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75 lines
2.8 KiB
Markdown
75 lines
2.8 KiB
Markdown
# Python Auto Runner
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## Case Illustration
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This case is built using ReactAgentTemplate, creating a simple application that can automatically generate and execute Python code.
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This case is leverages the QianWen large language model and the google_search function, necessitating the configuration of environment variables DASHSCOPE_API_KEY and SERPER_API_KEY prior to use.
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## Quick Start
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### Configure API Key
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For instance, configure key information in the custom_key.toml file, which is where agentUniverse manages private keys (the default setup uses qwen as the base model and serper as the google search tool).
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```toml
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[KEY_LIST]
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# serper google search key
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SERPER_API_KEY='xxx'
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# openai api key
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DASHSCOPE_API_KEY='xxx'
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```
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### Agent Configuration
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```yaml
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info:
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name: 'demo_react_agent'
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description: 'react agent'
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profile:
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prompt_version: qwen_react_agent.cn
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llm_model:
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name: 'qwen_llm'
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model_name: 'qwen-max'
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stop: 'Observation'
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temperature: 0.1
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action:
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tool:
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- 'google_search_tool'
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- 'python_runner'
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knowledge:
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- 'law_knowledge'
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memory:
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name: 'demo_memory'
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metadata:
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type: 'AGENT'
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module: 'agentuniverse.agent.template.react_agent_template'
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class: 'ReActAgentTemplate'
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```
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Here we used two tools: google_search_tool and python_runner. The relevant tool code links are as follows:
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- [google_search_tool](../../../../examples/sample_apps/react_agent_app/intelligence/agentic/tool/google_search_tool.yaml)
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- [python_runner](../../../../examples/sample_apps/react_agent_app/intelligence/agentic/tool/python_runner.yaml)
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### Case Run
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1. Test Case Run
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Directly run with test code[test_case](../../../../examples/sample_apps/react_agent_app/intelligence/test/test_react_agent.py)
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2. Interface Run
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After configuring the related keys, start the web service and use the following curl for testing.
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```shell
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curl --location --request POST 'http://localhost:8888/service_run' \
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--header 'Cookie: spanner=fQ47DxJmWYzf8rKDhs69LExySZYZFUiVXt2T4qEYgj0' \
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--header 'Content-Type: application/json' \
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--data-raw '{
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"service_id": "demo_react_service",
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"params": {
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"input": "请帮我生成一段python代码，可以计算三数之和"
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}
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}'
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```
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### Result
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In the image, React underwent a total of three steps:
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Step 1: The model provided a piece of Python code and submitted it to the Python Runner tool for execution. However, the execution failed due to an inability to use print to output the execution result.
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Step 2: The model recognized the mistake and proactively revised the code. It then resubmitted the code to the Python Runner tool for execution, which was successful this time.
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Step 3: The model conveyed the successfully executed code to the user.
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### Please note
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Due to limitations of the model's capabilities, it is recommended to use the qwen-max model for testing. |