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* docs(docs): start implementing docs website * update video url * add autogenerated codebase docs for backend * precommit * update links * fix config and video * gh actions * rename * workdirs * path * path * fix doc1 * redo markdown * docs * change main folder name * simplify readme * add back architecture * Fix lint errors * lint * update poetry lock --------- Co-authored-by: Jim Su <jimsu@protonmail.com>
50 lines
1.0 KiB
Markdown
50 lines
1.0 KiB
Markdown
---
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sidebar_label: prompt
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title: agenthub.planner_agent.prompt
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---
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#### get\_hint
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```python
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def get_hint(latest_action_id: str) -> str
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```
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Returns action type hint based on given action_id
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#### get\_prompt
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```python
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def get_prompt(plan: Plan, history: List[Tuple[Action, Observation]]) -> str
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```
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Gets the prompt for the planner agent.
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Formatted with the most recent action-observation pairs, current task, and hint based on last action
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**Arguments**:
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- plan (Plan): The original plan outlined by the user with LLM defined tasks
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- history (List[Tuple[Action, Observation]]): List of corresponding action-observation pairs
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**Returns**:
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- str: The formatted string prompt with historical values
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#### parse\_response
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```python
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def parse_response(response: str) -> Action
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```
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Parses the model output to find a valid action to take
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**Arguments**:
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- response (str): A response from the model that potentially contains an Action.
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**Returns**:
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- Action: A valid next action to perform from model output
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