Extract Text with LLMWhisperer API on New Record (of Selectable Type) from Salesforce API

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New Record (of Selectable Type) from the Salesforce API

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Extract Text with the LLMWhisperer API

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Getting Started#

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

Select this integration
Configure the New Record (of Selectable Type) trigger
1. Connect your Salesforce account
2. Configure timer
3. Select a Object Type
Configure the Extract Text action
1. Connect your LLMWhisperer account
2. Select a Processing Mode
3. Select a Output Mode
4. Optional- Configure Page Seperator
5. Optional- Configure Force Text Processing
6. Optional- Configure Pages To Extract
7. Optional- Configure Timeout
8. Optional- Configure Store Metadata for Highlighting
9. Optional- Configure Median Filter Size
10. Optional- Configure Gaussian Blur Radius
11. Optional- Select a OCR Provider
12. Optional- Configure Line Splitter Tolerance
13. Optional- Configure Horizontal Stretch Factor
14. Configure URL In Post
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 Record (of Selectable Type) on Salesforce

Description:Emit new event (at regular intervals) when a record of arbitrary object type (selected as an input parameter by the user) is created. See [the docs](https://sforce.co/3yPSJZy) for more information.

Version:0.0.6

Key:salesforce_rest_api-new-record

View on GitHub

Salesforce Overview#

The Salesforce (REST API) provides a powerful platform for creating and managing customer relationships with a wide array of features like data manipulation, querying, and complex automation. With Pipedream's serverless execution, you can create workflows that automate your sales processes, sync data with other platforms, enhance customer engagement, and trigger actions based on specific events. Dive into Salesforce data, streamline lead management, track customer interactions, and push or pull data to or from Salesforce seamlessly.

Trigger Code#

import startCase from "lodash/startCase.js";

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

export default {
  ...common,
  type: "source",
  name: "New Record (of Selectable Type)",
  key: "salesforce_rest_api-new-record",
  description: "Emit new event (at regular intervals) when a record of arbitrary object type (selected as an input parameter by the user) is created. See [the docs](https://sforce.co/3yPSJZy) for more information.",
  version: "0.0.6",
  hooks: {
    ...common.hooks,
    async activate() {
      const {
        objectType,
        getObjectTypeDescription,
        setObjectTypeColumns,
      } = this;

      await common.hooks.activate.call(this);

      const { fields } = await getObjectTypeDescription(objectType);
      const columns = fields.map(({ name }) => name);

      setObjectTypeColumns(columns);
    },
  },
  methods: {
    ...common.methods,
    generateMeta(item, fieldName) {
      const { objectType } = this;
      const {
        CreatedDate: createdDate,
        [fieldName]: name,
        Id: id,
      } = item;
      const entityType = startCase(objectType);
      const summary = `New ${entityType} created: ${name}`;
      const ts = Date.parse(createdDate);
      return {
        id,
        summary,
        ts,
      };
    },
    async processEvent(eventData) {
      const {
        paginate,
        objectType,
        setLatestDateCovered,
        getObjectTypeColumns,
        getNameField,
        generateMeta,
        $emit: emit,
      } = this;

      const {
        startTimestamp,
        endTimestamp,
      } = eventData;

      const fieldName = getNameField();
      const columns = getObjectTypeColumns();

      const events = await paginate({
        objectType,
        startTimestamp,
        endTimestamp,
        columns,
      });

      const [
        latestEvent,
      ] = events;

      if (latestEvent?.CreatedDate) {
        const latestDateCovered = new Date(latestEvent.CreatedDate);
        latestDateCovered.setSeconds(0);
        setLatestDateCovered(latestDateCovered.toISOString());
      }

      Array.from(events)
        .reverse()
        .forEach((item) => {
          const meta = generateMeta(item, fieldName);
          emit(item, meta);
        });
    },
  },
};

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
Salesforce	`salesforce`	`app`	This component uses the Salesforce app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`
Object Type	`objectType`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

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

About Salesforce#

Web services API for interacting with Salesforce

Action#

Extract Text on LLMWhisperer

Description:Convert your PDF/scanned documents to text format which can be used by LLMs. [See the documentation](https://docs.unstract.com/llm_whisperer/apis/llm_whisperer_text_extraction_api)

Version:0.0.1

Key:llmwhisperer-extract-text

View on GitHub

Action Code#

import fs from "fs";
import app from "../../llmwhisperer.app.mjs";

export default {
  key: "llmwhisperer-extract-text",
  name: "Extract Text",
  description: "Convert your PDF/scanned documents to text format which can be used by LLMs. [See the documentation](https://docs.unstract.com/llm_whisperer/apis/llm_whisperer_text_extraction_api)",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    processingMode: {
      type: "string",
      label: "Processing Mode",
      description: "The processing mode to be used. Choose between `ocr` and `text`.",
      options: [
        "ocr",
        "text",
      ],
    },
    outputMode: {
      type: "string",
      label: "Output Mode",
      description: "The output mode to be used. Choose between `line-printer` and `text`.",
      options: [
        "line-printer",
        "text",
      ],
    },
    pageSeperator: {
      type: "string",
      label: "Page Seperator",
      description: "The string to be used as a page separator. Eg: `<<<`",
      optional: true,
    },
    forceTextProcessing: {
      type: "boolean",
      label: "Force Text Processing",
      description: "If set to true, the document will be processed as text only. If set to false, the document will be processed based on LLMWhisperer's chosed stratergy.",
      optional: true,
    },
    pagesToExtract: {
      type: "string",
      label: "Pages To Extract",
      description: "Define which pages to extract. By default all pages are extracted. You can specify which pages to extract with this parameter. Example `1-5,7,21-` will extract pages **1,2,3,4,5,7,21,22,23,24...** till the last page.",
      optional: true,
    },
    timeout: {
      type: "integer",
      label: "Timeout",
      description: "The time in seconds after which the request will automatically switch to async mode. If a timeout occurs then the API will return a 202 message along with `whisper-hash` which can be used later to check processing status and retrieve the text. Refer to the async operation documentation for more information",
      optional: true,
    },
    storeMetadataForHighlighting: {
      type: "boolean",
      label: "Store Metadata for Highlighting",
      description: "If set to true, metadata required for the highlighting is stored. If you do not require highlighting API, set this to false. Note that setting this to true will store your text in our servers",
      optional: true,
    },
    medianFilterSize: {
      type: "integer",
      label: "Median Filter Size",
      description: "The size of the median filter to be applied to the image. This is used to remove noise from the image. This parameter works only in on-prem version of LLMWhisperer.",
      optional: true,
    },
    gaussianBlurRadius: {
      type: "integer",
      label: "Gaussian Blur Radius",
      description: "The radius of the gaussian blur to be applied to the image. This is used to remove noise from the image. This parameter works only in on-prem version of LLMWhisperer.",
      optional: true,
    },
    ocrProvider: {
      type: "string",
      label: "OCR Provider",
      description: "The OCR provider to be used. Choose between `simple` and `advanced`. This parameter works only in on-prem version of LLMWhisperer.",
      optional: true,
      options: [
        "simple",
        "advanced",
      ],
    },
    lineSplitterTolerance: {
      type: "string",
      label: "Line Splitter Tolerance",
      description: "Factor to decide when to move text to the next line when it is above or below the baseline. The default value of `0.4` signifies 40% of the average character height.",
      optional: true,
    },
    horizontalStretchFactor: {
      type: "string",
      label: "Horizontal Stretch Factor",
      description: "Factor by which a horizontal stretch has to applied. It defaults to `1.0`. A stretch factor of `1.1` would mean at 10% stretch factor applied. Normally this factor need not be adjusted. You might want to use this parameter when multi column layouts back into each other. For example in a two column layout, the two columns get merged into one.",
      optional: true,
    },
    urlInPost: {
      type: "boolean",
      label: "URL In Post",
      description: "If set to `true`, the headers will be set to `text/plain`. If set to `false`, the headers will be set to `application/octet-stream`.",
      reloadProps: true,
      default: true,
    },
  },
  additionalProps() {
    const { urlInPost } = this;
    return {
      data: {
        type: "string",
        label: urlInPost
          ? "Document URL"
          : "Document Path",
        description: urlInPost
          ? "The URL of the document to process."
          : "Document path of the file previously downloaded in Pipedream E.g. (`/tmp/my-file.txt`). [Download a file to the `/tmp` directory](https://pipedream.com/docs/code/nodejs/http-requests/#download-a-file-to-the-tmp-directory)",
      },
    };
  },
  methods: {
    getHeaders(urlInPost) {
      return {
        "Content-Type": urlInPost
          ? "text/plain"
          : "application/octet-stream",
      };
    },
    getData(urlInPost, data) {
      return urlInPost
        ? data
        : fs.readFileSync(data);
    },
    extractText(args = {}) {
      return this.app.post({
        path: "/whisper",
        ...args,
      });
    },
  },
  async run({ $ }) {
    const {
      extractText,
      getHeaders,
      getData,
      urlInPost,
      processingMode,
      outputMode,
      pageSeperator,
      forceTextProcessing,
      pagesToExtract,
      timeout,
      storeMetadataForHighlighting,
      medianFilterSize,
      gaussianBlurRadius,
      ocrProvider,
      lineSplitterTolerance,
      horizontalStretchFactor,
      data,
    } = this;

