diff --git a/docs/resources/spo/SPO-closed_task_figure.png b/docs/resources/spo/SPO-closed_task_figure.png
new file mode 100644
index 000000000..329ff5461
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diff --git a/docs/resources/spo/SPO-closed_task_table.png b/docs/resources/spo/SPO-closed_task_table.png
new file mode 100644
index 000000000..f5bc16523
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diff --git a/docs/resources/spo/SPO-method.png b/docs/resources/spo/SPO-method.png
new file mode 100644
index 000000000..7d73a63c9
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diff --git a/docs/resources/spo/SPO-open-ended _task_figure.png b/docs/resources/spo/SPO-open-ended _task_figure.png
new file mode 100644
index 000000000..99d2b733d
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diff --git a/examples/spo/README.md b/examples/spo/README.md
index 602ad9faa..129ab191b 100644
--- a/examples/spo/README.md
+++ b/examples/spo/README.md
@@ -4,6 +4,10 @@ # SPO 🤖 | Self-Supervised Prompt PromptOptimizer
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_
@@ -11,6 +15,25 @@ ## ✨ Core Advantages
- ⚡ **Universal Adaptation** - _Closed & open-ended tasks supported_
- 🔄 **Self-Evolving** - _Auto-optimization via LLM-as-judge mechanism_
+[Read our paper on arXiv](coming soon)
+
+## 📊 Experiment
+
+### Closed Tasks
+
+
+
+
+
+*SPO demonstrates superior cost efficiency, requiring only 1.1% to 5.6% of the cost of state-of-the-art methods while maintaining competitive performance.*
+
+### Open-ended Tasks
+
+
+
+
+*SPO significantly improves model performance across all model configurations in open-ended tasks.*
+
## 🚀 Quick Start
### 1. Configure Your API Key ⚙️
diff --git a/examples/spo/optimize.py b/examples/spo/optimize.py
index 0fa110f65..735fdd0b6 100644
--- a/examples/spo/optimize.py
+++ b/examples/spo/optimize.py
@@ -1,38 +1,27 @@
import argparse
+
from metagpt.ext.spo.components.optimizer import PromptOptimizer
from metagpt.ext.spo.utils.llm_client import SPO_LLM
def parse_args():
- parser = argparse.ArgumentParser(description='SPO PromptOptimizer CLI')
+ parser = argparse.ArgumentParser(description="SPO PromptOptimizer CLI")
# LLM parameter
- parser.add_argument('--opt-model', type=str, default='claude-3-5-sonnet-20240620',
- help='Model for optimization')
- parser.add_argument('--opt-temp', type=float, default=0.7,
- help='Temperature for optimization')
- parser.add_argument('--eval-model', type=str, default='gpt-4o-mini',
- help='Model for evaluation')
- parser.add_argument('--eval-temp', type=float, default=0.3,
- help='Temperature for evaluation')
- parser.add_argument('--exec-model', type=str, default='gpt-4o-mini',
- help='Model for execution')
- parser.add_argument('--exec-temp', type=float, default=0,
- help='Temperature for execution')
+ parser.add_argument("--opt-model", type=str, default="claude-3-5-sonnet-20240620", help="Model for optimization")
+ parser.add_argument("--opt-temp", type=float, default=0.7, help="Temperature for optimization")
+ parser.add_argument("--eval-model", type=str, default="gpt-4o-mini", help="Model for evaluation")
+ parser.add_argument("--eval-temp", type=float, default=0.3, help="Temperature for evaluation")
+ parser.add_argument("--exec-model", type=str, default="gpt-4o-mini", help="Model for execution")
