diff --git a/README.md b/README.md index 257767b6..4586e9f0 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,7 @@ English | [**中文**](./README_ZH.md) # MemoryScope -

- MemoryScope Logo -

+![MemoryScope Logo](./docs/images/logo_1.png) Equip your LLM chatbot with a powerful and flexible long term memory system. @@ -33,9 +31,8 @@ to form and update *insights*. Then, memory re-consolidation is performed to ens among memory pieces are properly handled. ### Framework -

- MemoryScope Framework -

+ +![MemoryScope Logo](./docs/images/framework.png) ### Main Features diff --git a/README_ZH.md b/README_ZH.md index c0f6b923..516c6bb6 100644 --- a/README_ZH.md +++ b/README_ZH.md @@ -2,6 +2,7 @@ # MemoryScope +![MemoryScope Logo](./docs/images/logo_1.png)

MemoryScope Logo

@@ -28,12 +29,25 @@ ## 什么是MemoryScope？ +💾 记忆数据库: +- MemoryScope 配备了一个 *ElasticSearch (ES)* 向量数据库，用于存储系统中记录的所有记忆片段。 + +🛠️ 系统操作: +- 记忆检索：当用户输入对话，此操作返回语义相关的记忆片段。如果输入对话包含对时间的指涉，则同时返回相应时间中的记忆片段。 +- 记忆巩固：此操作接收一批用户的输入对话，并从对话中提取重要的用户信息，将其作为 *observation* 形式的记忆片段存储在记忆数据库中。 +- 反思与再巩固：每隔一段时间，此操作对新记录的 *observations* 进行反思，以形成和更新 *insight* 形式的记忆片段。然后执行记忆再巩固， +以确保记忆片段之间的矛盾和重复得到妥善处理。 + +### 框架 MemoryScope可以为LLM聊天机器人提供强大且灵活的长期记忆能力，并提供了构建长期记忆能力的框架。 MemoryScope可以用于个人助理、情感陪伴等记忆场景，通过长期记忆能力来不断学习，记得用户的基础信息以及各种习惯和喜好，使得用户在使用LLM时逐渐感受到一种“默契”。

MemoryScope Framework

+![MemoryScope Logo](./docs/images/framework.png) + +### 主要特点 ### 长期记忆的框架： 💾 记忆数据库: MemoryScope配备了向量数据库(默认是*ElasticSearch*)，用于存储系统中记录的所有记忆片段。 diff --git a/docs/installation.md b/docs/installation.md index c6655b89..3b16a594 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -43,28 +43,13 @@ 3. Run `docker-compose up` to build and launch the memory-scope cli interface. -## III. Install from PYPI [Linux only] +## III. Install from PyPI [Linux] -1. Install from pypi: - - ```bash - pip install memoryscope - ``` - - -2. test Chinese / Dashscope chat configuration: - - ```bash - export DASHSCOPE_API_KEY="sk-0000000000" - memoryscope --config_path=memoryscope/core/config/demo_config_zh.yaml - ``` - -3. test English / OpenAI chat configuration: - - ```bash - export OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" - python quick-start-demo.py --config_path=memoryscope/core/config/demo_config.yaml - ``` +```bash +pip install memoryscope +export DASHSCOPE_API_KEY="sk-0000000000" +memoryscope --config_path=memoryscope/core/config/demo_config_zh.yaml +``` ## IV. Install from source [Linux only] @@ -78,7 +63,7 @@ vim memoryscope/core/config/demo_config_zh.yaml ``` -2. Install +2. Install ```bash pip install -e . ``` diff --git a/docs/sphinx_doc/build_sphinx_doc.sh b/docs/sphinx_doc/build_sphinx_doc.sh index ece876e9..cce54cc2 100755 --- a/docs/sphinx_doc/build_sphinx_doc.sh +++ b/docs/sphinx_doc/build_sphinx_doc.sh @@ -1,7 +1,41 @@ #!/bin/bash +cd docs/sphinx_doc + + +# remove build rm -rf build/html/* rm en/source/memoryscope*.rst rm zh_CN/source/memoryscope*.rst + + +# copy related files +cd ../../ + +cp README.md docs/sphinx_doc/en/source/README.md +cp docs/installation.md docs/sphinx_doc/en/source/docs/installation.md +cp -r docs/images docs/sphinx_doc/en/source/docs/images +cp -r examples docs/sphinx_doc/en/source/examples + +cp README_ZH.md docs/sphinx_doc/zh_CN/source/README.md +cp docs/installation_ZH.md docs/sphinx_doc/zh_CN/source/docs/installation.md +cp -r docs/images docs/sphinx_doc/zh_CN/source/docs/images +cp -r examples docs/sphinx_doc/zh_CN/source/examples + + +# build +cd docs/sphinx_doc sphinx-apidoc -f -o en/source ../../memoryscope -t template -e sphinx-apidoc -f -o zh_CN/source ../../memoryscope -t template -e -make clean all \ No newline at end of file + +# clear redundant files +make clean all + +rm en/source/README.md +rm en/source/docs/installation.md +rm -rf en/source/docs/images +rm -rf en/source/examples + +rm zh_CN/source/README.md +rm zh_CN/source/docs/installation.md +rm -rf zh_CN/source/docs/images +rm -rf zh_CN/source/examples \ No newline at end of file diff --git a/docs/sphinx_doc/en/source/conf.py b/docs/sphinx_doc/en/source/conf.py index 6592ef61..ca707ba1 100644 --- a/docs/sphinx_doc/en/source/conf.py +++ b/docs/sphinx_doc/en/source/conf.py @@ -39,11 +39,12 @@ extensions = [ "sphinxcontrib.mermaid", "myst_parser", "sphinx.ext.autosectionlabel", - 'sphinxcontrib.autodoc_pydantic' + "sphinxcontrib.autodoc_pydantic", + "nbsphinx" ] autodoc_pydantic_model_show_json = True -autodoc_pydantic_settings_show_json = True +autodoc_pydantic_settings_show_json = True # Prefix document path to section labels, otherwise autogenerated labels would # look like 'heading' rather than 'path/to/file:heading' @@ -82,13 +83,16 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # html_theme = "sphinx_rtd_theme" +# html_logo = "_static/logo.png" + # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] html_theme_options = { - "navigation_depth": 10, + # "logo_only": True, + "navigation_depth": 4, } source_suffix = { diff --git a/docs/sphinx_doc/en/source/api.rst b/docs/sphinx_doc/en/source/docs/api.rst similarity index 95% rename from docs/sphinx_doc/en/source/api.rst rename to docs/sphinx_doc/en/source/docs/api.rst index da34f758..e7ddd17f 100644 --- a/docs/sphinx_doc/en/source/api.rst +++ b/docs/sphinx_doc/en/source/docs/api.rst @@ -1,9 +1,5 @@ .. _api: -============= -API Reference -============= - MemoryScope API Documentation diff --git a/docs/sphinx_doc/en/source/index.rst b/docs/sphinx_doc/en/source/index.rst index 783f976b..6bd1076e 100644 --- a/docs/sphinx_doc/en/source/index.rst +++ b/docs/sphinx_doc/en/source/index.rst @@ -6,25 +6,48 @@ :github_url: https://github.com/modelscope/memoryscope MemoryScope Documentation -====================================== +========================= +Welcome to MemoryScope Tutorial +------------------------------- -.. include:: tutorial/main.md - :parser: myst_parser.sphinx_ +.. image:: docs/images/logo_1.png + :align: center + +MemoryScope is a powerful and flexible long term memory system for LLM chatbots. It consists +of a memory database and three customizable system operations, which can be flexibly combined to provide +robust long term memory services for your LLM chatbot. + +💾 Memory Database: +^^^^^^^^^^^^^^^^^^^^ + +- MemoryScope comes with an *ElasticSearch (ES)* vector database to store all the +memory pieces recorded in the system. + +🛠️ System operations: +^^^^^^^^^^^^^^^^^^^^^ + +- Memory Retrieval: Upon arrival of a user query, this operation returns the semantically related memory pieces +and/or those from the corresponding time if the query involves reference to time. + +- Memory Consolidation: This operation takes in a batch of user queries and returns important user information +extracted from the queries as consolidated *observations* to be stored in the memory database. + +- Reflection and Re-consolidation: At regular intervals, this operation performs reflection upon newly recorded *observations* +to form and update *insights*. Then, memory re-consolidation is performed to ensure contradictions and repetitions +among memory pieces are properly handled. .. toctree:: - :maxdepth: 1 - :glob: - :hidden: + :maxdepth: 2 :caption: MemoryScope Tutorial - tutorial/101-memoryscope.md - tutorial/102-installation.md - tutorial/103-example.md + About MemoryScope + 🚀 Installation + Cli Client + Simple Usages .. toctree:: :maxdepth: 6 :caption: MemoryScope API Reference - memoryscope - \ No newline at end of file + API diff --git a/docs/sphinx_doc/en/source/tutorial/101-memoryscope.md b/docs/sphinx_doc/en/source/tutorial/101-memoryscope.md deleted file mode 100644 index 2e8d6de4..00000000 --- a/docs/sphinx_doc/en/source/tutorial/101-memoryscope.md +++ /dev/null @@ -1,116 +0,0 @@ -(101-memoryscope-en)= - -# About AgentScope - -In this tutorial, we will provide an overview of AgentScope by answering -several questions, including what's AgentScope, what can AgentScope provide, -and why we should choose AgentScope. Let's get started! - -## What is AgentScope? - -AgentScope is a developer-centric multi-agent platform, which enables -developers to build their LLM-empowered multi-agent applications with less -effort. - -With the advance of large language models, developers are able to build -diverse applications. -In order to connect LLMs to data and services and solve complex tasks, -AgentScope provides a series of development tools and components for ease of -development. -It features - -- **usability**, -- **robustness**, -- **the support of multi-modal data**, -- **distributed deployment**. - -## Key Concepts - -### Message - -Message is a carrier of information (e.g. instructions, multi-modal -data, and dialogue). In AgentScope, message is a Python dict subclass -with `name` and `content` as necessary fields, and `url` as an optional -field referring to additional resources. - -### Agent - -Agent is an autonomous entity capable of interacting