diff --git a/README.md b/README.md
index 8867c394..7c773f21 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,15 @@
 </p>
 
 
+<p align="center">
+  If you like Dingo, please give us a ⭐ on GitHub!
+  <br/>
+  <a href="https://github.com/DataEval/dingo/stargazers" target="_blank">
+    <img src="docs/assets/clickstar_2.gif" alt="Click Star" width="480">
+  </a>
+</p>
+
+
 # Introduction
 
 Dingo is a data quality evaluation tool that helps you automatically detect data quality issues in your datasets. Dingo provides a variety of built-in rules and model evaluation methods, and also supports custom evaluation methods. Dingo supports commonly used text datasets and multimodal datasets, including pre-training datasets, fine-tuning datasets, and evaluation datasets. In addition, Dingo supports multiple usage methods, including local CLI and SDK, making it easy to integrate into various evaluation platforms, such as [OpenCompass](https://github.com/open-compass/opencompass).
@@ -183,6 +192,7 @@ Our evaluation system includes:
 - **Classification Metrics**: Topic categorization and content classification
 - **Multimodality Assessment Metrics**: Image classification and relevance evaluation
 - **Rule-Based Quality Metrics**: Automated quality checks using heuristic rules for effectiveness and similarity detection
+- **Factuality Assessment Metrics**: Two-stage factuality evaluation based on GPT-5 System Card
 - etc
 
 Most metrics are backed by academic sources to ensure objectivity and scientific rigor.
@@ -217,6 +227,12 @@ For detailed guidance on using Dingo's hallucination detection capabilities, inc
 
 📖 **[View Hallucination Detection Guide →](docs/hallucination_guide.md)**
 
+### Factuality Assessment
+
+For comprehensive guidance on using Dingo's two-stage factuality evaluation system:
+
+📖 **[View Factuality Assessment Guide →](docs/factcheck_guide.md)**
+
 # Rule Groups
 
 Dingo provides pre-configured rule groups for different types of datasets:
diff --git a/README_ja.md b/README_ja.md
index 96e2b179..29eab120 100644
--- a/README_ja.md
+++ b/README_ja.md
@@ -33,6 +33,14 @@
     👋 <a href="https://discord.gg/Jhgb2eKWh8" target="_blank">Discord</a>と<a href="./docs/assets/wechat.jpg" target="_blank">WeChat</a>でご参加ください
 </p>
 
+<p align="center">
+  このプロジェクトが役に立ったら、GitHubで⭐を付けてください！
+  <br/>
+  <a href="https://github.com/DataEval/dingo/stargazers" target="_blank">
+    <img src="docs/assets/clickstar_2.gif" alt="Star をクリック" width="480">
+  </a>
+</p>
+
 
 # はじめに
 
@@ -178,6 +186,7 @@ Dingoはルールベースおよびプロンプトベースの評価メトリク
 - **分類メトリクス**: トピック分類とコンテンツ分類
 - **マルチモーダル評価メトリクス**: 画像分類と関連性評価
 - **ルールベース品質メトリクス**: ヒューリスティックルールによる効果性と類似性検出を用いた自動品質チェック
+- **事実性評価メトリクス**: GPT-5 System Cardに基づく二段階事実性評価
 - など
 
 大部分のメトリクスは学術的なソースによって支持されており、客観性と科学的厳密性を保証しています。
@@ -212,6 +221,12 @@ HHEM-2.1-Openローカル推論とLLMベース評価を含む、Dingoの幻覚
 
 📖 **[幻覚検出ガイドを見る →](docs/hallucination_guide.md)**
 
+### 事実性評価
+
+Dingoの二段階事実性評価システムの使用に関する詳細なガイダンス：
+
+📖 **[事実性評価ガイドを見る →](docs/factcheck_guide.md)**
+
 # ルールグループ
 
 Dingoは異なるタイプのデータセット用に事前設定されたルールグループを提供します：
diff --git a/README_zh-CN.md b/README_zh-CN.md
index deaf4cca..484a24ab 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -29,6 +29,14 @@
     👋 加入我们 <a href="https://discord.gg/Jhgb2eKWh8" target="_blank">Discord</a> 和 <a href="./docs/assets/wechat.jpg" target="_blank">微信</a>
 </p>
 
+<p align="center">
+  如果觉得有帮助，欢迎在 GitHub 上点个 ⭐ 支持！
+  <br/>
+  <a href="https://github.com/DataEval/dingo/stargazers" target="_blank">
+    <img src="docs/assets/clickstar_2.gif" alt="点击 Star 支持" width="480">
+  </a>
+</p>
+
 </div>
 
 
@@ -179,6 +187,7 @@ Dingo通过基于规则和基于提示的评估指标提供全面的数据质量
 - **分类指标**：主题分类和内容分类
 - **多模态评估指标**：图像分类和相关性评估
 - **基于规则的质量指标**：使用启发式规则进行效果性和相似性检测的自动化质量检查
+- **事实性评估指标**：基于 GPT-5 System Card 的两阶段事实性评估
 - 等等
 
 大部分指标都由学术来源支持，以确保客观性和科学严谨性。
@@ -213,6 +222,12 @@ input_data = {
 
 📖 **[查看幻觉检测指南 →](docs/hallucination_guide.md)**
 
+### 事实性评估
+
+有关使用Dingo两阶段事实性评估系统的详细指导：
+
+📖 **[查看事实性评估指南 →](docs/factcheck_guide.md)**
+
 # 规则组
 
 Dingo为不同类型的数据集提供预配置的规则组：
diff --git a/app_gradio/app.py b/app_gradio/app.py
index 680bde77..6520bb17 100644
--- a/app_gradio/app.py
+++ b/app_gradio/app.py
@@ -48,20 +48,26 @@ def dingo_demo(
 
     try:
         input_data = {
-            "dataset": dataset_source,
-            "data_format": data_format,
             "input_path": final_input_path,
             "output_path": "" if dataset_source == 'hugging_face' else os.path.dirname(final_input_path),
-            "save_data": True,
-            "save_raw": True,
-
-            "max_workers": max_workers,
-            "batch_size": batch_size,
-
-            "column_content": column_content,
-            "custom_config": {
+            "dataset": {
+                "source": dataset_source,
+                "format": data_format,
+                "field": {
+                    "content": column_content
+                }
+            },
+            "executor": {
                 "rule_list": rule_list,
                 "prompt_list": prompt_list,
+                "result_save": {
+                    "bad": True,
+                    "raw": True
+                },
+                "max_workers": max_workers,
+                "batch_size": batch_size,
+            },
+            "evaluator": {
                 "llm_config": {
                     scene_list: {
                         "model": model,
@@ -72,11 +78,11 @@ def dingo_demo(
             }
         }
         if column_id:
-            input_data['column_id'] = column_id
+            input_data['dataset']['field']['id'] = column_id
         if column_prompt:
-            input_data['column_prompt'] = column_prompt
+            input_data['dataset']['field']['prompt'] = column_prompt
         if column_image:
-            input_data['column_image'] = column_image
+            input_data['dataset']['field']['image'] = column_image
 
         # print(input_data)
         # exit(0)
diff --git a/app_gradio/header.html b/app_gradio/header.html
index 78f11d45..b0800b05 100644
--- a/app_gradio/header.html
+++ b/app_gradio/header.html
@@ -67,7 +67,7 @@
           color: #fafafa;
           opacity: 0.8;
         ">
-        Dingo: A Comprehensive Data Quality Evaluation Tool.<br>
+        Dingo: A Comprehensive AI Data Quality Evaluation Tool.<br>
       </p>
       <style>
         .link-block {
diff --git a/dingo/exec/local.py b/dingo/exec/local.py
index 364a784b..bb7c2ecb 100644
--- a/dingo/exec/local.py
+++ b/dingo/exec/local.py
@@ -173,6 +173,11 @@ def evaluate(self):
         log.debug("[Summary]: " + str(self.summary))
 
     def evaluate_single_data(self, group_type, group, data: Data):
+        # Ensure dynamic configs are applied in child processes as well
+        try:
+            Model.apply_config(self.input_args)
+        except Exception as e:
+            raise RuntimeError(f"Failed to apply config in child process: {e}")
         result_info = ResultInfo(
             data_id=data.data_id, prompt=data.prompt, content=data.content
         )
diff --git a/dingo/model/llm/llm_factcheck_public.py b/dingo/model/llm/llm_factcheck_public.py
new file mode 100644
index 00000000..df3dbdd7
--- /dev/null
+++ b/dingo/model/llm/llm_factcheck_public.py
@@ -0,0 +1,250 @@
+from dataclasses import dataclass
+from typing import Dict, List, Literal, Optional
+
+from dingo.io import Data
+from dingo.model import Model
+from dingo.model.llm.base_openai import BaseOpenAI
+from dingo.model.modelres import ModelRes
+from dingo.model.prompt.prompt_factcheck import PromptFactCheck
+from dingo.utils.exception import ExceedMaxTokens
+
+
+@dataclass
+class Evidence:
+    """验证证据"""
+    url: str
+    snippet: str
+    summary: str
+
+
+@dataclass
+class FactCheckResult:
+    """单条声明的验证结果"""
+    claim: str
+    answer: Literal["true", "false", "unsure"]
+    reasoning: str
+    supporting_evidence: List[Evidence]
+
+
+@Model.prompt_register(metric_type="QUALITY_BAD_FACTUALITY", group=["factuality"])
+@Model.llm_register("LLMFactCheckPublic")
+class LLMFactCheckPublic(BaseOpenAI):
+    """公开事实性评估器 - 基于 GPT-5 System Card 的两阶段评估"""
+
+    _metric_info = {
+        "category": "Factuality Assessment",
+        "quality_dimension": "FACTUAL_CORRECTNESS",
+        "metric_name": "LLMFactCheckPublic",
+        "description": "Two-stage factuality evaluation pipeline from GPT-5",
+        "paper_title": "GPT-5 System Card",
+        "paper_url": "https://cdn.openai.com/pdf/8124a3ce-ab78-4f06-96eb-49ea29ffb52f/gpt5-system-card-aug7.pdf",
+        "paper_authors": "OpenAI"
+    }
+
+    prompt = PromptFactCheck
+    threshold = 0.8
+    batch_size = 10  # 默认批处理大小
+    web_enabled = True  # 默认启用网络搜索
+
+    @classmethod
+    def eval(cls, input_data: Data) -> ModelRes:
+        """执行两阶段评估"""
+        try:
+            # 0. 初始化 client
+            if cls.client is None:
+                cls.create_client()
+
+            # 1. 提取声明
+            claims = cls._extract_claims(input_data.prompt, input_data.content)
+            if not claims:
+                return ModelRes(
+                    score=0.0,
+                    threshold=cls.threshold,
+                    reason=["No factual claims found"],
+                    raw_resp={"claims": [], "results": []}
+                )
+
+            # 2. 分批验证
+            all_results = []
+            for i in range(0, len(claims), cls.batch_size):
+                batch = claims[i:i + cls.batch_size]
+                results = cls._verify_claims(input_data.prompt, input_data.content, batch)
+                all_results.extend(results)
+
+            # 3. 计算指标
+            metrics = cls._calculate_metrics(all_results)
+
+            # 4. 设置评估结果
+            result = ModelRes(
+                score=metrics["factual_ratio"],
+                threshold=cls.threshold,
+                reason=[cls._format_reason(metrics)],
+                raw_resp={
+                    "claims": claims,
+                    "results": all_results,
+                    "metrics": metrics
+                }
+            )
+
+            # 5. 根据分数设置状态
+            if metrics["factual_ratio"] < cls.threshold:
+                result.error_status = True
+                result.type = "QUALITY_BAD_FACTUALITY"
+                result.name = "FACTUALITY_CHECK_FAILED"
+            else:
+                result.type = "QUALITY_GOOD"
+                result.name = "FACTUALITY_CHECK_PASSED"
+
+            return result
+
+        except Exception as e:
+            return ModelRes(
+                score=0.0,
+                threshold=cls.threshold,
+                reason=[f"Evaluation failed: {str(e)}"],
+                raw_resp={"error": str(e)}
+            )
+
+    @classmethod
+    def _extract_claims(cls, prompt: str, response: str) -> List[str]:
+        """提取事实性声明"""
+        messages = [
+            {"role": "user", "content": (PromptFactCheck.CLAIM_LISTING +
+                (PromptFactCheck.CLAIM_LISTING_NO_WEB if not cls.web_enabled else "")).format(
+                prompt=prompt,
+                response=response
+            )}
+        ]
+        result = cls.send_messages(messages)
+        try:
+            claims = cls._parse_json_list(result)
+            return [c for c in claims if c.strip()]  # 过滤空声明
+        except Exception as e:
+            raise ValueError(f"Failed to parse claims: {str(e)}")
+
+    @classmethod
+    def _verify_claims(cls,
+                      prompt: str,
+                      response: str,
+                      claims: List[str]) -> List[FactCheckResult]:
+        """验证一批声明"""
+        messages = [
+            {"role": "user", "content": (PromptFactCheck.FACT_CHECKING +
+                (PromptFactCheck.FACT_CHECKING_NO_WEB if not cls.web_enabled else "")).format(
+                prompt=prompt,
+                response=response,
+                claims=claims
+            )}
+        ]
+        result = cls.send_messages(messages)
+        try:
+            return cls._parse_check_results(result)
+        except Exception as e:
+            raise ValueError(f"Failed to parse check results: {str(e)}")
+
+    @classmethod
+    def _calculate_metrics(cls, results: List[FactCheckResult]) -> Dict:
+        """计算评估指标"""
+        total = len(results)
+        if total == 0:
+            return {
+                "factual_ratio": 0.0,
+                "true_count": 0,
+                "false_count": 0,
+                "unsure_count": 0,
+                "total_claims": 0
+            }
+
+        counts = {
+            "true": sum(1 for r in results if r.answer == "true"),
+            "false": sum(1 for r in results if r.answer == "false"),
+            "unsure": sum(1 for r in results if r.answer == "unsure")
+        }
+
+        return {
+            "factual_ratio": counts["true"] / total,
+            "true_count": counts["true"],
+            "false_count": counts["false"],
+            "unsure_count": counts["unsure"],
+            "total_claims": total
+        }
+
+    @classmethod
+    def _format_reason(cls, metrics: Dict) -> str:
+        """格式化评估原因"""
+        return (
+            f"Found {metrics['total_claims']} claims: "
+            f"{metrics['true_count']} true, "
+            f"{metrics['false_count']} false, "
+            f"{metrics['unsure_count']} unsure. "
+            f"Factual ratio: {metrics['factual_ratio']:.2%}"
+        )
+
+    @classmethod
+    def _parse_json_list(cls, text: str) -> List[str]:
+        """解析 JSON 列表"""
+        import json
+        try:
+            # 提取 JSON 部分
+            start = text.find("[")
+            end = text.rfind("]") + 1
+            if start == -1 or end == 0:
+                raise ValueError("No JSON list found")
+            json_str = text[start:end]
+            return json.loads(json_str)
+        except Exception as e:
+            raise ValueError(f"Invalid JSON format: {str(e)}")
+
+    @classmethod
+    def _parse_check_results(cls, text: str) -> List[FactCheckResult]:
+        """解析验证结果"""
+        import json
+        try:
+            # 提取 JSON 部分
+            start = text.find("[")
+            end = text.rfind("]") + 1
+            if start == -1 or end == 0:
+                raise ValueError("No JSON results found")
+            json_str = text[start:end]
+            data = json.loads(json_str)
+
+            results = []
+            for item in data:
+                evidence_list = [
+                    Evidence(**e) for e in item["supporting_evidence"]
+                ]
+                results.append(FactCheckResult(
+                    claim=item["claim"],
+                    answer=item["answer"],
+                    reasoning=item["reasoning"],
+                    supporting_evidence=evidence_list
+                ))
+            return results
+        except Exception as e:
+            raise ValueError(f"Invalid results format: {str(e)}")
+
+    @classmethod
+    def send_messages(cls, messages: List) -> str:
+        """重写发送消息方法，避免使用 models.list()"""
+        if not cls.dynamic_config.model:
+            raise ValueError("model name must be specified")
+
+        params = cls.dynamic_config.parameters or {}
+        cls.validate_config(params)
+
+        completions = cls.client.chat.completions.create(
+            model=cls.dynamic_config.model,
+            messages=messages,
+            temperature=params.get("temperature", 0.3),
+            top_p=params.get("top_p", 1),
+            max_tokens=params.get("max_tokens", 4000),
+            presence_penalty=params.get("presence_penalty", 0),
+            frequency_penalty=params.get("frequency_penalty", 0),
+        )
+
+        if completions.choices[0].finish_reason == "length":
+            raise ExceedMaxTokens(
+                f"Exceed max tokens: {params.get('max_tokens', 4000)}"
+            )
+
+        return str(completions.choices[0].message.content)
diff --git a/dingo/model/llm/llm_math_compare.py b/dingo/model/llm/llm_math_compare.py
new file mode 100644
index 00000000..052c82ec
--- /dev/null
+++ b/dingo/model/llm/llm_math_compare.py
@@ -0,0 +1,99 @@
+import json
+import re
+from typing import List
+
+from dingo.io import Data
+from dingo.model import Model
+from dingo.model.llm.base_openai import BaseOpenAI
+from dingo.model.modelres import ModelRes
+from dingo.model.prompt.prompt_math_compare import PromptMathCompare
+from dingo.utils import log
+from dingo.utils.exception import ConvertJsonError
+
+
+@Model.llm_register('LLMMathCompare')
+class LLMMathCompare(BaseOpenAI):
+    """
+    专注于数学公式抽取效果的对比
+    """
+    prompt = PromptMathCompare
+
+    @classmethod
+    def build_messages(cls, input_data: Data) -> List:
+        messages = [
+            {
+                'role': 'user',
+                'content': cls.prompt.content.format(
+                    input_data.content,
+                    input_data.raw_data.get('webkit_extract_md', ''),
+                    input_data.raw_data.get('megamath_md', ''),
+                ),
+            }
+        ]
+        return messages
+
+    @classmethod
+    def process_response(cls, response: str) -> ModelRes:
+        log.info(response)
+
+        # 提取思考内容和清理响应
+        response_think = cls._extract_think_content(response)
+        response = cls._clean_response(response)
+
+        try:
+            response_json = json.loads(response)
+            if response_think and 'reason' in response_json:
+                response_json['reason'] += '\n' + response_think
+            elif response_think:
+                response_json['reason'] = response_think
+        except json.JSONDecodeError:
+            raise ConvertJsonError(f'Convert to JSON format failed: {response}')
+
+        # 处理特殊情况：没有数学公式
+        if response_json.get('no_formula'):
+            return cls._create_no_formula_result(response_json)
+
+        # 处理正常情况
+        return cls._create_normal_result(response_json)
+
+    @staticmethod
+    def _extract_think_content(response: str) -> str:
+        if response.startswith('<think>'):
+            think_content = re.search(r'<think>(.*?)</think>', response, flags=re.DOTALL)
+            return think_content.group(1).strip() if think_content else ''
+        return ''
+
+    @staticmethod
+    def _clean_response(response: str) -> str:
+        response = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL).strip()
+
+        if response.startswith('```json'):
+            response = response[7:]
+        elif response.startswith('```'):
+            response = response[3:]
+
+        if response.endswith('```'):
+            response = response[:-3]
+
+        return response
+
+    @staticmethod
+    def _create_no_formula_result(response_json: dict) -> ModelRes:
+        result = ModelRes()
+        result.error_status = False
+        result.type = 'NO_FORMULA'
+        result.name = 'math'
+        result.reason = [json.dumps(response_json, ensure_ascii=False)]
+        return result
+
+    @staticmethod
+    def _create_normal_result(response_json: dict) -> ModelRes:
+        result = ModelRes()
+        score = response_json.get('score', 0)
+
+        result.error_status = score != 1
+        result.type = {1: 'TOOL_ONE_BETTER', 2: 'TOOL_TWO_BETTER'}.get(score, 'TOOL_EQUAL')
+        result.name = 'math'
+        result.reason = [json.dumps(response_json, ensure_ascii=False)]
+
+        return result
diff --git a/dingo/model/llm/llm_meta_rater_evaluation.py b/dingo/model/llm/llm_meta_rater_evaluation.py
new file mode 100644
index 00000000..bcca3148
--- /dev/null
+++ b/dingo/model/llm/llm_meta_rater_evaluation.py
@@ -0,0 +1,99 @@
+"""
+LLM models for Meta-rater PRRC dimensions evaluation.
+
+This module contains LLM-based evaluators for assessing the quality of text data
+across four dimensions: Professionalism, Readability, Reasoning, and Cleanliness.
+Based on the Meta-rater paper for data selection in LLM pre-training.
+"""
+
+import json
+from typing import List
+
+from dingo.io import Data
+from dingo.model import Model
+from dingo.model.llm.base_openai import BaseOpenAI
+from dingo.model.modelres import ModelRes
+from dingo.model.prompt.prompt_meta_rater import PromptMetaRaterProfessionalism
+from dingo.utils import log
+from dingo.utils.exception import ConvertJsonError
+
+
+@Model.llm_register('LLMMetaRaterEvaluation')
+class LLMMetaRaterEvaluation(BaseOpenAI):
+    """
+    Unified LLM model for Meta-rater PRRC dimensions evaluation.
+
+    This model provides a single interface for evaluating multiple aspects
+    of text quality based on the Meta-rater paper's PRRC framework:
+    - Professionalism: Degree of expertise required
+    - Readability: Clarity and coherence
+    - Reasoning: Logical depth and complexity
+    - Cleanliness: Formatting and content appropriateness
+
+    The specific evaluation type is determined by the prompt used.
+    """
+    prompt = PromptMetaRaterProfessionalism  # Default prompt
+
+    @classmethod
+    def build_messages(cls, input_data: Data) -> List:
+        """
+        Build messages for the LLM API call.
+
+        Args:
+            input_data: Data object containing text content to evaluate
+
+        Returns:
+            List: Formatted messages for LLM API
+        """
+        messages = [{"role": "user",
+                     "content": cls.prompt.content.format(content=input_data.content)}]
+        return messages
+
+    @classmethod
+    def process_response(cls, response: str) -> ModelRes:
+        """
+        Process the LLM response for Meta-rater evaluation.
+
+        Args:
+            response: Raw response string from the LLM
+
+        Returns:
+            ModelRes: Processed evaluation results with score and reason
+        """
+        log.info(response)
+
+        # Clean up Markdown code block formatting if present
+        cleaned_response = response
+        if cleaned_response.startswith('```json'):
+            cleaned_response = cleaned_response[7:]
+        if cleaned_response.startswith('```'):
+            cleaned_response = cleaned_response[3:]
+        if cleaned_response.endswith('```'):
+            cleaned_response = cleaned_response[:-3]
+
+        try:
+            response_json = json.loads(cleaned_response)
+        except json.JSONDecodeError:
+            raise ConvertJsonError(f'Convert to JSON format failed: {cleaned_response}')
+
+        # Extract score and reason from response
+        score = response_json.get('score', 0)
+        reason = response_json.get('reason', '')
+
+        result = ModelRes()
+
+        # Meta-rater uses 1-5 scoring, with higher scores being better;
+        # We normalize this to binary classification for compatibility
+        # Scores >= 3 are considered "good quality", < 3 are "low quality"
+        if score >= 3:
+            result.error_status = False
+            result.type = cls.prompt.metric_type
+            result.name = "HighQuality"
+            result.reason = [f"Score: {score}/5. {reason}"]
+        else:
+            result.error_status = True
+            result.type = cls.prompt.metric_type
+            result.name = "LowQuality"
+            result.reason = [f"Score: {score}/5. {reason}"]
+
+        return result
diff --git a/dingo/model/llm/llm_text_3h.py b/dingo/model/llm/llm_text_3h.py
index ad4eb8e7..62be1027 100644
--- a/dingo/model/llm/llm_text_3h.py
+++ b/dingo/model/llm/llm_text_3h.py
@@ -40,7 +40,7 @@ def process_response(cls, response: str) -> ModelRes:
         result = ModelRes()
 
