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add aworld docs #667
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Summary of ChangesHello @rainsonGain, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly expands and reorganizes the AWorld documentation to provide a more structured and comprehensive resource for users. The changes aim to clarify core concepts, offer detailed usage examples for various features, and introduce new advanced topics. By restructuring the content into logical sections and adding in-depth guides on agent customization, runtime mechanisms, and key components like Context and Memory, the documentation now offers a clearer path for understanding and leveraging the AWorld framework for building complex multi-agent systems. Highlights
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Code Review
This pull request significantly refactors and expands the documentation, moving from markdown headers to HTML headers and reorganizing the file structure. The new content is largely a positive addition, providing more detailed explanations of core concepts. However, there are several critical issues that need to be addressed, including broken intranet image links and syntax errors in code examples. Additionally, some documentation sections have been simplified to the point of being unhelpful, and there are various consistency issues like typos, mixed languages in headings, and non-standard punctuation. Addressing these issues will greatly improve the quality and usability of the new documentation.
docs/Basic Usage/HITL.md
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| AWorld implements HITL based on tools and events, [example](https://github.com/inclusionAI/AWorld/blob/main/examples/aworld_quick_start/HITL/run.py). | ||
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| <h3 id="G4ris">HITL process</h3> | ||
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docs/Runtime/Overview.md
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| ```python | ||
| from aworld.core.task import Task | ||
| from aworld core.common import StreamingMode |
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docs/Get Start/Overview.md
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| + **Experience**: Through interaction with the environment, the Agent collects trajectory data (**Trajectory**) that captures both successful and failed attempts. | ||
| + **Learn**: These trajectories are logged and used for subsequent **optimization** via reinforcement learning (RL) or in-context learning, enabling the Agent to continuously self-improve and evolve. | ||
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docs/Get Start/Core Capabilities.md
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| llm_model_name="gpt-4", | ||
| llm_api_key="your-api-key", | ||
| llm_base_url='available url' | ||
| ) |
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docs/Basic Usage/Parallel Tasks.md
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| + **Strong Extensibility</font>**: A modular, plugin-based architecture makes it easy to add support for new compute engines to meet evolving requirements.</font> | ||
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| <h3 id="UuBBo">Tasks Execution Process</h3> | ||
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docs/Get Start/Quick Start.md
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| } | ||
| ``` | ||
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| <h2 id="gKF0N">Trainning</h2> |
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docs/Get Start/Quick Start.md
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| Example with Verl | ||
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| <h4 id="g0QLt">1. Docker Image</h4> | ||
| Use a mirror of the specified training framework,example:**verl0.5-cu126-torch2.7.1-fa2.8.0,**URL:[https://github.com/volcengine/verl/tree/main/docker](https://github.com/volcengine/verl/tree/main/docker) |
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This line uses full-width punctuation (,, :), which is inconsistent with the English used in the rest of the document. Please use standard half-width punctuation (,, :) for consistency.
| Use a mirror of the specified training framework,example:**verl0.5-cu126-torch2.7.1-fa2.8.0,**URL:[https://github.com/volcengine/verl/tree/main/docker](https://github.com/volcengine/verl/tree/main/docker) | |
| Use a mirror of the specified training framework, example: **verl0.5-cu126-torch2.7.1-fa2.8.0,** URL: [https://github.com/volcengine/verl/tree/main/docker](https://github.com/volcengine/verl/tree/main/docker) |
docs/Get Start/Quick Start.md
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| <h4 id="nSTzo">4. 训练配置</h4> | ||
| Customize configuration items based on specific training frameworks. | ||
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| Note: It is necessary to define a reward function for evaluating agent behavior based on specific task objectives, which can be set in the training configuration. | ||
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| ```python | ||
| # define train config | ||
| custom_train_config = "string or json" | ||
| # define reward | ||
| reward_func = "None or string or code reference" | ||
| ``` | ||
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| <h4 id="DUYK7">5. 运行</h4> |
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| data = message.payload | ||
| if "clause1" in data: | ||
| # Handle clause1 logic | ||
| pass | ||
| elif "clause2" in data: | ||
| # Handle clause2 logic | ||
| pass | ||
| ``` |
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The example code for 'Dynamic Routing' has been oversimplified and is no longer illustrative. The previous version demonstrated how to check the payload and yield ToolMessage or AgentMessage, which was a concrete and helpful example. The new version with if "clause1" in data: pass is too abstract to be useful. Please consider restoring the more detailed example or providing a new one that is equally informative.
data = message.payload[0]
content = data.policy_info
if "use_tool1" in content:
# not tool call, but want to use tools
yield ToolMessage(
payload=...,
caller=...,
sender=...,
receiver="tool1",
session_id=...,
headers=...
)
elif "use_agent1" in data:
# want to use agent as next step
yield AgentMessage(
payload=...,
caller=...,
sender=...,
receiver="agent1",
session_id=...,
headers=...
)
else:
...
docs/Runtime/Custom Runner.md
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| """Clear processes and modules""" | ||
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| async def do_run(self, context): | ||
| # 只处理主要流程 |
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add
Get Start,Basic Usage,Agents,Environment,Context,Memorydocs