41 lines
1.6 KiB
Markdown
41 lines
1.6 KiB
Markdown
# Distributed Conversation
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This example will show
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- How to set up and run a distributed conversation.
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- How to configure and use different language models in the system.
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## Background
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This example demonstrates a distributed dialog system leveraging various language models. The system is designed to handle conversational AI tasks in a distributed manner, allowing for scalable and efficient dialog management.
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## Tested Models
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These models are tested in this example. For other models, some modifications may be needed.
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- Ollama Chat (llama3_8b)
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- Dashscope Chat (qwen-Max)
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- Gemini Chat (gemini-pro)
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## Prerequisites
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Before running the example, please install the distributed version of Agentscope, fill in your model configuration correctly in `configs/model_configs.json`, and modify the `model_config_name` field in `distributed_dialog.py` accordingly.
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## Running the Example
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Use the following command to start the assistant agent:
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```
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cd examples/distributed_basic
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python distributed_dialog.py --role assistant --assistant-host localhost --assistant-port 12010
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# Please make sure the port is available.
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# If the assistant agent and the user agent are started on different machines,
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# please fill in the ip address of the assistant agent in the host field
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```
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Then, run the user agent:
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```
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python distributed_dialog.py --role user --assistant-host localhost --assistant-port 12010
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# If the assistant agent is started on another machine,
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# please fill in the ip address of the assistant agent in the host field
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```
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Now, you can chat with the assistant agent using the command line. |