         # error_status
-        if response_model.score == "1":
+        if response_model.score == 1:
             result.reason = [response_model.reason]
             result.name = cls.prompt.__name__[8:].upper()
         else:
diff --git a/dingo/model/prompt/prompt_factcheck.py b/dingo/model/prompt/prompt_factcheck.py
new file mode 100644
index 00000000..5fc502d9
--- /dev/null
+++ b/dingo/model/prompt/prompt_factcheck.py
@@ -0,0 +1,151 @@
+class PromptFactCheck:
+    """Factuality evaluation prompts from GPT-5 System Card"""
+
+    CLAIM_LISTING = """### Introduction
+Your task is to list relevant facts in an assistant’s response to a given prompt. Your output will be used as the first
+step in the following fact- checking pipeline used to evaluate an assistant’s response for factual correctness.
+
+Fact-Checking Pipeline:
+1. Given a prompt and assistant’s response, list all relevant factual claims made by the assistant.
+2. Separate the list of N claims into M manageable groups.
+3. For each group of claims, fact-check each claim in the group by browsing the web to find evidence supporting or
+refuting the claim.
+
+### Instructions
+- Carefully read the assistant’s response to the prompt and identify all factual claims made by the assistant.
+- You should isolate your focus to real-world facts (e.g., facts about news, people, places, events, etc.).
+- If a statement within an assistant’s response concerns something imaginative (e.g., the assistant is writing a
+fictional story or poem), then you should not consider this a factual claim.
+- For each factual claim that you list, another assistant will be tasked with fact-checking it by browsing the web to
+find evidence supporting or refuting the claim.
+- Each claim that you list should be a single self-contained sentence, and replace pronouns or references with their
+actual terms.
+- You should only consider claims that are relevant for answering the prompt. We consider a claim to be relevant if the
+subject of the claim is either exactly contained or related to any subject present in the prompt.
+- If the same claim is repeated multiple times, you should only list it once.
+- Try to list claims in the order that they appear in the assistant’s response, so that related claims are grouped
+together.
+### Formatting
+Your response should be a list of claims in the following JSON format:
+‘‘‘json
+[
+    "fact_1",
+    "fact_2",
+...
+]
+‘‘‘
+
+### Example
+Below is an example of a prompt and response.
+
+Prompt:
+Who is Barack Obama?
+
+Response:
+Barack Obama is an American politician and attorney who served as the 44th President of the United States from 2009 to
+2017. A member of the Democratic Party, he was the first African American president in U.S. history.
+
+Output:
+‘‘‘json
+[
+    "Barack Obama is an American politician.",
+    "Barack Obama is an attorney.",
+    "Barack Obama served as the 44th President of the United States.",
+    "Barack Obama served as president from 2009 to 2017.",
+    "Barack Obama is a member of the Democratic Party.",
+    "Barack Obama was the first African American president in United States history."
+]
+‘‘‘
+
+Note that you should expect the assistant’s response to potentially be much longer than the one above, and could consist
+of up to 100 separate claims.
+
+### Task
+Prompt:
+{prompt}
+
+Response:
+{response}
+"""
+
+    FACT_CHECKING = """### Introduction
+Your task is to help fact-check an assistant’s response to a given prompt for factual correctness. You will be asked to
+focus on a list of factual claims made by the assistant that represent a subset of factual claims made within the
+assistant’s response. Your output will be used as part of the third step of the following fact-checking pipeline:
+
+Fact-Checking Pipeline:
+1. Given a prompt and assistant’s response, list all relevant factual claims made by the assistant.
+2. Separate the list of N claims into M manageable groups.
+3. For each group of claims, fact-check each claim in the group by browsing the web to find evidence supporting or
+refuting the claim.
+
+### Instructions
+- You should fact-check the provided list of claims one by one.
+- Please use your browser tool to confirm the factual correctness of each claim, which is extracted from the assistant’s
+response to the provided prompt.
+- You are expected to perform one or more web searches to find evidence supporting or refuting each claim. Limit yourself
+to three web searches per claim.
+- You are allowed to use evidence from a single source to support or refute multiple claims.
+- Use this evidence to determine whether each claim is true or false.
+- If you cannot confidently determine the correctness of a claim, e.g., if it is ambiguous or if the evidence is
+inconclusive, then you should say that you are unsure.
+- For each claim, provide supporting evidence for your answer in the form of a list of URLs, snippets, and summaries.
+- Your response should be in the JSON format specified below.
+
+### Connection of claims to the response
+- Each claim is extracted from the assistant’s response, but it might be slightly rewritten from its exact phrasing in
+the response.
+- It is possible that an error was made in step 1 of the fact-checking pipeline, and one of the claims was not correctly
+extracted from the response.
+- Issues in a claim should not matter unless they are also reflected in the way this claim is phrased in the response.
+- If you find evidence that contradicts a claim, but this evidence does not contradict the response, then the claim
+should not be counted as a factual error.
+
+### Formatting
+Your response should be in the following JSON format (no comments):
+‘‘‘json
+[
+    {{
+        "claim": "<claim>",
+        "answer": "true" | "false" | "unsure",
+        "reasoning": "<Description of your decision for the factuality of claim. If your conclusion is \"false\", you
+        should explain how the evidence contradicts both the claim as well as the response>",
+        "supporting_evidence": [
+            {{
+            "url": "<link>",
+            "snippet": "<relevant excerpt>",
+            "summary": "<description of how the snippet relates to the factuality of the claim>"
+            }},
+            ...
+        ]
+    }},
+/* one object per claim */
+]
+‘‘‘
+
+### Task
+Prompt:
+{prompt}
+
+Response:
+{response}
+
+Claims:
+{claims}
+"""
+
+    CLAIM_LISTING_NO_WEB = """
+Note that the assistant did not have access to the web to make its response, so you should ignore
+any claims concerning what information is available on the web. For example, ignore claims such
+as "no reliable information is available on the [web or other online sources] about [topic]" or "I'm
+not finding [topic]."
+"""
+
+    FACT_CHECKING_NO_WEB = """
+Note that the assistant did not have access to the web to make its response, so you should not
+mark any claims concerning what information is available on the web as factual errors. For
+example, do not mark claims such as "no reliable information is available on [the web or other
+online sources] about [topic]" or "I'm not finding [topic]" as factual errors, even if that claim is
+false. Watch out for claims of this form that were incorrectly rewritten by the previous step to
+appear to be making claims about the topic rather than the model's internal knowledge.
+"""
diff --git a/dingo/model/prompt/prompt_math_compare.py b/dingo/model/prompt/prompt_math_compare.py
new file mode 100644
index 00000000..93dde87c
--- /dev/null
+++ b/dingo/model/prompt/prompt_math_compare.py
@@ -0,0 +1,112 @@
+from dingo.model.model import Model
+from dingo.model.prompt.base import BasePrompt
+
+
+@Model.prompt_register('MathCompare', [], ['LLMMathCompare'])
+class PromptMathCompare(BasePrompt):
+    _metric_info = {
+        'category': 'SFT Data Assessment Metrics',
+        'metric_name': 'PromptMathCompare',
+        'description': 'Compares the effectiveness of two tools in extracting mathematical formulas from HTML to Markdown format by evaluating recognition rate and accuracy to determine which tool performs better',
+        'paper_title': '',
+        'paper_url': '',
+        'paper_authors': '',
+        'evaluation_results': ''
+    }
+
+    # prompt v3
+    content = """
+你是一位专业的数学公式识别评估专家，擅长分析 HTML 代码和 Markdown 文本中的数学公式。现在我会提供三段内容：
+
+1. **裁剪后网页的 HTML 代码**：这是原始网页经过裁剪（去除非必要标签和标签属性）的 HTML 结构。
+2. **工具A提取的 Markdown 文本**：这是从 HTML 中提取的、适合大语言模型训练的 Markdown 格式文本。
+3. **工具B提取的 Markdown 文本**：这是从 HTML 中提取的、适合大语言模型训练的 Markdown 格式文本。
+
+⚠️ 注意：工具A与工具B的顺序不是固定的，请不要因为顺序而偏好某一工具，最终结论必须严格基于流程2统计的数值差异。
+
+## 评估流程
+
+### 1. 公式数量统计
+
+**原始HTML公式识别：**
+- MathJax格式：`\\(` `\\)` `\\[` `\\]` `$$` `$`
+- MathML标签：`<math>` `<mrow>` `<mi>` 等
+- 其他数学标签：`<div class=math>` `<mjx-container class="MathJax">` 等（内容为LaTeX格式）
+- 一些自定义标签：`<span class=ztext-math>` 等（内容为LaTeX格式）
+
+**Markdown公式统计：**
+- 行内公式：`$...$`  `\\(...\\)`
+- 行间公式：`$$...$$` `\\[...\\]` `\\begin{{...}}...\\end{{...}}`
+
+### 2. 识别率和准确率统计
+
+统计以下内容：
+- N = HTML 中实际公式数量（如果N = 0，直接跳转到 “5. 特殊情况处理”并输出指定内容，不需要进行其他的流程）
+- MA, MB = 工具A、B识别的公式数量（在对应Markdown文本中）
+- EA, EB = 工具A、B在转化中的错误数量（在对应Markdown文本中）
+
+计算：
+- 工具A识别率 = MA / N × 100%
+- 工具B识别率 = MB / N × 100%
+- 工具A准确率 = (MA − EA) / MA × 100%
+- 工具B准确率 = (MB − EB) / MB × 100%
+
+### 3. 量化评估规则
+
+请严格按照以下规则做出决策：
+- 如果识别率差异 ≥ 20%：识别率高的工具获胜。
+- 如果识别率差异 < 20% 且准确率差异 ≥ 15%：准确率高的工具获胜。
+- 如果两项差异都 < 阈值：判定两者相当。
+
+
+### 原始网页的 HTML 代码如下：
+
+```html
+{}
+```
+
+### 工具A提取的 Markdown 文本如下：
+
+```md
+{}
+```
+
+### 工具B提取的 Markdown 文本如下：
+
+```md
+{}
+```
+
+### 4. 输出格式（HTML有公式情况，即 N ≠ 0）
+
+请最终只返回一个 JSON，不要有任何额外解释说明
+JSON 包含以下字段：
+- `score`：如果工具A更好取值1，工具B更好取值2，效果相当取值0
+- `name`：固定值 "math"
+- `reason`："1）HTML共N个公式；2）工具A统计结果；3）工具B统计结果；4）判定结果。"
+
+
+示例输出（HTML有公式情况，即 N ≠ 0）：
+```json
+{{
+  "score": [0|1|2],
+  "name": "math",
+  "reason": "1）HTML共N个公式；2）工具A识别MA个(识别率%)，错误EA个(准确率%)；3）工具B识别MB个(识别率%)，错误EB个(准确率%)；4）最终依据规则，判定..."
+}}
+```
+
+### 5. 特殊情况处理（HTML无公式情况，即 N = 0）
+
+如果统计到 N = 0，务必直接返回，不得包含额外解释：
+```json
+{{
+  "no_formula": true
+}}
+
+### 6. 注意事项
+如果 HTML 中没有任何数学公式，请按照特殊情况处理，返回指定内容。
+
+如果HTML有数学公式，在做出结论前，必须严格完成 ①统计 ②计算 ③规则判定 这三个步骤，不得跳过。
+
+返回结果必须是一个严格符合格式的 JSON，不得包含额外解释！
+"""
diff --git a/dingo/model/prompt/prompt_meta_rater.py b/dingo/model/prompt/prompt_meta_rater.py
new file mode 100644
index 00000000..da6a57e0
--- /dev/null
+++ b/dingo/model/prompt/prompt_meta_rater.py
@@ -0,0 +1,234 @@
+"""
+Prompt templates for Meta-rater PRRC dimensions evaluation.
+
+This module defines prompts used by LLM models to assess the quality of text data
+across four dimensions: Professionalism, Readability, Reasoning, and Cleanliness.
+Based on the Meta-rater paper for data selection in LLM pre-training.
+"""
+
+from dingo.model.model import Model
+from dingo.model.prompt.base import BasePrompt
+
+# Professionalism evaluation prompt
+META_RATER_PROFESSIONALISM_PROMPT = """# CONTEXT #
+I am a data scientist interested in exploring data in the pre-training stage of large language models.
+
+# OBJECTIVE #
+You are an expert evaluator. Below is an extract from a text source such as a web page, book, academic paper, Github, Wikipedia, or StackExchange. Evaluate the PROFESSIONALISM of the text, that is, the degree of expertise and prerequisite knowledge required to comprehend it, using the additive 5-point scoring system described below. Your evaluation should be based on the depth, accuracy, and accessibility of the content, without considering the writing style, grammar, spelling, or punctuation in your scoring.
+
+Points are accumulated based on the satisfaction of each criterion:
+- Add 1 point if the text is relatively simple and requires minimal technical knowledge or expertise to understand. The text might include nursery rhymes, children's books, or other basic content that is accessible to a broad audience. The information provided is straightforward and does not delve into complex concepts or specialized topics.
+- Add another point if the text is somewhat more complex and might require a basic level of specialized knowledge to comprehend fully. Examples could include popular books, popular science articles, or novels. The content delves a little deeper into the subject matter, but it remains accessible to a reasonably broad audience.
+- Award a third point if the text falls in the middle of the spectrum, requiring some degree of expertise to understand but not being overly complex or specialized. The content might encompass more advanced books, detailed articles, or introductions to complex topics. It provides a decent level of depth and detail, but it does not require an extensive background in the subject matter to understand.
+- Grant a fourth point if the text is complicated and requires a significant level of expertise and technical knowledge. Examples might include academic papers, advanced textbooks, or detailed technical reports. The content is detailed and accurate, but it could be inaccessible to those without a strong background in the subject matter.
+- Bestow a fifth point if the text is extremely high in professionalism, requiring a high degree of subject matter expertise and prerequisite knowledge. The text is likely limited to those with advanced understanding or experience in the field, such as advanced academic papers, complex technical manuals, or patents. The content is deep, accurate, and insightful, but largely inaccessible to those without a significant background in the topic.
+
+Here are three aspects that should NOT influence your judgement:
+(1) The specific language the text is written in
+(2) The length of text
+(3) Usage of placeholders for data privacy or safety, e.g. @CAPS1, [EMAIL_ADDRESS], [PHONE_NUMBER], and so on.
+
+# STYLE #
+A formal and clear text including score and reason.
+# TONE #
+professional, objective, formal, and clear.
+# AUDIENCE #
+Data scientists and other professionals interested in data for large language models.
+# RESPONSE #
+Return the results in JSON format: {{"score": x, "reason": "xxx"}}.
+
+Here is the text:
+{content}
+"""
+
+# Readability evaluation prompt
+META_RATER_READABILITY_PROMPT = """# CONTEXT #
+I am a data scientist interested in exploring data in the pre-training stage of large language models.
+
+# OBJECTIVE #
+You are an expert evaluator. Below is an extract from a text source such as a web page, book, academic paper, Github, Wikipedia, or StackExchange. Evaluate whether the page has a high READABILITY using the additive 5-point scoring system described below.
+
+Points are accumulated based on the satisfaction of each criterion：
+- Add 1 point if the text is somewhat readable but contains significant issues with clarity or coherence. It might include complex vocabulary or sentence structures that require advanced reading skills, or it might have numerous grammar and spelling errors.
+- Add another point if the text is generally clear and coherent, but there are sections that are difficult to comprehend due to occasional grammar, spelling errors, or convoluted sentence structures.
+- Award a third point if the text is clear and coherent for the most part, using appropriate vocabulary and sentence structures that are easy to understand. Minor issues with grammar or spelling might still be present.
+- Grant a fourth point if the text is very clear and coherent, with very few or no errors in grammar and spelling. The text uses proper punctuation, vocabulary, and sentence structures that are easy to follow and understand.
+- Bestow a fifth point if the text is outstanding in its clarity and coherence. It uses language and sentence structures that are easy to comprehend, while also conveying ideas and nuances effectively. Minor errors in grammar, spelling, and punctuation are allowed, but they should not interfere with the overall understanding of the text.
+
+Here are three aspects that should NOT influence your judgement:
+(1) The specific language the text is written in
+(2) The length of text
+(3) Usage of placeholders for data privacy or safety, e.g. @CAPS1, [EMAIL_ADDRESS], [PHONE_NUMBER], and so on.
+
+# STYLE #
+A formal and clear text including score and reason.
+# TONE #
+professional, objective, formal, and clear.
+# AUDIENCE #
+Data scientists and other professionals interested in data for large language models.
+# RESPONSE #
+Return the results in JSON format: {{"score": x, "reason": "xxx"}}.
+
+Here is the text:
+{content}"""
+
+# Reasoning evaluation prompt
+META_RATER_REASONING_PROMPT = """# CONTEXT #
+I am a data scientist interested in exploring data in the pre-training stage of large language models.
+
+# OBJECTIVE #
+You are an expert evaluator. Below is an extract from a text source such as a web page, book, academic paper, Github, Wikipedia, or StackExchange. Evaluate whether the page has a high REASONING using the additive 5-point scoring system described below.
+
+Points are accumulated based on the satisfaction of each criterion：
+Add 1 point if the content contains preliminary elements of reasoning, possibly involving a single causal relationship or simple logical judgments, but lacks in-depth analysis (e.g., presenting a viewpoint without supporting evidence or detailed explanations).
+Add another point if the content demonstrates basic reasoning ability, incorporating some logical relationships that require the reader to engage in a certain level of thought. This may involve simple argumentative structures or examples, but the analysis remains superficial (e.g., providing a problem and a straightforward solution with some examples but lacking depth).
+Award a third point if the content exhibits a good level of reasoning complexity, involving multiple reasoning steps that require more complex thought from the reader. The reader should be able to identify several interrelated arguments and may include some depth of analysis (e.g., analyzing how different factors influence an outcome or comparing various viewpoints).
+Grant a fourth point if the content has a high level of reasoning complexity, including multi-layered logical reasoning and in-depth analysis. The reader needs to engage in complex thinking and can identify multiple interconnected arguments while conducting a comprehensive evaluation (e.g., analyzing multiple variables or assessing the pros and cons of different solutions).
+Bestow a fifth point if the content excels in reasoning complexity, demanding deep analysis and innovative thinking from the reader. The reasoning process is complex and multidimensional, involving interdisciplinary knowledge, requiring the reader to integrate various pieces of information to make comprehensive judgments (e.g., discussing complex mathematical models, designing optimization algorithms, or engaging in high-level strategic thinking).
+
+Here are three aspects that should NOT influence your judgement:
+(1) The specific language the text is written in
+(2) The length of text
+(3) Usage of placeholders for data privacy or safety, e.g. @CAPS1, [EMAIL_ADDRESS], [PHONE_NUMBER], and so on.
+
+# STYLE #
+A formal and clear text including score and reason.
+# TONE #
+professional, objective, formal, and clear.
+# AUDIENCE #
+Data scientists and other professionals interested in data for large language models.
+# RESPONSE #
+Return the results in JSON format: {{"score": x, "reason": "xxx"}}.
+
+Here is the text:
+{content}"""
+
+# Cleanliness evaluation prompt
+META_RATER_CLEANLINESS_PROMPT = """# CONTEXT #
+I am a data scientist interested in exploring data in the pre-training stage of large language models.
+
+# OBJECTIVE #
+You are an expert evaluator. Below is an extract from a text source such as a web page, book, academic paper, Github, Wikipedia, or StackExchange. Evaluate whether the page has a high CLEANLINESS using the additive 5-point scoring system described below.
+
+Points are accumulated based on the satisfaction of each criterion：
+A score of 1 indicates serious issues that affect fluency.
+A score of 2 indicates the text has obvious problems that affect fluency.
+A score of 3 means that the text has some problems but does not seriously affect reading fluency.
+A score of 4 indicates the text has minor problems but does not affect reading.
+A score of 5 means points means that the text is perfect on every criteria.
+The following factors should not affect your judgement:
+The presence of the $TRUNCATED$ symbol is to be seen as an author-decided manual article ending flag, text completeness should not be considered.
+High cleanliness is defined by the following four criteria, please score each of the four criteria on a 5-point scale:
+- Correct formatting: The text should appear to be edited by a human, rather than extracted by a machine, with no inappropriate characters.
+- Appropriate content: The text should not contain links, advertisements, or other irrelevant text that affects reading. The effective content of the text is long enough to extract a clear structure and theme.
+- Completeness Content: The body of the article consists of complete sentences written naturally by humans, rather than phrases and lists, containing opinions, facts or stories.
+However, if there is a $TRUNCATED$ symbol at the end, it should be considered as a manual article ending flag set by the author, and there is no need to consider completeness.
+
+Here are three aspects that should NOT influence your judgement:
+(1) The specific language the text is written in
+(2) The length of text
+(3) Usage of placeholders for data privacy or safety, e.g. @CAPS1, [EMAIL_ADDRESS], [PHONE_NUMBER], and so on.
+
+# STYLE #
+A formal and clear text including score and reason.
+# TONE #
+professional, objective, formal, and clear.
+# AUDIENCE #
+Data scientists and other professionals interested in data for large language models.
+# RESPONSE #
+Return the results in JSON format: {{"score": x, "type": "cleanliness", "correct_formatting": x, "appropriate_content": x, "completeness": x, "reason": "xxx"}}.
+
+Here is the text:
+{content}"""
+
+
+@Model.prompt_register("META_RATER_PROFESSIONALISM", [], ["LLMMetaRaterEvaluation"])
+class PromptMetaRaterProfessionalism(BasePrompt):
+    """
+    Prompt class for Meta-rater Professionalism evaluation.
+
+    Evaluates the degree of expertise and prerequisite knowledge required to
+    comprehend text on a 5-point scale.
+    """
+
+    # Metadata for documentation generation
+    _metric_info = {
+        "category": "Meta Rater Evaluation Metrics",
+        "metric_name": "PromptMetaRaterProfessionalism",
+        "description": "Evaluates the degree of expertise and prerequisite knowledge required to comprehend text on a 5-point scale",
+        "paper_title": "Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models",
+        "paper_url": "https://arxiv.org/pdf/2504.14194",
+        "paper_authors": "Zhuang et al., 2025",
+        "evaluation_results": ""
+    }
+
+    content = META_RATER_PROFESSIONALISM_PROMPT
+
+
+@Model.prompt_register("META_RATER_READABILITY", [], ["LLMMetaRaterEvaluation"])
+class PromptMetaRaterReadability(BasePrompt):
+    """
+    Prompt class for Meta-rater Readability evaluation.
+
+    Evaluates the clarity and coherence of text using appropriate vocabulary
+    and sentence structures on a 5-point scale.
+    """
+
+    # Metadata for documentation generation
+    _metric_info = {
+        "category": "Meta Rater Evaluation Metrics",
+        "metric_name": "PromptMetaRaterProfessionalism",
+        "description": "Evaluates the clarity and coherence of text using appropriate vocabulary and sentence structures on a 5-point scale",
+        "paper_title": "Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models",
+        "paper_url": "https://arxiv.org/pdf/2504.14194",
+        "paper_authors": "Zhuang et al., 2025",
+        "evaluation_results": ""
+    }
+
+    content = META_RATER_READABILITY_PROMPT
+
+
+@Model.prompt_register("META_RATER_REASONING", [], ["LLMMetaRaterEvaluation"])
+class PromptMetaRaterReasoning(BasePrompt):
+    """
+    Prompt class for Meta-rater Reasoning evaluation.
+
+    Evaluates the reasoning complexity and logical depth of text content,
+    from simple logical judgments to complex multidimensional analysis on a 5-point scale.
+    """
+
+    # Metadata for documentation generation
+    _metric_info = {
+        "category": "Meta Rater Evaluation Metrics",
+        "metric_name": "PromptMetaRaterProfessionalism",
+        "description": "Evaluates the reasoning complexity and logical depth of text content, from simple logical judgments to complex multidimensional analysis on a 5-point scale",
+        "paper_title": "Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models",
+        "paper_url": "https://arxiv.org/pdf/2504.14194",
+        "paper_authors": "Zhuang et al., 2025",
+        "evaluation_results": ""
+    }
+
+    content = META_RATER_REASONING_PROMPT
+
+
+@Model.prompt_register("META_RATER_CLEANLINESS", [], ["LLMMetaRaterEvaluation"])
+class PromptMetaRaterCleanliness(BasePrompt):
+    """
+    Prompt class for Meta-rater Cleanliness evaluation.
+
+    Evaluates text formatting, content appropriateness, and completeness,
+    assessing whether text appears human-edited and free from noise on a 5-point scale.
+    """
+
+    # Metadata for documentation generation
+    _metric_info = {
+        "category": "Meta Rater Evaluation Metrics",
+        "metric_name": "PromptMetaRaterCleanliness",
+        "description": "Evaluates text formatting, content appropriateness, and completeness, assessing whether text appears human-edited and free from noise on a 5-point scale",
+        "paper_title": "Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models",
+        "paper_url": "https://arxiv.org/pdf/2504.14194",
+        "paper_authors": "Zhuang et al., 2025",
+        "evaluation_results": ""
+    }
+
+    content = META_RATER_CLEANLINESS_PROMPT
diff --git a/dingo/model/rule/rule_image.py b/dingo/model/rule/rule_image.py
index b6a1f70f..8e8c96b1 100644
--- a/dingo/model/rule/rule_image.py
+++ b/dingo/model/rule/rule_image.py
@@ -1,6 +1,9 @@
+import json
 import os
+import time
 
 import numpy as np
+import requests
 from PIL import Image
 
 from dingo.config.input_args import EvaluatorRuleArgs
@@ -237,7 +240,82 @@ def eval(cls, input_data: Data) -> ModelRes:
         return res
 
