VectorBlaze 🔥

We implement a Innovative Cron-Job Website Scrapper with continuous streaming to the Backend Product Analytics part with G2 that blazingly processess 1000+ different products in less than 10 seconds.

We have Focused on Fast Web scrapping and Blazing analysis of the product leveraging tools like Qdrant Vector Database , Vector similarity indexing and Fast API Interaction

Architecture 🗺️

Key Features 🔑

• Web Scraping with Selenium: Utilizes Selenium to scrape data from websites periodically as a cron job, ensuring compatibility with dynamic web pages and periodic updates.
• Lightning Fast Communication using Redis Pub/Sub: Integrates Redis Pub/Sub to efficiently communicate between components.
• Data Analysis and Indexing: Employs similarity indexing to quickly check if scraped data already exists in the database.
• Vector Database: Utilizes the power of Vector Database for efficient data storage and retrieval, enhanced by custom queries and a neural engine for faster search.
• Streamlit Frontend: Offers a user-friendly interface for visualizing products not present in the database, enhancing user interaction and data exploration.

Performance Highlights 🏎

• Speed: Processes over 1000+ products in just 10 seconds, showcasing the framework's high-speed capabilities.
• Efficiency: Utilizes similarity indexing to quickly identify existing data, reducing unnecessary processing.
• Scalability: Designed with scalability in mind, allowing for easy expansion and integration of new features.

Prerequisites 😎

• Python 3.6+
• Redis Stack (local or cloud-based)
• Selenium WebDriver (local or containerised)
• qdrant vector db
• Drive to have fun!

Run Locally - start cookin... 💻

Setting Up Redis Stack Container

To start a Redis Stack container using the redis-stack image, follow these steps:

1. Run the following command in your terminal:

docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest

This command launches a Redis Stack container and exposes RedisInsight on port 8001. You can access RedisInsight by opening your browser and navigating to localhost:8001.

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Setting Up Qdrant

To set up Qdrant, follow these steps:

1. Download the Qdrant image from DockerHub:

docker pull qdrant/qdrant

2. Start Qdrant inside Docker with the following command:

docker run -p 6333:6333 \
  -v $(pwd)/qdrant_storage:/qdrant/storage \
  qdrant/qdrant

Once Qdrant is running, you can access its Web UI by navigating to localhost:6333/dashboard.

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Setting Up Environment Variables

Before proceeding further, create a .env file in your project directory and add the following line:

BEARER_TOKEN=your_bearer_token_here

Replace your_bearer_token_here with your actual bearer token.

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Generating Data

Now, generate the required data by following these steps:

1. Run the ProductsCollector.py script to create G2_Products.json.
2. Pre-process G2_Products.json to produce G2_Cleaned.json.

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Building the Neural Search Engine

To build the neural search engine, follow these steps:

1. Open the Qdrant_store.ipynb notebook.
2. Sequentially run the cells in the notebook to vectorize the G2 Products data and prepare the neural search engine.

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Running the Processor

Once everything is set up, run the processor.py script to perform high-speed processing:

python processor.py

This script will handle the heavy lifting tasks.

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Running Selenium Web Scraper

To set up and run the Selenium web scraper, setup the container:

docker run -d -p 4000:4444 -v /dev/shm:/dev/shm selenium/standalone-chrome

• installing python libraries

pip install -r requirements.txt

• run selenium web scraper

python3 scraper/fetchSourceForge.py

• visit http://localhost:4000/ to see scraper in action

Now your environment should be set up and ready to go! If you encounter any issues, feel free to reach out for assistance.

References 🌐

• QDrant Neural Search
• Selenium Docs
• Redis Pub/Sub