fix: rags of 5 metrics

dingo/model/llm/rag/llm_rag_answer_relevancy.py

@@ -22,7 +22,7 @@
 # 用于embedding的模型，支持OpenAI和HuggingFace
 class EmbeddingModel:
     """Embedding模型接口，支持OpenAI和HuggingFace模型"""
-    def __init__(self, model_name: str = "text-embedding-3-large", is_openai: bool = True):
+    def __init__(self, model_name: str = "text-embedding-3-large", is_openai: bool = True, api_key: str = None, base_url: str = None):
         self.is_openai = is_openai
         self.model_name = model_name
 
@@ -32,8 +32,8 @@ def __init__(self, model_name: str = "text-embedding-3-large", is_openai: bool =
 
             from openai import OpenAI
             self.client = OpenAI(
-                api_key="API-KEY",
-                base_url="API-KEY-BASE-URL"
+                api_key=api_key,
+                base_url=base_url
             )
         else:
             # 使用HuggingFace Embeddings
@@ -127,7 +127,18 @@ def init_embedding_model(cls, model_name: str = "text-embedding-3-large"):
         """初始化embedding模型"""
         # 检查是否是OpenAI模型
         is_openai = model_name.startswith("text-embedding-")
-        cls.embedding_model = EmbeddingModel(model_name, is_openai)
+        api_key = None
+        base_url = None
+        if is_openai:
+            # 从配置中获取API密钥和base_url
+            if not cls.dynamic_config.key:
+                raise ValueError("key cannot be empty in llm config.")
+            elif not cls.dynamic_config.api_url:
+                raise ValueError("api_url cannot be empty in llm config.")
+            else:
+                api_key = cls.dynamic_config.key
+                base_url = cls.dynamic_config.api_url
+        cls.embedding_model = EmbeddingModel(model_name, is_openai, api_key, base_url)
 
     @classmethod
     def build_messages(cls, input_data: Data) -> List:
@@ -265,7 +276,7 @@ def eval(cls, input_data: Data) -> ModelRes:
             result = ModelRes()
             result.score = score
 
-            # 根据分数判断是否通过（默认阈值5，满分10分）
+            # 根据分数判断是否通过，默认阈值为5
             threshold = 5
             if hasattr(cls, 'dynamic_config') and cls.dynamic_config.parameters:
                 threshold = cls.dynamic_config.parameters.get('threshold', 5)

dingo/model/llm/rag/llm_rag_context_precision.py

@@ -254,7 +254,7 @@ def process_response(cls, responses: List[str]) -> ModelRes:
         result = ModelRes()
         result.score = score
 
-        # 根据分数判断是否通过（默认阈值5，满分10分）
+        # 根据分数判断是否通过，默认阈值为5
         threshold = 5
         if hasattr(cls, 'dynamic_config') and cls.dynamic_config.parameters:
             threshold = cls.dynamic_config.parameters.get('threshold', 5)

dingo/model/llm/rag/llm_rag_context_recall.py

@@ -49,6 +49,7 @@ class LLMRAGContextRecall(BaseOpenAI):
 
     prompt = """上下文召回评估提示词，用于分类陈述归因"""
 
+    @staticmethod
     def context_recall_prompt(question: str, context: str, answer: str) -> str:
         """
         生成上下文召回评估的提示词
@@ -200,7 +201,7 @@ def process_response(cls, response: str) -> ModelRes:
         result = ModelRes()
         result.score = score
 
-        # 根据分数判断是否通过（默认阈值5，满分10分）
+        # 根据分数判断是否通过，默认阈值为5
         threshold = 5
         if hasattr(cls, 'dynamic_config') and cls.dynamic_config.parameters:
             threshold = cls.dynamic_config.parameters.get('threshold', 5)

dingo/model/llm/rag/llm_rag_context_relevancy.py

@@ -45,6 +45,7 @@ class LLMRAGContextRelevancy(BaseOpenAI):
         "source_frameworks": "Ragas + DeepEval + TruLens"
     }
 
+    @staticmethod
     def context_relevance_judge1_prompt(query: str, context: str) -> str:
         """
         First judge template for context relevance evaluation (Chinese version).
@@ -80,6 +81,7 @@ def context_relevance_judge1_prompt(query: str, context: str) -> str:
 请不要尝试解释。
 分析上下文和问题后，相关性分数为 """
 
+    @staticmethod
     def context_relevance_judge2_prompt(query: str, context: str) -> str:
         """
         Second judge template for context relevance evaluation (Chinese version).
@@ -200,7 +202,7 @@ def process_response(cls, response: str) -> ModelRes:
         result = ModelRes()
         result.score = score
 
-        # 根据分数判断是否通过（默认阈值5，满分10分）
+        # 根据分数判断是否通过，默认阈值为5
         threshold = 5
         if hasattr(cls, 'dynamic_config') and cls.dynamic_config.parameters:
             threshold = cls.dynamic_config.parameters.get('threshold', 5)

dingo/model/llm/rag/llm_rag_faithfulness.py

@@ -43,6 +43,7 @@ class LLMRAGFaithfulness(BaseOpenAI):
         "source_frameworks": "Ragas + DeepEval"
     }
 
+    @staticmethod
     def statement_generator_prompt(question: str, answer: str) -> str:
         """
         Prompt to generate statements from answer (Chinese version).
@@ -67,18 +68,19 @@ def statement_generator_prompt(question: str, answer: str) -> str:
 
 请以JSON格式返回结果，格式如下：
 ```json
-{
+{{
     "statements": [
         "陈述1",
         "陈述2",
         "陈述3"
     ]
-}
+}}
 ```
 
 请不要输出其他内容，只返回JSON格式的结果。
 """
 
+    @staticmethod
     def faithfulness_judge_prompt(context: str, statements: List[str]) -> str:
         """
         Prompt to judge faithfulness of statements (Chinese version).
@@ -103,15 +105,15 @@ def faithfulness_judge_prompt(context: str, statements: List[str]) -> str:
 
 请以JSON格式返回结果，格式如下：
 ```json
-{
+{{
     "statements": [
-        {
+        {{
             "statement": "原始陈述，一字不差",
             "reason": "判断理由",
             "verdict": 0或1
-        }
+        }}
     ]
-}
+}}
 ```
 
 请不要输出其他内容，只返回JSON格式的结果。
@@ -284,7 +286,7 @@ def process_response(cls, response: str) -> ModelRes:
         result = ModelRes()
         result.score = score
 
-        # 根据分数判断是否通过（默认阈值5，满分10分）
+        # 根据分数判断是否通过，默认阈值为5
         threshold = 5
         if hasattr(cls, 'dynamic_config') and cls.dynamic_config.parameters:
             threshold = cls.dynamic_config.parameters.get('threshold', 5)