Enterprise Deployment Program
MemOS's text memory management feature is particularly suitable for enterprise knowledge base construction:
- batch import: Support CSV/JSON format knowledge entry
Example command:python -m memos.importer --format=json --file=product_specs.json --tag=产品文档 - Search Optimization::
- Structured retrieval:
search_memory(query="QA流程", field="title") - Semantic Search:
search_memory(embedding=vector, threshold=0.85)
- Structured retrieval:
- privilege control: Access is managed through the RBAC model:
mag.set_access_rule(department="HR", memory_tag="薪酬制度", access_level=2)
Integration Recommendations: Can be interfaced with enterprise WeChat/pinning, automatically triggered when knowledge is updatednotify_subscribers()Push alerts. Document version traceability is available through themag.get_history(memory_id)View the modification log.
This answer comes from the articleMemOS: An Open Source System for Enhancing the Memory Capacity of Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " How can MemOS be applied in an enterprise knowledge base scenario to enable rapid knowledge updates?
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