[debug] update consistentchat & condor text operators for prompt_template arguments

dataflow/operators/conversations/generate/consistent_chat_generator.py

@@ -6,25 +6,24 @@
 from dataflow.utils.storage import DataFlowStorage
 import pandas as pd
 from dataflow.core import LLMServingABC
-from dataflow.prompts.general_text import ConsistentQueryPrompt, ConsistentResponsePrompt
-from dataflow.core.prompt import prompt_restrict
+from dataflow.prompts.general_text import ConsistentChatPrompt
+from dataflow.core.prompt import DIYPromptABC, prompt_restrict
+from typing import Union
 
 @prompt_restrict(
-    ConsistentQueryPrompt,
-    ConsistentResponsePrompt
+    ConsistentChatPrompt
 ) 
 
 @OPERATOR_REGISTRY.register()
 class ConsistentChatGenerator(OperatorABC):
-    def __init__(self, llm_serving: LLMServingABC = None, num_dialogs_per_intent = 20, num_turns_per_dialog = 6, temperature = 0.9):
+    def __init__(self, llm_serving: LLMServingABC = None, num_dialogs_per_intent = 20, num_turns_per_dialog = 6, temperature = 0.9, prompt_template : Union[ConsistentChatPrompt, DIYPromptABC] = None):
         self.logger = get_logger()
         self.logger.info(f'Initializing {self.__class__.__name__}...')
         self.llm_serving = llm_serving
         self.num_dialogs_per_intent = num_dialogs_per_intent # Based on the topic_dict in the existing prompt, it is recommended to set the value to below 1000 (which can generate 9000 conversation data). Otherwise, it is recommended to add more topic_dict in dataflow.prompts.general_text.ConsistentChatPrompt to increase data richness
         self.num_turns_per_dialog = num_turns_per_dialog
         self.temperature = temperature
-        self.query_prompt = ConsistentQueryPrompt()
-        self.response_prompt = ConsistentResponsePrompt()
+        self.prompt_template = prompt_template
         self.logger.info(f'{self.__class__.__name__} initialized.')
 
     @staticmethod
@@ -37,6 +36,7 @@ def get_desc(lang: str = "zh"):
                 "- num_dialogs_per_intent：每个意图生成的对话数量，默认20\n"
                 "- num_turns_per_dialog：每个对话的轮次数量，默认6\n"
                 "- temperature：生成温度，控制输出随机性，默认0.9\n"
+                "- prompt_template：提示词模板对象，用于定义提示结构\n"
                 "输出参数：\n"
                 "- 包含category和conversation字段的DataFrame，其中conversation为多轮对话列表"
             )
@@ -48,6 +48,7 @@ def get_desc(lang: str = "zh"):
                 "- num_dialogs_per_intent: Number of dialogs generated per intent, default 20\n"
                 "- num_turns_per_dialog: Number of turns per dialog, default 6\n"
                 "- temperature: Sampling temperature for generation, default 0.9\n"
+                "- prompt_template: Prompt template object, for defining the prompt structure\n"
                 "Output Parameters:\n"
                 "- DataFrame containing 'category' and 'conversation' fields, where conversation is a list of multi-turn dialogues"
             )
@@ -57,7 +58,7 @@ def get_desc(lang: str = "zh"):
     def run(self, storage: DataFlowStorage):
 
         # Step 1: Generate all queries using LLM
-        all_query_prompts = self.query_prompt.build_prompt(num_dialogs_per_intent=self.num_dialogs_per_intent)
+        all_query_prompts = self.prompt_template.build_prompt(mode="query", num_dialogs_per_intent=self.num_dialogs_per_intent)
         # Step 2: Generate queries by calling llm_serving once
         self.logger.info("Generating queries...")
         queries_list = self.llm_serving.generate_from_input(user_inputs=all_query_prompts)
@@ -78,7 +79,7 @@ def run(self, storage: DataFlowStorage):
         for queries in valid_queries:
             category = queries.get("category")
             turns = queries.get("turns")
-            all_response_prompts.append(self.response_prompt.build_prompt(topic=category, queries=turns))
+            all_response_prompts.append(self.prompt_template.build_prompt(mode="response", topic=category, queries=turns))
         self.logger.info("Generating responses...")
         responses_list = self.llm_serving.generate_from_input(user_inputs=all_response_prompts)
 

dataflow/operators/text_sft/generate/condor_generator.py

@@ -7,21 +7,22 @@
 import pandas as pd
 from dataflow.core import LLMServingABC
 from dataflow.prompts.general_text import CondorQuestionPrompt
-from dataflow.core.prompt import prompt_restrict
+from dataflow.core.prompt import DIYPromptABC, prompt_restrict
+from typing import Union
 
 @prompt_restrict(
     CondorQuestionPrompt
 ) 
 
 @OPERATOR_REGISTRY.register()
 class CondorGenerator(OperatorABC):
-    def __init__(self, llm_serving: LLMServingABC = None, num_samples=15, use_task_diversity=True):
+    def __init__(self, llm_serving: LLMServingABC = None, num_samples=15, use_task_diversity=True, prompt_template: Union[CondorQuestionPrompt, DIYPromptABC] = None):
         # Based on the existing topics, it is recommended to set num_samples below 5000. Otherwise, it is recommended to add topics in dataflow.prompts.general_text.CondorPrompt on your own to increase data richness
         self.logger = get_logger()
         self.logger.info(f'Initializing {self.__class__.__name__}...')
         self.llm_serving = llm_serving
         self.num_questions = num_samples // 3  # 每个prompt生成3个难度的问题
-        self.prompt = CondorQuestionPrompt()
+        self.prompt = prompt_template
         self.use_task_diversity = use_task_diversity  # 是否使用任务场景增强多样性
         self.logger.info(f'{self.__class__.__name__} initialized.')
 
