Add README for SPO

examples/spo/README.md

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+# SPO 🤖 | Self-Supervised Prompt Optimizer
+
+An automated prompt engineering tool for Large Language Models (LLMs), designed for universal domain adaptation.
+
+A next-generation prompt engineering system implementing **Self-Supervised Prompt Optimization (SPO)**. Achieves state-of-the-art performance with 17.8-90.9× higher cost efficiency than conventional methods. 🚀
+
+## ✨ Core Advantages
+
+- 💸 **Ultra-Low Cost** - _$0.15 per task optimization_
+- 🏷️ **Zero Supervision** - _No ground truth/human feedback required_
+- ⚡ **Universal Adaptation** - _Closed & open-ended tasks supported_
+- 🔄 **Self-Evolving** - _Auto-optimization via LLM-as-judge mechanism_
+
+## 🚀 Quick Start
+
+### 1. Configure Your API Key ⚙️
+
+Configure LLM parameters in `config/config2.yaml` (see `examples/aflow/config2.example.yaml` for reference)
+### 2. Define Your Iteration template 📝
+
+Create a Iteration template file `metagpt/ext/spo/settings/task_name.yaml`:
+```yaml
+prompt: |
+  solve question.
+
+requirements: |
+  ...
+
+count: None
+
+faq:
+  - question: |
+      ...
+    answer: |
+      ...
+
+  - question: |
+      ...
+    answer: |
+      ...
+```
+
+### 3. Implement the Optimizer 🔧
+
+我帮你完成这个 Readme 部分：
+
+### 3. Implement the Optimizer 🔧
+
+Use `metagpt/ext/spo/optimize.py` to execute:
+
+```python
+from metagpt.ext.spo.scripts.optimizer import Optimizer
+from metagpt.ext.spo.scripts.utils.llm_client import SPO_LLM
+
+if __name__ == "__main__":
+    # Initialize LLM settings
+    SPO_LLM.initialize(
+        optimize_kwargs={"model": "claude-3-5-sonnet-20240620", "temperature": 0.7},
+        evaluate_kwargs={"model": "gpt-4o-mini", "temperature": 0.3},
+        execute_kwargs={"model": "gpt-4o-mini", "temperature": 0}
+    )
+
+    # Create and run optimizer
+    optimizer = Optimizer(
+        optimized_path="workspace",    # Output directory
+        initial_round=1,               # Starting round
+        max_rounds=10,                 # Maximum optimization rounds
+        template="Poem.yaml",          # Template file
+        name="Poem",                   # Project name
+        iteration=True,                # Enable iteration mode
+    )
+
+    optimizer.optimize()
+```
+
+Or you can use command line interface:
+
+```bash
+python optimize.py [options]
+```
+
+Available command line options:
+```
+--optimize-model         Model for optimization (default: claude-3-5-sonnet-20240620)
+--optimize-temperature  Temperature for optimization (default: 0.7)
+--evaluate-model        Model for evaluation (default: gpt-4o-mini)
+--evaluate-temperature  Temperature for evaluation (default: 0.3)
+--execute-model         Model for execution (default: gpt-4o-mini)
+--execute-temperature   Temperature for execution (default: 0)
+--workspace            Output directory path (default: workspace)
+--initial-round        Initial round number (default: 1)
+--max-rounds          Maximum number of rounds (default: 10)
+--template            Template file name (default: Poem.yaml)
+--name                Project name (default: Poem)
+--no-iteration        Disable iteration mode (iteration enabled by default)
+```
+
+For help:
+```bash
+python optimize.py --help
+```

examples/spo/config2.example.yaml

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+models:
+ "<model_name>": # model: "gpt-4-turbo"  # or gpt-3.5-turbo
+   api_type: "openai"  # or azure / ollama / groq etc.
+   base_url: "<your base url>" 
+   api_key: "<your api key>"
+   temperature: 0
+ "<model_name>":  
+   api_type: "openai"  
+   base_url: "<your base url>"
+   api_key: "<your api key>"
+   temperature: 0
+

examples/spo/optimize.py

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+import argparse
+from metagpt.ext.spo.scripts.optimizer import Optimizer
+from metagpt.ext.spo.scripts.utils.llm_client import SPO_LLM
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='SPO Optimizer CLI')
+
+    # LLM parameter
+    parser.add_argument('--optimize-model', type=str, default='claude-3-5-sonnet-20240620',
+                        help='Model for optimization')
+    parser.add_argument('--optimize-temperature', type=float, default=0.7,
+                        help='Temperature for optimization')
+    parser.add_argument('--evaluate-model', type=str, default='gpt-4o-mini',
+                        help='Model for evaluation')
+    parser.add_argument('--evaluate-temperature', type=float, default=0.3,
+                        help='Temperature for evaluation')
+    parser.add_argument('--execute-model', type=str, default='gpt-4o-mini',
+                        help='Model for execution')
+    parser.add_argument('--execute-temperature', type=float, default=0,
+                        help='Temperature for execution')
+
+    # Optimizer parameter
+    parser.add_argument('--workspace', type=str, default='workspace',
+                        help='Path for optimized output')
+    parser.add_argument('--initial-round', type=int, default=1,
+                        help='Initial round number')
+    parser.add_argument('--max-rounds', type=int, default=10,
+                        help='Maximum number of rounds')
+    parser.add_argument('--template', type=str, default='Poem.yaml',
+                        help='Template file name')
+    parser.add_argument('--name', type=str, default='Poem',
+                        help='Project name')
+    parser.add_argument('--no-iteration', action='store_false', dest='iteration',
+                        help='Disable iteration mode')
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    SPO_LLM.initialize(
+        optimize_kwargs={
+            "model": args.optimize_model,
+            "temperature": args.optimize_temperature
+        },
+        evaluate_kwargs={
+            "model": args.evaluate_model,
+            "temperature": args.evaluate_temperature
+        },
+        execute_kwargs={
+            "model": args.execute_model,
+            "temperature": args.execute_temperature
+        }
+    )
+
+    optimizer = Optimizer(
+        optimized_path=args.workspace,
+        initial_round=args.initial_round,
+        max_rounds=args.max_rounds,
+        template=args.template,
+        name=args.name,
+        iteration=args.iteration,
+    )
+
+    optimizer.optimize()
+
+
+if __name__ == "__main__":
+    main()

metagpt/ext/spo/optimize.py

@@ -7,7 +7,7 @@ if __name__ == "__main__":
     SPO_LLM.initialize(
         optimize_kwargs={"model": "claude-3-5-sonnet-20240620", "temperature": 0.7},
         evaluate_kwargs={"model": "gpt-4o-mini", "temperature": 0.3},
-        execute_kwargs={"model": "gpt-4o-mini", "temperature": 0.3}
+        execute_kwargs={"model": "gpt-4o-mini", "temperature": 0}
     )
 
     optimizer = Optimizer(