Asynchronous processing mechanism and performance optimization
II-Researcher adopts an asynchronous programming paradigm to build its core architecture, which provides significant performance advantages in information acquisition and processing. The system can perform web crawling, content extraction, and multi-model inference tasks in parallel, maximizing the use of hardware resources through an event-driven mechanism. For the technical implementation, Python's asyncio framework is used to coordinate the search API calls (Firecrawl/Tavily) and the LLM inference process, while Docker containerized deployment further ensures service stability.
- Default 300-second timeout protection for search tasks
- Supports streaming (-stream) to reduce response latency
- Logging system (docker compose logs) for easy performance monitoring
Tests show that the asynchronous architecture is 3-5 times faster than traditional synchronous solutions when processing complex queries with more than 20 data sources, which is especially suitable for scenarios such as large-scale literature reviews.
This answer comes from the articleII-Researcher: Deep Search and Stepwise Reasoning to Answer Complex QuestionsThe
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