docs: update documentation structure and content

Author: jinliyl

.gitignore

@@ -28,4 +28,5 @@ cookbook/appworld/exp_result/*
 file_vector_store/*
 cookbook/appworld/file_vector_store/*
 /.venv/
-site/*
+site/*
+docs/_build/*

docs/_config.yml

@@ -19,6 +19,9 @@ execute:
 parse:
   myst_enable_extensions:
     - colon_fence
+    - deflist
+    - attrs_inline
+    - dollarmath
 
 # Define the name of the latex output file for PDF builds
 latex:

docs/_toc.yml

@@ -31,11 +31,6 @@ parts:
       - file: tool_memory/tool_summary_ops
       - file: tool_memory/tool_bench
 
-  - caption: SOP Memory
-    maxdepth: 1
-    chapters:
-      - file: sop_memory/making_sop_memories
-
   - caption: Extensions
     maxdepth: 1
     chapters:

docs/index.md

@@ -17,7 +17,7 @@ kernelspec:
 
 <div class="flex justify-center space-x-3">
   <a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
-  <a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1-blue?logo=pypi" alt="PyPI Version"></a>
+  <a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1.10.3-blue?logo=pypi" alt="PyPI Version"></a>
   <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
   <a href="https://github.com/modelscope/ReMe"><img src="https://img.shields.io/github/stars/modelscope/ReMe?style=social" alt="GitHub Stars"></a>
 </div>
@@ -41,44 +41,45 @@ Personal memory helps "**understand user preferences**", task memory helps agent
 
 ReMe integrates three complementary memory capabilities:
 
-```{admonition} Task Memory/Experience
+:::{admonition} Task Memory/Experience
+:class: note
 
-    Procedural knowledge reused across agents
+Procedural knowledge reused across agents
 
-    - **Success Pattern Recognition**: Identify effective strategies and understand their underlying principles
-    - **Failure Analysis Learning**: Learn from mistakes and avoid repeating the same issues
-    - **Comparative Patterns**: Different sampling trajectories provide more valuable memories through comparison
-    - **Validation Patterns**: Confirm the effectiveness of extracted memories through validation modules
+- **Success Pattern Recognition**: Identify effective strategies and understand their underlying principles
+- **Failure Analysis Learning**: Learn from mistakes and avoid repeating the same issues
+- **Comparative Patterns**: Different sampling trajectories provide more valuable memories through comparison
+- **Validation Patterns**: Confirm the effectiveness of extracted memories through validation modules
 
-```
+:::
 
 Learn more about how to use task memory from [task memory](task_memory/task_memory.md)
 
-```{admonition} Personal Memory
-
+:::{admonition} Personal Memory
+:class: note
 
-    Contextualized memory for specific users
+Contextualized memory for specific users
 
+- **Individual Preferences**: User habits, preferences, and interaction styles
+- **Contextual Adaptation**: Intelligent memory management based on time and context
+- **Progressive Learning**: Gradually build deep understanding through long-term interaction
+- **Time Awareness**: Time sensitivity in both retrieval and integration
 
-    - **Individual Preferences**: User habits, preferences, and interaction styles
-    - **Contextual Adaptation**: Intelligent memory management based on time and context
-    - **Progressive Learning**: Gradually build deep understanding through long-term interaction
-    - **Time Awareness**: Time sensitivity in both retrieval and integration
-   
-```
+:::
 
 Learn more about how to use personal memory from [personal memory](personal_memory/personal_memory.md)
 
-```{admonition} Tool Memory
+:::{admonition} Tool Memory
+:class: note
 
-    Data-driven tool selection and usage optimization
+Data-driven tool selection and usage optimization
 
-    - **Historical Performance Tracking**: Success rates, execution times, and token costs from real usage
-    - **LLM-as-Judge Evaluation**: Qualitative insights on why tools succeed or fail
-    - **Parameter Optimization**: Learn optimal parameter configurations from successful calls
-    - **Dynamic Guidelines**: Transform static tool descriptions into living, learned manuals
+- **Historical Performance Tracking**: Success rates, execution times, and token costs from real usage
+- **LLM-as-Judge Evaluation**: Qualitative insights on why tools succeed or fail
+- **Parameter Optimization**: Learn optimal parameter configurations from successful calls
+- **Dynamic Guidelines**: Transform static tool descriptions into living, learned manuals
 
-```
+:::
 
 Learn more about how to use tool memory from [tool memory](tool_memory/tool_memory.md)
 

docs/quick_start.md

@@ -42,6 +42,27 @@ reme \
 
 `````{tab-set}
 
+````{tab-item} python(http)
+```{code-block}
+import requests
+
+# Experience Summarizer: Learn from execution trajectories
+response = requests.post("http://localhost:8002/summary_task_memory", json={
+    "workspace_id": "task_workspace",
+    "trajectories": [
+        {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
+    ]
+})
+
+# Retriever: Get relevant memories
+response = requests.post("http://localhost:8002/retrieve_task_memory", json={
+    "workspace_id": "task_workspace",
+    "query": "How to efficiently manage project progress?",
+    "top_k": 1
+})
+```
+````
+
 ````{tab-item} python(import)
 ```{code-block}
 import asyncio
@@ -82,27 +103,6 @@ if __name__ == "__main__":
 ```
 ````
 
-````{tab-item} python(http)
-```{code-block}
-import requests
-
-# Experience Summarizer: Learn from execution trajectories
-response = requests.post("http://localhost:8002/summary_task_memory", json={
-    "workspace_id": "task_workspace",
-    "trajectories": [
-        {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
-    ]
-})
-
-# Retriever: Get relevant memories
-response = requests.post("http://localhost:8002/retrieve_task_memory", json={
-    "workspace_id": "task_workspace",
-    "query": "How to efficiently manage project progress?",
-    "top_k": 1
-})
-```
-````
-
 ````{tab-item} curl
 ```bash
 # Experience Summarizer: Learn from execution trajectories
@@ -164,11 +164,37 @@ fetch("http://localhost:8002/retrieve_task_memory", {
 
 #### Personal Memory Management
 
-<summary> import version</summary>
 `````{tab-set}
 