with environment and -agents, and taking actions to change the environment. In AgentScope, an -agent takes message as input and generates corresponding response message. - -### Service - -Service refers to the functional APIs that enable agents to perform -specific tasks. In AgentScope, services are categorized into model API -services, which are channels to use the LLMs, and general API services, -which provide a variety of tool functions. - -### Workflow - -Workflow represents ordered sequences of agent executions and message -exchanges between agents, analogous to computational graphs in TensorFlow, -but with the flexibility to accommodate non-DAG structures. - -## Why AgentScope? - -**Exceptional usability for developers.** -AgentScope provides high usability for developers with flexible syntactic -sugars, ready-to-use components, and pre-built examples. - -**Robust fault tolerance for diverse models and APIs.** -AgentScope ensures robust fault tolerance for diverse models, APIs, and -allows developers to build customized fault-tolerant strategies. - -**Extensive compatibility for multi-modal application.** -AgentScope supports multi-modal data (e.g., files, images, audio and videos) -in both dialog presentation, message transmission and data storage. - -**Optimized efficiency for distributed multi-agent operations.** AgentScope -introduces an actor-based distributed mechanism that enables centralized -programming of complex distributed workflows, and automatic parallel -optimization. - -## How is AgentScope designed? - -The architecture of AgentScope comprises three hierarchical layers. The -layers provide supports for multi-agent applications from different levels, -including elementary and advanced functionalities of a single agent -(**utility layer**), resources and runtime management (**manager and wrapper -layer**), and agent-level to workflow-level programming interfaces (**agent -layer**). AgentScope introduces intuitive abstractions designed to fulfill -the diverse functionalities inherent to each layer and simplify the -complicated interlayer dependencies when building multi-agent systems. -Furthermore, we offer programming interfaces and default mechanisms to -strengthen the resilience of multi-agent systems against faults within -different layers. - -## AgentScope Code Structure - -```bash -AgentScope -├── src -│ ├── agentscope -│ | ├── agents # Core components and implementations pertaining to agents. -│ | ├── memory # Structures for agent memory. -│ | ├── models # Interfaces for integrating diverse model APIs. -│ | ├── pipelines # Fundamental components and implementations for running pipelines. -│ | ├── rpc # Rpc module for agent distributed deployment. -│ | ├── service # Services offering functions independent of memory and state. -| | ├── web # WebUI used to show dialogs. -│ | ├── utils # Auxiliary utilities and helper functions. -│ | ├── message.py # Definitions and implementations of messaging between agents. -│ | ├── prompt.py # Prompt engineering module for model input. -│ | ├── ... .. -│ | ├── ... .. -├── scripts # Scripts for launching local Model API -├── examples # Pre-built examples of different applications. -├── docs # Documentation tool for API reference. -├── tests # Unittest modules for continuous integration. -├── LICENSE # The official licensing agreement for AgentScope usage. -└── setup.py # Setup script for installing. -├── ... .. -└── ... .. -``` - -[[Return to the top]](#101-memoryscope-en) diff --git a/docs/sphinx_doc/en/source/tutorial/102-installation.md b/docs/sphinx_doc/en/source/tutorial/102-installation.md deleted file mode 100644 index e1f16d30..00000000 --- a/docs/sphinx_doc/en/source/tutorial/102-installation.md +++ /dev/null @@ -1,68 +0,0 @@ -(102-installation-en)= - -# Installation - -To install AgentScope, you need to have Python 3.9 or higher installed. We recommend setting up a new virtual environment specifically for AgentScope: - -## Create a Virtual Environment - -### Using Conda - -If you're using Conda as your package and environment management tool, you can create a new virtual environment with Python 3.9 using the following commands: - -```bash -# Create a new virtual environment named 'agentscope' with Python 3.9 -conda create -n agentscope python=3.9 - -# Activate the virtual environment -conda activate agentscope -``` - -### Using Virtualenv - -Alternatively, if you prefer `virtualenv`, you