Data Interpreter Multi-Agent Pipeline #469
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update update update updated Update di_multiagent.py update update Create README.md Update README.md undo unnecessary changes undo unnecessary changes Update .pre-commit-config.yaml Update .pre-commit-config.yaml Update common.py remove changes in other files Update README.md update updated README and directory name remove Update di_multiagent.py
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Slight modification to the format_instruction to improve the success rate of `RegexTaggedContentParser` parsing the model API output.
src/agentscope/agents/react_agent.py
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| @@ -109,7 +109,7 @@ def __init__( | |||
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| # Initialize a parser object to formulate the response from the model | |||
| self.parser = RegexTaggedContentParser( | |||
| format_instruction="""Respond with specific tags as outlined below: | |||
| format_instruction="""Respond with specific tags as outlined below in json format: | |||
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Why do we need to add the requirement "in json format" here?
For string type, the json format is
<thought>'this is a test'</thought>
while the non-json format is
<thought>this is a test</thought>
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Based on my testing, without adding 'in json format', RegexTaggedContentParser cannot successfully parse boolean variables or None (will treat them as strings) and hence cannot be recognized correctly by tools like 'execute_python_code'. E.g., if the RegexTaggedContentParser returns a string None, if use_docker is None will be false.
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Update: I have modified my PR to override RegexTaggedContentParser's format_instruction within my example. I would caution against using RegexTaggedContentParser when try_parse_json=True without explicit instruction of adhering the arguments of interest to JSON format in format_instruction, since LLMs will likely output contents in other formats and cause json.loads to fail.
…reinforce the output adhere to json format within this example
…o reinforce the output adhere to json format within this example.
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| This step enables the executed code by the agents to perform required operations that are otherwise restricted by default. Ensure you understand the security implications of modifying these restrictions. | ||
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| - Comment out the following in `src/agentscope/utils/common.py`: |
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Why we have specific working directory here?
| from agentscope.service.service_status import ServiceExecStatus | ||
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| def read_csv_file(file_path: str) -> ServiceResponse: |
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Group this read_csv_file with the one in di_multiagent.py into a util.py file?
| "dependent_task_ids": list[str] = "ids of tasks prerequisite to this task", | ||
| "instruction": "what you should do in this task, one short phrase or sentence", | ||
| "task_type": "type of this task, should be one of Available Task Types", | ||
| "task_type": "type of this task, should be one of Available Task Types", |
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- Duplicate lines?
- what are the
Available Task Types?
| "dependent_task_ids": list[str] = "ids of tasks prerequisite to this task", | ||
| "instruction": "what you should do in this task, one short phrase or sentence", | ||
| "task_type": "type of this task, should be one of Available Task Types", | ||
| "task_type": "type of this task, should be one of Available Task Types", |
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Same questions as in the comment above
| "dependent_task_ids": list[str] = "ids of tasks prerequisite to this task", | ||
| "instruction": "what you should do in this task, one short phrase or sentence", | ||
| "task_type": "type of this task, should be one of Available Task Types", | ||
| "task_type": "type of this task, should be one of Available Task Types", |
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Same questions as the comments above
| ) | ||
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| _ = load_dotenv(find_dotenv()) # read local .env file |
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Why here need to have load_dotenv?
name: Pull Request
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Description
An example to demonstrate the problem-solving capability of multiagent LLMs with adaptive planning on various tasks.
Checklist
Please check the following items before code is ready to be reviewed.