    const response = await extractText({
      $,
      headers: getHeaders(urlInPost),
      params: {
        url_in_post: urlInPost,
        processing_mode: processingMode,
        output_mode: outputMode,
        page_seperator: pageSeperator,
        force_text_processing: forceTextProcessing,
        pages_to_extract: pagesToExtract,
        timeout,
        store_metadata_for_highlighting: storeMetadataForHighlighting,
        median_filter_size: medianFilterSize,
        gaussian_blur_radius: gaussianBlurRadius,
        ocr_provider: ocrProvider,
        line_splitter_tolerance: lineSplitterTolerance,
        horizontal_stretch_factor: horizontalStretchFactor,
      },
      data: getData(urlInPost, data),
    });

    $.export("$summary", "Successfully extracted text from document.");
    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
LLMWhisperer	`app`	`app`	This component uses the LLMWhisperer app.
Processing Mode	`processingMode`	`string`	Select a value from the drop down menu:`ocrtext`
Output Mode	`outputMode`	`string`	Select a value from the drop down menu:`line-printertext`
Page Seperator	`pageSeperator`	`string`	The string to be used as a page separator. Eg: `<<<`
Force Text Processing	`forceTextProcessing`	`boolean`	If set to true, the document will be processed as text only. If set to false, the document will be processed based on LLMWhisperer's chosed stratergy.
Pages To Extract	`pagesToExtract`	`string`	Define which pages to extract. By default all pages are extracted. You can specify which pages to extract with this parameter. Example `1-5,7,21-` will extract pages 1,2,3,4,5,7,21,22,23,24... till the last page.
Timeout	`timeout`	`integer`	The time in seconds after which the request will automatically switch to async mode. If a timeout occurs then the API will return a 202 message along with `whisper-hash` which can be used later to check processing status and retrieve the text. Refer to the async operation documentation for more information
Store Metadata for Highlighting	`storeMetadataForHighlighting`	`boolean`	If set to true, metadata required for the highlighting is stored. If you do not require highlighting API, set this to false. Note that setting this to true will store your text in our servers
Median Filter Size	`medianFilterSize`	`integer`	The size of the median filter to be applied to the image. This is used to remove noise from the image. This parameter works only in on-prem version of LLMWhisperer.
Gaussian Blur Radius	`gaussianBlurRadius`	`integer`	The radius of the gaussian blur to be applied to the image. This is used to remove noise from the image. This parameter works only in on-prem version of LLMWhisperer.
OCR Provider	`ocrProvider`	`string`	Select a value from the drop down menu:`simpleadvanced`
Line Splitter Tolerance	`lineSplitterTolerance`	`string`	Factor to decide when to move text to the next line when it is above or below the baseline. The default value of `0.4` signifies 40% of the average character height.
Horizontal Stretch Factor	`horizontalStretchFactor`	`string`	Factor by which a horizontal stretch has to applied. It defaults to `1.0`. A stretch factor of `1.1` would mean at 10% stretch factor applied. Normally this factor need not be adjusted. You might want to use this parameter when multi column layouts back into each other. For example in a two column layout, the two columns get merged into one.
URL In Post	`urlInPost`	`boolean`	If set to `true`, the headers will be set to `text/plain`. If set to `false`, the headers will be set to `application/octet-stream`.

Action Authentication#

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

About LLMWhisperer#

LLMWhisperer is a technology that presents data from complex documents to LLMs in a way that they can best understand.

More Ways to Connect LLMWhisperer + Salesforce#

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Extract Text with LLMWhisperer API on New Record (of Selectable Type) from Salesforce API

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

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

Developers ♥ Pipedream

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