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https://github.com/agentscope-ai/ReMe.git
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docs(config): update documentation config and quick start examples
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dengjiaji
parent
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commit
82d0adc86e
3 changed files with 98 additions and 85 deletions
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@ -45,6 +45,7 @@ sphinx:
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- sphinx.ext.intersphinx
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- sphinx.ext.autosummary
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- sphinxcontrib.mermaid
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- sphinx_design
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config:
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# API Documentation Configuration
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autosummary_generate: True
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@ -121,4 +122,7 @@ sphinx:
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color-admonition-background: "#f8f9fa"
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dark_css_variables:
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color-brand-primary: "#64b5f6"
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color-brand-content: "#64b5f6"
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color-brand-content: "#64b5f6"
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# jupyter-book build --all .
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# ghp-import -n -p -f _build/html
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@ -40,7 +40,50 @@ reme \
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#### Task Memory Management
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```{code-cell}
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`````{tab-set}
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````{tab-item} python(import)
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```{code-block}
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import asyncio
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from reme_ai import ReMeApp
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async def main():
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async with ReMeApp(
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"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
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"embedding_model.default.model_name=text-embedding-v4",
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"vector_store.default.backend=memory"
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) as app:
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# Experience Summarizer: Learn from execution trajectories
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result = await app.async_execute(
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name="summary_task_memory",
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workspace_id="task_workspace",
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trajectories=[
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{
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"messages": [
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{"role": "user", "content": "Help me create a project plan"}
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],
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"score": 1.0
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}
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]
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)
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print(result)
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# Retriever: Get relevant memories
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result = await app.async_execute(
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name="retrieve_task_memory",
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workspace_id="task_workspace",
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query="How to efficiently manage project progress?",
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top_k=1
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)
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print(result)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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````
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````{tab-item} python(http)
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```{code-block}
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import requests
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# Experience Summarizer: Learn from execution trajectories
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@ -58,9 +101,9 @@ response = requests.post("http://localhost:8002/retrieve_task_memory", json={
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"top_k": 1
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})
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```
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````
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````{dropdown} curl version
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````{tab-item} curl
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```bash
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# Experience Summarizer: Learn from execution trajectories
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curl -X POST http://localhost:8002/summary_task_memory \
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@ -83,7 +126,7 @@ curl -X POST http://localhost:8002/retrieve_task_memory \
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```
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````
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````{dropdown} Node.js version
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````{tab-item} Node.js
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```{code-block} javascript
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// Experience Summarizer: Learn from execution trajectories
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fetch("http://localhost:8002/summary_task_memory", {
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@ -117,9 +160,55 @@ fetch("http://localhost:8002/retrieve_task_memory", {
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.then(data => console.log(data));
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```
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````
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`````
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#### Personal Memory Management
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<summary> import version</summary>
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`````{tab-set}
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````{tab-item} Python(import)
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```{code-cell}
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import asyncio
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from reme_ai import ReMeApp
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async def main():
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async with ReMeApp(
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"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
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"embedding_model.default.model_name=text-embedding-v4",
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"vector_store.default.backend=memory"
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) as app:
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# Memory Integration: Learn from user interactions
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result = await app.async_execute(
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name="summary_personal_memory",
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workspace_id="task_workspace",
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trajectories=[
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{
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"messages": [
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{"role": "user", "content": "I like to drink coffee while working in the morning"},
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{"role": "assistant",
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"content": "I understand, you prefer to start your workday with coffee to stay energized"}
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]
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}
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]
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)
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print(result)
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# Memory Retrieval: Get personal memory fragments
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result = await app.async_execute(
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name="retrieve_personal_memory",
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workspace_id="task_workspace",
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query="What are the user's work habits?",
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top_k=5
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)
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print(result)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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````
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```{code-cell}
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# Memory Integration: Learn from user interactions
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response = requests.post("http://localhost:8002/summary_personal_memory", json={
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80
mkdocs.yml
80
mkdocs.yml
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@ -1,80 +0,0 @@
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site_name: ReMe
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site_url: https://modelscope.github.io/ReMe/
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site_description: "Memory Management Framework for Agents"
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repo_url: https://github.com/modelscope/ReMe
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repo_name: modelscope/ReMe
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theme:
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name: shadcn
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image: figure/reme_logo.png
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show_stargazers: true
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nav:
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- Welcome: index.md
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- Library:
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- Library Home: library/library.md
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- Use Library: library/use_library.md
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- Personal Memory:
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- Overview: personal_memory/personal_memory.md
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- Retrieve Ops: personal_memory/personal_retrieve_ops.md
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- Summary Ops: personal_memory/personal_summary_ops.md
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- Task Memory:
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- Overview: task_memory/task_memory.md
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- Retrieve Ops: task_memory/task_retrieve_ops.md
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- Summary Ops: task_memory/task_summary_ops.md
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- Tool Memory:
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- Overview: tool_memory/tool_memory.md
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- Retrieve Ops: tool_memory/tool_retrieve_ops.md
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- Summary Ops: tool_memory/tool_summary_ops.md
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- Benchmark: tool_memory/tool_bench.md
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- SOP Memory:
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- Making SOP Memories: sop_memory/making_sop_memories.md
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- Extensions:
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- MCP: mcp_quick_start.md
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- Vector Store: vector_store_api_guide.md
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- Experimental Tutorials:
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- Overview: cookbook/experiment_overview.md
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- AppWorld: cookbook/appworld/quickstart.md
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- BFCL: cookbook/bfcl/quickstart.md
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- FrozenLake: cookbook/frozenlake/quickstart.md
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- Future Work:
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- future_work.md
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- Contribution Guide: contribution.md
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plugins:
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- search
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- excalidraw
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markdown_extensions:
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admonition:
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codehilite:
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fenced_code:
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footnotes:
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extra:
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pymdownx.blocks.details:
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pymdownx.tabbed:
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pymdownx.blocks.tab:
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combine_header_slug: true
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separator: ___
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pymdownx.progressbar:
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pymdownx.snippets:
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pymdownx.arithmatex:
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generic: true
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shadcn.extensions.echarts.alpha:
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shadcn.extensions.codexec:
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shadcn.extensions.iconify:
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# pip install mkdocs-shadcn
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# mkdocs build
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# mkdocs serve
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# mkdocs gh-deploy --force
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