Chat using File Search with OpenAI (ChatGPT) API on New Image (Instant) from DynaPictures API

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New Image (Instant) from the DynaPictures API

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Chat using File Search with the OpenAI (ChatGPT) API

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Getting Started#

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

Select this integration
Configure the New Image (Instant) trigger
1. Connect your DynaPictures account
2. Select a Template ID
Configure the Chat using File Search action
1. Connect your OpenAI (ChatGPT) account
2. Configure alert
3. Select a Model
4. Select a Vector Store ID
5. Configure Chat Input
6. Optional- Configure Instructions
7. Optional- Configure Include Search Results
8. Optional- Configure Max Number of Results
9. Optional- Configure Metadata Filtering
10. Optional- Configure Previous Response ID
11. Optional- Select a Truncation
12. Optional- Select a Response Format
13. Optional- Configure Skip This Step
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 Image (Instant) on DynaPictures

Description:Emit new event when an image has been generated. [See the documentation](https://dynapictures.com/docs/#webhook-notification)

Version:0.0.1

Key:dynapictures-new-image-instant

View on GitHub

DynaPictures Overview#

DynaPictures API allows users to automate image generation with dynamic content. It's useful for creating personalized images for marketing campaigns, social media, or e-commerce products. In Pipedream, you can harness DynaPictures API to build serverless workflows that trigger based on various events, manipulate and generate images, and connect with other services for a seamless automation pipeline.

Trigger Code#

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

export default {
  key: "dynapictures-new-image-instant",
  name: "New Image (Instant)",
  description: "Emit new event when an image has been generated. [See the documentation](https://dynapictures.com/docs/#webhook-notification)",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  props: {
    app,
    http: {
      type: "$.interface.http",
      customResponse: true,
    },
    templateId: {
      propDefinition: [
        app,
        "templateId",
      ],
    },
  },
  hooks: {
    async activate() {
      const {
        createWebhook,
        getWebhookData,
      } = this;

      const response = await createWebhook(getWebhookData());
      if (response?.error) {
        const msg = "Error creating webhook";
        console.log(msg, response);
        throw new Error(response?.message || msg);
      }
    },
    async deactivate() {
      const {
        deleteWebhook,
        getWebhookData,
      } = this;

      const response = await deleteWebhook(getWebhookData());
      if (response?.error) {
        const msg = "Error deleting webhook";
        console.log(msg, response);
        throw new Error(response?.message || msg);
      }
    },
  },
  methods: {
    getWebhookData() {
      const {
        http,
        templateId,
      } = this;

      return {
        data: {
          targetUrl: http.endpoint,
          eventType: constants.EVENT.NEW_IMAGE,
          templateId,
        },
      };
    },
    createWebhook(args = {}) {
      return this.app.post({
        path: "/hooks",
        ...args,
      });
    },
    deleteWebhook(args = {}) {
      return this.app.delete({
        path: "/hooks",
        ...args,
      });
    },
  },
  async run({ body }) {
    this.http.respond({
      status: 200,
    });

    this.$emit(body, {
      id: body.id,
      summary: `New Image: ${body.id}`,
      ts: Date.now(),
    });
  },
};

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
DynaPictures	`app`	`app`	This component uses the DynaPictures app.
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.
Template ID	`templateId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

About DynaPictures#

Generate Hundreds of Personalized Images in Minutes. DynaPictures is a cloud-based solution for dynamic image generation. Connect your preferred data source like Google Sheets to create personalized images and display your client names, coupons, or virtually anything else.

Action#

Chat using File Search on OpenAI (ChatGPT)

Description:Chat with your files knowledge base (vector stores). [See the documentation](https://platform.openai.com/docs/guides/tools-file-search)

Version:0.0.3

Key:openai-chat-using-file-search

View on GitHub

OpenAI (ChatGPT) Overview#

OpenAI provides a suite of powerful AI models through its API, enabling developers to integrate advanced natural language processing and generative capabilities into their applications. Here’s an overview of the services offered by OpenAI's API:

Use Python or Node.js code to make fully authenticated API requests with your OpenAI account:

Action Code#

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

export default {
  ...common,
  name: "Chat using File Search",
  version: "0.0.3",
  key: "openai-chat-using-file-search",
  description: "Chat with your files knowledge base (vector stores). [See the documentation](https://platform.openai.com/docs/guides/tools-file-search)",
  type: "action",
  props: {
    openai,
    alert: {
      type: "alert",
      alertType: "info",
      content: "To use this action, you need to have set up a knowledge base in a vector store and uploaded files to it. [More infomation here](https://platform.openai.com/docs/guides/tools-file-search?lang=javascript#overview).",
    },
    modelId: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    vectorStoreId: {
      propDefinition: [
        openai,
        "vectorStoreId",
      ],
      description: "The identifier of a vector store. Currently supports only one vector store at a time",
    },
    input: {
      type: "string",
      label: "Chat Input",
      description: "Text inputs to the model used to generate a response",
    },
    instructions: {
      type: "string",
      label: "Instructions",
      description: "Inserts a system (or developer) message as the first item in the model's context",
      optional: true,
    },
    includeSearchResults: {
      type: "boolean",
      label: "Include Search Results",
      description: "Include the search results in the response",
      default: false,
      optional: true,
    },
    maxNumResults: {
      type: "integer",
      label: "Max Number of Results",
      description: "Customize the number of results you want to retrieve from the vector store",
      optional: true,
    },
    metadataFiltering: {
      type: "boolean",
      label: "Metadata Filtering",
      description: "Configure how the search results are filtered based on file metadata",
      optional: true,
      reloadProps: true,
    },
    previousResponseId: {
      type: "string",
      label: "Previous Response ID",
      description: "The unique ID of the previous response to the model. Use this to create multi-turn conversations",
      optional: true,
    },
    truncation: {
      type: "string",
      label: "Truncation",
      description: "Specifies the truncation mode for the response if it's larger than the context window size",
      optional: true,
      default: "auto",
      options: [
        "auto",
        "disabled",
      ],
    },
    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](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses).",
      options: [
        "text",
        "json_schema",
      ],
      default: "text",
      optional: true,
      reloadProps: true,
    },
    skipThisStep: {
      type: "boolean",
      label: "Skip This Step",
      description: "Pass in a boolean custom expression to skip this step's execution at runtime",
      optional: true,
      default: false,
    },
  },
  additionalProps() {
    const {
      modelId,
      metadataFiltering,
      responseFormat,
    } = this;
    const props = {};

    if (this.openai.isReasoningModel(modelId)) {
      props.reasoningEffort = {
        type: "string",
        label: "Reasoning Effort",
        description: "Constrains effort on reasoning for reasoning models",
        optional: true,
        options: [
          "low",
          "medium",
          "high",
        ],
      };

      // aparrently not supported yet as of 12/march/2025
      // props.generateSummary = {
      //   type: "string",
      //   label: "Generate Reasoning Summary",
      //   description: "A summary of the reasoning performed by the model",
      //   optional: true,
      //   options: [
      //     "concise",
      //     "detailed",
      //   ],
      // };
    }

    // TODO: make this configuration user-friendly
    // https://platform.openai.com/docs/guides/retrieval?attributes-filter-example=region#attribute-filtering
    if (metadataFiltering) {
      props.filters = {
        type: "object",
        label: "Filters",
        description: "Filter the search results based on file metadata. [See the documentation here](https://platform.openai.com/docs/guides/retrieval#attribute-filtering)",
      };
    }

    if (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. [Generate one here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas).",
      };
    }

    return props;
  },
  methods: {
    ...common.methods,
  },
  async run({ $ }) {
    if (this.skipThisStep) {
      $.export("$summary", "Step execution skipped");
      return;
    }

    const data = {
      model: this.modelId,
      input: this.input,
      instructions: this.instructions,
      previous_response_id: this.previousResponseId,
      truncation: this.truncation,
      tools: [
        {
          type: "file_search",
          vector_store_ids: [
            this.vectorStoreId,
          ],
          max_num_results: this.maxNumResults,
        },
      ],
    };

    if (this.includeSearchResults) {
      data.include = [
        "output[*].file_search_call.search_results",
      ];
    }

    if (this.filters) {
      data.tools[0].filters = this.filters;
    }

    if (this.openai.isReasoningModel(this.modelId) && this.reasoningEffort) {
      data.reasoning = {
        ...data.reasoning,
        effort: this.reasoningEffort,
      };
    }

    if (this.openai.isReasoningModel(this.modelId) && this.generateSummary) {
      data.reasoning = {
        ...data.reasoning,
        generate_summary: this.generateSummary,
      };
    }

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

    const response = await this.openai.responses({
      $,
      data,
    });

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

    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
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Model	`modelId`	`string`	Select a value from the drop down menu.
Vector Store ID	`vectorStoreId`	`string`	Select a value from the drop down menu.
Chat Input	`input`	`string`	Text inputs to the model used to generate a response
Instructions	`instructions`	`string`	Inserts a system (or developer) message as the first item in the model's context
Include Search Results	`includeSearchResults`	`boolean`	Include the search results in the response
Max Number of Results	`maxNumResults`	`integer`	Customize the number of results you want to retrieve from the vector store
Metadata Filtering	`metadataFiltering`	`boolean`	Configure how the search results are filtered based on file metadata
Previous Response ID	`previousResponseId`	`string`	The unique ID of the previous response to the model. Use this to create multi-turn conversations
Truncation	`truncation`	`string`	Select a value from the drop down menu:`autodisabled`
Response Format	`responseFormat`	`string`	Select a value from the drop down menu:`textjson_schema`
Skip This Step	`skipThisStep`	`boolean`	Pass in a boolean custom expression to skip this step's execution at runtime

Chat using File Search with OpenAI (ChatGPT) API on New Image (Instant) from DynaPictures API

Pipedream makes it easy to connect APIs for OpenAI (ChatGPT), DynaPictures 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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