kazparserbot 0.1.1

Keyword-driven web scraping pipeline (Serper + OpenAI).

Keyword Processor with Serper.dev and GPT

Description

This script:

1. Expands each keyword into 2 * queries_to_generate_per_keyword queries (the first half in Russian, the second half in Kazakh).
2. Uses serper.dev to retrieve search results for these queries.
3. De-duplicates and merges those results.
4. Asks GPT to pick the top top_results_to_get results for each keyword.
5. Scrapes those pages via serper.dev to get full text.
6. Outputs everything to a JSON file.

Setup

• Python 3.11+
• poetry install
• poetry shell
• Copy .env.template and fill in the OpenAI and Serper keys

Usage

1. Create a JSON file with a list of keywords, for example:

["Dimash Qudaibergen", "Apple Inc"]

2. Run:

python scrap_by_keywords.py keywords.json output.json \
    --google_results_to_get_per_query=10 \
    --top_results_to_get=5 \
    --queries_to_generate_per_keyword=3 \
    --collect_imgs_and_context=True

• google_results_to_get_per_query controls how many results to retrieve for each query.
• top_results_to_get is how many of those results to parse further.
• queries_to_generate_per_keyword is how many queries to generate in Russian (and then again in Kazakh), so total is double that.
• collect_imgs_and_context controls whether to collect images and context for each result after the main results are collected. Will output <output_json>_imgs.json and imgs folder with images. Json will contain list of dicts with 'url', 'img_url', 'context_text_before', 'context_text_after', 'file_path' fields. Only keep images > N pixels to prevent getting trash, technical imgs, headers, etc.

3. The output will be in path/to/output.json, for instance:

{
  "Dimash Qudaibergen": [
    {
      "url": "...",
      "google_snippet": "...",
      "full_text": "..."
    },
    ...
  ],
  ...
}