can install it first (if it's not already installed) and then create a new virtual environment as shown: - -```bash -# Install virtualenv if it is not already installed -pip install virtualenv - -# Create a new virtual environment named 'agentscope' with Python 3.9 -virtualenv agentscope --python=python3.9 - -# Activate the virtual environment -source agentscope/bin/activate # On Windows use `agentscope\Scripts\activate` -``` - -## Installing AgentScope - -### Install with Pip - -If you prefer to install AgentScope from Pypi, you can do so easily using `pip`: - -```bash -# For centralized multi-agent applications -pip install agentscope --pre -# For distributed multi-agent applications -pip install agentscope[distribute] --pre # On Mac use `pip install agentscope\[distribute\] --pre` -``` - -### Install from Source - -For users who prefer to install AgentScope directly from the source code, follow these steps to clone the repository and install the platform in editable mode: - -**_Note: This project is under active development, it's recommended to install AgentScope from source._** - -```bash -# Pull the source code from Github -git clone https://github.com/modelscope/agentscope.git -cd agentscope - -# For centralized multi-agent applications -pip install -e . -# For distributed multi-agent applications -pip install -e .[distribute] # On Mac use `pip install -e .\[distribute\]` -``` - -**Note**: The `[distribute]` option installs additional dependencies required for distributed applications. Remember to activate your virtual environment before running these commands. - -[[Return to the top]](#102-installation-en) diff --git a/docs/sphinx_doc/en/source/tutorial/103-example.md b/docs/sphinx_doc/en/source/tutorial/103-example.md deleted file mode 100644 index 563d072d..00000000 --- a/docs/sphinx_doc/en/source/tutorial/103-example.md +++ /dev/null @@ -1,108 +0,0 @@ -(103-start-en)= - -# Quick Start - -AgentScope is designed with a flexible communication mechanism. -In this tutorial, we will introduce the basic usage of AgentScope via a -simple standalone conversation between two agents (e.g. user and assistant -agents). - -## Step1: Prepare Model - -AgentScope decouples the deployment and invocation of models to better build multi-agent applications. - -In terms of model deployment, users can use third-party model services such -as OpenAI API, Google Gemini API, HuggingFace/ModelScope Inference API, or -quickly deploy local open-source model services through the [scripts](https://github.com/modelscope/agentscope/blob/main/scripts/README.md) in -the repository. - -While for model invocation, users should prepare a model configuration to specify the model service. Taking OpenAI Chat API as an example, the model configuration is like this: - -```python -model_config = { - "config_name": "{config_name}", # A unique name for the model config. - "model_type": "openai_chat", # Choose from "openai_chat", "openai_dall_e", or "openai_embedding". - - "model_name": "{model_name}", # The model identifier used in the OpenAI API, such as "gpt-3.5-turbo", "gpt-4", or "text-embedding-ada-002". - "api_key": "xxx", # Your OpenAI API key. If unset, the environment variable OPENAI_API_KEY is used. - "organization": "xxx", # Your OpenAI organization ID. If unset, the environment variable OPENAI_ORGANIZATION is used. -} -``` - -More details about model invocation, deployment and open-source models please refer to [Model](203-model-en) section. - -After preparing the model configuration, you can register your configuration by calling the `init` method of AgentScope. Additionally, you can load multiple model configurations at once. - -```python -import agentscope - -# init once by passing a list of config dict -openai_cfg_dict = { - # ... -} -modelscope_cfg_dict = { - # ... -} -agentscope.init(model_configs=[openai_cfg_dict, modelscope_cfg_dict]) -``` - -## Step2: Create Agents - -Creating agents is straightforward in AgentScope. After initializing AgentScope with your model configurations (Step 1 above), you can then define each agent with its corresponding role and specific model. - -```python -import agentscope -from agentscope.agents import DialogAgent, UserAgent - -# read model configs -agentscope.init(model_configs="./openai_model_configs.json") - -# Create a dialog agent and a user agent -dialogAgent = DialogAgent(name="assistant", model_config_name="gpt-4", sys_prompt="You are a helpful ai assistant") -userAgent = UserAgent() -``` - -**NOTE**: Please refer to [Customizing Your Own Agent](201-agent-en) for all available agents. - -## Step3: Agent Conversation - -"Message" is the primary means of communication between agents in AgentScope. They are Python dictionaries comprising essential fields like the actual `content` of this message and the sender's `name`. Optionally, a message can include a `url` to either a local file (image, video or audio) or website. - -```python -from agentscope.message import Msg - -# Example of a simple text message from Alice -message_from_alice = Msg("Alice", "Hi!") - -# Example of a message from Bob with an attached image -message_from_bob = Msg("Bob", "What about this picture I took?", url="/path/to/picture.jpg") -``` - -To start a conversation between two agents, such as `dialog_agent` and `user_agent`, you can use the following loop. The conversation continues until the user inputs `"exit"` which terminates the interaction. - -```python -x = None -while True: - x = dialogAgent(x) - x = userAgent(x) - - # Terminate the conversation if the user types "exit" - if x.content == "exit": - print("Exiting the conversation.") - break -``` - -For a more advanced approach, AgentScope offers the option of using pipelines to manage the flow of messages between agents. The `sequentialpipeline` stands for sequential speech, where each agent receive message from last agent and generate its response accordingly. - -```python -from agentscope.pipelines.functional import sequentialpipeline - -# Execute the conversation loop within a pipeline structure -x = None -while x is None or x.content != "exit": - x = sequentialpipeline([dialog_agent, user_agent]) -``` - -For more details about how to utilize pipelines for complex agent interactions, please refer to [Pipeline and MsgHub](202-pipeline-en). - -[[Return to the top]](#103-start-en) diff --git a/docs/sphinx_doc/en/source/tutorial/main.md b/docs/sphinx_doc/en/source/tutorial/main.md deleted file mode 100644 index 403fae43..00000000 --- a/docs/sphinx_doc/en/source/tutorial/main.md +++ /dev/null @@ -1,15 +0,0 @@ -# Welcome to MemoryScope Tutorial - -MemoryScope is an innovative multi-agent platform designed to empower developers to build multi-agent applications with ease, reliability, and high performance. It features three high-level capabilities: - -- **Easy-to-Use**: Programming in pure Python with various prebuilt components for immediate use, suitable for developers or users with different levels of customization requirements. - -- **High Robustness**: Supporting customized fault-tolerance controls and retry mechanisms to enhance application stability. - -- **Actor-Based Distribution**: Enabling developers to build distributed multi-agent applications in a centralized programming manner for streamlined development. - -## Tutorial Navigator - -- [About MemoryScope](101-memoryscope.md) -- [Installation](102-installation.md) -- [Quick Start](103-example.md) diff --git a/docs/sphinx_doc/requirements.txt b/docs/sphinx_doc/requirements.txt index e7733563..d0763f76 100644 --- a/docs/sphinx_doc/requirements.txt +++ b/docs/sphinx_doc/requirements.txt @@ -9,4 +9,5 @@ sphinx-autobuild sphinx_rtd_theme sphinxcontrib-mermaid myst-parser -autodoc_pydantic \ No newline at end of file +autodoc_pydantic +nbsphinx \ No newline at end of file diff --git a/docs/sphinx_doc/zh_CN/source/conf.py b/docs/sphinx_doc/zh_CN/source/conf.py index b509f2f9..2b8f00ad 100644 --- a/docs/sphinx_doc/zh_CN/source/conf.py +++ b/docs/sphinx_doc/zh_CN/source/conf.py @@ -39,11 +39,12 @@ extensions = [ "sphinxcontrib.mermaid", "myst_parser", "sphinx.ext.autosectionlabel", - 'sphinxcontrib.autodoc_pydantic' + "sphinxcontrib.autodoc_pydantic", + "nbsphinx" ] autodoc_pydantic_model_show_json = True -autodoc_pydantic_settings_show_json = True +autodoc_pydantic_settings_show_json = True # Prefix document path to section labels, otherwise autogenerated labels would # look like 'heading' rather than 'path/to/file:heading' @@ -82,12 +83,15 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # html_theme = "sphinx_rtd_theme" +# html_logo = "_static/logo.png" + # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] html_theme_options = { + # "logo_only": True, "navigation_depth": 4, } diff --git a/docs/sphinx_doc/zh_CN/source/api.rst b/docs/sphinx_doc/zh_CN/source/docs/api.rst similarity