Automated in-depth research solutions
DeepResearch offers the following systematic solutions to the problem of inefficient research data collection:
- Automated multi-round iterative study: Users only need to input the core problem (e.g. "the latest discussion focus on AI ethics"), and the system will automatically decompose the problem → formulate a plan → execute 5 rounds of iteration, each time optimizing the direction based on the previous results.
- Integrated Data Acquisition: Synchronized access via search engine API + web crawling technology:
- Abstracts of academic papers (preferred to grab .edu/.org domains)
- Industry reports (recognize PDF document features)
- News (chronological order)
- Structured Output: The final generation standard report contains:
- Background to the issue
- Data Matrix (Comparison Table of Core Views)
- References (automatically formatted citations)
In practice, it is recommended to validate the crawling rules in a test environment first (modifying research_plan.py) and then tweak them via docker-compose.yml:
research_depth: 3(number of iterations)
timeout: 30(Single grab timeout)
This answer comes from the articleDeepResearch: a fully open source AI assistant for automated deep researchThe
May not be reproduced without permission:AI productivity tools " How do you address the inefficiency of manually collecting large amounts of research information?
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