+ parser.add_argument("--exec-temp", type=float, default=0, help="Temperature for execution")
# PromptOptimizer 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')
+ 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()
@@ -41,18 +30,9 @@ def main():
args = parse_args()
SPO_LLM.initialize(
- optimize_kwargs={
- "model": args.opt_model,
- "temperature": args.opt_temp
- },
- evaluate_kwargs={
- "model": args.eval_model,
- "temperature": args.eval_temp
- },
- execute_kwargs={
- "model": args.exec_model,
- "temperature": args.exec_temp
- }
+ optimize_kwargs={"model": args.opt_model, "temperature": args.opt_temp},
+ evaluate_kwargs={"model": args.eval_model, "temperature": args.eval_temp},
+ execute_kwargs={"model": args.exec_model, "temperature": args.exec_temp},
)
optimizer = PromptOptimizer(
diff --git a/metagpt/ext/spo/components/evaluator.py b/metagpt/ext/spo/components/evaluator.py
index f59213996..e4f23519c 100644
--- a/metagpt/ext/spo/components/evaluator.py
+++ b/metagpt/ext/spo/components/evaluator.py
@@ -3,11 +3,12 @@
# @Author : all
# @Desc : Evaluation for different datasets
import asyncio
-from typing import Dict, Any
-from metagpt.ext.spo.utils import load
-from metagpt.ext.spo.prompts.evaluate_prompt import EVALUATE_PROMPT
import random
-from metagpt.ext.spo.utils.llm_client import SPO_LLM, extract_content
+from typing import Any, Dict
+
+from metagpt.ext.spo.prompts.evaluate_prompt import EVALUATE_PROMPT
+from metagpt.ext.spo.utils import load
+from metagpt.ext.spo.utils.llm_client import SPO_LLM, RequestType, extract_content
from metagpt.logs import logger
@@ -17,7 +18,6 @@ class QuickExecute:
"""
def __init__(self, prompt: str):
-
self.prompt = prompt
self.llm = SPO_LLM.get_instance()
@@ -28,12 +28,12 @@ class QuickExecute:
async def fetch_answer(q: str) -> Dict[str, Any]:
messages = [{"role": "user", "content": f"{self.prompt}\n\n{q}"}]
try:
- answer = await self.llm.responser(type="execute", messages=messages)
- return {'question': q, 'answer': answer}
+ answer = await self.llm.responser(request_type=RequestType.EXECUTE, messages=messages)
+ return {"question": q, "answer": answer}
except Exception as e:
- return {'question': q, 'answer': str(e)}
+ return {"question": q, "answer": str(e)}
- tasks = [fetch_answer(item['question']) for item in qa]
+ tasks = [fetch_answer(item["question"]) for item in qa]
answers = await asyncio.gather(*tasks)
return answers
@@ -56,15 +56,18 @@ class QuickEvaluate:
else:
is_swapped = False
- messages = [{"role": "user", "content": EVALUATE_PROMPT.format(
- requirement=requirement,
- sample=samples,
- new_sample=new_samples,
- answers=str(qa))}]
+ messages = [
+ {
+ "role": "user",
+ "content": EVALUATE_PROMPT.format(
+ requirement=requirement, sample=samples, new_sample=new_samples, answers=str(qa)
+ ),
+ }
+ ]
try:
- response = await self.llm.responser(type="evaluate", messages=messages)
- choose = extract_content(response, 'choose')
+ response = await self.llm.responser(request_type=RequestType.EVALUATE, messages=messages)
+ choose = extract_content(response, "choose")
return choose == "A" if is_swapped else choose == "B"
except Exception as e:
@@ -72,9 +75,8 @@ class QuickEvaluate:
return False
-
if __name__ == "__main__":
- execute = QuickExecute(prompt="Answer the Question")
+ executor = QuickExecute(prompt="Answer the Question")
- answers = asyncio.run(execute.prompt_evaluate())
