }) {
    const {
      getDownloadCounts,
      period,
      packageName,
    } = this;

    const response = await getDownloadCounts({
      period,
      packageName,
    });

    this.$emit(response, {
      id: ts,
      summary: `New Download Count ${response.downloads}`,
      ts,
    });
  },
};

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
npm	`app`	`app`	This component uses the npm app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`	One day interval time is recommended because NPM only update metrics once a day. See the documentation.
Period	`period`	`string`	Select a value from the drop down menu:`last-daylast-weeklast-month`
Package	`packageName`	`string`	Enter an npm package name. Leave blank for all

Trigger Authentication#

The npm API does not require authentication.

About npm#

Node package manager

Action#

Generate Text on Cohere

Description:This action generates realistic text conditioned on a given input. [See the docs here](https://docs.cohere.ai/reference/generate)

Version:0.0.2

Key:cohere_platform-generate-text

View on GitHub

Cohere Overview#

The Cohere API enables the development of apps with advanced natural language understanding capabilities. Utilizing machine learning, it can help with tasks like text generation, summarization, sentiment analysis, and more. On Pipedream, you can seamlessly integrate Cohere's features into serverless workflows, triggering actions based on text input, processing large volumes of data, or even enhancing chatbots with more human-like responses.

Action Code#

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

export default {
  ...common,
  key: "cohere_platform-generate-text",
  name: "Generate Text",
  version: "0.0.2",
  description: "This action generates realistic text conditioned on a given input. [See the docs here](https://docs.cohere.ai/reference/generate)",
  type: "action",
};

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
Cohere	`coherePlatform`	`app`	This component uses the Cohere app.
Prompt	`prompt`	`string`	Represents the prompt or text to be completed. Trailing whitespaces will be trimmed. If your use case requires trailing whitespaces, please contact ivan@cohere.ai.
Model	`model`	`string`	Select a value from the drop down menu:`mediumxlarge`
Number of generations	`numGenerations`	`integer`	Defaults to 1, min value of 1, max value of 5. Denotes the maximum number of generations that will be returned.
Max Tokens	`maxTokens`	`integer`	Denotes the number of tokens to predict per generation, defaults to 20. See BPE Tokens for more details. Can only be set to `0` if `return_likelihoods` is set to `ALL` to get the likelihood of the prompt.
Preset	`preset`	`string`	The ID of a custom playground preset. You can create presets in the playground. If you use a preset, all other parameters become optional, and any included parameters will override the preset's parameters.
Temperature	`temperature`	`string`	Defaults to `0.75`, min value of `0.0`, max value of `5.0`. A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations. See Temperature for more details.
K	`k`	`integer`	Defaults to `0`(disabled), which is the minimum. Maximum value is `500`. Ensures only the top `k` most likely tokens are considered for generation at each step.
P	`p`	`string`	Defaults to `0.75`. Set to `1.0` or `0` to disable. If set to a probability `0.0 < p < 1.0`, it ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
Frequency Penalty	`frequencyPenalty`	`string`	Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Can be used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
End Sequences	`endSequences`	`string[]`	The generated text will be cut at the beginning of the earliest occurence of an end sequence. The sequence will be excluded from the text.
Stop Sequences	`stopSequences`	`string[]`	The generated text will be cut at the end of the earliest occurence of a stop sequence. The sequence will be included the text.
Return Likelihoods	`returnLikelihoods`	`string`	Select a value from the drop down menu:`GENERATIONALLNONE`
Logit Bias	`logitBias`	`object`	Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. The format is `{token_id: bias}` where bias is a float between `-10` and `10`. Tokens can be obtained from text using Tokenize. For example, if the value `{'11': -10}` is provided, the model will be very unlikely to include the token 11 (`" "`, the newline character) anywhere in the generated text. In contrast `{'11': 10}` will result in generations that nearly only contain that token. Values between -10 and 10 will proportionally affect the likelihood of the token appearing in the generated text. Note: logit bias may not be supported for all custom models.
Truncate	`truncate`	`string`	Select a value from the drop down menu:`NONESTARTEND`