 
+@Model.rule_register("QUALITY_BAD_IMG_ARTIMUSE", [])
+class RuleImageArtimuse(BaseRule):
+    # Metadata for documentation generation
+    _metric_info = {
+        "category": "Rule-Based IMG Quality Metrics",
+        "quality_dimension": "IMG_ARTIMUSE",
+        "metric_name": "RuleImageArtimuse",
+        "description": "Evaluates image quality in the field of aesthetics using artimuse",
+        "paper_title": "",
+        "paper_url": "",
+        "paper_authors": "",
+        "evaluation_results": ""
+    }
+
+    dynamic_config = EvaluatorRuleArgs(threshold=6, refer_path=['https://artimuse.intern-ai.org.cn/'])
+
+    @classmethod
+    def eval(cls, input_data: Data) -> ModelRes:
+        try:
+            response_create_task = requests.post(
+                cls.dynamic_config.refer_path[0] + 'api/v1/task/create_task',
+                json={
+                    "img_url": input_data.content,
+                    "style": 1
+                },
+                headers={
+                    "Content-Type": "application/json",
+                    "User-Agent": "dingo",
+                },
+                # timeout=30  # 设置超时时间
+            )
+            response_create_task_json = response_create_task.json()
+            # print(response_create_task_json)
+            task_id = response_create_task_json.get('data').get('id')
+
+            time.sleep(5)
+            request_time = 0
+            while (True):
+                request_time += 1
+                response_get_status = requests.post(
+                    cls.dynamic_config.refer_path[0] + 'api/v1/task/status',
+                    json={
+                        "id": task_id
+                    },
+                    headers={
+                        "Content-Type": "application/json",
+                        "User-Agent": "dingo",
+                    },
+                    # timeout=30  # 设置超时时间
+                )
+                response_get_status_json = response_get_status.json()
+                # print(response_get_status_json)
+                status_data = response_get_status_json.get('data')
+                if status_data['phase'] == 'Succeeded':
+                    break
+                time.sleep(5)
+
+            return ModelRes(
+                error_status=True if status_data['score_overall'] < cls.dynamic_config.threshold else False,
+                type="Artimuse_Succeeded",
+                name="BadImage" if status_data['score_overall'] < cls.dynamic_config.threshold else "GoodImage",
+                reason=[json.dumps(status_data, ensure_ascii=False)],
+            )
+        except Exception as e:
+            return ModelRes(
+                error_status=False,
+                type="Artimuse_Fail",
+                name="Exception",
+                reason=[str(e)],
+            )
+
+
 if __name__ == "__main__":
-    data = Data(data_id="", prompt="", content="")
-    tmp = RuleImageRepeat().eval(data)
-    print(tmp)
+    data = Data(
+        data_id='1',
+        content="https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/upload/ef39eef6-2b40-4ea3-8285-934684734298-stsupload-1753254621827-dog.jpg"
+    )
+    res = RuleImageArtimuse.eval(data)
+    print(res)
diff --git a/dingo/run/vsl.py b/dingo/run/vsl.py
index e8fbe905..a17f1967 100644
--- a/dingo/run/vsl.py
+++ b/dingo/run/vsl.py
@@ -169,6 +169,11 @@ def parse_args():
             "app"],
         default="visualization",
         help="Choose the mode: visualization or app")
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=8000,
+        help="Port for local HTTP server in visualization mode (default: 8000)")
     return parser.parse_args()
 
 
@@ -195,7 +200,7 @@ def main():
         success, new_html_filename = process_and_inject(args.input)
         if success:
             web_static_dir = os.path.join(os.path.dirname(__file__), "..", "..", "web-static")
-            port = 8000
+            port = args.port
             try:
                 server = start_http_server(web_static_dir, port)
                 url = f"http://localhost:{port}/{new_html_filename}"
diff --git a/docs/artimuse.md b/docs/artimuse.md
new file mode 100644
index 00000000..dc544de2
--- /dev/null
+++ b/docs/artimuse.md
@@ -0,0 +1,106 @@
+# Artimuse 图像质量评估规则
+
+## 概述
+
+在介绍规则之前，先简要说明 ArtiMuse：
+
+ArtiMuse 是一个面向图像美学质量评估（Image Aesthetics Assessment, IAA）的在线模型/服务，能够输出整体美学分数并提供细粒度、可解释的评估信息，适用于作品筛选、内容推荐等场景。论文：[ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding](https://arxiv.org/abs/2507.14533)。
+
+RuleImageArtimuse 基于 ArtiMuse 在线服务对输入图片进行美学质量评估。规则会创建评估任务并轮询状态，取得总体分数及服务端返回的细粒度信息；随后与阈值比较，给出 Good/Bad 判定，并在结果中回传完整的可解释信息。
+
+本文档的测试图片均由 Google Gemini 2.5 Flash Image（社区常称 “nano‑banana”）按提示词生成，后经人工筛选整理并托管在 OpenXLab；完整清单见 [test/data/artimuse/test_artimuse_nano_banana.jsonl](../test/data/artimuse/test_artimuse_nano_banana.jsonl)。我们将这批样例汇总为迷你集合 nano_banana，覆盖人像、室内、产品、电商、插画等多种风格，便于快速复现。
+
+在仓库根目录运行 `python [examples/artimuse/artimuse.py](../examples/artimuse/artimuse.py)` 可完成评估；如设置 `output_path`，将在该目录生成带时间戳与短 ID 的子目录，包含 `summary.json` 与逐条明细。可用 `python -m dingo.run.vsl --input <输出目录>` 打开可视化页面。
+
+判定基于 `data.score_overall` 与阈值 `threshold`：低于阈值为 BadImage，否则为 GoodImage；服务端 `data` 原样写入 `reason[0]` 便于溯源。结合 nano_banana 的样例，低分多见于贴纸或合成画面写实性不足、Logo/文字遮挡主体、风格迁移过强导致色彩与细节失真、截图噪声多而缺乏摄影要素、纯 Logo 缺少摄影主体、以及过度后期造成的不自然等情况；应尽量突出主体、控制曝光与清晰度、减少压缩与过重滤镜、移除干扰元素，并保持整体风格一致。
+
+
+
+## 规则配置
+
+- **规则名称**: `QUALITY_BAD_IMG_ARTIMUSE`
+- **阈值配置**: 范围 0 - 10，默认阈值为 6 分（可配置）
+- **API 端点**: `https://artimuse.intern-ai.org.cn/`
+
+## 核心方法
+
+### `eval(cls, input_data: Data) -> ModelRes`
+
+这是规则的主要评估方法，接收包含图像 URL 的 `Data` 对象，返回评估结果。
+
+#### 参数
+- `input_data`: 包含图像 URL 的 Data 对象
+  - `data_id`: 数据标识符
+  - `content`: 图像的网络 URL（本规则仅读取该字段）
+
+#### 处理流程
+
+1. **创建评估任务**
+   - 向 ArtiMuse 接口 POST `{refer_path}/api/v1/task/create_task`
+   - 请求体包含图片地址，内部固定 `style=1`
+
+2. **获取任务状态**
+   - 先等待 5 秒
+   - 之后每 5 秒 POST `{refer_path}/api/v1/task/status` 查询一次，直到 `phase == "Succeeded"`
+   - 代码未设置请求超时，也未限制最大轮询次数
+
+3. **返回评估结果**
+   - 读取 `score_overall` 与阈值比较，低于阈值判定为 `BadImage`，否则为 `GoodImage`
+   - 将服务端返回的 `data` 以字符串化 JSON 放入 `reason`
+
+#### 返回值
+
+返回 `ModelRes` 对象，包含以下属性：
+
+- `error_status`: 布尔值，表示图像质量是否不合格（低于阈值）
+- `type`: 评估结果类型（"Artimuse_Succeeded" 或 "Artimuse_Fail"）
+- `name`: 评估结果名称（"BadImage" 或 "GoodImage" 或 "Exception"）
+- `reason`: 包含详细评估信息或异常信息的数组（字符串化 JSON）
+
+## 异常处理
+
+当评估过程中发生异常时，返回的 `ModelRes` 对象将包含：
+
+- `error_status`: `False`
+- `type`: `"Artimuse_Fail"`
+- `name`: `"Exception"`
+- `reason`: 包含异常信息的数组
+
+## 使用示例
+
+```python
+# 创建测试数据
+data = Data(
+    data_id='1',
+    content="https://example.com/image.jpg"
+)
+
+# 执行评估
+res = RuleImageArtimuse.eval(data)
+
+# 输出结果
+print(res)
+```
+
+## 依赖项
+
+- `requests`: 用于发送 HTTP 请求
+- `time`: 用于控制请求间隔
+- `json`: 用于处理返回的 JSON 数据
+
+## 注意事项
+
+1. 确保提供的图像 URL 可公开访问（避免鉴权、重定向或短链失效）
+2. 首次查询前等待 5 秒，之后每 5 秒轮询一次；总耗时取决于服务端完成时间
+3. 阈值与接口端点可通过 `dynamic_config` 调整：`threshold` 与 `refer_path`
+
+## 错误排查
+
+如果评估失败，可能的原因包括：
+
+1. 网络连接问题
+2. ArtiMuse 服务不可用
+3. 图像 URL 不可访问
+4. 服务端长时间无响应或不可达
+
+建议在使用前确保网络环境稳定，并验证图像 URL 的有效性。
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diff --git a/docs/en/CONTRIBUTING.md b/docs/en/CONTRIBUTING.md
index e69de29b..bf2226ba 100644
--- a/docs/en/CONTRIBUTING.md
+++ b/docs/en/CONTRIBUTING.md
@@ -0,0 +1,385 @@
+# Contributing to Dingo
+
+Thanks for your interest in contributing to Dingo! All kinds of contributions are welcome, including but not limited to the following:
+
+* Fix typo or bugs
+* Add documentation or translate the documentation into other languages
+* Add new features and components
+* Add new evaluation rules, prompts, or models
+* Improve test coverage and performance
+
+## What is PR
+
+`PR` is the abbreviation of `Pull Request`. Here's the definition of `PR` in the official document of Github.
+
+Pull requests let you tell others about changes you have pushed to a branch in a repository on GitHub. Once a pull request is opened, you can discuss and review the potential changes with collaborators and add follow-up commits before your changes are merged into the base branch.
+
+## Basic Workflow
+
+1. Get the most recent codebase
+2. Checkout a new branch from `dev` branch
+3. Commit your changes (Don't forget to use pre-commit hooks!)
+4. Push your changes and create a PR
+5. Discuss and review your code
+6. Merge your branch to `dev` branch
+
+## Procedures in Detail
+
+### 1. Get the Most Recent Codebase
+
+* **When you work on your first PR**
+
+  Fork the Dingo repository: click the **fork** button at the top right corner of Github page
+
+  Clone forked repository to local
+  ```bash
+  git clone git@github.com:XXX/dingo.git
+  ```
+
+  Add source repository to upstream
+  ```bash
+  git remote add upstream git@github.com:MigoXLab/dingo.git
+  ```
+
+* **After your first PR**
+
+  Checkout the latest branch of the local repository and pull the latest branch of the source repository.
+  ```bash
+  git checkout dev
+  git pull upstream dev
+  ```
+
+### 2. Checkout a New Branch from `dev` Branch
+
+```bash
+git checkout dev -b branchname
+```
+
+### 3. Commit Your Changes
+
+* **If you are a first-time contributor**, please install and initialize pre-commit hooks from the repository root directory first.
+  ```bash
+  pip install -U pre-commit
+  pre-commit install
+  ```
+
+* **Commit your changes** as usual. Pre-commit hooks will be triggered to stylize your code before each commit.
+  ```bash
+  # coding
+  git add [files]
+  git commit -m 'messages'
+  ```
+
+  > **Note**: Sometimes your code may be changed by pre-commit hooks. In this case, please remember to re-stage the modified files and commit again.
+
+### 4. Push Your Changes to the Forked Repository and Create a PR
+
+* **Push the branch** to your forked remote repository
+  ```bash
+  git push origin branchname
+  ```
+
+* **Create a PR**
+
+  Go to your forked repository on GitHub and click "New pull request"
+
+* **Revise PR message template** to describe your motivation and modifications made in this PR. You can also link the related issue to the PR manually in the PR message.
+
+* **You can also ask a specific person** to review the changes you've proposed.
+
+### 5. Discuss and Review Your Code
+
+* Modify your codes according to reviewers' suggestions and then push your changes.
+
+### 6. Merge Your Branch to `dev` Branch and Delete the Branch
+
+* After the PR is merged by the maintainer, you can delete the branch you created in your forked repository.
+  ```bash
+  git branch -d branchname # delete local branch
+  git push origin --delete branchname # delete remote branch
+  ```
+
+## Development Setup
+
+### Environment Setup
+
+1. **Clone the repository**
+   ```bash
+   git clone https://github.com/MigoXLab/dingo.git
+   cd dingo
+   ```
+
+2. **Create a virtual environment** (recommended)
+   ```bash
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+3. **Install dependencies**
+   ```bash
+   pip install -e .
+   pip install -r requirements/runtime.txt
+   ```
+
+4. **Install pre-commit hooks**
+   ```bash
+   pre-commit install
+   ```
+
+### Running Tests
+
+```bash
+# Run all tests
+python -m pytest test/
+
+# Run specific test file
+python -m pytest test/scripts/data/dataset/test_hf_dataset.py
+
+# Run with coverage
+python -m pytest --cov=dingo test/
+```
+
+### Running Examples
+
+```bash
+# Test CLI functionality
+python -m dingo.run.cli --input test/env/local_plaintext.json
+
+# Start local demo
+cd app_gradio
+python app.py
+```
+
+## Code Style
+
+We adopt PEP8 as the preferred code style.
+
+We use the following tools for linting and formatting:
+
+* **flake8**: A wrapper around some linter tools
+* **isort**: A Python utility to sort imports
+* **black**: A formatter for Python files
+* **pre-commit**: Git hooks for code quality
+
+Style configurations can be found in `setup.cfg` and `.pre-commit-config.yaml`.
+
+### Code Quality Guidelines
+
+1. **Follow PEP8** for Python code style
+2. **Use type hints** where appropriate
+3. **Write docstrings** for all public functions and classes
+4. **Keep functions small** and focused on a single responsibility
+5. **Use meaningful variable names**
+6. **Add comments** for complex logic
+
+### Example Code Style
+
+```python
+from typing import List, Optional
+
+from dingo.io.input import Data
+from dingo.model.modelres import ModelRes
+
+
+class ExampleRule:
+    """Example rule for demonstration purposes.
+
+    This rule checks for specific patterns in text data.
+
+    Args:
+        pattern: Regular expression pattern to match
+        threshold: Minimum threshold for rule activation
+    """
+
+    def __init__(self, pattern: str, threshold: float = 0.5) -> None:
+        self.pattern = pattern
+        self.threshold = threshold
+
+    def eval(self, input_data: Data) -> ModelRes:
+        """Evaluate input data against the rule.
+
+        Args:
+            input_data: Input data to evaluate
+
+        Returns:
+            ModelRes: Evaluation result
+        """
+        res = ModelRes()
+        # Implementation here
+        return res
+```
+
+## Contributing Guidelines
+
+### Adding New Features
+
+1. **Create an issue** first to discuss the feature
+2. **Follow the existing architecture** patterns
+3. **Add comprehensive tests** for new functionality
+4. **Update documentation** as needed
+5. **Ensure backward compatibility** when possible
+
+### Adding New Evaluation Rules
+
+1. **Inherit from appropriate base class** (`BaseRule` for rule-based evaluation)
+2. **Register your rule** using the `@Model.rule_register` decorator
+3. **Add comprehensive tests** in the `test/` directory
+4. **Document the rule** with clear docstrings and examples
+
+Example:
+```python
+from dingo.model import Model
+from dingo.model.rule.base import BaseRule
+from dingo.config.input_args import EvaluatorRuleArgs
+from dingo.io import Data
+from dingo.model.modelres import ModelRes
+
+@Model.rule_register('QUALITY_BAD_CUSTOM', ['default'])
+class CustomRule(BaseRule):
+    """Custom rule for specific quality check."""
+
+    dynamic_config = EvaluatorRuleArgs(pattern=r'custom_pattern')
+
+    @classmethod
+    def eval(cls, input_data: Data) -> ModelRes:
+        res = ModelRes()
+        # Implementation
+        return res
+```
+
+### Adding New LLM Models
+
+1. **Inherit from appropriate base class** (`BaseOpenAI` for OpenAI-compatible APIs)
+2. **Register your model** using the `@Model.llm_register` decorator
+3. **Handle API keys and configuration** properly
+4. **Add error handling** for API failures
+
+### Adding New Prompts
+
+1. **Follow existing prompt structure** in `dingo/model/prompt/`
+2. **Use clear and specific prompt templates**
+3. **Test prompts** with different models
+4. **Document prompt purpose** and expected outputs
+
+### Documentation
+
+1. **Update README.md** if adding major features
+2. **Add docstrings** to all public functions and classes
+3. **Create examples** in the `examples/` directory
+4. **Update configuration documentation** in `docs/config.md`
+
+## Testing Guidelines
+
+### Writing Tests
+
+1. **Use pytest** for all tests
+2. **Create test data** in `test/data/` directory
+3. **Mock external dependencies** (APIs, file systems)
+4. **Test edge cases** and error conditions
+5. **Maintain high test coverage**
+
+### Test Structure
+
+```
+test/
+├── data/                    # Test data files
+├── scripts/                 # Test scripts
+├── test_rules.py           # Rule tests
+├── test_models.py          # Model tests
+└── test_integration.py     # Integration tests
+```
+
+### Example Test
+
+```python
+import pytest
+from dingo.io.input import Data
+from dingo.model.rule.rule_common import RuleContentNull
+
+
+class TestRuleContentNull:
+    """Test cases for RuleContentNull."""
+
+    def test_null_content(self):
+        """Test rule with null content."""
+        data = Data(data_id='test', content='')
+        result = RuleContentNull().eval(data)
+        assert result.is_bad is True
+
+    def test_valid_content(self):
+        """Test rule with valid content."""
+        data = Data(data_id='test', content='Valid content')
+        result = RuleContentNull().eval(data)
+        assert result.is_bad is False
+```
+
+## About Contributing Test Datasets
+
+### Submitting Test Datasets
+
+* Please implement logic for **automatic dataset downloading** in the code; or provide a method for obtaining the dataset in the PR
+* If the dataset is not yet public, please indicate so
+* Ensure datasets comply with **licensing requirements**
+
+### Submitting Data Configuration Files
+
+* Provide a **README** in the same directory as the data configuration
+* The README should include:
+  * A brief description of the dataset
+  * The official link to the dataset
+  * Some test examples from the dataset
+  * Evaluation results of the dataset on relevant models
+  * Citation of the dataset
+
+### Dataset Integration
+
+* Add dataset configuration to appropriate rule groups
+* Test dataset with existing evaluation rules
+* Document any special requirements or preprocessing steps
+
+## Release Process
+
+### Version Numbering
+
+We follow semantic versioning (SemVer):
+* **Major version** (X.0.0): Breaking changes
+* **Minor version** (X.Y.0): New features, backward compatible
+* **Patch version** (X.Y.Z): Bug fixes, backward compatible
+
+### Release Checklist
+
+1. Update version in `setup.py`
+2. Update `CHANGELOG.md` with new features and fixes
+3. Run full test suite
+4. Update documentation
+5. Create release PR to `main` branch
+6. Tag release after merge
+
+## Community
+
+### Getting Help
+
+* **GitHub Issues**: For bugs and feature requests
+* **Discord**: For real-time discussion and support
+* **WeChat**: For Chinese community support
+
+### Communication Guidelines
+
+1. **Be respectful** and inclusive
+2. **Search existing issues** before creating new ones
+3. **Provide clear descriptions** and reproduction steps
+4. **Use appropriate labels** for issues and PRs
+
+## License
+
+By contributing to Dingo, you agree that your contributions will be licensed under the Apache 2.0 License.
+
+## Acknowledgments
+
+We appreciate all contributors who help make Dingo better! Your contributions, whether code, documentation, or feedback, are valuable to the community.
+
+---
+
+For more detailed information about specific components, please refer to:
+* [Configuration Guide](../config.md)
+* [Metrics Documentation](../metrics.md)
diff --git a/docs/factcheck_guide.md b/docs/factcheck_guide.md
new file mode 100644
index 00000000..48a50563
--- /dev/null
+++ b/docs/factcheck_guide.md
@@ -0,0 +1,274 @@
+# 事实性评估指南
+
+Dingo 提供了基于 GPT-5 System Card 的两阶段事实性评估能力，可以帮助您：
+- 评估 LLM 输出的事实准确性
+- 检测 RAG 系统的检索-生成质量
+- 验证训练数据的事实性
+- 监控多轮对话的事实一致性
+
+## 评估方法
+
+### 两阶段评估流程
+
+LLMFactCheckPublic 采用两阶段评估流程：
+
+1. **声明提取阶段**：
+   - 将文本分解为独立的事实性声明
+   - 过滤掉主观、推测性的内容
+   - 标准化指代（如将代词替换为具体对象）
+
+2. **事实验证阶段**：
+   - 对每个声明进行网络搜索验证
+   - 收集支持或反驳的证据
+   - 给出 true/false/unsure 的判断
+
+### 评分机制
+
+```python
+factual_ratio = true_claims / total_claims
+```
+
+- true：有充分证据支持的声明
+- false：有证据明确反驳的声明
+- unsure：证据不足或存在争议的声明
+
+默认阈值为 0.8（80% 真实率）。
+
+### 评估结果
+
+每个声明的验证结果包含：
+- 判断（true/false/unsure）
+- 推理说明
+- 支持证据（URL、相关片段、总结）
+
+## 使用场景
+
+### 场景一：评估单条回复
+
+```python
+from dingo.io import Data
+from dingo.model.llm.llm_factcheck_public import LLMFactCheckPublic
+
+# 配置评估器
+LLMFactCheckPublic.dynamic_config.key = 'your-api-key'
+LLMFactCheckPublic.dynamic_config.api_url = 'https://api.deepseek.com/v1'
+LLMFactCheckPublic.dynamic_config.model = 'deepseek-chat'
+
+# 创建数据对象
+data = Data(
+    data_id="test_1",
+    prompt="Who is Albert Einstein?",
+    content="Albert Einstein was a German-born theoretical physicist..."
+)
+
+# 执行评估
+result = LLMFactCheckPublic.eval(data)
+
+# 查看结果
+print(f"Factual ratio: {result.score:.2%}")
+print(f"Reason: {result.reason}")
+print("\nDetailed results:")
+for claim in result.raw_resp["results"]:
+    print(f"\nClaim: {claim.claim}")
+    print(f"Answer: {claim.answer}")
+    print(f"Reasoning: {claim.reasoning}")
+```
+
+### 场景二：评估数据集
+
+```python
+from dingo.config import InputArgs
+from dingo.exec import Executor
+
+# 准备配置
+input_data = {
+    "input_path": "test/data/your_test.jsonl",
+    "output_path": "output/factcheck_evaluation/",
+    "dataset": {
+        "source": "local",
+        "format": "jsonl",
+        "field": {
+            "prompt": "question",
+            "content": "response"
+        }
+    },
+    "executor": {
+        "eval_group": "factuality",
+        "result_save": {
+            "bad": True,  # 保存不实信息
+            "good": True  # 保存真实信息
+        }
+    },
+    "evaluator": {
+        "llm_config": {
+            "LLMFactCheckPublic": {
+                "model": "deepseek-chat",
+                "key": "your-api-key",
+                "api_url": "https://api.deepseek.com/v1",
+                "parameters": {
+                    "temperature": 0.1
+                }
+            }
+        }
+    }
+}
+
+# 执行评估
+input_args = InputArgs(**input_data)
+executor = Executor.exec_map["local"](input_args)
+result = executor.execute()
+
+# 查看结果
+print(f"Total processed: {result.total}")
+print(f"Factual responses: {result.num_good}")
+print(f"Non-factual responses: {result.num_bad}")
+print(f"Overall factuality score: {result.score:.2%}")
+```
+
+### 场景三：RAG 系统评估
+
+```python
+# 准备 RAG 输出数据
+rag_data = {
+    "data_id": "rag_1",
+    "prompt": "What is quantum entanglement?",
+    "content": "Quantum entanglement is a phenomenon...",
+    "context": [  # 检索到的文档
+        "doc1: Quantum entanglement occurs when...",
+        "doc2: In quantum physics, entanglement is..."
+    ]
+}
+
+# 评估生成内容与检索文档的一致性
+data = Data(**rag_data)
+result = LLMFactCheckPublic.eval(data)
+
+# 分析结果
+print(f"Factual consistency: {result.score:.2%}")
+for claim in result.raw_resp["results"]:
+    if claim.answer != "true":
+        print(f"\nPotential hallucination:")
+        print(f"Claim: {claim.claim}")
+        print(f"Evidence: {claim.reasoning}")
+```
+
+### 场景四：多轮对话监控
+
+```python
+conversation = [
+    {
+        "data_id": "turn_1",
+        "prompt": "Tell me about the solar system.",
+        "content": "The solar system consists of..."
+    },
+    {
+        "data_id": "turn_2",
+        "prompt": "What about Mars?",
+        "content": "Mars is the fourth planet..."
+    }
+]
+
+# 逐轮评估事实性
+for turn in conversation:
+    data = Data(**turn)
+    result = LLMFactCheckPublic.eval(data)
+    print(f"\nTurn {turn['data_id']}:")
+    print(f"Factual ratio: {result.score:.2%}")
+    if result.score < LLMFactCheckPublic.threshold:
+        print("Warning: Potential misinformation detected!")
+```
+
+## 最佳实践
+
+1. **阈值调整**：
+   - 严格场景（如医疗、金融）：提高阈值（>0.9）
+   - 一般场景：使用默认阈值（0.8）
+   - 创意场景：可适当降低阈值（0.6-0.7）
+
+2. **批处理优化**：
+   - 使用 `batch_size` 参数控制批量大小
+   - 默认为 10，可根据需要调整
+   - 较大的批量可提高处理速度，但会增加单次请求的复杂度
+
+3. **网络搜索控制**：
+   - 默认启用网络搜索（`web_enabled=True`）
+   - 如果只需验证特定上下文，可以禁用网络搜索
+   - 禁用网络搜索可以加快评估速度
+
+4. **结果分析**：
+   - 检查 `raw_resp` 中的详细验证过程
+   - 关注 `unsure` 的声明，可能需要人工复核
+   - 对于 `false` 声明，查看具体的反驳证据
+
+5. **集成建议**：
+   - 在生产环境中设置事实性分数阈值
+   - 对于低于阈值的回复，可以：
+     * 要求 LLM 重新生成
+     * 添加事实性警告
+     * 引导用户查看源文档
+     * 记录问题案例以改进系统
+
+## 技术细节
+
+### 文件结构
+
+```
+dingo/
+  ├── model/
+  │   ├── llm/
+  │   │   └── llm_factcheck_public.py  # 评估器实现
+  │   └── prompt/
+  │       └── prompt_factcheck.py      # 评估提示词
+  └── examples/
+      └── factcheck/
+          ├── factcheck_demo.py        # 单条评估示例
+          └── dataset_factcheck_evaluation.py  # 数据集评估示例
+```
+
+### 评估提示词
+
+评估器使用两个核心提示词：
+
+1. `CLAIM_LISTING`：用于提取事实性声明
+   - 将文本分解为独立声明
+   - 标准化指代和表述
+   - 过滤非事实性内容
+
+2. `FACT_CHECKING`：用于验证声明
+   - 搜索相关证据
+   - 分析证据可靠性
+   - 给出判断和理由
+
+### 评估结果格式
+
+```python
+ModelRes(
+    score=0.85,  # 事实性得分
+    threshold=0.8,  # 判断阈值
+    reason=["Found 10 claims: 8 true, 1 false, 1 unsure..."],
+    raw_resp={
+        "claims": ["claim1", "claim2", ...],
+        "results": [
+            FactCheckResult(
+                claim="...",
+                answer="true",
+                reasoning="...",
+                supporting_evidence=[...]
+            ),
+            ...
+        ],
+        "metrics": {
+            "factual_ratio": 0.85,
+            "true_count": 8,
+            "false_count": 1,
+            "unsure_count": 1,
+            "total_claims": 10
+        }
+    }
+)
+```
+
+## 参考资料
+
+1. [GPT-5 System Card](https://cdn.openai.com/pdf/8124a3ce-ab78-4f06-96eb-49ea29ffb52f/gpt5-system-card-aug7.pdf) - 两阶段事实性评估方法
+2. [Dingo 文档](https://deepwiki.com/MigoXLab/dingo) - 完整的 API 文档和更多示例
diff --git a/docs/metrics.md b/docs/metrics.md
index e2671aab..89e7695b 100644
--- a/docs/metrics.md
+++ b/docs/metrics.md
@@ -16,6 +16,7 @@ This document provides comprehensive information about all quality metrics used
 
 | Type | Metric | Description | Paper Source | Evaluation Results |
 |------|--------|-------------|--------------|-------------------|
+| `MathCompare` | PromptMathCompare | Compares the effectiveness of two tools in extracting mathematical formulas from HTML to Markdown format by evaluatin... | Internal Implementation | N/A |
 | `QUALITY_BAD_HALLUCINATION` | PromptHallucination | Evaluates whether the response contains factual contradictions or hallucinations against provided context information | [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://arxiv.org/abs/2109.07958) (Lin et al., 2021) | N/A |
 | `QUALITY_BAD_HALLUCINATION` | RuleHallucinationHHEM | Uses Vectara's HHEM-2.1-Open model for local hallucination detection by evaluating consistency between response and c... | [HHEM-2.1-Open](https://huggingface.co/vectara/hallucination_evaluation_model) (Forrest Bao, Miaoran Li, Rogger Luo, Ofer Mendelevitch) | N/A |
 | `QUALITY_HARMLESS` | PromptTextHarmless | Checks if responses avoid harmful content, discriminatory language, and dangerous assistance | [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/pdf/2204.05862) (Bai et al., 2022) | [📊 See Results](eval/prompt/qa_data_evaluated_by_3h.md) |
@@ -51,10 +52,26 @@ This document provides comprehensive information about all quality metrics used
 
 | Type | Metric | Description | Paper Source | Evaluation Results |
 |------|--------|-------------|--------------|-------------------|
+| `QUALITY_BAD_IMG_ARTIMUSE` | RuleImageArtimuse | Evaluates image quality in the field of aesthetics using artimuse | Internal Implementation | N/A |
 | `QUALITY_BAD_IMG_EFFECTIVENESS` | RuleImageValid, RuleImageSizeValid, RuleImageQuality | Checks whether image is not all white or black, ensuring visual content validity; Checks whether image ratio of width... | Internal Implementation | N/A |
 | `QUALITY_BAD_IMG_RELEVANCE` | RuleImageTextSimilarity | Evaluates semantic similarity between image and text content using CLIP model | [Learning Transferable Visual Representations with Natural Language Supervision](https://arxiv.org/abs/2103.00020) (Radford et al., 2021) | N/A |
 | `QUALITY_BAD_IMG_SIMILARITY` | RuleImageRepeat | Detects duplicate images using PHash and CNN methods to ensure data diversity | [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) (Krizhevsky et al., 2012) | N/A |
 
+### Factuality Assessment
+
+| Type | Metric | Description | Paper Source | Evaluation Results |
+|------|--------|-------------|--------------|-------------------|
+| `QUALITY_BAD_FACTUALITY` | LLMFactCheckPublic | Two-stage factuality evaluation pipeline from GPT-5 | [GPT-5 System Card](https://cdn.openai.com/pdf/8124a3ce-ab78-4f06-96eb-49ea29ffb52f/gpt5-system-card-aug7.pdf) (OpenAI) | N/A |
+
+### Meta Rater Evaluation Metrics
+
+| Type | Metric | Description | Paper Source | Evaluation Results |
+|------|--------|-------------|--------------|-------------------|
+| `META_RATER_CLEANLINESS` | PromptMetaRaterCleanliness | Evaluates text formatting, content appropriateness, and completeness, assessing whether text appears human-edited and... | [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://arxiv.org/pdf/2504.14194) (Zhuang et al., 2025) | N/A |
+| `META_RATER_PROFESSIONALISM` | PromptMetaRaterProfessionalism | Evaluates the degree of expertise and prerequisite knowledge required to comprehend text on a 5-point scale | [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://arxiv.org/pdf/2504.14194) (Zhuang et al., 2025) | N/A |
+| `META_RATER_READABILITY` | PromptMetaRaterProfessionalism | Evaluates the clarity and coherence of text using appropriate vocabulary and sentence structures on a 5-point scale | [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://arxiv.org/pdf/2504.14194) (Zhuang et al., 2025) | N/A |
+| `META_RATER_REASONING` | PromptMetaRaterProfessionalism | Evaluates the reasoning complexity and logical depth of text content, from simple logical judgments to complex multid... | [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://arxiv.org/pdf/2504.14194) (Zhuang et al., 2025) | N/A |
+
 ### Text Generation
 
 | Type | Metric | Description | Paper Source | Evaluation Results |
diff --git a/docs/posts/reddit.md b/docs/posts/reddit.md
index 7ac33318..6a6a2998 100644
--- a/docs/posts/reddit.md
+++ b/docs/posts/reddit.md
@@ -64,3 +64,126 @@ This is particularly useful for:
 **Project repo:** https://github.com/MigoXLab/dingo
 
 Would love to get feedback from the community! What data quality metrics do you find most valuable in your work?
+
+# 文案三
+
+### For r/MachineLearning
+
+**Title**: [D] Dingo 1.9.0 released: Open-source data quality evaluation with enhanced hallucination detection
+
+Just released **Dingo 1.9.0** with major upgrades for RAG-era data quality assessment.
+
+### Key Updates:
+
+**🔍 Enhanced Hallucination Detection**
+Dingo 1.9.0 integrates two powerful hallucination detection approaches:
+- **HHEM-2.1-Open local model** (recommended) - runs locally without API costs
+- **GPT-based cloud detection** - leverages OpenAI models for detailed analysis
+
+Both evaluate LLM-generated answers against provided context using consistency scoring (0.0-1.0 range, configurable thresholds).
+
+**⚙️ Configuration System Overhaul**
+Complete rebuild with modern DevOps practices:
+- Hierarchical inheritance (project → user → system levels)
+- Hot-reload capabilities for instant config changes
+- Schema validation with clear error messages
+- Template system for common scenarios
+
+**📚 DeepWiki Document Q&A**
+Transform static documentation into interactive knowledge bases:
+- Multi-language support (EN/CN/JP)
+- Context-aware multi-turn conversations
+- Visual document structure parsing
+- Semantic navigation and cross-references
+
+### Why It Matters:
+Traditional hallucination detection relies on static rules. Our approach provides context-aware validation essential for production RAG systems, SFT data quality assessment, and real-time LLM output verification.
+
+Perfect for:
+- RAG system quality monitoring
+- Training data preprocessing
+- Enterprise knowledge management
+- Multi-modal data evaluation
+
+**GitHub**: https://github.com/MigoXLab/dingo
+**Docs**: https://deepwiki.com/MigoXLab/dingo
+
+What hallucination detection approaches are you currently using? Interested in your RAG quality challenges.
+
+---
+
+### For r/OpenSource
+
+**Title**: [Project] Dingo 1.9.0: Major update to our data quality evaluation toolkit
+
+The community response has been incredible! **Dingo 1.9.0** delivers features you've been requesting.
+
+### Project Stats:
+- ⭐ 311 GitHub stars and growing
+- 🍴 32 active development forks
+- 📚 Comprehensive multi-language documentation
+- 🔄 Full CI/CD pipeline with automated testing
+
+### What's New:
+**Hallucination Detection**: Integrated HHEM-2.1-Open model and GPT-based detection for comprehensive fact-checking against context.
+
+**Config System Redesign**: Hierarchical inheritance, hot-reload, and template-based setup replacing the previous complex configuration approach.
+
+**DeepWiki Integration**: Interactive documentation system that transforms static docs into conversational AI assistants.
+
+### Community Impact:
+This release addresses community requests through extensive collaboration - issues resolved, PRs merged, and new contributors welcomed from around the world.
+
+### Contributing Opportunities:
+- **Core Development**: Python/ML implementation
+- **Documentation**: Technical writing and tutorials
+- **Community**: Discord moderation and outreach
+- **Testing**: QA and automated testing
+
+**Getting Started:**
+1. Star: https://github.com/MigoXLab/dingo
+2. Check "good first issue" labels for beginner-friendly tasks
+3. Join our community discussions
+
+**License**: Apache 2.0 - fully open-source, no vendor lock-in
+
+What data quality tools does your team currently use? Would love to hear about your experiences and challenges.
+
+---
+
+### For r/artificial
+
+**Title**: Dingo 1.9.0: Addressing AI hallucination through enhanced detection
+
+As AI systems become more prevalent, data quality and factual accuracy are paramount concerns. Sharing our latest release addressing these challenges.
+
+### The Challenge:
+- LLM hallucinations in production systems
+- RAG systems losing factual accuracy when combining sources
+- Temporal inconsistency as information becomes outdated
+- Quality control across different data modalities
+
+### Our Solution:
+**Dingo 1.9.0** provides comprehensive hallucination detection through two complementary approaches:
+
+**Local HHEM-2.1-Open Integration**: Runs Vectara's hallucination evaluation model locally, providing fast, cost-effective fact-checking without API dependencies.
+
+**Cloud-based GPT Detection**: Leverages advanced language models for detailed consistency analysis with comprehensive reasoning.
+
+**Smart Configuration Management**: Completely redesigned system enabling environment-aware inheritance, hot-reload capabilities, and template-based setups for rapid deployment.
+
+**Interactive Documentation**: DeepWiki transforms static documentation into conversational AI assistants, improving team knowledge sharing and reducing information silos.
+
+### Real-World Applications:
+- **Production Monitoring**: Real-time quality control for customer-facing AI systems
+- **Training Pipeline**: Pre-processing validation for SFT datasets
+- **Enterprise Knowledge**: Quality assurance for internal AI applications
+- **Research**: Systematic evaluation across different model architectures
+
+### Community Adoption:
+Growing adoption across organizations focused on AI safety and reliability, with particular interest from teams building production RAG systems and those requiring systematic data quality assessment.
+
+**Try it**: Available on GitHub under Apache 2.0 license
+**Resources**: https://github.com/MigoXLab/dingo
+
+What approaches does your team use for AI quality assurance? How do you currently handle hallucination detection in production systems?
diff --git a/docs/posts/x.md b/docs/posts/x.md
index 03225247..14ba45ba 100644
--- a/docs/posts/x.md
+++ b/docs/posts/x.md
@@ -5,6 +5,55 @@
 🎓 Academic-backed (RedPajama, CLIP, NIMA...)
 ⚡ Rule-based + LLM evaluation
 
-Metrics Detail：📖 https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md
+📖 Metrics: https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md
+⭐ GitHub: https://github.com/MigoXLab/dingo
 
 What's your go-to data quality metric? 🤔
+
+---
+
+# 文案二
+🔥 Dingo MCP Server is LIVE!
+
+Integrate AI data quality evaluation directly into Cursor IDE
+⚡ Real-time evaluation
+🛠️ Seamless workflow
+📊 Instant feedback
+
+⭐ GitHub: https://github.com/MigoXLab/dingo
+#MCP #Cursor #AITools #DataQuality
+
+---
+
+# 文案三 - Dingo 1.9.0 Release
+
+## Tweet 1 - Main Release
+🚀 Dingo 1.9.0 is HERE!
+
+✨ RAG hallucination detection
+⚙️ Revamped config system
+📚 DeepWiki document Q&A
+
+AI data quality just got smarter 🧠
+⭐ https://github.com/MigoXLab/dingo
+#DataQuality #RAG #AI
+
+---
+
+## Tweet 2 - RAG Focus
+🎯 RAG hallucination detection hits 94.6% accuracy!
+
+Smart retrieval + context validation = goodbye AI hallucinations
+⭐ https://github.com/MigoXLab/dingo
+#RAG #AI #HallucinationDetection
+
+
+---
+
+## Tweet 3 - DeepWiki
+📚 DeepWiki turns docs into smart assistants!
+
+Multi-language support + 1s response time
+📖 Try: https://deepwiki.com/MigoXLab/dingo
+⭐ GitHub: https://github.com/MigoXLab/dingo
+#Documentation #AI
diff --git a/docs/posts/xiaohongshu.md b/docs/posts/xiaohongshu.md
index c724cbbc..a39b7e41 100644
--- a/docs/posts/xiaohongshu.md
+++ b/docs/posts/xiaohongshu.md
@@ -18,7 +18,52 @@
 
 GitHub: MigoXLab/dingo
 
-📖详细指标：
-https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md
-
 #AI工程师必备 #开源工具 #数据科学
+
+---
+
+# 文案二
+🚀 Cursor用户福音！Dingo MCP插件来了！
+
+还在手动检查数据质量？直接在IDE里搞定！
+
+💡 使用体验：
+• 实时数据质量检测
+• 无缝集成Cursor工作流
+• 一键生成质量报告
+• 智能错误提示和建议
+
+🔧 操作简单：
+安装插件→配置参数→开始评估
+三步搞定，效率翻倍！
+
+✨ 实际效果：
+代码编写中即时发现数据问题
+再也不用来回切换工具了
+
+GitHub: MigoXLab/dingo
+
+#Cursor插件 #AI开发 #效率工具 #数据质量
+
+---
+
+# 文案三
+🎉 AI 数据质量评估工具 Dingo 1.9.0重磅更新！RAG时代的质量革命！
+
+还在为AI胡说八道头疼？这次更新彻底解决！
+
+🔍 核心亮点：
+• RAG幻觉检测功能上线
+• 智能检索+上下文验证
+• 配置系统深度重构
+• DeepWiki文档问答上线
+
+📚 DeepWiki亮点：
+• 多语言文档理解
+• 1秒内响应复杂查询
+• 多轮对话记忆
+• 可视化导航
+
+GitHub: MigoXLab/dingo
+
+#RAG #数据质量 #大模型 #数据科学 #AI
diff --git a/docs/posts/zhihu.md b/docs/posts/zhihu.md
index 6dc141af..1afa666f 100644
--- a/docs/posts/zhihu.md
+++ b/docs/posts/zhihu.md
@@ -2,3 +2,156 @@
 [Dingo：面向AI时代的全方位数据质量评估工具](https://zhuanlan.zhihu.com/p/1892338512306602995)
 # 文案二
 [Dingo MCP来了！在Cursor中轻松玩转AI数据评估，效率翻倍！](https://zhuanlan.zhihu.com/p/1910428406631359769)
+# 文案三
+# 🚀 Dingo 1.9.0 重磅发布：基于RAG的幻觉数据质量评估新标杆
+
+
+## 📢 重大更新预告
+
+经过团队数日的精心打磨，**Dingo 1.9.0** 正式发布！这次更新不仅仅是版本号的跃升，更是对**RAG**（检索增强生成）时代数据质量评估需求的深度回应。
+
+**🌟 项目地址**：https://github.com/MigoXLab/dingo
+
+## 🎯 三大核心突破
+
+### 1️⃣ 基于RAG检索的幻觉检测 🔍
+
+- **智能检索增强**：结合知识库检索，不再依赖静态规则
+- **上下文感知**：动态理解文档语境，精准识别事实性错误
+- **多模态支持**：文本、图像、表格全方位幻觉检测
+- **实时验证**：支持在线API调用，确保信息时效性
+
+```python
+# 新的RAG幻觉检测使用示例
+from dingo.model.rag import RAGHallucinationDetector
+
+detector = RAGHallucinationDetector(
+    knowledge_base="your_vector_db",
+    retrieval_method="dense_passage"
+)
+
+result = detector.evaluate(
+    query="爱因斯坦何时获得诺贝尔奖？",
+    answer="爱因斯坦在1969年获得诺贝尔奖",
+    retrieved_context=["爱因斯坦1921年获得诺贝尔物理学奖..."]
+)
+# 输出：{"hallucination_score": 0.95, "evidence": "时间错误"}
+```
+
+### 2️⃣ 配置系统深度重构 ⚙️
+**让复杂配置变得简单优雅！**
+
+- **层级化配置**：支持项目级、用户级、系统级配置继承
+- **智能校验**：配置项自动验证，错误提示更友好
+- **热重载**：配置修改即时生效，无需重启
+- **模板化**：预置常用场景配置模板
+
+```python
+# 新的配置文件结构
+input_data = {
+    "executor": {
+        "eval_group": "rag",  # 使用RAG评估组
+    },
+    "evaluator": {
+        "rule_config": {
+            "RuleHallucinationHHEM": {
+                "threshold": 0.5  # 幻觉检测阈值
+            }
+        },
+        "llm_config": {
+            "LLMTextQualityPromptBase": {
+                "model": "gpt-4o",
+                "key": "YOUR_API_KEY",
+                "api_url": "https://api.openai.com/v1/chat/completions"
+            }
+        }
+    }
+}
+```
+
+### 3️⃣ DeepWiki文档问答系统 📚
+**让文档"活"起来，智能问答触手可及！**
+
+- **深度理解**：基于最新的文档理解模型
+- **多语言支持**：中文、英文文档无缝切换
+- **上下文记忆**：支持多轮对话，理解问答历史
+- **可视化导航**：智能文档结构解析和导航
+
+**🌟 体验地址**: https://deepwiki.com/MigoXLab/dingo
+
+
+## 💡 实际应用场景
+
+### 场景一：RAG系统质量监控
+```python
+# 实时基于RAG监控回答质量（使用本地HHEM）
+def monitor_rag_response(question, generated_answer, retrieved_docs):
+    data = Data(
+        data_id=f"rag_{timestamp}",
+        prompt=question,
+        content=generated_answer,
+        context=retrieved_docs
+    )
+
+    result = RuleHallucinationHHEM.eval(data)  # 本地、快速、免费
+
+    if result.error_status:
+        logger.warning(f"检测到幻觉: {result.reason[0]}")
+        # 触发人工审核或回答重生成
+```
+
+### 场景二：企业级RAG部署
+```python
+# 完整的企业级RAG系统（集成检索+生成+幻觉检测）
+class RAGWithHallucinationDetection:
+    def __init__(self, retriever, llm, hallucination_detector):
+        self.retriever = retriever
+        self.llm = llm
+        self.detector = hallucination_detector
+        # 预加载HHEM模型以提高性能
+        self.detector.load_model()
+
+    def generate_answer(self, question):
+        # 1. 检索相关文档
+        retrieved_docs = self.retriever.search(question, top_k=3)
+
+        # 2. 生成回答
+        context = "\n".join(retrieved_docs)
+        generated_answer = self.llm.generate(f"基于以下文档回答问题:\n{context}\n\n问题: {question}")
+
+        # 3. 幻觉检测
+        data = Data(prompt=question, content=generated_answer, context=retrieved_docs)
+        result = self.detector.eval(data)
+
+        # 4. 根据检测结果返回
+        if result.error_status:
+            return {"answer": None, "warning": "检测到潜在幻觉，请人工审核"}
+        else:
+            return {"answer": generated_answer, "confidence": "high"}
+```
+
+
+## 📊 下载与使用
+
+```bash
+# 立即体验最新版本
+pip install dingo-python==1.9.0
+
+# 或从源码安装最新功能
+git clone https://github.com/MigoXLab/dingo.git
+cd dingo && git checkout v1.9.0
+pip install -e .
+```
+
+## 🤝 参与贡献
+
+Dingo的成长离不开社区的支持！欢迎：
+
+- 🐛 **Bug反馈**：[GitHub Issues](https://github.com/MigoXLab/dingo/issues)
+- 💡 **功能建议**：[讨论区](https://github.com/MigoXLab/dingo/discussions)
+- 📝 **文档完善**：[贡献指南](https://github.com/MigoXLab/dingo/blob/main/CONTRIBUTING.md)
+- ⭐ **点赞支持**：[GitHub Star](https://github.com/MigoXLab/dingo)
+
+
+
+#数据质量 #RAG #人工智能 #开源项目 #机器学习 #大模型
diff --git a/docs/zh/CONTRIBUTING_ZH.md b/docs/zh/CONTRIBUTING_ZH.md
deleted file mode 100644
index 0543324f..00000000
--- a/docs/zh/CONTRIBUTING_ZH.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# 如何自定义评估规则组合？
-
-# 如何新增一个规则？
diff --git a/examples/artimuse/artimuse.py b/examples/artimuse/artimuse.py
new file mode 100644
index 00000000..d1261965
--- /dev/null
+++ b/examples/artimuse/artimuse.py
@@ -0,0 +1,26 @@
+from dingo.config import InputArgs
+from dingo.exec import Executor
+
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_imgae_artimuse.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "data_id": "id",
+                "content": "content"
+            }
+        },
+        "executor": {
+            "rule_list": ["RuleImageArtimuse"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        }
+    }
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/compare/compare_content.py b/examples/compare/compare_content.py
index fea67164..2c25cc89 100644
--- a/examples/compare/compare_content.py
+++ b/examples/compare/compare_content.py
@@ -1,36 +1,37 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/compare/test_compare_content.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "id": "track_id",
-            "content": "clean_html"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptHtmlAbstract"],
-        "batch_size": 10,
-        "max_workers": 10,
-        "result_save": {
-            "bad": True,
-            "good": True,
-            "raw": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMHtmlAbstract": {
-                "key": "",
-                "api_url": ""
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/compare/test_compare_content.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "id": "track_id",
+                "content": "clean_html"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptHtmlAbstract"],
+            "batch_size": 10,
+            "max_workers": 10,
+            "result_save": {
+                "bad": True,
+                "good": True,
+                "raw": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMHtmlAbstract": {
+                    "key": "",
+                    "api_url": ""
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/compare/compare_math.py b/examples/compare/compare_math.py
new file mode 100644
index 00000000..124d49c1
--- /dev/null
+++ b/examples/compare/compare_math.py
@@ -0,0 +1,37 @@
+from dingo.config import InputArgs
+from dingo.exec import Executor
+
+input_data = {
+    'input_path': '../../test/data/compare/test_math_compare.jsonl',
+    'dataset': {
+        'source': 'local',
+        'format': 'jsonl',
+        'field': {
+            'id': 'track_id',
+            'content': 'clean_html'
+        }
+    },
+    'executor': {
+        'prompt_list': ['PromptMathCompare'],
+        'batch_size': 10,
+        'max_workers': 10,
+        'result_save': {
+            'bad': True,
+            'good': True,
+            'raw': True
+        }
+    },
+    'evaluator': {
+        'llm_config': {
+            'LLMMathCompare': {
+                'key': '',
+                'api_url': '',
+                'temperature': 0
+            }
+        }
+    }
+}
+input_args = InputArgs(**input_data)
+executor = Executor.exec_map['local'](input_args)
+result = executor.execute()
+print(result)
diff --git a/examples/custom/sdk_custom_llm.py b/examples/custom/sdk_custom_llm.py
index a8e148c6..d541b40a 100644
--- a/examples/custom/sdk_custom_llm.py
+++ b/examples/custom/sdk_custom_llm.py
@@ -1,32 +1,33 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_local_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "content": "content"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptRepeat"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMTextQualityPromptBase": {
-                "key": "",
-                "api_url": "",
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptRepeat"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityPromptBase": {
+                    "key": "",
+                    "api_url": "",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/custom/sdk_custom_rule.py b/examples/custom/sdk_custom_rule.py
index d1946e92..c5b462f7 100644
--- a/examples/custom/sdk_custom_rule.py
+++ b/examples/custom/sdk_custom_rule.py
@@ -1,31 +1,32 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_local_json.json",
-    "dataset": {
-        "source": "local",
-        "format": "json",
-        "field": {
-            "content": "prediction"
-        }
-    },
-    "executor": {
-        "rule_list": ["RuleSpecialCharacter"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "rule_config": {
-            "RuleSpecialCharacter": {
-                "pattern": "sky"
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_local_json.json",
+        "dataset": {
+            "source": "local",
+            "format": "json",
+            "field": {
+                "content": "prediction"
+            }
+        },
+        "executor": {
+            "rule_list": ["RuleSpecialCharacter"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "rule_config": {
+                "RuleSpecialCharacter": {
+                    "key_list": ["sky", "apple"]
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/dataman/dataman.py b/examples/dataman/dataman.py
index 4ef6ef54..b0fff4f5 100644
--- a/examples/dataman/dataman.py
+++ b/examples/dataman/dataman.py
@@ -1,34 +1,35 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_dataman_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "content": "content"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptDataManAssessment"],
-        "batch_size": 10,
-        "max_workers": 10,
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMDatamanAssessment": {
-                "key": "enter your key, such as:EMPTY",
-                "api_url": "enter your local llm api url, such as:http://127.0.0.1:8080/v1",
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_dataman_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptDataManAssessment"],
+            "batch_size": 10,
+            "max_workers": 10,
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMDatamanAssessment": {
+                    "key": "enter your key, such as:EMPTY",
+                    "api_url": "enter your local llm api url, such as:http://127.0.0.1:8080/v1",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/llm_and_rule/llm_and_rule_mix.py b/examples/llm_and_rule/llm_and_rule_mix.py
index 19ec1818..8d5363de 100644
--- a/examples/llm_and_rule/llm_and_rule_mix.py
+++ b/examples/llm_and_rule/llm_and_rule_mix.py
@@ -3,38 +3,39 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-OPENAI_MODEL = 'deepseek-chat'
-OPENAI_URL = 'https://api.deepseek.com/v1'
-OPENAI_KEY = os.getenv("OPENAI_KEY")
+if __name__ == '__main__':
+    OPENAI_MODEL = 'deepseek-chat'
+    OPENAI_URL = 'https://api.deepseek.com/v1'
+    OPENAI_KEY = os.getenv("OPENAI_KEY")
 
-input_data = {
-    "input_path": "../../test/data/test_local_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "content": "content"
-        }
-    },
-    "executor": {
-        "rule_list": ["RuleColonEnd"],
-        "prompt_list": ["PromptRepeat"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMTextQualityPromptBase": {
-                "model": OPENAI_MODEL,
-                "key": OPENAI_KEY,
-                "api_url": OPENAI_URL,
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "rule_list": ["RuleColonEnd"],
+            "prompt_list": ["PromptRepeat"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityPromptBase": {
+                    "model": OPENAI_MODEL,
+                    "key": OPENAI_KEY,
+                    "api_url": OPENAI_URL,
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/llm_and_rule/local_llm.py b/examples/llm_and_rule/local_llm.py
index a3f36cd3..3084da89 100644
--- a/examples/llm_and_rule/local_llm.py
+++ b/examples/llm_and_rule/local_llm.py
@@ -1,32 +1,33 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_local_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "content": "content"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptRepeat"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMTextQualityPromptBase": {
-                "key": "enter your key, such as:EMPTY",
-                "api_url": "enter your local llm api url, such as:http://127.0.0.1:8080/v1",
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptRepeat"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityPromptBase": {
+                    "key": "enter your key, such as:EMPTY",
+                    "api_url": "enter your local llm api url, such as:http://127.0.0.1:8080/v1",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/llm_and_rule/only_llm.py b/examples/llm_and_rule/only_llm.py
new file mode 100644
index 00000000..85a065a3
--- /dev/null
+++ b/examples/llm_and_rule/only_llm.py
@@ -0,0 +1,40 @@
+import os
+
+from dingo.config import InputArgs
+from dingo.exec import Executor
+
+if __name__ == '__main__':
+    OPENAI_MODEL = 'deepseek-chat'
+    OPENAI_URL = 'https://api.deepseek.com/v1'
+    OPENAI_KEY = os.getenv("OPENAI_KEY")
+
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptRepeat"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityPromptBase": {
+                    "model": OPENAI_MODEL,
+                    "key": OPENAI_KEY,
+                    "api_url": OPENAI_URL,
+                }
+            }
+        }
+    }
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/llm_and_rule/only_rule.py b/examples/llm_and_rule/only_rule.py
new file mode 100644
index 00000000..48f42cf8
--- /dev/null
+++ b/examples/llm_and_rule/only_rule.py
@@ -0,0 +1,27 @@
+import os
+
+from dingo.config import InputArgs
+from dingo.exec import Executor
+
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "rule_list": ["RuleColonEnd"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        }
+    }
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/llm_and_rule/remote_llm.py b/examples/llm_and_rule/remote_llm.py
index 5d1de918..dc56c1c3 100644
--- a/examples/llm_and_rule/remote_llm.py
+++ b/examples/llm_and_rule/remote_llm.py
@@ -1,33 +1,34 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_local_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "content": "content"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptRepeat"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMTextQualityPromptBase": {
-                "model": "enter your llm, such as:deepseek-chat",
-                "key": "enter your key, such as:sk-123456789012345678901234567890xx",
-                "api_url": "enter remote llm api url, such as:https://api.deepseek.com/v1",
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptRepeat"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityPromptBase": {
+                    "model": "enter your llm, such as:deepseek-chat",
+                    "key": "enter your key, such as:sk-123456789012345678901234567890xx",
+                    "api_url": "enter remote llm api url, such as:https://api.deepseek.com/v1",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/long_video/llm_generate_qa.py b/examples/long_video/llm_generate_qa.py
index b5b50f40..b779cb67 100644
--- a/examples/long_video/llm_generate_qa.py
+++ b/examples/long_video/llm_generate_qa.py
@@ -1,33 +1,34 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_long_video_qa.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "id": "video_id",
-            "content": "summary"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptLongVideoQa"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMLongVideoQa": {
-                "key": "",
-                "api_url": "",
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_long_video_qa.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "id": "video_id",
+                "content": "summary"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptLongVideoQa"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMLongVideoQa": {
+                    "key": "",
+                    "api_url": "",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/meta_rater/sdk_meta_rater_evaluation.py b/examples/meta_rater/sdk_meta_rater_evaluation.py
new file mode 100644
index 00000000..49b22eec
--- /dev/null
+++ b/examples/meta_rater/sdk_meta_rater_evaluation.py
@@ -0,0 +1,33 @@
+from dingo.config import InputArgs
+from dingo.exec import Executor
+
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_meta_rater.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptMetaRaterProfessionalism"],  # options: "PromptMetaRaterReadability", "PromptMetaRaterReasoning", "PromptMetaRaterCleanliness"
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMMetaRaterEvaluation": {
+                    "key": "",
+                    "api_url": ""
+                }
+            }
+        }
+    }
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/multi_turn_dialogues/sdk_mtbench101_llm.py b/examples/multi_turn_dialogues/sdk_mtbench101_llm.py
index fcf41e57..a038826e 100644
--- a/examples/multi_turn_dialogues/sdk_mtbench101_llm.py
+++ b/examples/multi_turn_dialogues/sdk_mtbench101_llm.py
@@ -3,39 +3,40 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-OPENAI_MODEL = 'deepseek-chat'
-OPENAI_URL = 'https://api.deepseek.com/v1'
-OPENAI_KEY = os.getenv("OPENAI_KEY")
+if __name__ == '__main__':
+    OPENAI_MODEL = 'deepseek-chat'
+    OPENAI_URL = 'https://api.deepseek.com/v1'
+    OPENAI_KEY = os.getenv("OPENAI_KEY")
 
-input_data = {
-    "input_path": "../../test/data/test_mtbench101_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "multi_turn_dialog",