+ answers = asyncio.run(executor.prompt_execute())
print(answers)
diff --git a/metagpt/ext/spo/components/optimizer.py b/metagpt/ext/spo/components/optimizer.py
index 7c5585158..8d41a8aac 100644
--- a/metagpt/ext/spo/components/optimizer.py
+++ b/metagpt/ext/spo/components/optimizer.py
@@ -4,27 +4,26 @@
# @Desc : optimizer for prompt
import asyncio
-from metagpt.ext.spo.utils.data_utils import DataUtils
-from metagpt.ext.spo.utils.evaluation_utils import EvaluationUtils
-from metagpt.ext.spo.utils.prompt_utils import PromptUtils
+
from metagpt.ext.spo.prompts.optimize_prompt import PROMPT_OPTIMIZE_PROMPT
from metagpt.ext.spo.utils import load
+from metagpt.ext.spo.utils.data_utils import DataUtils
+from metagpt.ext.spo.utils.evaluation_utils import EvaluationUtils
+from metagpt.ext.spo.utils.llm_client import SPO_LLM, RequestType, extract_content
+from metagpt.ext.spo.utils.prompt_utils import PromptUtils
from metagpt.logs import logger
-from metagpt.ext.spo.utils.llm_client import extract_content, SPO_LLM
-
class PromptOptimizer:
def __init__(
- self,
- optimized_path: str = None,
- initial_round: int = 1,
- max_rounds: int = 10,
- name: str = "",
- template: str = "",
- iteration: bool = True,
+ self,
+ optimized_path: str = None,
+ initial_round: int = 1,
+ max_rounds: int = 10,
+ name: str = "",
+ template: str = "",
+ iteration: bool = True,
) -> None:
-
self.dataset = name
self.root_path = f"{optimized_path}/{self.dataset}"
self.top_scores = []
@@ -40,7 +39,6 @@ class PromptOptimizer:
def optimize(self):
if self.iteration:
-
for opt_round in range(self.max_rounds):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
@@ -55,7 +53,6 @@ class PromptOptimizer:
logger.info(f"Prompt generated in round {self.round}: {prompt}")
async def _optimize_prompt(self):
-
prompt_path = f"{self.root_path}/prompts"
load.set_file_name(self.template)
@@ -69,18 +66,16 @@ class PromptOptimizer:
self.prompt = prompt
self.prompt_utils.write_prompt(directory, prompt=self.prompt)
new_samples = await self.evaluation_utils.execute_prompt(self, directory, initial=True)
- _, answers = await self.evaluation_utils.evaluate_prompt(self, None, new_samples, path=prompt_path,
- data=data, initial=True)
+ _, answers = await self.evaluation_utils.evaluate_prompt(
+ self, None, new_samples, path=prompt_path, data=data, initial=True
+ )
self.prompt_utils.write_answers(directory, answers=answers)
-
_, requirements, qa, count = load.load_meta_data()
directory = self.prompt_utils.create_round_directory(prompt_path, self.round + 1)
- top_round = self.data_utils.get_best_round()
-
- samples = top_round
+ samples = self.data_utils.get_best_round()
logger.info(f"choose {samples['round']}")
@@ -88,12 +83,16 @@ class PromptOptimizer:
best_answer = self.data_utils.list_to_markdown(samples["answers"])
optimize_prompt = PROMPT_OPTIMIZE_PROMPT.format(
- prompt=samples["prompt"], answers=best_answer,
+ prompt=samples["prompt"],
+ answers=best_answer,
requirements=requirements,
golden_answers=golden_answer,
- count=count)
+ count=count,
+ )
- response = await self.llm.responser(type="optimize", messages=[{"role": "user", "content": optimize_prompt}])
+ response = await self.llm.responser(
+ request_type=RequestType.OPTIMIZE, messages=[{"role": "user", "content": optimize_prompt}]
+ )
modification = extract_content(response, "modification")
@@ -110,8 +109,9 @@ class PromptOptimizer:
new_samples = await self.evaluation_utils.execute_prompt(self, directory, data)
- success, answers = await self.evaluation_utils.evaluate_prompt(self, samples, new_samples, path=prompt_path,
- data=data, initial=False)
+ success, answers = await self.evaluation_utils.evaluate_prompt(
+ self, samples, new_samples, path=prompt_path, data=data, initial=False
+ )
self.prompt_utils.write_answers(directory, answers=answers)
@@ -122,7 +122,6 @@ class PromptOptimizer:
return prompt
async def _test_prompt(self):
-
load.set_file_name(self.template)
prompt_path = f"{self.root_path}/prompts"
diff --git a/metagpt/ext/spo/utils/data_utils.py b/metagpt/ext/spo/utils/data_utils.py
index cb75504b0..7bf57dbf7 100644
--- a/metagpt/ext/spo/utils/data_utils.py
+++ b/metagpt/ext/spo/utils/data_utils.py
@@ -1,11 +1,11 @@
import datetime
import json
import os
-import random
-from typing import Union, List, Dict
-import pandas as pd
-from metagpt.logs import logger
+from typing import Dict, List, Union
+import pandas as pd
+
+from metagpt.logs import logger
class DataUtils:
@@ -14,7 +14,7 @@ class DataUtils:
self.top_scores = []
def load_results(self, path: str) -> list:
- result_path = os.path.join(path, "results.json")
+ result_path = self.get_results_file_path(path)
if os.path.exists(result_path):
with open(result_path, "r") as json_file:
try:
@@ -24,7 +24,6 @@ class DataUtils:
return []
def get_best_round(self):
-
self._load_scores()
for entry in self.top_scores:
@@ -44,11 +43,6 @@ class DataUtils:
with open(json_file_path, "w") as json_file:
json.dump(data, json_file, default=str, indent=4)
- def save_cost(self, directory: str, data: Union[List, Dict]):
- json_file = os.path.join(directory, 'cost.json')
- with open(json_file, "w", encoding="utf-8") as file:
- json.dump(data, file, default=str, indent=4)
-
def _load_scores(self):
rounds_dir = os.path.join(self.root_path, "prompts")
result_file = os.path.join(rounds_dir, "results.json")
@@ -65,12 +59,14 @@ class DataUtils:
df = pd.DataFrame(data)
for index, row in df.iterrows():
- self.top_scores.append({
- "round": row["round"],
- "succeed": row["succeed"],
- "prompt": row["prompt"],
- "answers": row['answers']
- })
+ self.top_scores.append(
+ {
+ "round": row["round"],
+ "succeed": row["succeed"],
+ "prompt": row["prompt"],
+ "answers": row["answers"],
+ }
+ )
self.top_scores.sort(key=lambda x: x["round"], reverse=True)
diff --git a/metagpt/ext/spo/utils/evaluation_utils.py b/metagpt/ext/spo/utils/evaluation_utils.py
index 5b598070b..c44192d69 100644
--- a/metagpt/ext/spo/utils/evaluation_utils.py
+++ b/metagpt/ext/spo/utils/evaluation_utils.py
@@ -1,22 +1,24 @@
+import tiktoken
+
from metagpt.ext.spo.components.evaluator import QuickEvaluate, QuickExecute
from metagpt.logs import logger
-import tiktoken
EVALUATION_REPETITION = 4
+
def count_tokens(sample):
- if sample is None:
+ if not sample:
return 0
else:
encoding = tiktoken.get_encoding("cl100k_base")
- return len(encoding.encode(str(sample['answers'])))
+ return len(encoding.encode(str(sample["answers"])))
+
class EvaluationUtils:
def __init__(self, root_path: str):
self.root_path = root_path
async def execute_prompt(self, optimizer, prompt_path, initial=False):
-
optimizer.prompt = optimizer.prompt_utils.load_prompt(optimizer.round, prompt_path)
executor = QuickExecute(prompt=optimizer.prompt)
@@ -29,7 +31,6 @@ class EvaluationUtils:
return new_data
async def evaluate_prompt(self, optimizer, samples, new_samples, path, data, initial=False):
-
evaluator = QuickEvaluate()
new_token = count_tokens(new_samples)
@@ -47,8 +48,9 @@ class EvaluationUtils:
false_count = evaluation_results.count(False)
succeed = true_count > false_count
- new_data = optimizer.data_utils.create_result_data(new_samples['round'], new_samples['answers'],
- new_samples['prompt'], succeed, new_token)
+ new_data = optimizer.data_utils.create_result_data(
+ new_samples["round"], new_samples["answers"], new_samples["prompt"], succeed, new_token
+ )
data.append(new_data)
@@ -56,6 +58,6 @@ class EvaluationUtils:
optimizer.data_utils.save_results(result_path, data)
- answers = new_samples['answers']
+ answers = new_samples["answers"]
return succeed, answers
diff --git a/metagpt/ext/spo/utils/llm_client.py b/metagpt/ext/spo/utils/llm_client.py
index 3ad31fcd5..689d2a5ef 100644
--- a/metagpt/ext/spo/utils/llm_client.py
+++ b/metagpt/ext/spo/utils/llm_client.py
@@ -1,12 +1,20 @@
+import asyncio
import re
+from enum import Enum
from typing import Optional
+
from metagpt.configs.models_config import ModelsConfig
from metagpt.llm import LLM
-import asyncio
+
+
+class RequestType(Enum):
+ OPTIMIZE = "optimize"
+ EVALUATE = "evaluate"
+ EXECUTE = "execute"
class SPO_LLM:
- _instance: Optional['SPO_LLM'] = None
+ _instance: Optional["SPO_LLM"] = None
def __init__(self, optimize_kwargs=None, evaluate_kwargs=None, execute_kwargs=None):
self.evaluate_llm = LLM(llm_config=self._load_llm_config(evaluate_kwargs))
@@ -14,7 +22,7 @@ class SPO_LLM:
self.execute_llm = LLM(llm_config=self._load_llm_config(execute_kwargs))
def _load_llm_config(self, kwargs: dict):
- model = kwargs.get('model')
+ model = kwargs.get("model")
if not model:
raise ValueError("'model' parameter is required")
@@ -31,23 +39,24 @@ class SPO_LLM:
return config
- except AttributeError as e:
+ except AttributeError:
raise ValueError(f"Model '{model}' not found in configuration")
except Exception as e:
raise ValueError(f"Error loading configuration for model '{model}': {str(e)}")
- async def responser(self, type: str, messages):
- if type == "optimize":
- response = await self.optimize_llm.acompletion(messages)
- elif type == "evaluate":
- response = await self.evaluate_llm.acompletion(messages)
- elif type == "execute":
- response = await self.execute_llm.acompletion(messages)
- else:
- raise ValueError("Please set the correct name: optimize, evaluate or execute")
+ async def responser(self, request_type: RequestType, messages: list):
+ llm_mapping = {
+ RequestType.OPTIMIZE: self.optimize_llm,
+ RequestType.EVALUATE: self.evaluate_llm,
+ RequestType.EXECUTE: self.execute_llm,
+ }
- rsp = response.choices[0].message.content
- return rsp
+ llm = llm_mapping.get(request_type)
+ if not llm:
+ raise ValueError(f"Invalid request type. Valid types: {', '.join([t.value for t in RequestType])}")
+
+ response = await llm.acompletion(messages)
+ return response.choices[0].message.content
@classmethod
def initialize(cls, optimize_kwargs, evaluate_kwargs, execute_kwargs):
@@ -61,8 +70,9 @@ class SPO_LLM:
raise RuntimeError("SPO_LLM not initialized. Call initialize() first.")
return cls._instance
+
def extract_content(xml_string, tag):
- pattern = rf'<{tag}>(.*?)'
+ pattern = rf"<{tag}>(.*?)"
match = re.search(pattern, xml_string, re.DOTALL)
return match.group(1).strip() if match else None
@@ -72,23 +82,20 @@ async def spo():
SPO_LLM.initialize(
optimize_kwargs={"model": "gpt-4o", "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": 0.3},
)
llm = SPO_LLM.get_instance()
# test messages
hello_msg = [{"role": "user", "content": "hello"}]
- response = await llm.responser(type='execute', messages=hello_msg)
+ response = await llm.responser(request_type=RequestType.EXECUTE, messages=hello_msg)
print(f"AI: {response}")
- response = await llm.responser(type='optimize', messages=hello_msg)
+ response = await llm.responser(request_type=RequestType.OPTIMIZE, messages=hello_msg)
print(f"AI: {response}")
- response = await llm.responser(type='evaluate', messages=hello_msg)
+ response = await llm.responser(request_type=RequestType.EVALUATE, messages=hello_msg)
print(f"AI: {response}")
if __name__ == "__main__":
asyncio.run(spo())
-
-
-
diff --git a/metagpt/ext/spo/utils/load.py b/metagpt/ext/spo/utils/load.py
index 2b3a1371d..3f9ab0c27 100644
--- a/metagpt/ext/spo/utils/load.py
+++ b/metagpt/ext/spo/utils/load.py
@@ -1,10 +1,12 @@
-import yaml
-import random
import os
+import random
-FILE_NAME = ''
+import yaml
+
+FILE_NAME = ""
SAMPLE_K = 3
+
def set_file_name(name):
global FILE_NAME
FILE_NAME = name
@@ -12,13 +14,13 @@ def set_file_name(name):
def load_meta_data(k=SAMPLE_K):
# load yaml file
- config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'settings', FILE_NAME)
+ config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "settings", FILE_NAME)
if not os.path.exists(config_path):
raise FileNotFoundError(f"Configuration file '{FILE_NAME}' not found in settings directory")
try:
- with open(config_path, 'r', encoding='utf-8') as file:
+ with open(config_path, "r", encoding="utf-8") as file:
data = yaml.safe_load(file)
except yaml.YAMLError as e:
raise ValueError(f"Error parsing YAML file '{FILE_NAME}': {str(e)}")
@@ -27,14 +29,14 @@ def load_meta_data(k=SAMPLE_K):
qa = []
- for item in data['faq']:
- question = item['question']
- answer = item['answer']
- qa.append({'question': question, 'answer': answer})
+ for item in data["faq"]:
+ question = item["question"]
+ answer = item["answer"]
+ qa.append({"question": question, "answer": answer})
- prompt = data['prompt']
- requirements = data['requirements']
- count = data['count']
+ prompt = data["prompt"]
+ requirements = data["requirements"]
+ count = data["count"]
if isinstance(count, int):
count = f", within {count} words"
@@ -44,4 +46,3 @@ def load_meta_data(k=SAMPLE_K):
random_qa = random.sample(qa, min(k, len(qa)))
return prompt, requirements, random_qa, count
-
diff --git a/metagpt/ext/spo/utils/prompt_utils.py b/metagpt/ext/spo/utils/prompt_utils.py
index 8bf1ec898..449611219 100644
--- a/metagpt/ext/spo/utils/prompt_utils.py
+++ b/metagpt/ext/spo/utils/prompt_utils.py
@@ -1,4 +1,5 @@
import os
+
from metagpt.logs import logger
@@ -15,14 +16,13 @@ class PromptUtils:
prompt_file_name = f"{prompts_path}/prompt.txt"
try:
- with open(prompt_file_name, 'r', encoding='utf-8') as file:
+ with open(prompt_file_name, "r", encoding="utf-8") as file:
return file.read()
except FileNotFoundError as e:
logger.info(f"Error loading prompt for round {round_number}: {e}")
raise
def write_answers(self, directory: str, answers: dict, name: str = "answers.txt"):
-
with open(os.path.join(directory, name), "w", encoding="utf-8") as file:
for item in answers:
file.write(f"Question:\n{item['question']}\n")
@@ -30,7 +30,5 @@ class PromptUtils:
file.write("\n")
def write_prompt(self, directory: str, prompt: str):
-
with open(os.path.join(directory, "prompt.txt"), "w", encoding="utf-8") as file:
file.write(prompt)
-