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Merge pull request #1692 from XiangJinyu/main
modify SPO README and app
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6 changed files with 67 additions and 31 deletions
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@ -1,5 +1,7 @@
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# SPO | Self-Supervised Prompt Optimization <img src="../../docs/resources/spo/SPO-logo.png" width="60" height="60" style="vertical-align: middle; margin-left: 10px; position: relative; top: -5px;">
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[](https://arxiv.org/pdf/2502.06855)
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[](https://huggingface.co/spaces/XiangJinYu/SPO)
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An automated prompt engineering tool for Large Language Models (LLMs), designed for universal domain adaptation.
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@ -16,7 +18,10 @@ ## ✨ Core Advantages
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- ⚡ **Universal Adaptation** - _Closed & open-ended tasks supported_
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- 🔄 **Self-Evolving** - _Auto-optimization via LLM-as-judge mechanism_
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[Read our paper on arXiv](https://arxiv.org/pdf/2502.06855)
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## 🔗 Quick Links
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- [📝 Read our paper](https://arxiv.org/pdf/2502.06855)
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- [🤗 Try our demo](https://huggingface.co/spaces/XiangJinYu/SPO)
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## 📊 Experiment
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@ -52,7 +57,7 @@ ### 2. Define Your Iteration template 📝
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count: None
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faq:
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qa:
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- question: |
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...
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answer: |
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@ -1,12 +1,25 @@
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llm:
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api_type: "openai"
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model: "gpt-4o-mini"
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base_url: "<your base url>"
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api_key: "<your api key>"
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temperature: 0
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models:
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"<model_name>": # model: "gpt-4-turbo" # or gpt-3.5-turbo
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"gpt-4o": # model: "gpt-4-turbo" # or gpt-3.5-turbo
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api_type: "openai" # or azure / ollama / groq etc.
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base_url: "<your base url>"
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base_url: "<your base url>"
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api_key: "<your api key>"
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temperature: 0
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"<model_name>":
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api_type: "openai"
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"deepseek-chat": # api_type: "openai" # or azure / ollama / groq etc.
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api_type: "openai" # or azure / ollama / groq etc.
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base_url: "<your base url>"
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api_key: "<your api key>"
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temperature: 0
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"gpt-4o-mini": # api_type: "openai" # or azure / ollama / groq etc.
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api_type: "openai" # or azure / ollama / groq etc.
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base_url: "<your base url>"
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api_key: "<your api key>"
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temperature: 0
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# Other models
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@ -15,7 +15,7 @@ def load_yaml_template(template_path: Path) -> Dict:
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if template_path.exists():
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with open(template_path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f)
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return {"prompt": "", "requirements": "", "count": None, "faq": [{"question": "", "answer": ""}]}
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return {"prompt": "", "requirements": "", "count": None, "qa": [{"question": "", "answer": ""}]}
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def save_yaml_template(template_path: Path, data: Dict) -> None:
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@ -23,9 +23,9 @@ def save_yaml_template(template_path: Path, data: Dict) -> None:
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"prompt": str(data.get("prompt", "")),
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"requirements": str(data.get("requirements", "")),
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"count": data.get("count"),
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"faq": [
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{"question": str(faq.get("question", "")).strip(), "answer": str(faq.get("answer", "")).strip()}
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for faq in data.get("faq", [])
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"qa": [
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{"question": str(qa.get("question", "")).strip(), "answer": str(qa.get("answer", "")).strip()}
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for qa in data.get("qa", [])
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],
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}
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@ -76,7 +76,25 @@ def main():
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if "optimization_results" not in st.session_state:
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st.session_state.optimization_results = []
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st.title("SPO | Self-Supervised Prompt Optimization 🤖")
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st.markdown(
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"""
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<div style="background-color: #f0f2f6; padding: 20px; border-radius: 10px; margin-bottom: 25px">
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<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px">
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<h1 style="margin: 0;">SPO | Self-Supervised Prompt Optimization 🤖</h1>
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</div>
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<div style="display: flex; gap: 20px; align-items: center">
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<a href="https://arxiv.org/pdf/2502.06855" target="_blank" style="text-decoration: none;">
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<img src="https://img.shields.io/badge/Paper-PDF-red.svg" alt="Paper">
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</a>
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<a href="https://github.com/geekan/MetaGPT/blob/main/examples/spo/README.md" target="_blank" style="text-decoration: none;">
