- Introduced RedisManager class in scraper.py for centralized Redis operations including job tracking and caching.
- Enhanced job scraping logic in MultiPlatformJobScraper to support multiple platforms (Ashby, Lever, Greenhouse).
- Updated browser initialization and context management to ensure better resource handling.
- Improved error handling and logging throughout the scraping process.
- Added SSL connection parameters management in a new ssl_connection.py module for RabbitMQ connections.
- Refactored sender.py to utilize RedisManager for job deduplication and improved logging mechanisms.
- Enhanced CSV processing logic in sender.py with better validation and error handling.
- Updated connection parameters for RabbitMQ to support SSL configurations based on environment variables.
- Replace XAI/Grok integration with DeepSeek's OpenAI-compatible API
- Remove schema generation and caching logic
- Simplify prompt structure and response parsing
- Standardize database schema and markdown output format
- Update config to use DEEPSEEK_API_KEY instead of XAI_API_KEY
- Change default search keyword in linkedin_main.py
The previous timeout values were too short for slower network conditions, causing premature timeouts during job scraping. Increased wait_for_function timeout from 30s to 80s and load_state timeout from 30s to 60s to accommodate slower page loads.
refactor: update timeout values in job scraper classes
feat: add spoof config for renderers and vendors
build: update pycache files for config and modules
- Implement LLMJobRefiner class for processing job data with Gemini API
- Add new job_scraper2.py with enhanced scraping capabilities
- Remove search_keywords parameter from scraping engine
- Add environment variable loading in config.py
- Update main script to use new scraper and target field