index 100% rename from docs/sphinx_doc/zh_CN/source/api.rst rename to docs/sphinx_doc/zh_CN/source/docs/api.rst diff --git a/docs/sphinx_doc/zh_CN/source/index.rst b/docs/sphinx_doc/zh_CN/source/index.rst index f830497f..5301c8d5 100644 --- a/docs/sphinx_doc/zh_CN/source/index.rst +++ b/docs/sphinx_doc/zh_CN/source/index.rst @@ -6,24 +6,44 @@ :github_url: https://github.com/modelscope/memoryscope MemoryScope 文档 -====================================== +========================= +欢迎浏览MemoryScope相关文档 +------------------------------- -.. include:: tutorial/main.md - :parser: myst_parser.sphinx_ +.. image:: ./docs/images/logo_1.png + :align: center + +MemoryScope 是一个为LLM聊天机器人服务的强大且灵活的长期记忆系统。它由一个记忆数据库和三个可定制的系统操作组成，这些操作可以灵活组合， +为您的LLM聊天机器人提供强大的长期记忆服务。 + +💾 记忆数据库: +^^^^^^^^^^^^^ + +- MemoryScope 配备了一个 *ElasticSearch (ES)* 向量数据库，用于存储系统中记录的所有记忆片段。 + +🛠️ 系统操作: +^^^^^^^^^^^^ + +- 记忆检索：当用户输入对话，此操作返回语义相关的记忆片段。如果输入对话包含对时间的指涉，则同时返回相应时间中的记忆片段。 +- +- 记忆巩固：此操作接收一批用户的输入对话，并从对话中提取重要的用户信息，将其作为 *observation* 形式的记忆片段存储在记忆数据库中。 +- +- 反思与再巩固：每隔一段时间，此操作对新记录的 *observations* 进行反思，以形成和更新 *insight* 形式的记忆片段。然后执行记忆再巩固， +以确保记忆片段之间的矛盾和重复得到妥善处理。 .. toctree:: - :maxdepth: 1 - :glob: - :hidden: - :caption: MemoryScope Tutorial + :maxdepth: 2 + :caption: MemoryScope 教程 - tutorial/101-memoryscope.md - tutorial/102-installation.md - tutorial/103-example.md + 关于MemoryScope + 🚀 安装 + 命令行 + 简例 .. toctree:: - :maxdepth: 4 - :caption: MemoryScope API 文档 + :maxdepth: 6 + :caption: MemoryScope 接口 + + API - api \ No newline at end of file diff --git a/docs/sphinx_doc/zh_CN/source/tutorial/101-memoryscope.md b/docs/sphinx_doc/zh_CN/source/tutorial/101-memoryscope.md deleted file mode 100644 index dc35f9ec..00000000 --- a/docs/sphinx_doc/zh_CN/source/tutorial/101-memoryscope.md +++ /dev/null @@ -1,7 +0,0 @@ -(101-memoryscope-cn)= - -# 关于memoryscope - -TODO: 添加中文文档 - -[[Return to the top]](#101-memoryscope-cn) diff --git a/docs/sphinx_doc/zh_CN/source/tutorial/102-installation.md b/docs/sphinx_doc/zh_CN/source/tutorial/102-installation.md deleted file mode 100644 index 86650c92..00000000 --- a/docs/sphinx_doc/zh_CN/source/tutorial/102-installation.md +++ /dev/null @@ -1,7 +0,0 @@ -(102-installation-cn)= - -# 安装memoryscope - -TODO: 添加中文文档 - -[[Return to the top]](#102-installation-cn) diff --git a/docs/sphinx_doc/zh_CN/source/tutorial/103-example.md b/docs/sphinx_doc/zh_CN/source/tutorial/103-example.md deleted file mode 100644 index 407422df..00000000 --- a/docs/sphinx_doc/zh_CN/source/tutorial/103-example.md +++ /dev/null @@ -1,6 +0,0 @@ -(103-start-cn)= - -# 如何使用 -TODO: 添加中文 - -[[Return to the top]](#103-start-cn) diff --git a/docs/sphinx_doc/zh_CN/source/tutorial/main.md b/docs/sphinx_doc/zh_CN/source/tutorial/main.md deleted file mode 100644 index 1f46c420..00000000 --- a/docs/sphinx_doc/zh_CN/source/tutorial/main.md +++ /dev/null @@ -1,15 +0,0 @@ -# 欢迎来到 MemoryScope 教程 - -AgentScope是一款全新的Multi-Agent框架，专为应用开发者打造，旨在提供高易用、高可靠的编程体验！ - -- **高易用**：AgentScope支持纯Python编程，提供多种语法工具实现灵活的应用流程编排，内置丰富的API服务（Service）以及应用样例，供开发者直接使用。 - -- **高鲁棒**：确保开发便捷性和编程效率的同时，针对不同能力的大模型，AgentScope提供了全面的重试机制、定制化的容错控制和面向Agent的异常处理，以确保应用的稳定、高效运行； - -- **基于Actor的分布式机制**：AgentScope设计了一种新的基于Actor的分布式机制，实现了复杂分布式工作流的集中式编程和自动并行优化，即用户可以使用中心化编程的方式完成分布式应用的流程编排，同时能够零代价将本地应用迁移到分布式的运行环境中。 - -## 教程大纲 - -- [关于AgentScope](101-memoryscope.md) -- [安装](102-installation.md) -- [快速开始](103-example.md) \ No newline at end of file diff --git a/examples/api/simple_usages_cn.ipynb b/examples/api/simple_usages_cn.ipynb index 61b092d8..c590bcc4 100644 --- a/examples/api/simple_usages_cn.ipynb +++ b/examples/api/simple_usages_cn.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# chat 和 service 接口的示例用法\n", "这个笔记本展示了 MemoryScope 的 **chat** 和 **service** 接口的简单用法，以及它的主要功能。\n", @@ -14,9 +12,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## 初始化一个 MemoryScope 实例\n", "首先，我们需要指定一个配置并初始化一个 MemoryScope 实例。\n", @@ -26,14 +22,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:42:19.303078Z", - "start_time": "2024-08-02T14:42:17.332785Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import sys\n", @@ -59,9 +49,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## 聊天（不含记忆）\n", "MemoryScope 配有默认的 chat 接口，因此开始聊天非常容易，就像使用任何大型语言模型聊天机器人一样。" @@ -69,14 +57,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:42:24.843842Z", - "start_time": "2024-08-02T14:42:19.304038Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -96,9 +78,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "你可以选择进行含有或不含有多轮对话上下文的聊天。