Create Chat Completion with DeepSeek API on New Scheduled Tasks from AWS API

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New Scheduled Tasks from the AWS API

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Create Chat Completion with the DeepSeek API

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Getting Started#

This integration creates a workflow with a AWS trigger and DeepSeek 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 AWS account
2. Select a AWS Region
3. Optional- Configure Secret
Configure the Create Chat Completion action
1. Connect your DeepSeek account
2. Configure Messages
3. Optional- Configure Frequency Penalty
4. Optional- Configure Max Tokens
5. Optional- Configure Presence Penalty
6. Optional- Select a Response Format Type
7. Optional- Configure Stop Sequences
8. Optional- Configure Stream
9. Optional- Configure Stream Include Usage
10. Optional- Configure Temperature
11. Optional- Configure Top P
12. Optional- Configure Tools
13. Optional- Configure Tool Choice
14. Optional- Configure Log Probs
15. 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 Scheduled Tasks on AWS

Description:Creates a Step Function State Machine to publish a message to an SNS topic at a specific timestamp. The SNS topic delivers the message to this Pipedream source, and the source emits it as a new event.

Version:0.4.2

Key:aws-new-scheduled-tasks

View on GitHub

AWS Overview#

The AWS API unlocks endless possibilities for automation with Pipedream. With this powerful combo, you can manage your AWS services and resources, automate deployment workflows, process data, and react to events across your AWS infrastructure. Pipedream offers a serverless platform for creating workflows triggered by various events that can execute AWS SDK functions, making it an efficient tool to integrate, automate, and orchestrate tasks across AWS services and other apps.

Trigger Code#

import base from "../common/scheduled.mjs";
import { toSingleLineString } from "../../common/utils.mjs";

export default {
  ...base,
  key: "aws-new-scheduled-tasks",
  name: "New Scheduled Tasks",
  description: toSingleLineString(`
    Creates a Step Function State Machine to publish a message
    to an SNS topic at a specific timestamp. The SNS topic delivers
    the message to this Pipedream source, and the source emits it as a new event.
  `),
  version: "0.4.2",
  type: "source",
  dedupe: "unique", // Dedupe on SNS message ID
  methods: {
    ...base.methods,
    getStateMachineDefinition() {
      const { PD_COMPONENT: componentId } = process.env;
      const topicArn = this.getTopicArn();
      return {
        Comment: `Task Scheduler for Pipedream component ${componentId}`,
        StartAt: "Wait",
        States: {
          Wait: {
            Comment: "Wait until specified timestamp",
            Type: "Wait",
            TimestampPath: "$.timestamp",
            Next: "SendMessageToSNS",
          },
          SendMessageToSNS: {
            Type: "Task",
            Resource: "arn:aws:states:::sns:publish",
            Parameters: {
              "TopicArn": topicArn,
              "Message.$": "$",
            },
            End: true,
          },
        },
      };
    },
    getStateMachinePermissions() {
      const topicArn = this.getTopicArn();
      const document = {
        Version: "2012-10-17",
        Statement: [
          {
            Effect: "Allow",
            Action: [
              "sns:Publish",
            ],
            Resource: [
              topicArn,
            ],
          },
        ],
      };
      const name = "publish-messages-to-pipedream-sns-topic";
      return {
        document,
        name,
      };
    },
  },
};

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
AWS	`aws`	`app`	This component uses the AWS app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
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.
AWS Region	`region`	`string`	Select a value from the drop down menu.
Secret	`secret`	`string`	Optional but recommended: if you enter a secret here, you must pass this value in the `secret` parameter of each HTTP POST request

Trigger Authentication#

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

Follow the AWS Instructions for creating an IAM user with an associated access and secret key.

As a best practice, attach the minimum set of IAM permissions necessary to perform the specific task in Pipedream. If your workflow only needs to perform a single API call, you should create a user and associate an IAM group / policy with permission to do only that task. You can create as many linked AWS accounts in Pipedream as you'd like.

Enter your access and secret key below.

About AWS#

Amazon Web Services (AWS) offers reliable, scalable, and inexpensive cloud computing services.

Action#

Create Chat Completion on DeepSeek

Description:Creates a chat completion using the DeepSeek API. [See the documentation](https://api-docs.deepseek.com/api/create-chat-completion)

Version:0.0.1

Key:deepseek-create-chat-completion

View on GitHub

Action Code#

import { RESPONSE_FORMAT_TYPE_OPTIONS } from "../../common/constants.mjs";
import { parseObject } from "../../common/util.mjs";
import deepseek from "../../deepseek.app.mjs";