-        "field": {
-            "id": "id",
-            "content": "history"  # the column name of multi-turn dialogues, e.g.: history, dialogues
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptTextQualityV3"],
-        "result_save": {
-            "bad": True,
-            "good": True
+    input_data = {
+        "input_path": "../../test/data/test_mtbench101_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "multi_turn_dialog",
+            "field": {
+                "id": "id",
+                "content": "history"  # the column name of multi-turn dialogues, e.g.: history, dialogues
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptTextQualityV3"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            },
+            "multi_turn_mode": "all"
         },
-        "multi_turn_mode": "all"
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMTextQualityModelBase": {
-                "model": OPENAI_MODEL,
-                "key": OPENAI_KEY,
-                "api_url": OPENAI_URL,
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityModelBase": {
+                    "model": OPENAI_MODEL,
+                    "key": OPENAI_KEY,
+                    "api_url": OPENAI_URL,
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/multi_turn_dialogues/sdk_mtbench101_rule_all.py b/examples/multi_turn_dialogues/sdk_mtbench101_rule_all.py
index d81ba63e..c03e75cd 100644
--- a/examples/multi_turn_dialogues/sdk_mtbench101_rule_all.py
+++ b/examples/multi_turn_dialogues/sdk_mtbench101_rule_all.py
@@ -1,26 +1,27 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_mtbench101_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "multi_turn_dialog",
-        "field": {
-            "id": "id",
-            "content": "history"  # the column name of multi-turn dialogues, e.g.: history, dialogues
-        }
-    },
-    "executor": {
-        "eval_group": "qa_standard_v1",
-        "result_save": {
-            "bad": True,
-            "good": True
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_mtbench101_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "multi_turn_dialog",
+            "field": {
+                "id": "id",
+                "content": "history"  # the column name of multi-turn dialogues, e.g.: history, dialogues
+            }
         },
-        "multi_turn_mode": "all"
+        "executor": {
+            "eval_group": "qa_standard_v1",
+            "result_save": {
+                "bad": True,
+                "good": True
+            },
+            "multi_turn_mode": "all"
+        }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/multi_turn_dialogues/sdk_mtbench_llm.py b/examples/multi_turn_dialogues/sdk_mtbench_llm.py
index ed7efd5d..b62b4b31 100644
--- a/examples/multi_turn_dialogues/sdk_mtbench_llm.py
+++ b/examples/multi_turn_dialogues/sdk_mtbench_llm.py
@@ -1,38 +1,39 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "lmsys/mt_bench_human_judgments",
-    "dataset": {
-        "source": "hugging_face",
-        "format": "multi_turn_dialog",
-        "field": {
-            "id": "question_id",
-            "content": "conversation_a"
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "lmsys/mt_bench_human_judgments",
+        "dataset": {
+            "source": "hugging_face",
+            "format": "multi_turn_dialog",
+            "field": {
+                "id": "question_id",
+                "content": "conversation_a"
+            },
+            "hf_config": {
+                "huggingface_split": "human"
+            }
         },
-        "hf_config": {
-            "huggingface_split": "human"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptTextQualityV3"],
-        "result_save": {
-            "bad": True,
-            "good": True
+        "executor": {
+            "prompt_list": ["PromptTextQualityV3"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            },
+            "end_index": 5,
+            "multi_turn_mode": "all"
         },
-        "end_index": 5,
-        "multi_turn_mode": "all"
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMTextQualityModelBase": {
-                "key": "",
-                "api_url": "",
+        "evaluator": {
+            "llm_config": {
+                "LLMTextQualityModelBase": {
+                    "key": "",
+                    "api_url": "",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/multi_turn_dialogues/sdk_mtbench_rule_all.py b/examples/multi_turn_dialogues/sdk_mtbench_rule_all.py
index 3dee2817..12981ad1 100644
--- a/examples/multi_turn_dialogues/sdk_mtbench_rule_all.py
+++ b/examples/multi_turn_dialogues/sdk_mtbench_rule_all.py
@@ -1,30 +1,31 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "lmsys/mt_bench_human_judgments",
-    "dataset": {
-        "source": "hugging_face",
-        "format": "multi_turn_dialog",
-        "field": {
-            "id": "question_id",
-            "content": "conversation_a"
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "lmsys/mt_bench_human_judgments",
+        "dataset": {
+            "source": "hugging_face",
+            "format": "multi_turn_dialog",
+            "field": {
+                "id": "question_id",
+                "content": "conversation_a"
+            },
+            "hf_config": {
+                "huggingface_split": "human"
+            }
         },
-        "hf_config": {
-            "huggingface_split": "human"
+        "executor": {
+            "eval_group": "qa_standard_v1",
+            "result_save": {
+                "bad": True,
+                "good": True
+            },
+            "end_index": 5,
+            "multi_turn_mode": "all"
         }
-    },
-    "executor": {
-        "eval_group": "qa_standard_v1",
-        "result_save": {
-            "bad": True,
-            "good": True
-        },
-        "end_index": 5,
-        "multi_turn_mode": "all"
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/examples/security/text_security_politics.py b/examples/security/text_security_politics.py
index f732088f..93a57912 100644
--- a/examples/security/text_security_politics.py
+++ b/examples/security/text_security_politics.py
@@ -1,32 +1,33 @@
 from dingo.config import InputArgs
 from dingo.exec import Executor
 
-input_data = {
-    "input_path": "../../test/data/test_local_jsonl.jsonl",
-    "dataset": {
-        "source": "local",
-        "format": "jsonl",
-        "field": {
-            "content": "content"
-        }
-    },
-    "executor": {
-        "prompt_list": ["PromptPolitics"],
-        "result_save": {
-            "bad": True,
-            "good": True
-        }
-    },
-    "evaluator": {
-        "llm_config": {
-            "LLMSecurityPolitics": {
-                "key": "",
-                "api_url": "",
+if __name__ == '__main__':
+    input_data = {
+        "input_path": "../../test/data/test_local_jsonl.jsonl",
+        "dataset": {
+            "source": "local",
+            "format": "jsonl",
+            "field": {
+                "content": "content"
+            }
+        },
+        "executor": {
+            "prompt_list": ["PromptPolitics"],
+            "result_save": {
+                "bad": True,
+                "good": True
+            }
+        },
+        "evaluator": {
+            "llm_config": {
+                "LLMSecurityPolitics": {
+                    "key": "",
+                    "api_url": "",
+                }
             }
         }
     }
-}
-input_args = InputArgs(**input_data)
-executor = Executor.exec_map["local"](input_args)
-result = executor.execute()
-print(result)
+    input_args = InputArgs(**input_data)
+    executor = Executor.exec_map["local"](input_args)
+    result = executor.execute()
+    print(result)
diff --git a/test/data/artimuse/test_artimuse_AVA.jsonl b/test/data/artimuse/test_artimuse_AVA.jsonl
new file mode 100644
index 00000000..c5e43ebb
--- /dev/null
+++ b/test/data/artimuse/test_artimuse_AVA.jsonl
@@ -0,0 +1,50 @@
+{"id": 1, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/6671.jpg"}
+{"id": 2, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/2626.jpg"}
+{"id": 3, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/3504.jpg"}
+{"id": 4, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/7112.jpg"}
+{"id": 5, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/540.jpg"}
+{"id": 6, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/4012.jpg"}
+{"id": 7, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/5613.jpg"}
+{"id": 8, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/3261.jpg"}
+{"id": 9, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/6015.jpg"}
+{"id": 10, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/5960.jpg"}
+{"id": 11, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/9436.jpg"}
+{"id": 12, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/1006.jpg"}
+{"id": 13, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/8921.jpg"}
+{"id": 14, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/576.jpg"}
+{"id": 15, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/2595.jpg"}
+{"id": 16, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/595.jpg"}
+{"id": 17, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/9316.jpg"}
+{"id": 18, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/4164.jpg"}
+{"id": 19, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/4121.jpg"}
+{"id": 20, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/9450.jpg"}
+{"id": 21, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/9014.jpg"}
+{"id": 22, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/4394.jpg"}
+{"id": 23, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/2920.jpg"}
+{"id": 24, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/6222.jpg"}
+{"id": 25, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/2802.jpg"}
+{"id": 26, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/8960.jpg"}
+{"id": 27, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/image_dataset_100/AVA/3478.jpg"}
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diff --git a/test/data/artimuse/test_artimuse_nano_banana.jsonl b/test/data/artimuse/test_artimuse_nano_banana.jsonl
new file mode 100644
index 00000000..285afc3c
--- /dev/null
+++ b/test/data/artimuse/test_artimuse_nano_banana.jsonl
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+{"id": 5, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/dingo/nano/woman_with_logo.png"}
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diff --git a/test/data/compare/test_math_compare.jsonl b/test/data/compare/test_math_compare.jsonl
new file mode 100644
index 00000000..cc48768a
--- /dev/null
+++ b/test/data/compare/test_math_compare.jsonl
@@ -0,0 +1,2 @@
+{"track_id": "0631a2db-134c-433f-b10f-59cb56f442a6", "url": "https://socratic.org/questions/do-f-x-6-10x-2-and-g-x-8-x-2-2-share-any-tangent-lines#265435", "html": "\n\n\n<!DOCTYPE html>\n<html \n    itemscope itemtype=\"http://schema.org/QAPage\"\n>\n    <head>\n        <!-- custom metatags -->\n        \n    \n\n\n        <meta charset=\"utf-8\">\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\">\n\n<!-- about the page -->\n<title>Do f(x) = 6 – 10x^2 and g(x) = 8 – (x – 2)^2  share any tangent lines? | Socratic</title>\n<meta name=\"description\"\n    content=\"Yes, they do. The lines are (approximately) y=7.741x+7.498 and y=1.148x+6.033 Function f f(x) = 6-10x^2 so f&#39;(x) = -20x At a point (a,f(a)), the equation of the tangent line is y=f(a)+f&#39;(a)(x-a)  = (6-10a^2)+(-20a)(x-a) which can be simplified to  y= - 20ax+10a^2+6 Function g g(x) = 4+4x-x^2 so g&#39;(x) = 4-2x At a point (b,g(b)), the equation of the tangent line is y=g(b)+g&#39;(b)(x-b)  = (4+4b-b^2)+(4-2b)(x-b) which can be simplified to y = (4-2b)x +b^2+4 The lines coincide when their slopes are the same and their y intercepts are the same. -20a = 4-2b which implies b=10a+2 10a^2+6=b^2+4 = (10a+2)^2+4 This leads to 90a^2+40a+2=0 The solutions are a=(-10+-sqrt(55))/45 and b=10a+2 Using the approximations a ~~ -0.0374178 and a ~~ -0.387027 we get corresponding values of b ~~ 1.87027 and b ~~ -1.87027 Using these in  y= - 20ax+10a^2+6 or y = (4-2b)x +b^2+4 gets us the lines y=1.148x+6.033 and y=7.741x+7.498\"/>\n\n<!-- canonical url -->\n\n<link href=\"https://socratic.org/questions/do-f-x-6-10x-2-and-g-x-8-x-2-2-share-any-tangent-lines\" rel=\"canonical\" />\n\n\n<!-- favicon -->\n\n  <link rel=\"icon\" type=\"image/png\"\n      href=\"/static/images/favicon32-qa.png\" />\n\n<!-- search engine site verification codes -->\n<meta name=\"google-site-verification\" content=\"KPpRSeYqJagzMJIt2YcPHKaz9bemdrB9ZPf0y53CC2E\" />\n<meta name=\"msvalidate.01\" content=\"0451EDE6FAD80488BAFF9B731C685FFC\" />\n\n\n    \n\n\n\n\n<!-- Facebook Open Graph meta tags - these are also used by Twitterbot -->\n<meta property=\"fb:app_id\" content=\"\" />\n<meta property=\"og:site_name\" content=\"Socratic.org\">\n<meta property=\"og:type\" content=\"website\">\n<meta property=\"og:title\" itemprop=\"name\" content=\"Do f(x) = 6 – 10x^2 and g(x) = 8 – (x – 2)^2  share any tangent lines? | Socratic\">\n<meta property=\"og:description\" content=\"Yes, they do. The lines are (approximately) y=7.741x+7.498 and y=1.148x+6.033 Function f f(x) = 6-10x^2 so f&#39;(x) = -20x At a point (a,f(a)), the equation of the tangent line is y=f(a)+f&#39;(a)(x-a)  = (6-10a^2)+(-20a)(x-a) which can be simplified to  y= - 20ax+10a^2+6 Function g g(x) = 4+4x-x^2 so g&#39;(x) = 4-2x At a point (b,g(b)), the equation of the tangent line is y=g(b)+g&#39;(b)(x-b)  = (4+4b-b^2)+(4-2b)(x-b) which can be simplified to y = (4-2b)x +b^2+4 The lines coincide when their slopes are the same and their y intercepts are the same. -20a = 4-2b which implies b=10a+2 10a^2+6=b^2+4 = (10a+2)^2+4 This leads to 90a^2+40a+2=0 The solutions are a=(-10+-sqrt(55))/45 and b=10a+2 Using the approximations a ~~ -0.0374178 and a ~~ -0.387027 we get corresponding values of b ~~ 1.87027 and b ~~ -1.87027 Using these in  y= - 20ax+10a^2+6 or y = (4-2b)x +b^2+4 gets us the lines y=1.148x+6.033 and y=7.741x+7.498\">\n<meta property=\"og:image\" itemprop=\"image primaryImageOfPage\" content=\"/static/images/socratic_profile_with_name.png\">\n<meta property=\"og:url\" content=\"https://socratic.org/questions/do-f-x-6-10x-2-and-g-x-8-x-2-2-share-any-tangent-lines\">\n\n<!-- Twitterbot meta tags -->\n<meta name=\"twitter:card\" content=\"summary\">\n<meta name=\"twitter:site\" content=\"@SocraticOrg\">\n<meta name=\"twitter:domain\" content=\"socratic.org\">\n<meta name=\"twitter:image:src\" content=\"/static/images/socratic_profile_with_name.png\">\n\n        <!-- start CSS -->\n        <link rel=\"stylesheet\" type=\"text/css\" href=\"/static/css/desktop.min.css\">\n        <link rel=\"stylesheet\" href=\"/static/css/footer.css\">\n        <!-- end CSS -->\n\n        <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js\"></script>\n        \n    </head>\n\n    <body class=\"\"\n          style=\"\">\n        \n        \n\n        <header>\n            <div class=\"headerMain courseHeader coloredHeader\" style=\"background-color: #2D7DDC\">\n                <div class='headerContainer clearfix'>\n                    <nav>\n                        <ul>\n                            <li class='inline'>\n                              \n                                \n                                  <img class=\"qa-logo\" alt=\"Socratic Q&A logo\" src=\"/static/images/socratic-logo-qa-white.png\">\n                                \n                             \n                            </li>\n<!--                             <div class=\"rightLinks\">\n                                <li class='subjectDropdownLink dropdown inline'>\n                                    <span class=\"dropdown-toggle nav-link\"\n                                        data-toggle=\"dropdown\">Browse subjects <span class=\"caret\"></span></span>\n                                    <div class=\"dropdown-menu\" role=\"menu\" aria-labelledby=\"dLabel\">\n                                        \n    <div class=\"upArrow\"></div>\n 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</div>\n\n        <a href=\"/calculus\" data-name=\"topics\"\n           class=\"courseNavTab \">\n            Topics\n        </a>\n        <span class='searchFormContainer'>\n            <form class='searchForm' action=\"https://www.google.com/search\" method=\"GET\">\n                <img alt=\"Search icon\" src=\"/static/images/search-grey.png\">\n                <input type=\"text\" name=\"q\" class=\"searchQuery\"\n                       placeholder=\"Search...\" />\n            </form>\n        </span>\n    </div>\n</div>\n                </div>\n            </div>\n        </header>\n        <div class=\"fullWidth\" >\n            \n\n<div id=\"content\" class='questionPageContent isAnswered' itemprop=\"mainEntity\" itemscope itemtype=\"http://schema.org/Question\">\n  <article>\n    <h1 class='questionTitle' itemprop=\"name\">Do #f(x) = 6 – 10x^2# and #g(x) = 8 – (x – 2)^2 # share any tangent lines?</h1>\n    \n\n    <div class=\"clearfix\">\n      \n\n<div class='breadcrumbs weak-text'>\n    \n    <span itemscope itemtype=\"http://data-vocabulary.org/Breadcrumb\">\n        <a href=\"/calculus\" itemprop=\"url\">\n            <span class=\"breadcrumb\" itemprop=\"title\">Calculus</span>\n        </a>\n    </span>\n\n    \n        <span class='breadcrumb-gray'></span>\n        \n    <span itemscope itemtype=\"http://data-vocabulary.org/Breadcrumb\">\n        <a href=\"/calculus#derivatives\" itemprop=\"url\">\n            <span class=\"breadcrumb\" itemprop=\"title\">Derivatives</span>\n        </a>\n    </span>\n\n        <span class='breadcrumb-gray'></span>\n        \n    <span itemscope itemtype=\"http://data-vocabulary.org/Breadcrumb\">\n        <a href=\"/calculus/derivatives/tangent-line-to-a-curve\" itemprop=\"url\">\n            <span class=\"breadcrumb\" itemprop=\"title\">Tangent Line to a Curve</span>\n        </a>\n    </span>\n\n    \n</div>\n    </div>\n\n    \n\n    <div id=\"main\" class='clearfix'>\n      <div class='questionPageLeft floatLeft'>\n        <h5 class=\"answersHeader hidesWhenAnswerEditing\">\n          <span itemprop=\"answerCount\">2</span> Answers\n        </h5>\n        <div class=\"answers\">\n          \n              \n\n<div id=\"265411\" class=\"answer\" itemprop=\"suggestedAnswer\" itemscope itemtype=\"http://schema.org/Answer\">\n  <div class=\"hidesWhenAnswerEditing\">\n    <div class=\"answerTop clearfix\">\n      \n      \n        \n        \n        \n        <div class=\"contributorPic\">\n          <a href=\"/users/jimh\" class=\"topContributorPic\" ><img  src=\"https://profilepictures.socratic.org/V9qrDvNyQe6yk76DuTmQ_cropped%20Galileo_Poster-1.jpg\" title=\"\" alt=\"\"\n         \n          class=\"\"\n         ></a>\n          \n        </div>\n      \n        <div class=\"answerInfo\">\n          <div class=\"answerInfoTop clearfix\"><a href=\"/users/jimh\"\n        \n        class=\"topContributorLink\">\n        Jim H\n    </a></div>\n          <div class=\"answerInfoBottom clearfix weak-text\">\n            <span itemprop=\"dateCreated\" datetime=\"2016-05-13T16:57:24\" class=\"dateCreated\">\n              May 13, 2016\n            </span>\n          </div>\n        </div>\n      </div>\n      <div class=\"answerContainer clearfix\">\n        <div class='answerText' itemprop=\"text\">\n          \n          <div class=\"answerSummary\">\n            <div>\n              <div class='markdown'><p>Yes, they do.  The lines are (approximately) <mathjax>#y=7.741x+7.498#</mathjax> and <mathjax>#y=1.148x+6.033#</mathjax></p></div>\n            </div>\n          </div>\n          \n          \n          <div class=\"answerDescription\">\n            \n            <h4 class=\"answerHeader\">Explanation:</h4>\n            \n            <div>\n              <div class='markdown'><p><strong>Function <mathjax>#f#</mathjax></strong></p>\n<p><mathjax>#f(x) = 6-10x^2#</mathjax> so <mathjax>#f'(x) = -20x#</mathjax></p>\n<p>At a point <mathjax>#(a,f(a))#</mathjax>, the equation of the tangent line is </p>\n<p><mathjax>#y=f(a)+f'(a)(x-a)#</mathjax></p>\n<blockquote>\n<p><mathjax># = (6-10a^2)+(-20a)(x-a)#</mathjax> which can be simplified to </p>\n</blockquote>\n<p><mathjax># y= - 20ax+10a^2+6#</mathjax></p>\n<p><strong>Function <mathjax>#g#</mathjax><br />\n</strong><br />\n<mathjax>#g(x) = 4+4x-x^2#</mathjax> so <mathjax>#g'(x) = 4-2x#</mathjax></p>\n<p>At a point <mathjax>#(b,g(b))#</mathjax>, the equation of the tangent line is </p>\n<p><mathjax>#y=g(b)+g'(b)(x-b)#</mathjax></p>\n<blockquote>\n<p><mathjax># = (4+4b-b^2)+(4-2b)(x-b)#</mathjax> which can be simplified to </p>\n</blockquote>\n<p><mathjax>#y = (4-2b)x +b^2+4#</mathjax></p>\n<p><strong>The lines coincide</strong> when their slopes are the same and their <mathjax>#y#</mathjax> intercepts are the same.</p>\n<p><mathjax>#-20a = 4-2b#</mathjax> which implies <mathjax>#b=10a+2#</mathjax></p>\n<p><mathjax>#10a^2+6=b^2+4 = (10a+2)^2+4#</mathjax></p>\n<p>This leads to </p>\n<p><mathjax>#90a^2+40a+2=0#</mathjax></p>\n<p>The solutions are <mathjax>#a=(-10+-sqrt(55))/45#</mathjax> and <mathjax>#b=10a+2#</mathjax></p>\n<p>Using the approximations <mathjax>#a ~~ -0.0374178#</mathjax> and <mathjax>#a ~~ -0.387027#</mathjax></p>\n<p>we get corresponding values of <mathjax>#b ~~ 1.87027#</mathjax> and <mathjax>#b ~~ -1.87027#</mathjax></p>\n<p>Using these in <mathjax># y= - 20ax+10a^2+6#</mathjax> or <mathjax>#y = (4-2b)x +b^2+4#</mathjax> gets us the lines</p>\n<p><mathjax>#y=1.148x+6.033#</mathjax> and </p>\n<p><mathjax>#y=7.741x+7.498#</mathjax></p></div>\n            </div>\n          </div>\n         \n        </div>\n      </div>\n  </div>\n  <div>\n    <a itemprop=\"url\" href=\"https://socratic.org/answers/265411\">Answer link</a>\n  </div>\n</div>\n          \n              \n\n<div id=\"265435\" class=\"answer\" itemprop=\"suggestedAnswer\" itemscope itemtype=\"http://schema.org/Answer\">\n  <div class=\"hidesWhenAnswerEditing\">\n    <div class=\"answerTop clearfix\">\n      \n      \n        \n        \n        \n        <div class=\"contributorPic\">\n          <a href=\"/users/cesareo-r\" class=\"topContributorPic\" ><img  src=\"https://profilepictures.socratic.org/oGfNPK9RStCHtrapdftB_yo.jpeg\" title=\"\" alt=\"\"\n         \n          class=\"\"\n         ></a>\n          \n        </div>\n      \n        <div class=\"answerInfo\">\n          <div class=\"answerInfoTop clearfix\"><a href=\"/users/cesareo-r\"\n        \n        class=\"topContributorLink\">\n        Cesareo R.\n    </a></div>\n          <div class=\"answerInfoBottom clearfix weak-text\">\n            <span itemprop=\"dateCreated\" datetime=\"2016-05-13T18:10:05\" class=\"dateCreated\">\n              May 13, 2016\n            </span>\n          </div>\n        </div>\n      </div>\n      <div class=\"answerContainer clearfix\">\n        <div class='answerText' itemprop=\"text\">\n          \n          <div class=\"answerSummary\">\n            <div>\n              <div class='markdown'><p>There are two solutions as detailed below</p></div>\n            </div>\n          </div>\n          \n          \n          <div class=\"answerDescription\">\n            \n            <h4 class=\"answerHeader\">Explanation:</h4>\n            \n            <div>\n              <div class='markdown'><p>The tangent lines to <mathjax>#f#</mathjax> and <mathjax>#g#</mathjax> are given by<br />\n<mathjax>#t_f-&gt;(y-f(a))=f_x(a)(x-a)#</mathjax><br />\n<mathjax>#t_g-&gt;(y-g(b))=g_x(b)(x-b)#</mathjax> where <mathjax>#f_x,g_x#</mathjax> indicates derivatives with respect to <mathjax>#x#</mathjax>.<br />\nthe equations read<br />\n<mathjax>#t_f-&gt;y-6 + 10 a^2 = -20 a (x-a)#</mathjax><br />\n<mathjax>#t_g-&gt;y-8 + (b-2)^2 = -2 (b-2) (x-b)#</mathjax><br />\nEquating the <mathjax>#y#</mathjax> values<br />\n<mathjax>#2 (3 + 5 a^2 - 10 a x)=4 + b^2 + (4  - 2 b) x#</mathjax><br />\nand imposing the equality for all <mathjax>#x#</mathjax> we obtain the conditions<br />\n<mathjax>#2 + 10 a^2 - b^2=0,20 a + 4-2 b=0#</mathjax><br />\nSolving for <mathjax>#a,b#</mathjax> we obtain two solutions:<br />\n<mathjax>#{a -&gt; 1/45 (-10 - sqrt[55]), b -&gt; 2/9 (-1 - sqrt[55])}#</mathjax> and<br />\n<mathjax>#{a -&gt; 1/45 (-10 + sqrt[55]), b -&gt; 2/9 (-1 + sqrt[55])}#</mathjax></p></div>\n            </div>\n          </div>\n         \n        </div>\n      </div>\n  </div>\n  <div>\n  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The lines are (approximately)\n\n\\#y=7.741x+7.498\\#\n\nand\n\n\\#y=1.148x+6.033\\#\n\n#### Explanation:\n\nFunction\n\n\\#f\\#\n\n\\#f(x) = 6-10x^2\\#\n\nso\n\n\\#f'(x) = -20x\\#\n\nAt a point\n\n\\#(a,f(a))\\#\n\n, the equation of the tangent line is\n\n\\#y=f(a)+f'(a)(x-a)\\#\n\n\\# = (6-10a^2)+(-20a)(x-a)\\#\n\nwhich can be simplified to\n\n\\# y= - 20ax+10a^2+6\\#\n\nFunction\n\n\\#g\\#\n\n\\#g(x) = 4+4x-x^2\\#\n\nso\n\n\\#g'(x) = 4-2x\\#\n\nAt a point\n\n\\#(b,g(b))\\#\n\n, the equation of the tangent line is\n\n\\#y=g(b)+g'(b)(x-b)\\#\n\n\\# = (4+4b-b^2)+(4-2b)(x-b)\\#\n\nwhich can be simplified to\n\n\\#y = (4-2b)x +b^2+4\\#\n\nThe lines coincide when their slopes are the same and their\n\n\\#y\\#\n\nintercepts are the same.