Navigating the hackathon

I'm considering how Pathway can streamline the hackathon, involving Docker and GCP for Google Drive's PDF files. Seeking clarity on integrating these tools for optimal research paper classification.

Simplifying setup

OK, let me see: setting up Docker on Windows, integrating with GCP for Google Drive access, and finding a suitable Pathway framework from open-source/GitHub for research paper classification.

Cloning and configuring

Cloning the Pathway repo, building the Docker container from Dockerfile, and deciding on integrating PDF files from GCS bucket, local environment, or Google Drive.

Thought about Pathway and Docker integration for 15 seconds

Below is a step-by-step, beginner-friendly breakdown of how you might approach this challenge with Pathway, Docker, and Google Drive/GCP. I’ll keep it high-level and conceptual so you can adapt it to your use case.

1. Choosing a Suitable Pathway Repo/Template

Pathway is a real-time data processing framework that can handle streaming or batch data. For research paper classification, you’ll need to:

1. Ingest PDF files (training data).
2. Parse and preprocess PDF text.
3. Build a classification pipeline (e.g., using NLP or ML libraries).
4. Possibly serve predictions or export them somewhere.

Since Pathway is a bit general-purpose, there may not be a single “ready-made” repo for PDF classification. However, Pathway’s GitHub has examples that demonstrate data ingestion, transformation, and classification-like tasks. You’ll have to adapt one of these examples or create your own.

For a starting point, look at:

• pathway/examples/quickstart – minimal code for reading data, processing it, and producing output.
• pathway/examples/data_processing – more advanced ingestion/ETL examples.

If you want to get a feel for it, clone the entire pathway repo. You can later strip out just the parts you need.

git clone https://github.com/pathwaycom/pathway.git

2. Running Pathway in Docker on Windows

2.1. Basic Docker Concepts (Quick Overview)

• Dockerfile: A recipe that tells Docker how to build an image (like a lightweight virtual machine).
• Docker image: A snapshot built from the Dockerfile. You can run many containers from it.
• Docker container: A running instance of the image.

2.2. Pathway + Docker on Windows

1. Install Docker Desktop (Windows)
   You’ve done this already, but ensure Docker Desktop is running and you’re logged in.

2. Locate or Create a Dockerfile

   • The Pathway repo might have a Dockerfile in the root or in subfolders.
   • Or you can use Pathway’s official Docker image if they provide one (check Docker Hub or Pathway docs).

3. Build the Docker image (if you need to build from source).
   Inside the cloned pathway directory (assuming there’s a Dockerfile there):

   docker build -t pathway-image:latest .
   

   This gives you an image named pathway-image.

4. Run the container:

   docker run -it --name my_pathway_container pathway-image:latest
   

   • -it keeps it interactive.
   • --name my_pathway_container just names your container.
   • Alternatively, map local ports, mount volumes, etc.

5. Mount your local folder (optional but usually needed).
   If you have data in a local C:\Users\YourName\projects\papers directory, for example:

   docker run -it \
       -v "C:\Users\YourName\projects\papers:/app/data" \
       --name my_pathway_container \
       pathway-image:latest
   

   Now inside the container, the path /app/data references your local papers folder. You can read/write to it from Pathway scripts.

3. Accessing Google Drive Data

There are a few ways to do this:

3.1. Option A: Manual Download

1. Download the PDF files from Google Drive to your Windows machine.
2. Mount that local directory into Docker (as shown above).
3. The Pathway script runs inside Docker and sees the PDF files in the mounted volume.

For many hackathon scenarios, this is the easiest approach:

• No GCP bucket involved.
• No extra authentication.
• You just handle the PDF files locally.

3.2. Option B: Using GCP with Google Drive

If your hackathon setup is integrated with Google Cloud Platform, or if your dataset is large enough, you might:

1. Copy your PDFs from Drive to a GCP Storage Bucket.
   • You can either do this via the GCP console or use the gsutil command-line tool.
2. Access those PDFs from Docker by:
   • Installing gsutil inside your container.
   • Authenticating with your GCP credentials.
   • Downloading the files inside the container at runtime.
   • Or streaming them if you have them stored in GCS.

Typical steps if going that route:

gcloud auth login
gsutil cp gs://your-gcp-bucket/*.pdf .

And so on, inside the container.

3.3. Option C: Google Drive API (Less Common for Hackathons)

You could mount Google Drive via the Google Drive API or other sync methods. But for hackathons, this is usually overkill unless explicitly required.