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<img src="https://img.shields.io/badge/GitHub-Repository-blue.svg" alt="GitHub">
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</a>
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<span style="color: #666;">A framework for self-supervised prompt optimization</span>
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</div>
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</div>
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""",
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unsafe_allow_html=True,
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)
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# Sidebar for configurations
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with st.sidebar:
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@ -126,51 +144,51 @@ def main():
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if "current_template" not in st.session_state or st.session_state.current_template != template_name:
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st.session_state.current_template = template_name
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st.session_state.faqs = template_data.get("faq", [])
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st.session_state.qas = template_data.get("qa", [])
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# Edit template sections
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prompt = st.text_area("Prompt", template_data.get("prompt", ""), height=100)
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requirements = st.text_area("Requirements", template_data.get("requirements", ""), height=100)
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# FAQ section
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st.subheader("FAQ Examples")
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# qa section
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st.subheader("Q&A Examples")
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# Add new FAQ button
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if st.button("Add New FAQ"):
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st.session_state.faqs.append({"question": "", "answer": ""})
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# Add new qa button
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if st.button("Add New Q&A"):
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st.session_state.qas.append({"question": "", "answer": ""})
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# Edit FAQs
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new_faqs = []
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for i in range(len(st.session_state.faqs)):
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st.markdown(f"**FAQ #{i + 1}**")
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# Edit qas
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new_qas = []
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for i in range(len(st.session_state.qas)):
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st.markdown(f"**QA #{i + 1}**")
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col1, col2, col3 = st.columns([45, 45, 10])
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with col1:
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question = st.text_area(
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f"Question {i + 1}", st.session_state.faqs[i].get("question", ""), key=f"q_{i}", height=100
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f"Question {i + 1}", st.session_state.qas[i].get("question", ""), key=f"q_{i}", height=100
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)
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with col2:
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answer = st.text_area(
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f"Answer {i + 1}", st.session_state.faqs[i].get("answer", ""), key=f"a_{i}", height=100
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f"Answer {i + 1}", st.session_state.qas[i].get("answer", ""), key=f"a_{i}", height=100
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)
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with col3:
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if st.button("🗑️", key=f"delete_{i}"):
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st.session_state.faqs.pop(i)
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st.session_state.qas.pop(i)
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st.rerun()
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new_faqs.append({"question": question, "answer": answer})
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new_qas.append({"question": question, "answer": answer})
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# Save template button
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if st.button("Save Template"):
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new_template_data = {"prompt": prompt, "requirements": requirements, "count": None, "faq": new_faqs}
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new_template_data = {"prompt": prompt, "requirements": requirements, "count": None, "qa": new_qas}
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save_yaml_template(template_path, new_template_data)
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st.session_state.faqs = new_faqs
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st.session_state.qas = new_qas
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st.success(f"Template saved to {template_path}")
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st.subheader("Current Template Preview")
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preview_data = {"prompt": prompt, "requirements": requirements, "count": None, "faq": new_faqs}
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preview_data = {"qa": new_qas, "requirements": requirements, "prompt": prompt}
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st.code(yaml.dump(preview_data, allow_unicode=True), language="yaml")
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st.subheader("Optimization Logs")
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count: None
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faq:
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qa:
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- question: |
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If you follow these instructions, do you return to the starting point? Always face forward. Take 7 steps left. Take 2 steps backward. Take 7 steps backward. Take 7 steps backward. Take 3 steps forward.
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Options:
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@ -6,7 +6,7 @@ requirements: |
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count: None
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faq:
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qa:
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- question: |
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Write a modern sonnet about climate change
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answer: |
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@ -29,7 +29,7 @@ def load_meta_data(k: int = SAMPLE_K):
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qa = []
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for item in data["faq"]:
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for item in data["qa"]:
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question = item["question"]
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answer = item["answer"]
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qa.append({"question": question, "answer": answer})
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