Persistent AI Memory enables semantic search in the following ways:
- Use LM Studio's embedding technology to convert text into a vector representation that captures semantic information rather than surface features.
- pass (a bill or inspection etc)
search_memoriescap (a poem)search_conversationsfunction performs a search and returns the 10 most relevant results by default. - Support custom embedded service URL, users can use different embedded models according to the requirements.
This way of searching understands the deeper meaning of a query, e.g. a search for "Python programming" will return all memories related to Python, not just records containing the word "Python".
This answer comes from the articlePersistent AI Memory: Persistent Local Memory Storage for AI AssistantsThe
May not be reproduced without permission:AI productivity tools " How does Persistent AI Memory enable semantic search functionality?
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