4. Putting It All Together (Process Outline)

Below is a simplified “big picture” of how you might tackle the classification workflow:

1. Fetch PDFs

   • Easiest: Download them to a local folder: C:\my_hackathon\pdfs.
   • Alternatively: Use a GCP bucket or Drive API.

2. Build Docker/Pathway Environment

   • Clone Pathway’s GitHub.
   • Docker build the image if needed.
   • Docker run the container, mounting your local PDF folder.

3. Inside Your Container

   • Ensure you have a Python environment with PDF parsing libraries (e.g., PyPDF2, pdfminer.six, or pymupdf).
   • Write a small script that does the following:
     1. Reads all PDFs from the mounted folder.
     2. Converts each PDF to text.
     3. Uses Pathway to create a data pipeline that:
        • Ingests the text.
        • Applies any text preprocessing (cleaning, tokenization, etc.).
        • Feeds the text into a classification model (which might be something you train with scikit-learn, PyTorch, or another library).
     4. Outputs the classification results to a file or console.

4. Train Your Model

   • If you’re building a custom classifier, you can store your model (e.g., a .pkl file for scikit-learn or a saved PyTorch model) in the container or a mounted volume.
   • You might build the model offline (on your machine) and just ship it into Docker.

5. Iterate & Debug

   • Because hackathons move fast, you’ll likely keep refining.
   • Use docker exec -it my_pathway_container /bin/bash to hop into the container’s shell and check logs, run commands, etc.

5. Why Might GCP Be Involved?

• Storage: Your PDFs could be in a GCP Storage bucket if Drive is too large or not convenient.
• Compute: You might run Pathway containers on GCP VM instances or Cloud Run.
• Authentication: If your hackathon requires using GCP credentials or a service account to fetch data from Drive programmatically, you’ll integrate the GCP auth tokens inside your Docker container so it can gsutil or use the Drive API.

Often, though, for a small hackathon project:

1. Download from Drive to local.
2. Mount the local folder to Docker.
3. Problem solved (no extra GCP steps needed).

6. Tips for a Smooth Experience

1. Keep it Local First

   • Test everything locally (Docker + local PDF folder).
   • Only bring GCP or other cloud services in if the hackathon specifically requires them or your data is huge.

2. Lean on Familiar Tools

   • For classification: scikit-learn, huggingface Transformers, etc.
   • For PDF parsing: PyPDF2 or pdfminer.
   • Then wrap your data flow logic with Pathway.

3. Focus on the Core Challenge

   • The hackathon might not require a super-optimized approach, but a working pipeline.
   • Pathway is likely required for the data ingestion/processing pipeline, but your classification can still leverage standard Python ML libraries.

4. Keep Docker Simple

   • If possible, use Pathway’s prebuilt images or minimal Dockerfiles.
   • Keep an eye on Docker Hub for any official pathwaycom images.

5. Version Control

   • Put your Dockerfile, any scripts, and your classification code under Git.
   • This helps you avoid “it works on my machine” issues.

Summary

• Repository: Clone Pathway’s main repo or adapt an example.
• Docker: Build or pull a prebuilt Pathway Docker image, then run it interactively, mounting a local folder containing your PDFs.
• Data Access: Best to download PDFs from Google Drive to a local folder first and mount that folder in Docker for your Pathway pipeline. For heavier, more complex setups, consider GCP Storage or the Drive API.
• Process: Ingest & parse PDFs $\rightarrow$ run classification (NLP/ML) $\rightarrow$ output results.

If you follow this outline, you should be able to get a basic Pathway pipeline up and running with Docker on Windows, using your PDF data stored in Google Drive. Good luck with your hackathon!

Understanding Pathway's docstore

I'm digging into Pathway's docstore, which is a data processing framework for streaming data in Python. Clarification on how it might be related to retrieving text from PDF files and metadata is needed.

Structuring the answer

I’m outlining a high-level step-by-step explanation on Pathway’s "documentStore," including storing and retrieving PDF text from GDrive or local folder, and the importance of metadata.

Breaking down the steps

I’m outlining the steps to explain Pathway's docstore, covering document storage (in memory or DB), parsing PDF text, retrieving text, and integrating with Docker and Cursor Editor.

Thought about Pathway documentStore and PDFs for 6 seconds

Below is a beginner-friendly, conceptual walkthrough of using Pathway’s “documentStore,” explaining what it is, why metadata is talked about (and what it means for you if you don’t care about it), and how to ingest and retrieve text from PDF files—either from Google Drive or a local folder—within a Docker setup.

