scriptoriumadmin/ReMe
1
0
Fork 0
mirror of https://github.com/agentscope-ai/ReMe.git synced 2026-08-28 05:25:04 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 11

docs(service): update service description layout and content

Browse source 
- Adjust column widths for better readability
- Capitalize table header text
- Replace 'Desc' with 'Description' for clarity
- Make 'Note' column more concise and include GitHub link
- Update environment variable information formatting
This commit is contained in:
jinli.yl 2025-09-09 12:30:40 +08:00
parent 4834b53f22
commit a1fb20998b
1 changed files with 29 additions and 23 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

52
reme_ai/service/service_description.md
View file

@ -1,24 +1,30 @@
<table style="width: 100%; table-layout: fixed;">
<thead>
<tr>
<th style="width: 15%;">memory type</th>
<th style="width: 20%;">import</th>
<th style="width: 50%;">Desc</th>
<th style="width: 15%;">Note</th>
</tr>
</thead>
<tbody>
<tr>
<td>ReMe.PersonalMemoryService</td>
<td><code>from reme_ai.service.personal_memory_service import PersonalMemoryService</code></td>
<td>ReMe's personalized memory service (formerly MemoryScope) empowers you to generate, retrieve, and share customized memories. Leveraging advanced LLM, embedding, and vector store technologies, it builds a comprehensive memory system with intelligent, context- and time-aware memory management—seamlessly enabling you to configure and deploy powerful AI agents.</td>
<td>https://github.com/modelscope/ReMe Need to configure environment variables: <code>FLOW_EMBEDDING_API_KEY</code>, <code>FLOW_EMBEDDING_BASE_URL</code>, <code>FLOW_LLM_API_KEY</code> and <code>FLOW_LLM_BASE_URL</code></td>
</tr>
<tr>
<td>ReMe.TaskMemoryService</td>
<td><code>from reme_ai.service.task_memory_service import TaskMemoryService</code></td>
<td>ReMe's task-oriented memory service helps you efficiently manage and schedule task-related memories, enhancing both the accuracy and efficiency of task execution. Powered by LLM capabilities, it supports flexible creation, retrieval, update, and deletion of memories across diverse task scenarios—enabling you to effortlessly build and scale robust agent-based task systems.</td>
<td>the same as <code>ReMe.PersonalMemoryService</code></td>
</tr>
</tbody>
<table>
<colgroup>
<col style="width: 20%">
<col style="width: 25%">
<col style="width: 15%">
<col style="width: 40%">
</colgroup>
<thead>
<tr>
<th>Memory Type</th>
<th>Import</th>
<th>Note</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>ReMe.PersonalMemoryService</td>
<td><code>from reme_ai.service.personal_memory_service import PersonalMemoryService</code></td>
<td><a href="https://github.com/modelscope/ReMe">GitHub</a><br>Env: <code>FLOW_EMBEDDING_API_KEY</code>, <code>FLOW_EMBEDDING_BASE_URL</code>, <code>FLOW_LLM_API_KEY</code>, <code>FLOW_LLM_BASE_URL</code></td>
<td>ReMe's personalized memory service (formerly MemoryScope) empowers you to generate, retrieve, and share customized memories. Leveraging advanced LLM, embedding, and vector store technologies, it builds a comprehensive memory system with intelligent, context- and time-aware memory management—seamlessly enabling you to configure and deploy powerful AI agents.</td>
</tr>
<tr>
<td>ReMe.TaskMemoryService</td>
<td><code>from reme_ai.service.task_memory_service import TaskMemoryService</code></td>
<td>Same as above</td>
<td>ReMe's task-oriented memory service helps you efficiently manage and schedule task-related memories, enhancing both the accuracy and efficiency of task execution. Powered by LLM capabilities, it supports flexible creation, retrieval, update, and deletion of memories across diverse task scenarios—enabling you to effortlessly build and scale robust agent-based task systems.</td>
</tr>
</tbody>
</table>