Chat using File Search with OpenAI (ChatGPT) API on New Sign Request Completed from Stiply API

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New Sign Request Completed from the Stiply 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 Stiply 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 Sign Request Completed trigger
1. Connect your Stiply account
2. Configure timer
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 Sign Request Completed on Stiply

Description:Emit new event when a sign request is completed in Stiply. [See the documentation](https://app.stiply.nl/api-documentation/v2#tag/sign-requests/operation/GetSignRequests).

Version:0.0.1

Key:stiply-new-sign-request-completed

View on GitHub

Trigger Code#

import stiply from "../../stiply.app.mjs";
import { DEFAULT_POLLING_SOURCE_TIMER_INTERVAL } from "@pipedream/platform";
import sampleEmit from "./test-event.mjs";

export default {
  key: "stiply-new-sign-request-completed",
  name: "New Sign Request Completed",
  description: "Emit new event when a sign request is completed in Stiply. [See the documentation](https://app.stiply.nl/api-documentation/v2#tag/sign-requests/operation/GetSignRequests).",
  type: "source",
  dedupe: "unique",
  version: "0.0.1",
  props: {
    stiply,
    db: "$.service.db",
    timer: {
      type: "$.interface.timer",
      default: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
  },
  methods: {
    generateMeta(item) {
      return {
        id: item.id,
        summary: `New sign request completed: ${item.id}`,
        ts: Date.parse(item.updated_at),
      };
    },
  },
  async run() {
    const signRequests = await this.stiply.getPaginatedResults({
      fn: this.stiply.listSignRequests,
      params: {
        $orderby: "updated_at desc",
        status: "completed",
      },
    });
    signRequests.forEach((signRequest) => {
      const meta = this.generateMeta(signRequest);
      this.$emit(signRequest, meta);
    });
  },
  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.

Label	Prop	Type	Description
Stiply	`stiply`	`app`	This component uses the Stiply app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

Stiply uses OAuth authentication. When you connect your Stiply account, Pipedream will open a popup window where you can sign into Stiply and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Stiply API.

Pipedream requests the following authorization scopes when you connect your account:

SignRequest.ReadWrite

About Stiply#

Legally valid digital signing with a focus on speed and convenience, so you have more time for the moments that count.

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:0.0.8

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: "0.0.8",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: true,
  },
  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 Sign Request Completed from Stiply API

Pipedream makes it easy to connect APIs for OpenAI (ChatGPT), Stiply and 2,900+ other apps remarkably fast.

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

Developers ♥ Pipedream

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