-````{tab-item} Python(import)
-```{code-cell}
+````{tab-item} python(http)
+```{code-block}
+import requests
+
+# Memory Integration: Learn from user interactions
+response = requests.post("http://localhost:8002/summary_personal_memory", json={
+    "workspace_id": "task_workspace",
+    "trajectories": [
+        {"messages":
+            [
+                {"role": "user", "content": "I like to drink coffee while working in the morning"},
+                {"role": "assistant",
+                 "content": "I understand, you prefer to start your workday with coffee to stay energized"}
+            ]
+        }
+    ]
+})
+
+# Memory Retrieval: Get personal memory fragments
+response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
+    "workspace_id": "task_workspace",
+    "query": "What are the user's work habits?",
+    "top_k": 5
+})
+```
+````
+
+````{tab-item} python(import)
+```{code-block}
 import asyncio
 from reme_ai import ReMeApp
 
@@ -208,32 +234,7 @@ if __name__ == "__main__":
 ```
 ````
 
-
-```{code-cell}
-# Memory Integration: Learn from user interactions
-response = requests.post("http://localhost:8002/summary_personal_memory", json={
-    "workspace_id": "task_workspace",
-    "trajectories": [
-        {"messages":
-            [
-                {"role": "user", "content": "I like to drink coffee while working in the morning"},
-                {"role": "assistant",
-                 "content": "I understand, you prefer to start your workday with coffee to stay energized"}
-            ]
-        }
-    ]
-})
-
-# Memory Retrieval: Get personal memory fragments
-response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
-    "workspace_id": "task_workspace",
-    "query": "What are the user's work habits?",
-    "top_k": 5
-})
-```
-
-````{dropdown} curl version
-
+````{tab-item} curl
 ```bash
 # Memory Integration: Learn from user interactions
 curl -X POST http://localhost:8002/summary_personal_memory \
@@ -259,8 +260,7 @@ curl -X POST http://localhost:8002/retrieve_personal_memory \
 ```
 ````
 
-````{dropdown} Node.js version
-
+````{tab-item} Node.js
 ```{code-block} javascript
 // Memory Integration: Learn from user interactions
 fetch("http://localhost:8002/summary_personal_memory", {
@@ -297,11 +297,15 @@ fetch("http://localhost:8002/retrieve_personal_memory", {
 .then(data => console.log(data));
 ```
 ````
+`````
 
 
 #### Tool Memory Management
 
-```{code-cell}
+`````{tab-set}
+
+````{tab-item} python(http)
+```{code-block}
 import requests
 
 # Record tool execution results
@@ -332,10 +336,59 @@ response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
     "tool_names": "web_search"
 })
 ```
+````
 
+````{tab-item} python(import)
+```{code-block}
+import asyncio
+from reme_ai import ReMeApp
+
+async def main():
+    async with ReMeApp(
+        "llm.default.model_name=qwen3-30b-a3b-thinking-2507",
+        "embedding_model.default.model_name=text-embedding-v4",
+        "vector_store.default.backend=memory"
+    ) as app:
+        # Record tool execution results
+        result = await app.async_execute(
+            name="add_tool_call_result",
+            workspace_id="tool_workspace",
+            tool_call_results=[
+                {
+                    "create_time": "2025-10-21 10:30:00",
+                    "tool_name": "web_search",
+                    "input": {"query": "Python asyncio tutorial", "max_results": 10},
+                    "output": "Found 10 relevant results...",
+                    "token_cost": 150,
+                    "success": True,
+                    "time_cost": 2.3
+                }
+            ]
+        )
+        print(result)
+        
+        # Generate usage guidelines from history
+        result = await app.async_execute(
+            name="summary_tool_memory",
+            workspace_id="tool_workspace",
+            tool_names="web_search"
+        )
+        print(result)
+        
+        # Retrieve tool guidelines before use
+        result = await app.async_execute(
+            name="retrieve_tool_memory",
+            workspace_id="tool_workspace",
+            tool_names="web_search"
+        )
+        print(result)
 
-````{dropdown} curl version
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+````
 
+````{tab-item} curl
 ```bash
 # Record tool execution results
 curl -X POST http://localhost:8002/add_tool_call_result \
@@ -373,8 +426,7 @@ curl -X POST http://localhost:8002/retrieve_tool_memory \
 ```
 ````
 
-````{dropdown} Node.js version
-
+````{tab-item} Node.js
 ```{code-block} javascript
 // Record tool execution results
 fetch("http://localhost:8002/add_tool_call_result", {
@@ -428,4 +480,5 @@ fetch("http://localhost:8002/retrieve_tool_memory", {
 .then(response => response.json())
 .then(data => console.log(data));
 ```
-````
+````
+`````

pyproject.toml

@@ -27,6 +27,11 @@ dependencies = [
     "flowllm[reme]>=0.1.11.3",
 ]
 
+[project.optional-dependencies]
+dev = ["jupyter-book", "ghp-import", "myst-nb", "sphinxcontrib-bibtex", "furo", "sphinxcontrib-mermaid"]
+
+all = ["reme_ai[dev]"]
+
 [tool.setuptools.packages.find]
 where = ["."]
 include = ["reme_ai*"]