@@ -33,6 +34,7 @@ def get_desc(lang: str = "zh"):
                 "输入参数：\n"
                 "- llm_serving：LLM服务对象，需实现LLMServingABC接口\n"
                 "- num_samples：生成样本总数，建议小于5000，默认值为15\n"
+                "- prompt_template：提示词模板对象，用于定义提示结构\n"
                 "输出参数：\n"
                 "- 包含'difficulty'、'instruction'和'output'字段的DataFrame\n"
                 "- 返回生成的DataFrame用于后续处理"
@@ -44,6 +46,7 @@ def get_desc(lang: str = "zh"):
                 "Input Parameters:\n"
                 "- llm_serving: LLM serving object implementing LLMServingABC interface\n"
                 "- num_samples: Total number of samples to generate, recommended to be less than 5000, default is 15\n\n"
+                "- prompt_template: Prompt template object, for defining the prompt structure\n"
                 "Output Parameters:\n"
                 "- DataFrame containing 'difficulty', 'instruction', and 'output' fields\n"
                 "- Returns generated DataFrame for subsequent processing"

dataflow/operators/text_sft/refine/condor_refiner.py

@@ -6,22 +6,21 @@
 from dataflow.utils.storage import DataFlowStorage
 import pandas as pd
 from dataflow.core import LLMServingABC
-from dataflow.prompts.general_text import CondorCritiquePrompt, CondorRefinePrompt
-from dataflow.core.prompt import prompt_restrict
+from dataflow.prompts.general_text import CondorRefinePrompt
+from dataflow.core.prompt import prompt_restrict, DIYPromptABC
+from typing import Union
 
 @prompt_restrict(
-    CondorCritiquePrompt,
     CondorRefinePrompt
 )
 
 @OPERATOR_REGISTRY.register()
 class CondorRefiner(OperatorABC):
-    def __init__(self, llm_serving: LLMServingABC = None):
+    def __init__(self, llm_serving: LLMServingABC = None, prompt_template: Union[CondorRefinePrompt, DIYPromptABC] = None):
         self.logger = get_logger()
         self.logger.info(f'Initializing {self.__class__.__name__}...')
         self.llm_serving = llm_serving
-        self.critique_prompt = CondorCritiquePrompt()  # 创建 CondorPrompt 类的实例
-        self.refine_prompt = CondorRefinePrompt()
+        self.prompt_template = prompt_template
         self.logger.info(f'{self.__class__.__name__} initialized.')
 
     @staticmethod
@@ -33,6 +32,7 @@ def get_desc(lang: str = "zh"):
                 "- llm_serving：LLM服务对象，需实现LLMServingABC接口\n"
                 "- input_instruction_key：输入指令字段名，默认为'instruction'\n"
                 "- input_output_key：输入回复字段名，默认为'output'\n"
+                "- prompt_template：提示词模板对象，用于定义提示结构\n"
                 "输出参数：\n"
                 "- 包含优化后回复的DataFrame\n"
                 "- 返回包含优化后回复字段名的列表，用于后续算子引用"
@@ -44,7 +44,8 @@ def get_desc(lang: str = "zh"):
                 "Input Parameters:\n"
                 "- llm_serving: LLM serving object implementing LLMServingABC interface\n"
                 "- input_instruction_key: Field name for input instructions, default is 'instruction'\n"
-                "- input_output_key: Field name for input responses, default is 'output'\n\n"
+                "- input_output_key: Field name for input responses, default is 'output'\n"
+                "- prompt_template: Prompt template object, for defining the prompt structure\n"
                 "Output Parameters:\n"
                 "- DataFrame containing refined responses\n"
                 "- List containing refined response field name for subsequent operator reference"
@@ -56,13 +57,13 @@ def get_desc(lang: str = "zh"):
 
     def generate_critique(self, question, answer):
         # 批量生成 Critique
-        critique_prompts = [self.critique_prompt.build_prompt(q, a) for q, a in zip(question, answer)]
+        critique_prompts = [self.prompt_template.build_prompt(mode="critique", question=q, answer=a) for q, a in zip(question, answer)]
         critique_responses = self.llm_serving.generate_from_input(critique_prompts)
         return critique_responses
 
     def generate_refined_answer(self, question, answer, critique):
         # 批量生成修改后的答案
-        refine_prompts = [self.refine_prompt.build_prompt(q, a, c) for q, a, c in zip(question, answer, critique)]
+        refine_prompts = [self.prompt_template.build_prompt(mode="refine", question=q, answer=a, critique=c) for q, a, c in zip(question, answer, critique)]
         refined_answers = self.llm_serving.generate_from_input(refine_prompts)
         refined_answers = [answer.replace('[Improved Answer Start]', '').replace('[Improved Answer End]', '').strip() for answer in refined_answers]
         return refined_answers