← DocumentPro + OpenAI (ChatGPT) integrations

Chat using File Search with OpenAI (ChatGPT) API on New Document Updated (Instant) from DocumentPro API

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New Document Updated (Instant) from the DocumentPro 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 DocumentPro trigger and OpenAI (ChatGPT) action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

  1. Select this integration
  2. Configure the New Document Updated (Instant) trigger
    1. Connect your DocumentPro account
    2. Select a Parser ID
  3. 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
  4. Deploy the workflow
  5. Send a test event to validate your setup
  6. 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

Description:Emit new event when a file request status changes. You can only create one webhook in a parser at a time.
Version:0.0.1
Key:documentpro-new-document-updated-instant

Trigger Code

import documentpro from "../../documentpro.app.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  key: "documentpro-new-document-updated-instant",
  name: "New Document Updated (Instant)",
  description: "Emit new event when a file request status changes. You can only create one webhook in a parser at a time.",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  props: {
    documentpro,
    http: {
      type: "$.interface.http",
      customResponse: true,
    },
    db: "$.service.db",
    parserId: {
      propDefinition: [
        documentpro,
        "parserId",
      ],
    },
  },
  hooks: {
    async activate() {
      await this.documentpro.updateParser({
        parserId: this.parserId,
        data: {
          webhook_url: this.http.endpoint,
        },
      });
    },
    async deactivate() {
      await this.documentpro.updateParser({
        parserId: this.parserId,
        data: {
          webhook_url: null,
        },
      });
    },
  },
  async run({ body }) {
    this.$emit(body, {
      id: `${body.data.request_id}-${body.timestamp}`,
      summary: `New document (${body.data.response_body.file_name}) status updated: ${body.event} - ${body.data.request_status}`,
      ts: Date.parse(body.timestamp),
    });

    this.http.respond({
      status: 200,
      body: {
        message: "Received",
      },
    });
  },
  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.
LabelPropTypeDescription
DocumentProdocumentproappThis component uses the DocumentPro app.
N/Ahttp$.interface.httpThis component uses $.interface.http to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the run() method of the component.
N/Adb$.service.dbThis component uses $.service.db to maintain state between executions.
Parser IDparserIdstringSelect a value from the drop down menu.

Trigger Authentication

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

About DocumentPro

Automate data extraction with AI parsers. Extract Invoices from PDFs and Images to Excel & more using AI.

Action

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

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.

LabelPropTypeDescription
OpenAI (ChatGPT)openaiappThis component uses the OpenAI (ChatGPT) app.
ModelmodelIdstringSelect a value from the drop down menu.
Vector Store IDvectorStoreIdstringSelect a value from the drop down menu.
Chat Inputinputstring

Text inputs to the model used to generate a response

Instructionsinstructionsstring

Inserts a system (or developer) message as the first item in the model's context

Include Search ResultsincludeSearchResultsboolean

Include the search results in the response

Max Number of ResultsmaxNumResultsinteger

Customize the number of results you want to retrieve from the vector store

Metadata FilteringmetadataFilteringboolean

Configure how the search results are filtered based on file metadata

Previous Response IDpreviousResponseIdstring

The unique ID of the previous response to the model. Use this to create multi-turn conversations

TruncationtruncationstringSelect a value from the drop down menu:autodisabled
Response FormatresponseFormatstringSelect a value from the drop down menu:textjson_schema
Skip This StepskipThisStepboolean

Pass in a boolean custom expression to skip this step's execution at runtime

Action Authentication

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

About OpenAI (ChatGPT)

OpenAI is an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity. They are the makers of popular models like ChatGPT, DALL-E, and Whisper.

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