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Revert commit for build_prompt

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  1. README.md +15 -23
README.md CHANGED
@@ -200,30 +200,13 @@ def build_conversation_history_prompt(conversation_history: str):
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  # Helper function to build the input prompt for our model
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- def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str, conversation_history: list):
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- prompt = f"[BEGIN OF TASK INSTRUCTION]
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- {task_instruction}
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- [END OF TASK INSTRUCTION]
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-
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- "
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- prompt += f"[BEGIN OF AVAILABLE TOOLS]
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- {json.dumps(xlam_format_tools)}
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- [END OF AVAILABLE TOOLS]
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-
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- "
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- prompt += f"[BEGIN OF FORMAT INSTRUCTION]
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- {format_instruction}
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- [END OF FORMAT INSTRUCTION]
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-
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- "
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- prompt += f"[BEGIN OF QUERY]
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- {query}
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- [END OF QUERY]
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-
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- "
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-
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- if len(conversation_history) > 0: prompt += build_conversation_history_prompt(conversation_history)
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  return prompt
 
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  # Build the input and start the inference
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  xlam_format_tools = convert_to_xlam_tool(openai_format_tools)
@@ -493,6 +476,15 @@ If you find this repo helpful, please consider to cite our papers:
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  }
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  ```
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  ```bibtex
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  @article{zhang2024agentohana,
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  title={AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning},
 
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  # Helper function to build the input prompt for our model
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+ def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str):
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+ prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
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+ prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(xlam_format_tools)}\n[END OF AVAILABLE TOOLS]\n\n"
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+ prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
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+ prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  return prompt
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+
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  # Build the input and start the inference
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  xlam_format_tools = convert_to_xlam_tool(openai_format_tools)
 
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  }
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  ```
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+ ```bixtex
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+ @article{zhang2025actionstudio,
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+ title={ActionStudio: A Lightweight Framework for Data and Training of Action Models},
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+ author={Zhang, Jianguo and Hoang, Thai and Zhu, Ming and Liu, Zuxin and Wang, Shiyu and Awalgaonkar, Tulika and Prabhakar, Akshara and Chen, Haolin and Yao, Weiran and Liu, Zhiwei and others},
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+ journal={arXiv preprint arXiv:2503.22673},
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+ year={2025}
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+ }
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+ ```
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+
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  ```bibtex
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  @article{zhang2024agentohana,
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  title={AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning},