add examples

examples/omegaPRM_openR/.env.example

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+# When customizing the model, you need to set the model to call some model...like # When customizing the model, you need to set the model to call deepseek-chat model
+
+OPENAI_COMPATIBILIY_ModelType=deepseek-chat
+OPENAI_COMPATIBILIY_API_BASE_URL=https://api.deepseek.com
+OPENAI_COMPATIBILIY_API_KEY=

examples/omegaPRM_openR/config.yaml

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+input:
+  json_file_path: 'example_problems.json'
+
+output:
+  file_prefix: 'example'
+  log_file_path: 'example_processing.log'
+
+processing:
+  initial_rollouts: 20
+  num_rollouts: 20
+  max_iterations: 100
+
+model:
+  model_type: "camel"
+  model_name: "deepseek-chat"
+  model_args:
+    max_tokens: 200
+    temperature_range: [0.7, 1.0]
+
+
+# There are 32 initial scroll execution questions to choose from. 
+# If the questions are too easy (all right) or too hard (all wrong), 
+# then OmegaPRM will not deal with them. 
+# So you may need a problem set of the right difficulty and a model that is not too strong, 
+# such as qwen's open source small model. Then the node node data may be obtained

examples/omegaPRM_openR/example_problems.json

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+[
+    {
+        "problem": "How many ways can we put 3 math books and 5 English books on a shelf if all the math books must stay together and all the English books must also stay together?  (The math books are all different and so are the English books.)",
+        "final_answer": "1440"
+    }
+] 

examples/omegaPRM_openR/gen_data.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import json
+import logging
+
+import yaml
+from model_utils import LM
+from module import (
+    Node,
+    calculate_mc_score,
+    perform_rollouts,
+    process_annotations,
+)
+
+
+def load_config(config_path):
+    """
+    Load configuration from a YAML file.
+
+    Args:
+        config_path (str): Path to the YAML configuration file.
+
+    Returns:
+        dict: A dictionary containing the configuration.
+    """
+    with open(config_path, 'r') as file:
+        return yaml.safe_load(file)
+
+
+def load_json_file(file_path):
+    """
+    Load data from a JSON file.
+
+    Args:
+        file_path (str): Path to the JSON file.
+
+    Returns:
+        list: A list of dictionaries containing the problem and final answer.
+    """
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+    return data
+
+
+def setup_logging(log_file):
+    """
+    Set up logging configuration to output to file and console.
+
+    Args:
+        log_file (str): Path to the log file.
+    """
+    logging.basicConfig(
+        filename=log_file,
+        level=logging.INFO,
+        format='%(asctime)s - %(levelname)s - %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S',
+    )
+
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        '%(asctime)s - %(levelname)s - %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S',
+    )
+    console_handler.setFormatter(formatter)
+
+    root_logger = logging.getLogger()
+    root_logger.addHandler(console_handler)
+
+    logging.getLogger("openai").setLevel(logging.ERROR)
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+
+
+def main():
+    # Load configuration
+    config = load_config('config.yaml')
+
+    # Get parameters from config
+    json_file_path = config['input']['json_file_path']
+    log_file_path = config['output']['log_file_path']
+    file_prefix = config['output']['file_prefix']
+    num_rollouts = config['processing']['num_rollouts']
+    initial_rollouts = config['processing']['initial_rollouts']
+    max_iterations = config['processing']['max_iterations']
+
+    lm_model = LM(
+        model_type=config['model']['model_type'],
+        model_name=config['model']['model_name'],
+        num_rollouts=num_rollouts,
+        **config['model']['model_args'],
+    )
+
+    # Set up logging
+    setup_logging(log_file_path)
+
+    # Start the process and log it
+    logging.info("Started processing the JSON file.")
+
+    # Load the JSON data
+    data = load_json_file(json_file_path)
+
+    # Process each problem and its final answer
+    for i, item in enumerate(data):
+        problem = item.get('problem', 'No problem found')
+        final_answer = item.get('final_answer', 'No answer found')
+
+        # Log each problem and answer
+        logging.info(f"Processed Problem {i + 1}: {problem}")
+        logging.info(f"Final Answer: {final_answer}")
+
+        # Initialize the root node and perform rollouts
+        nodes = []
+        root_node = Node(problem, "", final_answer)
+        rollouts, correctness_flags = perform_rollouts(
+            root_node, lm_model, initial_rollouts
+        )
+        mc_score = calculate_mc_score(root_node)
+        root_node.mc_score = mc_score
+
+        nodes.append(root_node)
+
+        # Check if further processing is needed
+        if 0 < sum(correctness_flags) < initial_rollouts:
+            print("Processing annotations ...\n")
+            filename = f"{file_prefix}_{i+1}_nodes_data.json"
+            process_annotations(
+                problem, nodes, lm_model, filename, max_iterations
+            )
+
+    # Log completion
+    logging.info("Finished processing the JSON file.")
+
+
+if __name__ == "__main__":
+    main()

examples/omegaPRM_openR/model_utils.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import os
+import random
+
+from dotenv import load_dotenv
+from tqdm import tqdm
+
+from camel.agents import ChatAgent
+from camel.models import ModelFactory
+from camel.types import ModelPlatformType
+
+load_dotenv()
+
+
+class LM:
+    def __init__(self, model_type, model_name, num_rollouts=5, **kwargs):
+        self.model_type = model_type
+        self.model_name = model_name
+        self.num_rollouts = num_rollouts
+        self.max_tokens = kwargs.get('max_tokens', 4096)
+        self.temperature_range = kwargs.get('temperature_range', [0.7, 1.0])
+
+        if self.model_type != "camel":
+            raise ValueError("Only camel model type is supported")
+
+        # Initialize camel model
+        self.model = ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
+            model_type=os.environ.get("OPENAI_COMPATIBILIY_ModelType"),
+            api_key=os.environ.get("OPENAI_COMPATIBILIY_API_KEY"),
+            url=os.environ.get("OPENAI_COMPATIBILIY_API_BASE_URL"),
+            model_config_dict={
+                "temperature": random.uniform(*self.temperature_range),
+                "max_tokens": self.max_tokens,
+            },
+        )
+
+        # Initialize chat agent
+        self.agent = ChatAgent(
+            system_message='''You are a mathematical reasoning expert who
+             always solves problems step by step.
+For each step:
+1. Write down what you're calculating
+2. Show the calculation
+3. Explain the result
+Always show your work, even for simple calculations.
+End your solution with the final numerical answer.''',
+            model=self.model,
+            message_window_size=10,
+        )
+
+    def generate(self, question, partial_answer, num_rollouts=None):
+        results = []
+        if num_rollouts is None:
+            num_rollouts = self.num_rollouts
+
+        for _ in tqdm(range(num_rollouts)):
+            # Update temperature for each rollout
+            self.model.model_config_dict["temperature"] = random.uniform(
+                *self.temperature_range
+            )
+
+            # Construct the prompt
+            if partial_answer:
+                prompt = f"""Problem: {question}
+Current solution steps:
+{partial_answer}
+Continue the solution, showing all steps and calculations. 
+Make sure to explain each step:"""
+            else:
+                prompt = f"""Problem: {question}
+Please solve this step by step, showing all calculations and
+ explaining each step.
+Remember to:
+1. Break down the problem
+2. Show all calculations
+3. Explain each step
+4. End with the final numerical answer."""
+
+            # Get response from agent
+            response = self.agent.step(prompt)
+            results.append(response.msgs[0].content)
+
+        return results