然而，由于尚未调用**记忆巩固**功能，系统中还没有任何记忆片段。" @@ -106,14 +86,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:42:33.777924Z", - "start_time": "2024-08-02T14:42:24.845133Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -136,9 +110,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## **记忆巩固**\n", "现在，我们再聊多几句，然后尝试**记忆巩固**功能。" @@ -146,14 +118,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:42:52.517936Z", - "start_time": "2024-08-02T14:42:33.780200Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -183,14 +149,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:43:14.475175Z", - "start_time": "2024-08-02T14:42:52.518868Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -212,9 +172,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "**记忆巩固**从用户的7条聊天消息中提取了3条 *observations* ，其余无效的信息被过滤掉了。\n", @@ -224,14 +182,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:43:57.226685Z", - "start_time": "2024-08-02T14:43:14.475977Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -283,14 +235,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:44:26.290389Z", - "start_time": "2024-08-02T14:43:57.227597Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -309,9 +255,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "我们可以看到，**记忆巩固**成功过滤掉了虚假内容，并展示了良好的时间敏感性。\n", @@ -321,14 +265,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:44:57.748723Z", - "start_time": "2024-08-02T14:44:26.292543Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -370,14 +308,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:45:38.454853Z", - "start_time": "2024-08-02T14:44:57.750558Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -401,9 +333,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## **反思与再巩固**\n", "现在，我们在系统中已经积累了足够多的新的 *observations* ，因此我们可以调用**反思与再巩固**功能，让我们看看会得到什么。" @@ -411,14 +341,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:45:52.599528Z", - "start_time": "2024-08-02T14:45:38.455556Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -447,9 +371,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## 低用户时延（RT）\n", "\n", @@ -459,9 +381,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import time\n", @@ -483,14 +403,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:46:08.814486Z", - "start_time": "2024-08-02T14:45:52.601688Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -540,9 +454,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "我们可以看到，从 MemoryScope 检索记忆片段不会增加聊天的响应时间。" @@ -550,24 +462,13 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## 更多用法\n", "我们建议读者参考[进阶自定义用法](./examples/api/advanced_customization_cn.ipynb)来对MemoryScope系统进行各种自定义设置。您还可以通过自定义**workflow**和对应的**worker**来创建或定制满足您特定需求的**operation**。\n", "\n", - "此外，您还可以尝试使用[在命令行与MemoryScope聊天机器人交互](./examples/cli/dash_cli_cn1.sh)。我们在这里实现了始终在后台异步运行**记忆巩固**和**反思与再巩固**这两个操作，从而使得它们不会增加聊天的响应时间。" + "此外，您还可以尝试使用[在命令行与MemoryScope聊天机器人交互](../cli/README.md)。我们在这里实现了始终在后台异步运行**记忆巩固**和**反思与再巩固**这两个操作，从而使得它们不会增加聊天的响应时间。" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -585,8 +486,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython2" } }, "nbformat": 4, diff --git a/examples/api/simple_usages_en.ipynb b/examples/api/simple_usages_en.ipynb index 29cd896e..0f7b0292 100644 --- a/examples/api/simple_usages_en.ipynb +++ b/examples/api/simple_usages_en.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# Example usages of **chat** and **service** interfaces\n", "This notebook shows simple usages of MemoryScope's **chat** and **service** interfaces, along with its main features.\n", @@ -14,9 +12,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Initiate a MemoryScope instance\n", "First, we need to specify a configuration and initiate a MemoryScope instance.\n", @@ -25,14 +21,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:34:30.387354Z", - "start_time": "2024-08-02T14:34:28.512658Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import sys\n", @@ -58,9 +48,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Chat without memory\n", "MemoryScope comes with a default **chat** interface, so it's very easy to start chatting, just as what you'll do with any LLM chatbot." @@ -68,14 +56,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:34:35.313590Z", - "start_time": "2024-08-02T14:34:30.388733Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -95,9 +77,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "You can choose