1. What Is Pathway’s documentStore?

In many frameworks (Pathway or otherwise), a “document store” is a place to keep documents in a structured way (like a database or an in-memory structure). It allows you to:

1. Store documents (like PDFs or the text extracted from them).
2. Attach metadata (author, publication date, conference name, etc.).
3. Retrieve the full text or partial content later for further processing or classification.

While metadata can be very helpful for advanced filtering, searching, or classification, you’re free to ignore it if your only goal is to retrieve raw text. However, many data connectors or ingestion tools automatically generate or expect some metadata (like file name, upload time, etc.). That’s why you might see talk about metadata even if you don’t plan to use it.

2. Four Main Steps to Get Your PDFs into the Document Store

1. Get the PDF files (Google Drive or local folder).
2. Ingest/parse the PDF files to extract text.
3. Load them into Pathway’s documentStore.
4. Retrieve the text (and optional metadata) from documentStore.

We’ll break these down in more detail.

2.1. Getting Your PDF Files

Option A: Use Google Drive Connector

If Pathway has a Google Drive data connector, you can:

1. Authenticate with your Google account (e.g., via OAuth or a service account).
2. List/fetch the files from your Drive folder.
3. Download them into your environment (locally or inside the Docker container) automatically.

Challenges with GDrive

• You need the correct credentials.
• You have to configure the connector to know which folder to pull from.
• The connector often “pulls” the files locally within your code environment anyway.

Option B: Download PDFs to a Local Folder

1. Manually download the PDF files from Google Drive and place them in a local folder on your machine (e.g., C:\my_hackathon\pdfs).
2. Mount this folder into the Docker container so your Python code can see it when running inside Docker.

2.2. Ingest/Parse the PDF Files

Regardless of whether you got the PDF from Google Drive or your local folder, you still need to extract text from them before storing them in documentStore. Common Python libraries for PDF text extraction are:

• PyPDF2
• pdfminer.six
• pymupdf (fitz)

These libraries convert PDF pages to plain text, which you can then push into Pathway’s data structures. Here’s a generic example (not necessarily Pathway-specific):

import PyPDF2

def extract_text_from_pdf(pdf_path):
    text = ""
    with open(pdf_path, 'rb') as f:
        pdf_reader = PyPDF2.PdfReader(f)
        for page in pdf_reader.pages:
            text += page.extract_text() + "\n"
    return text

Then you’d do something like:

pdf_text = extract_text_from_pdf("path/to/file.pdf")
# ... store pdf_text into your docstore ...

2.3. Loading Text into the documentStore

2.3.1. What a documentStore Might Look Like

Depending on the version of Pathway or how they handle documents, you might see a code snippet in their examples like:

from pathway import DocumentStore

# Initialize your document store (the details might differ)
doc_store = DocumentStore()

# Store your text
doc_id = doc_store.write_document(
    text=pdf_text,
    metadata={"filename": "my_paper.pdf", "other_info": "..." }
)

This hypothetical snippet:

• Creates a DocumentStore() object.
• Writes a document’s text (and optional metadata) to the store.
• Returns an ID (doc_id) that you can use to retrieve or manage that document later.

Why Metadata:

• Even if you don’t care about it, you might see that DocumentStore expects or allows you to pass a dictionary of metadata. That’s because many projects rely on metadata for searching or classification. If you truly don’t need it, you can put an empty dictionary or skip it if the method allows.
• For example:

  doc_id = doc_store.write_document(
      text=pdf_text,
      metadata={}
  )
  

2.3.2. Data Connectors that Ingest PDFs Directly

Pathway might provide a data connector that automatically reads PDF files (from local or from Google Drive) and loads them into the documentStore. In that case, your job is simpler:

1. Configure the connector (where are the PDFs? what’s your doc store?).
2. The connector loops over each PDF, extracts text, creates a doc in documentStore.
3. You simply reference doc_store to retrieve them.

Look in the official Pathway docs or examples for something like “PDF connector” or “Google Drive connector.” Sometimes it’s as simple as:

from pathway.connectors.pdf import PdfConnector

doc_store = DocumentStore()
connector = PdfConnector(input_path="path/to/pdfs", document_store=doc_store)
connector.run()  # This might parse all PDFs in the folder and store them

(This code is for illustration; you’ll need to adapt it to your actual version of Pathway and connectors.)