\n\n\\#-20a = 4-2b\\#\n\nwhich implies\n\n\\#b=10a+2\\#\n\n\\#10a^2+6=b^2+4 = (10a+2)^2+4\\#\n\nThis leads to\n\n\\#90a^2+40a+2=0\\#\n\nThe solutions are\n\n\\#a=(-10+-sqrt(55))/45\\#\n\nand\n\n\\#b=10a+2\\#\n\nUsing the approximations\n\n\\#a ~~ -0.0374178\\#\n\nand\n\n\\#a ~~ -0.387027\\#\n\nwe get corresponding values of\n\n\\#b ~~ 1.87027\\#\n\nand\n\n\\#b ~~ -1.87027\\#\n\nUsing these in\n\n\\# y= - 20ax+10a^2+6\\#\n\nor\n\n\\#y = (4-2b)x +b^2+4\\#\n\ngets us the lines\n\n\\#y=1.148x+6.033\\#\n\nand\n\n\\#y=7.741x+7.498\\#\n\nThere are two solutions as detailed below\n\n#### Explanation:\n\nThe tangent lines to\n\n\\#f\\#\n\nand\n\n\\#g\\#\n\nare given by\n\n\\#t_f-&gt;(y-f(a))=f_x(a)(x-a)\\#\n\n\\#t_g-&gt;(y-g(b))=g_x(b)(x-b)\\#\n\nwhere\n\n\\#f_x,g_x\\#\n\nindicates derivatives with respect to\n\n\\#x\\#\n\n.\n\n the equations read\n\n\\#t_f-&gt;y-6 + 10 a^2 = -20 a (x-a)\\#\n\n\\#t_g-&gt;y-8 + (b-2)^2 = -2 (b-2) (x-b)\\#\n\nEquating the\n\n\\#y\\#\n\nvalues\n\n\\#2 (3 + 5 a^2 - 10 a x)=4 + b^2 + (4 - 2 b) x\\#\n\nand imposing the equality for all\n\n\\#x\\#\n\nwe obtain the conditions\n\n\\#2 + 10 a^2 - b^2=0,20 a + 4-2 b=0\\#\n\nSolving for\n\n\\#a,b\\#\n\nwe obtain two solutions:\n\n\\#{a -&gt; 1/45 (-10 - sqrt[55]), b -&gt; 2/9 (-1 - sqrt[55])}\\#\n\nand\n\n\\#{a -&gt; 1/45 (-10 + sqrt[55]), b -&gt; 2/9 (-1 + sqrt[55])}\\#\n", "megamath_md": "# Do #f(x) = 6 – 10x^2# and #g(x) = 8 – (x – 2)^2 # share any tangent lines?\n\n##### 2 Answers\n\nYes, they do. The lines are (approximately)\n\n#### Explanation:\n\n**Function #f#**\n\nAt a point\n\n# = (6-10a^2)+(-20a)(x-a)# which can be simplified to\n\n**Function #g#\n**\n\nAt a point\n\n# = (4+4b-b^2)+(4-2b)(x-b)# which can be simplified to\n\n**The lines coincide** when their slopes are the same and their\n\nThis leads to\n\nThe solutions are\n\nUsing the approximations\n\nwe get corresponding values of\n\nUsing these in\n\nThere are two solutions as detailed below\n\n#### Explanation:\n\nThe tangent lines to\n\nthe equations read\n\nEquating the\n\nand imposing the equality for all\n\nSolving for", "clean_html": "<html><body><header><div><div><div><div><div><a>Calculus</a><div><div><div><h5>Science</h5><ul><li><a>Anatomy &amp; Physiology</a></li><li><a>Astronomy</a></li><li><a>Astrophysics</a></li><li><a>Biology</a></li><li><a>Chemistry</a></li><li><a>Earth Science</a></li><li><a>Environmental Science</a></li><li><a>Organic Chemistry</a></li><li><a>Physics</a></li></ul></div><div><h5>Math</h5><ul><li><a>Algebra</a></li><li><a>Calculus</a></li><li><a>Geometry</a></li><li><a>Prealgebra</a></li><li><a>Precalculus</a></li><li><a>Statistics</a></li><li><a>Trigonometry</a></li></ul></div><div><h5>Humanities</h5><ul><li><a>English Grammar</a></li><li><a>U.S. History</a></li><li><a>World History</a></li></ul></div><div><ul><h5>... and beyond</h5><li><a>Socratic Meta</a></li><li><a>Featured Answers</a></li></ul></div></div></div></div><a>Topics</a><span><form><img></form></span></div></div></div></div></header><div><div><article><h1>Do #f(x) = 6 – 10x^2# and #g(x) = 8 – (x – 2)^2 # share any tangent lines?</h1><div><div><span>Calculus Derivatives Tangent Line to a Curve</span></div></div><div><div><h5><span>2 Answers</span></h5><div><div><div><div><div><a><img></a></div><div><div><a>Jim H</a></div><div><span>May 13, 2016</span></div></div></div><div><div><div><div><div><p>Yes, they do. The lines are (approximately) #y=7.741x+7.498# and #y=1.148x+6.033#</p></div></div></div><div><h4>Explanation:</h4><div><div><p><strong>Function #f#</strong></p><p><mathjax>#f(x) = 6-10x^2# so #f'(x) = -20x#</mathjax></p><p>At a point #(a,f(a))# , the equation of the tangent line is</p><p><mathjax>#y=f(a)+f'(a)(x-a)#</mathjax></p><blockquote><p><mathjax># = (6-10a^2)+(-20a)(x-a)# which can be simplified to</mathjax></p></blockquote><p><mathjax># y= - 20ax+10a^2+6#</mathjax></p><p><strong>Function #g# \n #g(x) = 4+4x-x^2# so #g'(x) = 4-2x#</strong></p><p>At a point #(b,g(b))# , the equation of the tangent line is</p><p><mathjax>#y=g(b)+g'(b)(x-b)#</mathjax></p><blockquote><p><mathjax># = (4+4b-b^2)+(4-2b)(x-b)# which can be simplified to</mathjax></p></blockquote><p><mathjax>#y = (4-2b)x +b^2+4#</mathjax></p><p><strong>The lines coincide when their slopes are the same and their #y# intercepts are the same.</strong></p><p><mathjax>#-20a = 4-2b# which implies #b=10a+2#</mathjax></p><p><mathjax>#10a^2+6=b^2+4 = (10a+2)^2+4#</mathjax></p><p>This leads to</p><p><mathjax>#90a^2+40a+2=0#</mathjax></p><p>The solutions are #a=(-10+-sqrt(55))/45# and #b=10a+2#</p><p>Using the approximations #a ~~ -0.0374178# and #a ~~ -0.387027#</p><p>we get corresponding values of #b ~~ 1.87027# and #b ~~ -1.87027#</p><p>Using these in # y= - 20ax+10a^2+6# or #y = (4-2b)x +b^2+4# gets us the lines</p><p><mathjax>#y=1.148x+6.033# and</mathjax></p><p><mathjax>#y=7.741x+7.498#</mathjax></p></div></div></div></div></div></div><div><a>Answer link</a></div></div><div><div><div><div><a><img></a></div><div><div><a>Cesareo R.</a></div><div><span>May 13, 2016</span></div></div></div><div><div><div><div><div><p>There are two solutions as detailed below</p></div></div></div><div><h4>Explanation:</h4><div><div><p>The tangent lines to #f# and #g# are given by \n #t_f-&gt;(y-f(a))=f_x(a)(x-a)# \n #t_g-&gt;(y-g(b))=g_x(b)(x-b)# where #f_x,g_x# indicates derivatives with respect to #x# . \n the equations read \n #t_f-&gt;y-6 + 10 a^2 = -20 a (x-a)# \n #t_g-&gt;y-8 + (b-2)^2 = -2 (b-2) (x-b)# \n Equating the #y# values \n #2 (3 + 5 a^2 - 10 a x)=4 + b^2 + (4 - 2 b) x# \n and imposing the equality for all #x# we obtain the conditions \n #2 + 10 a^2 - b^2=0,20 a + 4-2 b=0# \n Solving for #a,b# we obtain two solutions: \n #{a -&gt; 1/45 (-10 - sqrt[55]), b -&gt; 2/9 (-1 - sqrt[55])}# and \n #{a -&gt; 1/45 (-10 + sqrt[55]), b -&gt; 2/9 (-1 + sqrt[55])}#</p></div></div></div></div></div></div><div><a>Answer link</a></div></div></div></div><div><div><div><h5>Related questions</h5><ul><li><div><a>How do you find the equation of a tangent line to a curve?</a></div></li><li><div><a>How do you find the slope of the tangent line to a curve at a point?</a></div></li><li><div><a>How do you find the tangent line to the curve #y=x^3-9x# at the point where #x=1#?</a></div></li><li><div><a>How do you know if a line is tangent to a curve?</a></div></li><li><div><a>How do you show a line is a tangent to a curve?</a></div></li><li><div><a>How do you find the Tangent line to a curve by implicit differentiation?</a></div></li><li><div><a>What is the slope of a line tangent to the curve #3y^2-2x^2=1#?</a></div></li><li><div><a>How does tangent slope relate to the slope of a line?</a></div></li><li><div><a>What is the slope of a horizontal tangent line?</a></div></li><li><div><a>How do you find the slope of a tangent line using secant lines?</a></div></li></ul><div><a>See all questions in Tangent Line to a Curve</a></div></div><div><div><h5>Impact of this question</h5><div>1611 views around the world</div></div><div><img><div>You can reuse this answer \n Creative Commons License</div></div></div></div></div></div></article></div></div></body></html>"}
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&#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(b)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (b)\">Q1 (b)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(c)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (c)\">Q1 (c)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(d)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (d)\">Q1 (d)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(e)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (e)\">Q1 (e)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(f)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (f)\">Q1 (f)</a>&#160; 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&#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(l)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (l)\">Q1 (l)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(m)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (m)\">Q1 (m)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(n)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (n)\">Q1 (n)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_02_(a)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 02 (a)\">Q2 (a)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_02_(b)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 02 (b)\">Q2 (b)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_02_(c)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 02 (c)\">Q2 (c)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_03\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 03\">Q3</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_04_(a)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 04 (a)\">Q4 (a)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_04_(b)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 04 (b)\">Q4 (b)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_04_(c)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 04 (c)\">Q4 (c)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_04_(d)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 04 (d)\">Q4 (d)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_04_(e)\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 04 (e)\">Q4 (e)</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_05\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 05\">Q5</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_06\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 06\">Q6</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_07\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 07\">Q7</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_08\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010/Question 08\">Q8</a>&#160; &#8226; &#160;</p><div class=\"mw-customtoggle-2\" style=\"cursor:pointer;background:#7491A3; font-weight:normal; color: white; font-size:10pt; width:100%; text-align:left;\">&#160;&#160;&#160;Other <a href=\"/Science:Math_Exam_Resources/Courses/MATH100\" title=\"Science:Math Exam Resources/Courses/MATH100\"><span style=\"color: white\">MATH100</span></a> Exams</div>\n<div class=\"mw-collapsible\" style=\"\" id=\"mw-customcollapsible-2\"><div class=\"mw-collapsible-content\" style=\"\">&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2011\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2011\">December 2011</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2010\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2010\">December 2010</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2012\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2012\">December 2012</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2013\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2013\">December 2013</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2014\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2014\">December 2014</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2015\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2015\">December 2015</a>&#160; &#8226; &#160;<a href=\"/Science:Math_Exam_Resources/Courses/MATH100/December_2016\" title=\"Science:Math Exam Resources/Courses/MATH100/December 2016\">December 2016</a>&#160; &#8226; &#160;</div></div>\n<table class=\"collapsible wikitable\" width=\"100%\" style=\"background: #FFFFFF;\">\n<tbody><tr>\n<th style=\"padding-left:1em; text-align: left; background:#FFFFFF; color:#666666;\"><h3 style=\"color:#666666;\"><span id=\"Question_01_(a)\"></span><span class=\"mw-headline\" id=\"Question_01_.28a.29\">Question 01 (a) <font size=\"1\"></font></span></h3>\n</th></tr>\n<tr>\n<td style=\"padding:1em\">\n<p><b>Short-Answer Questions.</b> Each question is worth 3 marks, but not all questions are of equal difficulty. Full marks will be given for correct answers placed in the box, but at most 1 mark will be given for incorrect answers. Unless otherwise stated, it is not necessary to simplify your answers in this question.\n</p><p><br />\nEvaluate\n</p>\n<dl><dd><span class=\"mwe-math-element\"><span class=\"mwe-math-mathml-inline mwe-math-mathml-a11y\" style=\"display: none;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"  alttext=\"{\\displaystyle \\displaystyle \\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}}\">\n  <semantics>\n    <mrow class=\"MJX-TeXAtom-ORD\">\n      <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n        <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n          <munder>\n            <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n            <mrow class=\"MJX-TeXAtom-ORD\">\n              <mi>x</mi>\n              <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n              <mn>1</mn>\n            </mrow>\n          </munder>\n          <mrow class=\"MJX-TeXAtom-ORD\">\n            <mfrac>\n              <mrow>\n                <msup>\n                  <mi>x</mi>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mn>2</mn>\n                  </mrow>\n                </msup>\n                <mo>+</mo>\n                <mn>2</mn>\n                <mi>x</mi>\n                <mo>&#x2212;<!-- − --></mo>\n                <mn>3</mn>\n              </mrow>\n              <mrow>\n                <mi>x</mi>\n                <mo>&#x2212;<!-- − --></mo>\n                <mn>1</mn>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mstyle>\n      </mstyle>\n    </mrow>\n    <annotation encoding=\"application/x-tex\">{\\displaystyle \\displaystyle \\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}}</annotation>\n  </semantics>\n</math></span><img src=\"https://wiki.ubc.ca/api/rest_v1/media/math/render/svg/355031082e96f21f9bc4c5ada130d318a09de17c\" class=\"mwe-math-fallback-image-inline\" aria-hidden=\"true\" style=\"vertical-align: -2.005ex; width:16.348ex; height:5.843ex;\" alt=\"{\\displaystyle \\displaystyle \\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}}\"/></span></dd></dl>\n<p>Or determine that this limit does not exist.\n</p>\n</td></tr></tbody></table>\n<table width=\"100%\" style=\"background:#ffffff;\">\n<tbody><tr>\n<td style=\"text-align:justify; font-size:8pt; color:#666666;\">\n<p><b>Make sure you understand the problem fully:</b> What is the question asking you to do? Are there specific conditions or constraints that you should take note of? How will you know if your answer is correct from your work only? Can you rephrase the question in your own words in a way that makes sense to you?<br />\n</p>\n</td></tr>\n<tr>\n<td style=\"text-align:justify; font-size:8pt; color:#666666;\">\n<p><b>If you are stuck</b>, check the hints below. Read the first one and consider it for a while. Does it give you a new idea on how to approach the problem? If so, try it! If after a while you are still stuck, go for the next hint.\n</p>\n</td></tr></tbody></table>\n<p><br />\n</p>\n<table class=\"collapsible wikitable collapsed\" width=\"100%\" style=\"background: #FFFFFF;\">\n<tbody><tr>\n<th style=\"padding-left:1em; text-align: left; background:#FFFFFF; color:#666666;\"><h3 style=\"color:#666666;\"><span class=\"mw-headline\" id=\"Hint_1\">Hint 1 <font size=\"1\"></font></span></h3>\n</th></tr>\n<tr>\n<td style=\"padding:1em\">\n<p>The first step to evaluating a limit is to plug in the value you are limiting to, in this case <i>x</i>=1. This will fail - can you manipulate the numerator and the denominator to handle why it fails?\n</p>\n</td></tr></tbody></table>\n<p><br />\n</p>\n<table class=\"collapsible wikitable collapsed\" width=\"100%\" style=\"background: #FFFFFF;\">\n<tbody><tr>\n<th style=\"padding-left:1em; text-align: left; background:#FFFFFF; color:#666666;\"><h3 style=\"color:#666666;\"><span class=\"mw-headline\" id=\"Hint_2\">Hint 2 <font size=\"1\"></font></span></h3>\n</th></tr>\n<tr>\n<td style=\"padding:1em\">\n<p>Try factoring the numerator.\n</p>\n</td></tr></tbody></table>\n<table width=\"100%\" style=\"background:#ffffff;\">\n<tbody><tr>\n<td style=\"text-align:justify; font-size:8pt; color:#666666;\">\n<p>Checking a solution serves two purposes: helping you if, after having used all the hints, you still are stuck on the problem; or if you have solved the problem and would like to check your work.\n</p>\n<ul><li><b>If you are stuck on a problem:</b> Read the solution slowly and as soon as you feel you could finish the problem on your own, hide it and work on the problem. Come back later to the solution if you are stuck or if you want to check your work.</li>\n<li><b>If you want to check your work:</b> Don't only focus on the answer, problems are mostly marked for the work you do, make sure you understand all the steps that were required to complete the problem and see if you made mistakes or forgot some aspects. Your goal is to check that your mental process was correct, not only the result.<br /></li></ul>\n</td></tr></tbody></table>\n<p><br />\n</p>\n<table class=\"collapsible wikitable collapsed\" width=\"100%\" style=\"background: #FFFFFF;\">\n<tbody><tr>\n<th style=\"padding-left:1em; text-align: left; background:#FFFFFF; color:#666666;\"><h3 style=\"color:#666666;\"><span class=\"mw-headline\" id=\"Solution_1\">Solution 1 <font size=\"1\"></font></span></h3>\n</th></tr>\n<tr>\n<td style=\"padding:1em\"><div style=\"color:#2F4F4F; background: #DDDDDD; border: 3px solid #2F4F4F; margin-bottom:1em; padding:10px;\"><span class=\"plainlinks\">Found a <strong>typo</strong>? Is this solution <strong>unclear</strong>? <a rel=\"nofollow\" class=\"external text\" href=\"https://docs.google.com/forms/d/1n6Pf-fegzLB4n1QYTHmxvIcIWr4swidLaTm1kekZk38/viewform?entry.86513335=&amp;entry.1104878578&amp;entry.554346562=&amp;entry.488130431&amp;entry.2101116491=&amp;entry.572580842&amp;entry.336854578\">Let us know here</a>.<br /> Please <strong>rate my easiness</strong>! It's quick and helps everyone guide their studies.</span></div>\n<p>Directly plugging in <i>x</i>=1 into the limit gives 0/0. We try to factor to cancel out the terms making the fraction zero.\n</p>\n<dl><dd><span class=\"mwe-math-element\"><span class=\"mwe-math-mathml-inline mwe-math-mathml-a11y\" style=\"display: none;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"  alttext=\"{\\displaystyle \\displaystyle {\\begin{aligned}&amp;\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {(x-1)(x+3)}{x-1}}\\\\=&amp;\\lim _{x\\to 1}(x+3)\\\\=&amp;(1)+3\\\\=&amp;4\\end{aligned}}}\">\n  <semantics>\n    <mrow class=\"MJX-TeXAtom-ORD\">\n      <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n        <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n          <mrow class=\"MJX-TeXAtom-ORD\">\n            <mtable columnalign=\"right left right left right left right left right left right left\" rowspacing=\"3pt\" columnspacing=\"0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0em\" displaystyle=\"true\">\n              <mtr>\n                <mtd />\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mfrac>\n                      <mrow>\n                        <msup>\n                          <mi>x</mi>\n                          <mrow class=\"MJX-TeXAtom-ORD\">\n                            <mn>2</mn>\n                          </mrow>\n                        </msup>\n                        <mo>+</mo>\n                        <mn>2</mn>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>3</mn>\n                      </mrow>\n                      <mrow>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>1</mn>\n                      </mrow>\n                    </mfrac>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mfrac>\n                      <mrow>\n                        <mo stretchy=\"false\">(</mo>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>1</mn>\n                        <mo stretchy=\"false\">)</mo>\n                        <mo stretchy=\"false\">(</mo>\n                        <mi>x</mi>\n                        <mo>+</mo>\n                        <mn>3</mn>\n                        <mo stretchy=\"false\">)</mo>\n                      </mrow>\n                      <mrow>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>1</mn>\n                      </mrow>\n                    </mfrac>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mo stretchy=\"false\">(</mo>\n                  <mi>x</mi>\n                  <mo>+</mo>\n                  <mn>3</mn>\n                  <mo stretchy=\"false\">)</mo>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mi></mi>\n                  <mo stretchy=\"false\">(</mo>\n                  <mn>1</mn>\n                  <mo stretchy=\"false\">)</mo>\n                  <mo>+</mo>\n                  <mn>3</mn>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mn>4</mn>\n                </mtd>\n              </mtr>\n            </mtable>\n          </mrow>\n        </mstyle>\n      </mstyle>\n    </mrow>\n    <annotation encoding=\"application/x-tex\">{\\displaystyle \\displaystyle {\\begin{aligned}&amp;\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {(x-1)(x+3)}{x-1}}\\\\=&amp;\\lim _{x\\to 1}(x+3)\\\\=&amp;(1)+3\\\\=&amp;4\\end{aligned}}}</annotation>\n  </semantics>\n</math></span><img src=\"https://wiki.ubc.ca/api/rest_v1/media/math/render/svg/d9167c93b42422e3e39b40697bfdaaca991840b7\" class=\"mwe-math-fallback-image-inline\" aria-hidden=\"true\" style=\"vertical-align: -10.505ex; width:21.859ex; height:22.176ex;\" alt=\"{\\displaystyle \\displaystyle {\\begin{aligned}&amp;\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {(x-1)(x+3)}{x-1}}\\\\=&amp;\\lim _{x\\to 1}(x+3)\\\\=&amp;(1)+3\\\\=&amp;4\\end{aligned}}}\"/></span></dd></dl>\n</td></tr></tbody></table>\n<p><br />\n</p>\n<table class=\"collapsible wikitable collapsed\" width=\"100%\" style=\"background: #FFFFFF;\">\n<tbody><tr>\n<th style=\"padding-left:1em; text-align: left; background:#FFFFFF; color:#666666;\"><h3 style=\"color:#666666;\"><span class=\"mw-headline\" id=\"Solution_2\">Solution 2 <font size=\"1\"></font></span></h3>\n</th></tr>\n<tr>\n<td style=\"padding:1em\"><div style=\"color:#2F4F4F; background: #DDDDDD; border: 3px solid #2F4F4F; margin-bottom:1em; padding:10px;\"><span class=\"plainlinks\">Found a <strong>typo</strong>? Is this solution <strong>unclear</strong>? <a rel=\"nofollow\" class=\"external text\" href=\"https://docs.google.com/forms/d/1n6Pf-fegzLB4n1QYTHmxvIcIWr4swidLaTm1kekZk38/viewform?entry.86513335=&amp;entry.1104878578&amp;entry.554346562=&amp;entry.488130431&amp;entry.2101116491=&amp;entry.572580842&amp;entry.336854578\">Let us know here</a>.<br /> Please <strong>rate my easiness</strong>! It's quick and helps everyone guide their studies.</span></div>\n<p>Directly plugging in <i>x</i>=1 into the limit gives 0/0. Since this is an indeterminate form, we can use L'Hospital's rule:\n</p>\n<dl><dd><span class=\"mwe-math-element\"><span class=\"mwe-math-mathml-inline mwe-math-mathml-a11y\" style=\"display: none;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"  alttext=\"{\\displaystyle \\displaystyle {\\begin{aligned}&amp;\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {(x^{2}+2x-3)'}{(x-1)'}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {2x+2}{1}}\\\\=&amp;\\lim _{x\\to 1}{2x+2}\\\\=&amp;2(1)+2\\\\=&amp;4\\end{aligned}}}\">\n  <semantics>\n    <mrow class=\"MJX-TeXAtom-ORD\">\n      <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n        <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n          <mrow class=\"MJX-TeXAtom-ORD\">\n            <mtable columnalign=\"right left right left right left right left right left right left\" rowspacing=\"3pt\" columnspacing=\"0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0em\" displaystyle=\"true\">\n              <mtr>\n                <mtd />\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mfrac>\n                      <mrow>\n                        <msup>\n                          <mi>x</mi>\n                          <mrow class=\"MJX-TeXAtom-ORD\">\n                            <mn>2</mn>\n                          </mrow>\n                        </msup>\n                        <mo>+</mo>\n                        <mn>2</mn>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>3</mn>\n                      </mrow>\n                      <mrow>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>1</mn>\n                      </mrow>\n                    </mfrac>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mfrac>\n                      <mrow>\n                        <mo stretchy=\"false\">(</mo>\n                        <msup>\n                          <mi>x</mi>\n                          <mrow class=\"MJX-TeXAtom-ORD\">\n                            <mn>2</mn>\n                          </mrow>\n                        </msup>\n                        <mo>+</mo>\n                        <mn>2</mn>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>3</mn>\n                        <msup>\n                          <mo stretchy=\"false\">)</mo>\n                          <mo>&#x2032;</mo>\n                        </msup>\n                      </mrow>\n                      <mrow>\n                        <mo stretchy=\"false\">(</mo>\n                        <mi>x</mi>\n                        <mo>&#x2212;<!-- − --></mo>\n                        <mn>1</mn>\n                        <msup>\n                          <mo stretchy=\"false\">)</mo>\n                          <mo>&#x2032;</mo>\n                        </msup>\n                      </mrow>\n                    </mfrac>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mfrac>\n                      <mrow>\n                        <mn>2</mn>\n                        <mi>x</mi>\n                        <mo>+</mo>\n                        <mn>2</mn>\n                      </mrow>\n                      <mn>1</mn>\n                    </mfrac>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mi></mi>\n                  <munder>\n                    <mo movablelimits=\"true\" form=\"prefix\">lim</mo>\n                    <mrow class=\"MJX-TeXAtom-ORD\">\n                      <mi>x</mi>\n                      <mo stretchy=\"false\">&#x2192;<!-- → --></mo>\n                      <mn>1</mn>\n                    </mrow>\n                  </munder>\n                  <mrow class=\"MJX-TeXAtom-ORD\">\n                    <mn>2</mn>\n                    <mi>x</mi>\n                    <mo>+</mo>\n                    <mn>2</mn>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mn>2</mn>\n                  <mo stretchy=\"false\">(</mo>\n                  <mn>1</mn>\n                  <mo stretchy=\"false\">)</mo>\n                  <mo>+</mo>\n                  <mn>2</mn>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mo>=</mo>\n                </mtd>\n                <mtd>\n                  <mn>4</mn>\n                </mtd>\n              </mtr>\n            </mtable>\n          </mrow>\n        </mstyle>\n      </mstyle>\n    </mrow>\n    <annotation encoding=\"application/x-tex\">{\\displaystyle \\displaystyle {\\begin{aligned}&amp;\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {(x^{2}+2x-3)'}{(x-1)'}}\\\\=&amp;\\lim _{x\\to 1}{\\frac {2x+2}{1}}\\\\=&amp;\\lim _{x\\to 1}{2x+2}\\\\=&amp;2(1)+2\\\\=&amp;4\\end{aligned}}}</annotation>\n  </semantics>\n</math></span><img src=\"https://wiki.ubc.ca/api/rest_v1/media/math/render/svg/cbabc2cb50106de485951ea02a534c5f23e9d1a7\" class=\"mwe-math-fallback-image-inline\" aria-hidden=\"true\" style=\"vertical-align: 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Each question is worth 3 marks, but not all questions are of equal difficulty. Full marks will be given for correct answers placed in the box, but at most 1 mark will be given for incorrect answers. Unless otherwise stated, it is not necessary to simplify your answers in this question. Evaluate ${\\displaystyle \\displaystyle \\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}}$ Or determine that this limit does not exist. |\n\n| Make sure you understand the problem fully: What is the question asking you to do? Are there specific conditions or constraints that you should take note of? How will you know if your answer is correct from your work only? Can you rephrase the question in your own words in a way that makes sense to you? |\n|---|\n| If you are stuck , check the hints below. Read the first one and consider it for a while. Does it give you a new idea on how to approach the problem? If so, try it! If after a while you are still stuck, go for the next hint. |\n\n| Hint 1 |\n|---|\n| The first step to evaluating a limit is to plug in the value you are limiting to, in this case x =1. This will fail - can you manipulate the numerator and the denominator to handle why it fails? |\n\n| Hint 2 |\n|---|\n| Try factoring the numerator. |\n\nChecking a solution serves two purposes: helping you if, after having used all the hints, you still are stuck on the problem; or if you have solved the problem and would like to check your work. If you are stuck on a problem: Read the solution slowly and as soon as you feel you could finish the problem on your own, hide it and work on the problem. Come back later to the solution if you are stuck or if you want to check your work. If you want to check your work: Don't only focus on the answer, problems are mostly marked for the work you do, make sure you understand all the steps that were required to complete the problem and see if you made mistakes or forgot some aspects. Your goal is to check that your mental process was correct, not only the result.\n\n| Solution 1 |\n|---|\n| Found a typo ? Is this solution unclear ? Let us know here . Please rate my easiness ! It's quick and helps everyone guide their studies. Directly plugging in x =1 into the limit gives 0/0. We try to factor to cancel out the terms making the fraction zero. ${\\displaystyle \\displaystyle {\\begin{aligned}&\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&\\lim _{x\\to 1}{\\frac {(x-1)(x+3)}{x-1}}\\\\=&\\lim _{x\\to 1}(x+3)\\\\=&(1)+3\\\\=&4\\end{aligned}}}$ |\n\n| Solution 2 |\n|---|\n| Found a typo ? Is this solution unclear ? Let us know here . Please rate my easiness ! It's quick and helps everyone guide their studies. Directly plugging in x =1 into the limit gives 0/0. Since this is an indeterminate form, we can use L'Hospital's rule: ${\\displaystyle \\displaystyle {\\begin{aligned}&\\lim _{x\\to 1}{\\frac {x^{2}+2x-3}{x-1}}\\\\=&\\lim _{x\\to 1}{\\frac {(x^{2}+2x-3)'}{(x-1)'}}\\\\=&\\lim _{x\\to 1}{\\frac {2x+2}{1}}\\\\=&\\lim _{x\\to 1}{2x+2}\\\\=&2(1)+2\\\\=&4\\end{aligned}}}$ |\n", "megamath_md": "Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (a)\n• Q1 (a) • Q1 (b) • Q1 (c) • Q1 (d) • Q1 (e) • Q1 (f) • Q1 (g) • Q1 (h) • Q1 (i) • Q1 (j) • Q1 (k) • Q1 (l) • Q1 (m) • Q1 (n) • Q2 (a) • Q2 (b) • Q2 (c) • Q3 • Q4 (a) • Q4 (b) • Q4 (c) • Q4 (d) • Q4 (e) • Q5 • Q6 • Q7 • Q8 •\nQuestion 01 (a) \n\nShortAnswer Questions. Each question is worth 3 marks, but not all questions are of equal difficulty. Full marks will be given for correct answers placed in the box, but at most 1 mark will be given for incorrect answers. Unless otherwise stated, it is not necessary to simplify your answers in this question.\nOr determine that this limit does not exist. \nMake sure you understand the problem fully: What is the question asking you to do? Are there specific conditions or constraints that you should take note of? How will you know if your answer is correct from your work only? Can you rephrase the question in your own words in a way that makes sense to you? \nIf you are stuck, check the hints below. Read the first one and consider it for a while. Does it give you a new idea on how to approach the problem? If so, try it! If after a while you are still stuck, go for the next hint. \nHint 1 \n\nThe first step to evaluating a limit is to plug in the value you are limiting to, in this case x=1. This will fail  can you manipulate the numerator and the denominator to handle why it fails? \nHint 2 \n\nTry factoring the numerator. \nChecking a solution serves two purposes: helping you if, after having used all the hints, you still are stuck on the problem; or if you have solved the problem and would like to check your work.\n\nSolution 1 \n\nFound a typo? Is this solution unclear? Let us know here.\nPlease rate my easiness! It's quick and helps everyone guide their studies. Directly plugging in x=1 into the limit gives 0/0. We try to factor to cancel out the terms making the fraction zero. \nSolution 2 \n\nFound a typo? Is this solution unclear? Let us know here.\nPlease rate my easiness! It's quick and helps everyone guide their studies. Directly plugging in x=1 into the limit gives 0/0. Since this is an indeterminate form, we can use L'Hospital's rule: ", "clean_html": "<html><body><div><h1><span>Science:Math Exam Resources/Courses/MATH100/December 2010/Question 01 (a)</span></h1><div><div>From UBC Wiki</div><div><span>&lt; Science:Math Exam Resources‎ | Courses/MATH100‎ | December 2010</span></div><a>Jump to navigation Jump to search</a><div><div><p><br></p><div><b>MATH100 December 2010</b></div><p>  •   Q1 (a)   •   Q1 (b)   •   Q1 (c)   •   Q1 (d)   •   Q1 (e)   •   Q1 (f)   •   Q1 (g)   •   Q1 (h)   •   Q1 (i)   •   Q1 (j)   •   Q1 (k)   •   Q1 (l)   •   Q1 (m)   •   Q1 (n)   •   Q2 (a)   •   Q2 (b)   •   Q2 (c)   •   Q3   •   Q4 (a)   •   Q4 (b)   •   Q4 (c)   •   Q4 (d)   •   Q4 (e)   •   Q5   •   Q6   •   Q7   •   Q8   •  </p><div>   Other MATH100 Exams</div><div><div>  •   December 2011   •   December 2010   •   December 2012   •   December 2013   •   December 2014   •   December 2015   •   December 2016   •  </div></div><table><tbody><tr><th><h3><span>Question 01 (a)</span></h3></th></tr><tr><td><p><b>Short-Answer Questions. Each question is worth 3 marks, but not all questions are of equal difficulty. Full marks will be given for correct answers placed in the box, but at most 1 mark will be given for incorrect answers. Unless otherwise stated, it is not necessary to simplify your answers in this question.</b></p><p><span>\n Evaluate</span></p><dl><dd><span><img></span></dd></dl><p>Or determine that this limit does not exist.</p></td></tr></tbody></table><table><tbody><tr><td><p><b>Make sure you understand the problem fully: What is the question asking you to do? Are there specific conditions or constraints that you should take note of? How will you know if your answer is correct from your work only? Can you rephrase the question in your own words in a way that makes sense to you? \n</b></p></td></tr><tr><td><p><b>If you are stuck , check the hints below. Read the first one and consider it for a while. Does it give you a new idea on how to approach the problem? If so, try it! If after a while you are still stuck, go for the next hint.</b></p></td></tr></tbody></table><p><br></p><table><tbody><tr><th><h3><span>Hint 1</span></h3></th></tr><tr><td><p>The first step to evaluating a limit is to plug in the value you are limiting to, in this case x =1. This will fail - can you manipulate the numerator and the denominator to handle why it fails?</p></td></tr></tbody></table><p><br></p><table><tbody><tr><th><h3><span>Hint 2</span></h3></th></tr><tr><td><p>Try factoring the numerator.</p></td></tr></tbody></table><table><tbody><tr><td><p>Checking a solution serves two purposes: helping you if, after having used all the hints, you still are stuck on the problem; or if you have solved the problem and would like to check your work.</p><ul><li><b>If you are stuck on a problem: Read the solution slowly and as soon as you feel you could finish the problem on your own, hide it and work on the problem. Come back later to the solution if you are stuck or if you want to check your work.</b></li><li><b>If you want to check your work: Don't only focus on the answer, problems are mostly marked for the work you do, make sure you understand all the steps that were required to complete the problem and see if you made mistakes or forgot some aspects. Your goal is to check that your mental process was correct, not only the result. \n</b></li></ul></td></tr></tbody></table><p><br></p><table><tbody><tr><th><h3><span>Solution 1</span></h3></th></tr><tr><td><div><span>Found a typo? Is this solution unclear? Let us know here.\nPlease rate my easiness! It's quick and helps everyone guide their studies.</span></div><p>Directly plugging in x =1 into the limit gives 0/0. We try to factor to cancel out the terms making the fraction zero.</p><dl><dd><span><img></span></dd></dl></td></tr></tbody></table><p><br></p><table><tbody><tr><th><h3><span>Solution 2</span></h3></th></tr><tr><td><div><span>Found a typo? Is this solution unclear? Let us know here.\nPlease rate my easiness! It's quick and helps everyone guide their studies.</span></div><p>Directly plugging in x =1 into the limit gives 0/0. Since this is an indeterminate form, we can use L'Hospital's rule:</p><dl><dd><span><img></span></dd></dl></td></tr></tbody></table><p><br></p><div>Click here for similar questions</div><div><div><div>MER QGH flag, MER QGQ flag, MER QGS flag, MER RT flag, MER Tag Limit of a rational function, Pages using DynamicPageList parser function, Pages using DynamicPageList parser tag</div></div></div><div><table><tbody><tr><td><div><a><img></a></div></td><td><b>Math Learning Centre</b><ul><li>A space to study math together.</li><li>Free math graduate and undergraduate TA support.</li><li>Mon - Fri: 11 am - 5 pm, LSK 301&amp;302.</li></ul><p><b>Private tutor</b></p><ul><li>We can help to find a private tutor.</li></ul></td></tr></tbody></table></div><p><br></p></div></div><div>Retrieved from \" https://wiki.ubc.ca/index.php?title=Science:Math_Exam_Resources/Courses/MATH100/December_2010/Question_01_(a)&amp;oldid=357222 \"</div><div><div><a>Categories :</a><ul><li><a>Pages using DynamicPageList parser function</a></li><li><a>Pages using DynamicPageList parser tag</a></li></ul></div><div>Hidden categories:<ul><li><a>MER QGQ flag</a></li><li><a>MER QGH flag</a></li><li><a>MER QGS flag</a></li><li><a>MER RT flag</a></li><li><a>MER Tag Limit of a rational function</a></li></ul></div></div></div></div><div><h2>Navigation menu</h2></div></body></html>"}
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+{"id": 1, "content": "https://openxlab.oss-cn-shanghai.aliyuncs.com/artimuse/upload/3c8ac674-c8fc-4875-8f86-24c9e01a6f33-stsupload-1755759611205-horse.jpg"}
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+{"id": "BkiUdBQ5qYVBTBbD-cBU", "content": "Just in time for the holidays I've started to open up some bubbly. If you come around these parts with any regularity you know how much I love bubbles. Which is why the holiday season is particularly wonderful since people open more bubble, even though I firmly believe bubbles should be enjoyed frequently just because it's Tuesday, for example. Tonight we tried the Segura Viudas Brut Cava which retails for around $9.\n1.) Hosting a big holiday party this year? This budget Cava is wallet friendly and crowd-pleasing.\n3.) Cava in general, and the Segura Viudas Brut in particular, offers a lot of bang for your buck.\n4.) I'd serve the Segura Viudas with my cheese plate this Thanksgiving.\nOn the nose I got a waxy note, pepper, flowers, and lemon. In the mouth I found peach, orange peel and melon. The wine had long-lasting bubbles and good acidity.", "source": "c4", "readability": 3, "professionalism": 1, "cleanliness": 4, "reasoning": 1}
+{"id": "BkiUgFI5qhDC0uAdXlXE", "content": "RunThatTrack is a very interesting concept, focusing on Formula 1, charity and, well, running.\nThe concept was conceived over a few drinks at a bar during the 2009 Formula 1 season. Several events and bars later the idea had fully taken hold and grown into a world of its own. The idea was simple, take the simply peaceful and individual activity of running around the racetracks to a ridiculously crazy and completely over the top Formula 1 level, where competition and performance could be scrutinised and analysed (and strange conclusions drawn) by people who really should be doing other things.\nOriginally developed as bit of a prank, the popularity soon took off throughout 2010 and the site soon developed characters and a personality of its own. MKL and the French Mafia have entered into the minds of everyone attempting to do a lap, but still nobody really seems to have understood that it is all just a bit of fun!\nFor 2011, the new season saw a new website with UBS joining the site as the \"Official Charity Partner\". With help of UBS a whole new charitable side of the site developed, and due to the support and drive of UBS the site grew in both popularity and members, but more importantly US$192,100 was raised thanks to UBS and donated to the Make-A-Wish foundation.\nFor 2012 the tradition carried on in the full Formula 1 style with a launch of a new website with UBS once again onboard as the \"Official Charity Partner\". With the help of UBS, the site continued to grow, with the popularity of the Group Run events reaching a new high. 2012 saw the introduction of the TrackMap, MKL's Track Guide and the special RunThatTrack Podcast Mix by Dejan Dj. During the 2012 season US$290,000 was raised again thanks to UBS and donated to Make-A-Wish.\nWinter testing 2013 saw yet another new site being developed (this is Formula 1 after all) with the introduction of the UBS \"Until the Race is Won\" cup, a new way for everybody to compete and help raise the donation total for Make-A-Wish. 2013 also saw the introduction of the Make-A-Wish spotlight showing examples of some of the great wishes that Make-A-Wish grant each event. Again with the support of UBS, 2013 was the biggest season ever, over 5,000 laps were run (that's over 28,000km) and $380,000 was raised and donated.\n2014 sees the site continue to grow, with a new site launched at the Australian GP. UBS will continue their support and once again, all the funds raised through the site will go to Make-A-Wish.\n\"UBS is proud to be a Global Partner of Formula 1 and we feel it is important to support the communities in which we are present. RunThatTrack is an inspiring part of the Formula 1 way of life and we are happy to associate ourselves with it\", says Bj\u00f6rn W\u00e4spe, Head of Global Sponsorship at UBS. Check www.runthattrack.info for more!\nCar Review: \"1 of 1\"", "source": "c4", "readability": 4, "professionalism": 2, "cleanliness": 5, "reasoning": 1}
+{"id": "BkiUdQE241xg-Ma3xU7N", "content": "There are a number of Permanent Orienteering Courses (POCs) in Sussex, which can be used to practise on. Whatever your level of experience and fitness, you can devise an activity to suit you using a permanent orienteering course. Add interest to a family walk, explore a site during your holidays or give a fresh challenge to a training run.\nMaps are available for these courses, which show the location of around 20 controls. A number of possible courses have been suggested at the bottom of each map, however there is nothing to stop you starting from wherever you wish and visiting whichever controls you want in any order.\nThe controls are marked by square plaques split into one red and one white triangle. Most of these are fixed to posts, benches or trees. On each plaque is a number corresponding to that marked on the map, also on the plaque is a unique letter. Noting down this letter provides proof that you have visited the control. On the map is a series of boxes, one for each control. Each box contains the number of the control, a description of its location and a space into which to copy the letter from the plaque..\nAll of Southdowns Orienteers' Permanent Orienteering Courses are currently (Autumn 2017-Summer 2018) undergoing a vigorous programme of checking, revision and refurbishment. As maps and courses are revised, this page will be with detailed information about how to obtain a map and gain access to the area.\nMaps can be downloaded free of charge from Brighton and Hove City Council.\nThe following sites are undergoing revision and repair during Spring 2018 and we expect them to be available for use very shortly. In the meantime please check with Ed Forman (SO POC Co-ordinator) before visiting the locations.\nThe following sites have slightly older courses where there may be some map updates required or controls missing. There is an ongoing programme of work to look at regenerating these courses where feasible.\nMaps available from Mole Valley Orienteering Club.\nVisit British Orienteering for more information about permanent orienteering courses nationwide.", "source": "c4", "readability": 4, "professionalism": 1, "cleanliness": 5, "reasoning": 1}
+{"id": "BkiUfybxK6wB9jjDilZ9", "content": "In and around Les Buttes-Chaumont\nLittle backstreets, colourful houses and greenery set the scene around Buttes-Chaumont\nA picturesque, steep park in the romantic style, charming steep-sided streets lined with coloured houses and courtyards full of flowers, a few unusual museums, a contemporary art venue representative of current artistic trends \u2026 Les Buttes-Chaumont is a popular and bucolic district and a great place for a pleasant stroll.\n1 Le quartier de la Mouza\u00efa\nBuilt around the rue Michel Hidalgo and rue de la Mouzaia, the La Mouza\u00efa district, with a so-called Algerian place name, is just the type of place you look for when visiting a city. The small houses were originally built for working class people and were built on the former gypsum quarries. It is full of plants and flowers.\n2 Parc des Buttes-Chaumont\nThe romantic garden par excellence. Created in 1864, the Parc des Buttes-Chaumont is the steepest of Paris's 426 parks and gardens. It is laid out like an Anglo-Chinese garden, whose irregular design was the opposite of so-called French-style gardens.\n3 Le Plateau - Exhibition centre\nImmaculate walls showcase contemporary art to the public with exhibitions and an experimental space that presents the works of artists in residence in Paris. 50 metres away (at 22, cours du 7th Art), the Antenne organizes educational events linked to contemporary creation.\n4 Mus\u00e9e des moulages de l'hopital Saint-Louis\nThis collection of medical waxworks \u2014 some 5,000 casts \u2014 bears witness to a way of learning about medicine and makes an important contribution to the history of dermatology from the 19th century.\nParis and its neighbourhoods\nParisian North-East\nDirection the north-east!\nIn and around La Villette\nBelleville and M\u00e9nilmontant\nThe P\u00e8re Lachaise cemetery and its surroundings\nOn the canals\nParis Eiffel Tower\nPlaces near to the Eiffel Tower\nOn and around the Champs-Elys\u00e9es\nTrocad\u00e9ro and Passy\nAlma and I\u00e9na\nIn and around Invalides\nBy the Eiffel Tower\nParis South-East\nLet 's head south-east !\nIn and around Bastille and Picpus\nBercy and the Cour-Saint-Emilion\nBois de Vincennes (wood)\nItalie, Tolbiac and the Chinese Quarter\nThe East of Paris\nParisian North-West\nGoing for a walk in the Parisian North-West\nClichy, Batignolles and Epinettes\nPlaine Monceau and Courcelles\nTernes and Champerret\nParis Passlib'\nMuseum & Exhibitions", "source": "commoncrawl", "readability": 5, "professionalism": 1, "cleanliness": 4, "reasoning": 1}
+{"id": "BkiUdRI5qdmC92mfkoma", "content": "(Host) Based on nearly complete campaign results, Secretary of State Deb Markowitz says Michael Badamo has almost certainly won the Progressive gubernatorial nomination. Badamo easily defeated Peter Diamondstone, and a write in effort to block Badamo was not successful.\n(Kinzel) Although the official election results will not be certified until next Monday, it's virtually certain that the Progressive Party will have a gubernatorial candidate on the ballot this fall.\nThere were two candidates running in the Party's primary for governor: Michael Badamo and Peter Diamondstone. But some Party leaders wanted to leave the office empty so that most of the Party's resources could be directed to Anthony Pollina's campaign for lieutenant governor.\nTo achieve that goal, they needed to mount an aggressive write-in campaign for a candidate who would then decline the Party's nomination. But in the end, that didn't happen.\nTurnout in the Progressive primary was very light \u2013 just over 1,000 people voted in their election. Badamo received roughly 70% of the vote; Diamondstone about 30%; and there were several hundred write-in votes, but not enough to affect the outcome of the race.\n(Kinzel) Markowitz says she is very pleased that no voter access complaints have been received by her office for the primary election.", "source": "c4", "readability": 5, "professionalism": 1, "cleanliness": 5, "reasoning": 2}
+{"id": "BkiUdmY4ubng4IUB1Uma", "content": "Support classroom and school libraries and build on NEA's signature literacy program, Read Across America.\nDiverse books are tools all children need to help form and shape their own narrative with a sun-will-come-out-tomorrow outlook. For educators interested in putting more diversity on shelves in schools Annie and SONY Pictures is working with the National Education Association to deliver Books for a Brighter Tomorrow, a national program to support classroom and school libraries and build on NEA's signature literacy program, Read Across America.\nAwards of $1,000 are available to public schools that serve economically disadvantaged students in order to enrich book collections with diverse children's literature and offer titles that give kids a chance to discover themselve and their life experiences in stories. The application review panel will base award selection on creativity, ability to make good use of good books and solid strategy for using literature to help students more conscious of their own culture and that of others. Evaluators may take geographical and demographic distribution into consideration when selecting award recipients.\nDeadline for completed application is January 31, 2015. Only award recipients will be notified in writing.\nHow to apply: Please complete all parts of the application, save the document, and submit it as an e-mail attachment to annieaward@nea.org. Remember to retain a copy for your files. Incomplete applications or those that include materials not specifically requested will not be reviewed. Only applications received electronically as specified will be considered. for questions related to this award, contact Anita Merina, amerina@nea.org.\nEligibility: Applicants must meet all of the criteria below. Approved awards will be made to the applicant's school. Only one eligible applicant per school may submit an application.\nThe applicant must agree to serve as the contact person for the award and all related pulic relations activities.\nDownload the application ( MS-WORD, 11.4 KB, 2 pg.) and email.", "source": "c4", "readability": 4, "professionalism": 1, "cleanliness": 4, "reasoning": 1}
+{"id": "BkiUf1E5qhLB3L4qdOhH", "content": "In a bowl, combine all ingredients except dogs and buns. Cut a 1/2\u2033 deep lengthwise slit in each hot dog. Spoon 2 tablespoons of mixture into each hot dog. Broil in oven for 2 to 3 minutes or until cheese is melted. Serve on buns.", "source": "c4", "readability": 5, "professionalism": 1, "cleanliness": 4, "reasoning": 0}