to chat with or without multi-round conversation contexts. However, since **Memory Consolidation** has not been called, there's no memory pieces in the system yet." @@ -105,14 +85,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:34:42.621918Z", - "start_time": "2024-08-02T14:34:35.315441Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -135,9 +109,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Memory Consolidation\n", "Now, we do a bit more chatting and then try out **Memory Consolidation**." @@ -145,14 +117,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:35:07.399702Z", - "start_time": "2024-08-02T14:34:42.622804Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -182,14 +148,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:35:42.560772Z", - "start_time": "2024-08-02T14:35:07.401224Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -211,9 +171,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "**Memory Consolidation** extracted 3 *observations* out of the 7 chat messages from the user, with the uninformative ones being filtered out.\n", @@ -223,14 +181,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:36:30.548435Z", - "start_time": "2024-08-02T14:35:42.559854Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -294,14 +246,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:37:04.220392Z", - "start_time": "2024-08-02T14:36:30.549719Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -322,9 +268,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "We can see **Memory Consolidation** successfully filtered out fictitious contents, and shows good time sensitivity.\n", @@ -334,14 +278,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:37:25.239829Z", - "start_time": "2024-08-02T14:37:04.221032Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -383,14 +321,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:38:05.369583Z", - "start_time": "2024-08-02T14:37:25.241674Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -413,9 +345,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Reflection and Re-Consolidation\n", "Now, we have accumulated enough new *observations* in the system, so we can call **Reflection and Re-Consolidation**, let's see what will it get." @@ -423,14 +353,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:38:27.126860Z", - "start_time": "2024-08-02T14:38:05.370326Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -463,9 +387,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## Low response-time (RT) for the user\n", "Finally, we test the RT of MemoryScope system for the user. Specifically, we test the difference of RT when responding with and without retrieving memory pieces from the system." @@ -474,9 +396,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import time\n", @@ -498,14 +418,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-02T14:38:46.158169Z", - "start_time": "2024-08-02T14:38:27.128634Z" - }, - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -557,9 +471,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "----\n", "We can see responding with retrieving memory pieces from MemoryScope does not increase RT." @@ -567,25 +479,14 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "## More Examples\n", "We direct the reader to [Advanced Customization](./examples/api/advanced_customization_en.ipynb) for guidance on customizing the various settings of the MemoryScope system. It is also possible to create or customize your own MemoryScope **operations** by specifying a **workflow** and the corresponding **workers** that best meet your specific needs.\n", "\n", - "Additionally, you can also try out the [CLI with a MemoryScope Chatbot](./examples/cli/dash_cli_cn1.sh). We have implemented the chatbot so that the **Memory Consolidation** and **Reflection and Re-Consolidation** operations are always run asynchronously in the backend, ensuring that they do not incur any response time for the user.\n", + "Additionally, you can also try out the [CLI with a MemoryScope Chatbot](../cli/README.md). We have implemented the chatbot so that the **Memory Consolidation** and **Reflection and Re-Consolidation** operations are always run asynchronously in the backend, ensuring that they do not incur any response time for the user.\n", "\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -603,8 +504,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython2" } }, "nbformat": 4,