Building connector for data ingestion and data processing

[CodinjaoftheWorld]

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Provide a description of the help you need

I need help in building a connector for data ingestion and data processing. It would be helpful if some reference or sample is provided for the same.

[dosubot[bot]]

Hey there, @CodinjaoftheWorld! Long time no see. Hope you've been coding up a storm since we last chatted! 🌪️💻

Based on your request, it seems like you're looking to build a connector for data ingestion and data processing. Here's a general approach you can follow, inspired by the structure and methods found in the QAIndexProcessor class in the repository:

1. Data Ingestion (Extract): Implement a method similar to extract to ingest data from your data source (e.g., a database, an API, or files).

2. Data Transformation (Transform): Implement a method similar to transform to clean and transform your ingested data.

3. Data Formatting (Format): Define the expected input format and map it to your data model. Implement parsing logic that converts your input data into a structured format suitable for processing or indexing.

4. Data Loading (Load): Determine the target system for your processed data (e.g., a database, a search index). Implement logic to convert your documents into the appropriate format and load them into the target system.

Here's a simplified example of how you might structure a basic connector for data ingestion and processing:

class DataConnector:
    def extract(self, source):
        # Implement logic to ingest data from `source`
        pass

    def transform(self, data):
        # Implement logic to clean and transform `data`
        pass

    def load(self, data, target):
        # Implement logic to load `data` into `target`
        pass

# Example usage
connector = DataConnector()
raw_data = connector.extract(source="your_data_source")
transformed_data = connector.transform(raw_data)
connector.load(transformed_data, target="your_target_system")

This example is quite generic. You'll need to fill in the implementation details based on your specific data source, transformation requirements, and target system for the processed data.

For more details, you can refer to the QAIndexProcessor class in the api/core/rag/index_processor/processor/qa_index_processor.py file. This class provides a good example of how to structure a data processing pipeline, including methods for data extraction, transformation, formatting, and loading.

Please note that this is a high-level approach and the actual implementation will depend on the specifics of your data source and target system. If you need more specific guidance, please provide more details about your data source, the format of your data, and your target system.

Sources

• api/core/rag/index_processor/processor/qa_index_processor.py

About Dosu

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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[ZJ-CAI]

@dosu-bot, If i`d like to use self.create_blob_message() to show the audio player in the front page , what will the arguments be? Can you give me an example? As the blob should be input as a byte-like data, how may i transfer .wav file to it? Dose base64.b64decode(wavfile) work properly for it?

[CodinjaoftheWorld]

@dosu-bot Thanks for your response. To be very specific, i am looking for option to ingest data from a website and then preprocess the data for specific use.
Can you please suggest some sample code to ingest the PDFs and images from a website and preprocess it?

[dosubot[bot]]

Based on your request, you can follow a two-step approach to ingest and preprocess data from a website, specifically for PDFs and images.

Firstly, you need to fetch and upload the files. The provided TypeScript code snippet outlines a method for uploading images. For a comprehensive solution, you'll need to extend this approach to handle PDFs and include preprocessing steps.

type FileUploadParams = {
  file: File; // No change here, as File can represent any file type, including PDFs
  onProgressCallback: (progress: number) => void;
  onSuccessCallback: (res: { id: string }) => void;
  onErrorCallback: () => void;
};

// Renamed to a more generic function name
type FileUpload = (v: FileUploadParams, isPublic?: boolean, url?: string) => void;

export const fileUpload: FileUpload = ({
  file,
  onProgressCallback,
  onSuccessCallback,
  onErrorCallback,
}, isPublic, url) => {
  const formData = new FormData();
  formData.append('file', file);

  // The rest of the function remains the same
};

Secondly, after uploading, you might want to preprocess these files. Preprocessing can vary widely based on your specific use case. For images, preprocessing might include resizing, cropping, or format conversion. For PDFs, it could involve text extraction, compression, or conversion to another format.

For images, you can use libraries like Sharp (Node.js) for server-side processing or Pica for client-side processing to resize or transform images.

import pica from 'pica';

// This function resizes an image file and returns a Blob
const resizeImage = async (file) => {
  const offscreenCanvas = document.createElement('canvas');
  offscreenCanvas.width = 1000; // Target width
  offscreenCanvas.height = 1000; // Target height

  const img = await createImageBitmap(file);
  await pica().resize(img, offscreenCanvas);

  return pica().toBlob(offscreenCanvas, 'image/jpeg', 0.90);
};

For PDFs, if you need to extract text, libraries like PDF.js can be helpful. For server-side processing, libraries like PDFLib can be used for manipulation.

import * as pdfjsLib from 'pdfjs-dist/webpack';

const extractTextFromPDF = async (pdfFile) => {
  const pdf = await pdfjsLib.getDocument(pdfFile).promise;
  const page = await pdf.getPage(1); // Example: extracting text from the first page
  const textContent = await page.getTextContent();
  return textContent.items.map(item => item.str).join(' ');
};

The provided code snippet is a starting point for uploading files. For a complete solution, you'll need to adapt it for PDFs and implement preprocessing steps based on your specific requirements. The examples given for preprocessing images and PDFs should guide you in handling common tasks.

As for the libraries or frameworks currently being used in the codebase for file handling and data processing, the uuid library is being used. You can find this in the uuid_utils.py file.

Please note that the provided code snippets are just examples and might need to be adapted to fit into your specific use case or environment. If you have any further questions or need more specific help, feel free to ask.

Sources

• web/app/components/base/image-uploader/utils.ts
• api/core/tools/utils/uuid_utils.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.