export default {
  key: "deepseek-create-chat-completion",
  name: "Create Chat Completion",
  description: "Creates a chat completion using the DeepSeek API. [See the documentation](https://api-docs.deepseek.com/api/create-chat-completion)",
  version: "0.0.1",
  type: "action",
  props: {
    deepseek,
    messages: {
      type: "string[]",
      label: "Messages",
      description: "The messages for the chat conversation as JSON strings. Each message should be a JSON string like '{\"role\": \"user\", \"content\": \"Hello!\"}'. [See the documentation](https://api-docs.deepseek.com/api/create-chat-completion) for further details.",
    },
    frequencyPenalty: {
      type: "string",
      label: "Frequency Penalty",
      description: "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.",
      optional: true,
    },
    maxTokens: {
      type: "integer",
      label: "Max Tokens",
      description: "Integer between 1 and 8192. The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. If `max_tokens` is not specified, the default value 4096 is used.",
      optional: true,
    },
    presencePenalty: {
      type: "string",
      label: "Presence Penalty",
      description: "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.",
      optional: true,
    },
    responseFormatType: {
      type: "string",
      label: "Response Format Type",
      description: "The format that the model must output. Setting to JSON Object enables JSON Output, which guarantees the message the model generates is valid JSON.",
      options: RESPONSE_FORMAT_TYPE_OPTIONS,
      optional: true,
    },
    stop: {
      type: "string[]",
      label: "Stop Sequences",
      description: "Up to 16 sequences where the API will stop generating further tokens.",
      optional: true,
    },
    stream: {
      type: "boolean",
      label: "Stream",
      description: "If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events (SSE) as they become available, with the stream terminated by a `data: [DONE]` message.",
      optional: true,
      reloadProps: true,
    },
    streamIncludeUsage: {
      type: "string",
      label: "Stream Include Usage",
      description: "If set, an additional chunk will be streamed before the `data: [DONE]` message. The `usage` field on this chunk shows the token usage statistics for the entire request, and the `choices` field will always be an empty array. All other chunks will also include a `usage` field, but with a null value.",
      optional: true,
      hidden: true,
    },
    temperature: {
      type: "string",
      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. We generally recommend altering this or Top P but not both.",
      optional: true,
    },
    topP: {
      type: "string",
      label: "Top P",
      description: "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. We generally recommend altering this or Temperature but not both.",
      optional: true,
    },
    tools: {
      type: "string[]",
      label: "Tools",
      description: "A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.",
      optional: true,
    },
    toolChoice: {
      type: "string",
      label: "Tool Choice",
      description: "Controls which (if any) tool is called by the model. [See the documentation](https://api-docs.deepseek.com/api/create-chat-completion) for further details.",
      optional: true,
    },
    logprobs: {
      type: "boolean",
      label: "Log Probs",
      description: "Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`.",
      optional: true,
    },
    topLogprobs: {
      type: "string",
      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. logprobs must be set to true if this parameter is used.",
      optional: true,
    },
  },
  async additionalProps(props) {
    props.streamIncludeUsage.hidden = !this.stream;
    return {};
  },
  async run({ $ }) {
    const response = await this.deepseek.createModelResponse({
      $,
      data: {
        messages: parseObject(this.messages),
        model: "deepseek-chat",
        frequency_penalty: this.frequencyPenalty && parseInt(this.frequencyPenalty),
        max_tokens: this.maxTokens,
        presence_penalty: this.presencePenalty && parseInt(this.presencePenalty),
        response_format: this.responseFormatType
          ? {
            type: this.responseFormatType,
          }
          : null,
        stop: parseObject(this.stop),
        stream: this.stream,
        stream_options: this.stream
          ? {
            include_usage: this.streamIncludeUsage,
          }
          : null,
        temperature: this.temperature && parseInt(this.temperature),
        top_p: this.topP && parseInt(this.topP),
        tools: parseObject(this.tools),
        tool_choice: parseObject(this.toolChoice),
        logprobs: this.logprobs,
        top_logprobs: this.topLogprobs && parseInt(this.topLogprobs),
      },
    });
    $.export("$summary", "Chat completion created");
    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
DeepSeek	`deepseek`	`app`	This component uses the DeepSeek app.
Messages	`messages`	`string[]`	The messages for the chat conversation as JSON strings. Each message should be a JSON string like '{"role": "user", "content": "Hello!"}'. See the documentation for further details.
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.
Max Tokens	`maxTokens`	`integer`	Integer between 1 and 8192. The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. If `max_tokens` is not specified, the default value 4096 is used.
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.
Response Format Type	`responseFormatType`	`string`	Select a value from the drop down menu:`{ "label": "Text", "value": "text" }{ "label": "JSON Object", "value": "json_object" }`
Stop Sequences	`stop`	`string[]`	Up to 16 sequences where the API will stop generating further tokens.
Stream	`stream`	`boolean`	If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events (SSE) as they become available, with the stream terminated by a `data: [DONE]` message.
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. We generally recommend altering this or Top P but not both.
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. We generally recommend altering this or Temperature but not both.
Tools	`tools`	`string[]`	A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.
Tool Choice	`toolChoice`	`string`	Controls which (if any) tool is called by the model. See the documentation for further details.
Log Probs	`logprobs`	`boolean`	Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`.
Top Log Probabilities	`topLogprobs`	`string`	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. logprobs must be set to true if this parameter is used.

Action Authentication#

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

About DeepSeek#

DeepSeek is an AI-powered tool that helps developers quickly find, understand, and optimize code using natural language search and contextual suggestions. It integrates seamlessly with your workflow, offering insights into your codebase to improve productivity and code quality. Perfect for teams and solo developers, DeepSeek simplifies code navigation and accelerates development.

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Create Chat Completion with DeepSeek API on New Scheduled Tasks from AWS API

Pipedream makes it easy to connect APIs for DeepSeek, AWS and 2,500+ other apps remarkably fast.

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

Developers ♥ Pipedream

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