2.4. Retrieving Text from the documentStore

Once your documents are in the store, you can query them by ID or by metadata:

# If you have an ID
document = doc_store.get_document(doc_id)
text = document.text

# Or if you want to retrieve all:
all_docs = doc_store.get_all_documents()
for doc in all_docs:
    print(doc.text)

(Exact function names may differ, so check the reference or examples.)

3. Docker Basics (to Get PDFs or Code Running Inside Docker)

Since you have main.py and a Dockerfile, you’ll want to do something like:

1. Dockerfile (Example)

   FROM python:3.10-slim
   
   # 1. Set a working directory inside the container
   WORKDIR /app
   
   # 2. Copy your main.py and requirements file
   COPY requirements.txt /app
   RUN pip install --no-cache-dir -r requirements.txt
   
   # 3. Copy your source code
   COPY main.py /app
   
   # 4. (Optional) Copy or mount PDF data? We'll do that at runtime.
   
   # 5. Entry point (when the container starts, it will run main.py)
   CMD ["python", "main.py"]
   

2. Build your Docker image:

   docker build -t mypathwayapp:latest .
   

3. Run the container, mounting your local PDF folder (if you want to access local PDFs):

   docker run -it \
       -v "C:\my_hackathon\pdfs:/app/pdfs" \
       --name my_container \
       mypathwayapp:latest
   

   • Inside the container, you should see a /app/pdfs directory with all your local PDFs.
   • If you want to read them from main.py, you’d point the path to /app/pdfs.

4. Inside main.py:
   You’d do something like:

   import os
   from pathway import DocumentStore  # or whatever the real import is
   
   def main():
       doc_store = DocumentStore()
   
       pdf_folder = "/app/pdfs"
       for file_name in os.listdir(pdf_folder):
           if file_name.lower().endswith(".pdf"):
               pdf_path = os.path.join(pdf_folder, file_name)
               text = extract_text_from_pdf(pdf_path)
               doc_store.write_document(
                   text=text,
                   metadata={"filename": file_name}
               )
   
       # Now retrieve or do something with the doc_store data
       all_docs = doc_store.get_all_documents()
       for doc in all_docs:
           print("Text:", doc.text[:200])  # print first 200 chars
   
   if __name__ == "__main__":
       main()
   

5. Metadata:

   • You can add as much or as little as you want.
   • If the DocumentStore requires some minimal metadata, pass an empty dict or just the filename.

4. Summary of Step-by-Step Flow

(1) Decide how to get PDFs

• Use Google Drive connector if available: Provide credentials, specify which GDrive folder, let the connector handle the download.
• Or manually download the PDFs to a local directory.

(2) Docker Setup

• Create a Dockerfile with your Python environment.
• (Optional) Mount your local PDFs folder to the container.

(3) Parse the PDFs

• Inside main.py, use a PDF library to convert PDF pages to text.

(4) Push Text into documentStore

• For each PDF’s extracted text, call something like doc_store.write_document(text=pdf_text, metadata=...).

(5) Retrieve the Text

• Use doc_store.get_all_documents() or a similar method to fetch the stored text.

(6) Metadata

• It’s just extra info (like file name, authors, or conference).
• If you don’t need it, keep it minimal. But many ingestion pipelines still mention it because it’s standard practice.

5. Why Is Metadata Discussed So Often?

1. Organization: People often keep track of which PDF belongs to which category, or the date it was uploaded, or the author.
2. Filtering/Searching: If you store metadata like “conference = AAAI,” you can retrieve only AAAI papers.
3. Machine Learning: Sometimes metadata is used as features (e.g., the year of publication or the authors might matter for classification).

Since you can ignore it for now, just pass an empty dictionary or minimal data (like file name) so you know which PDF your text came from if needed later.

Final Tips

1. Start Simple:

   • Download PDFs locally.
   • Mount the folder into Docker.
   • Extract text in main.py.
   • Store the text in the documentStore.
   • Retrieve it to confirm it works.

2. Iterate:

   • If you need the Google Drive connector, set it up only after you’ve verified your local approach works.
   • If you want advanced features, look into using metadata or advanced indexing.

3. Check Pathway Examples:

   • If Pathway provides any “documentStore” or “connectors” examples in their GitHub or docs, open them up to see real function names or usage. The naming might differ from these conceptual examples.

That’s it! By following the above steps, you’ll be able to (a) get your PDF files (from GDrive or local), (b) store them in Pathway’s documentStore, and (c) retrieve the text for classification or other tasks, all while understanding where metadata fits into the picture.