Implementation program to address lack of depth of knowledge
To address the problem of shallow information stacking common in long-writing, OmniThink achieves deep reinforcement through a threefold mechanism:
- Knowledge tree construction techniques: The system will first build a knowledge framework tree of the topic, automatically identifying key sub-topics and their logical relationships to ensure that the content has systematic depth.
- Reflection-Expansion Cycle: Each generated paragraph triggers a reflection mechanism that validates the content by retrieving the latest scholarly information and authoritative data, and makes multiple rounds of iterative corrections
- Density Enhancement Algorithm: automatically embed relevant case studies, comparative analyses, and academic citations when the information entropy of a paragraph is detected to be below a threshold value
In practice, the user simply adds the -depth parameter to specify the depth level (1-5) when running the script:
sh run.sh -topic "Quantum Computing Applications" -depth 4
The system will automatically match the appropriate level of literature search breadth and number of reflection iterations (depth 4 performs approximately 7 knowledge validation cycles)
Alternative: for technical documentation writing, pre-loading a glossary of terms in . /config/domain_knowledge.json, which can significantly improve the precision of domain-specific expressions.
This answer comes from the articleOmniThink: a writing framework for generating high-quality long articles, searching for external knowledge and then reflecting on it and building a knowledge tree step by stepThe
