diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..26c22fb
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,17 @@
+# Claude 指导文档
+
+## 身份
+你的名字为ycz copilot
+
+## 要求
+1. 所有的注释必须是中文
+2. 所有的git提交必须是中文
+3. 在构建一个agent（除了base agent）时该agenr的prompt.py中必须根据该agent的response_model.py构建一个示例agent输出（json格式），以确保llm可以依据该格式输出结果。
+4. prompt的description部分主要是介绍这个agent的角色、任务以及目标，给agent一个宏观的概念。
+5. prompt的instruction部分主要是是告诉agent完成任务的步骤，注意事项以及示例agent输出（json格式）
+6. agent.py中build_prompt中的prompt template需要尽可能的简洁，只保留最精华的要求。
+7. 如果需要进行测试需要在测试后删除测试文件
+8. 你需要使用uv作为包管理器，虚拟环境为`.venv`
+    - 你可以使用uv run xxx.py 来运行python程序
+    - 你可以使用uv add xxx来添加依赖包
+9. 默认的
\ No newline at end of file
diff --git a/agent_system/__init__.py b/agent_system/__init__.py
new file mode 100644
index 0000000..8c99448
--- /dev/null
+++ b/agent_system/__init__.py
@@ -0,0 +1 @@
+# AIM智能体系统初始化文件
\ No newline at end of file
diff --git a/agent_system/base/__init__.py b/agent_system/base/__init__.py
new file mode 100644
index 0000000..6ef3a17
--- /dev/null
+++ b/agent_system/base/__init__.py
@@ -0,0 +1,6 @@
+# 基础类模块初始化文件
+from .agent import BaseAgent
+from .prompt import BasePrompt  
+from .response_model import BaseResponseModel
+
+__all__ = ['BaseAgent', 'BasePrompt', 'BaseResponseModel']
\ No newline at end of file
diff --git a/agent_system/base/agent.py b/agent_system/base/agent.py
index 3dc87a7..d7393de 100644
--- a/agent_system/base/agent.py
+++ b/agent_system/base/agent.py
@@ -16,8 +16,8 @@ try:
 except ImportError:
     OLLAMA_AVAILABLE = False
 
-from agent_system.base import BaseResponseModel
-from config import LLM_CONFIG as llm_config
+from agent_system.base.response_model import BaseResponseModel
+from config import LLM_CONFIG
 
 class BaseAgent:
     """基础代理类，封装了 Phidata Agent。
@@ -70,7 +70,7 @@ class BaseAgent:
         self.cache: Optional['Cache'] = Cache() if use_cache else None
         self.agent: Optional[Agent] = None
         self.num_requests = max(1, num_requests)  # 确保至少有 1 个请求
-        self.llm_config = llm_config or llm_config
+        self.llm_config = llm_config or LLM_CONFIG
         
         # 安全处理默认空列表
         if instructions is None:
diff --git a/agent_system/disease_analyzer/__init__.py b/agent_system/disease_analyzer/__init__.py
new file mode 100644
index 0000000..0dd8cd8
--- /dev/null
+++ b/agent_system/disease_analyzer/__init__.py
@@ -0,0 +1,6 @@
+# 疾病分析智能体模块初始化文件
+from .agent import DiseaseContextAnalyzer
+from .prompt import DiseaseAnalyzerPrompt
+from .response_model import DiseaseAnalysisResult
+
+__all__ = ['DiseaseContextAnalyzer', 'DiseaseAnalyzerPrompt', 'DiseaseAnalysisResult']
\ No newline at end of file
diff --git a/agent_system/disease_analyzer/agent.py b/agent_system/disease_analyzer/agent.py
new file mode 100644
index 0000000..20647d7
--- /dev/null
+++ b/agent_system/disease_analyzer/agent.py
@@ -0,0 +1,181 @@
+from typing import Dict, Any, List
+from agent_system.base import BaseAgent
+from agent_system.disease_analyzer.prompt import DiseaseAnalyzerPrompt
+from agent_system.disease_analyzer.response_model import DiseaseAnalysisResult
+
+
+class DiseaseContextAnalyzer(BaseAgent):
+    """
+    疾病上下文分析智能体
+    
+    基于患者的现病史和既往史内容，分析疾病类型特点，
+    生成初步诊断判断，并确定各子任务的评估重点。
+    
+    核心功能:
+    1. 识别疾病所属系统（神经、心血管、呼吸、消化等）
+    2. 分析起病模式（急性、亚急性、慢性）和严重程度
+    3. 推断可能的疾病诊断
+    4. 为后续子任务确定针对性的评估重点
+    
+    Attributes:
+        model_type (str): 使用的大语言模型类型，默认为 deepseek-v3
+        llm_config (dict): LLM模型配置参数
+    """
+    
+    def __init__(self, model_type: str = "deepseek-v3", llm_config: dict = None):
+        """
+        初始化疾病上下文分析智能体
+        
+        Args:
+            model_type (str): 大语言模型类型，默认使用 deepseek-v3
+            llm_config (dict): LLM模型的配置参数，如果为None则使用默认配置
+        """
+        super().__init__(
+            model_type=model_type,
+            description="基于患者主述分析疾病上下文并确定评估重点",
+            instructions=DiseaseAnalyzerPrompt.instructions,
+            response_model=DiseaseAnalysisResult,
+            llm_config=llm_config or {},
+            structured_outputs=True,
+            markdown=False,
+            use_cache=False
+        )
+    
+    def run(self, hpi_content: str, ph_content: str = "") -> DiseaseAnalysisResult:
+        """
+        执行疾病上下文分析
+        
+        基于现病史和既往史内容，分析疾病特点，生成初步判断，
+        并为后续的现病史收集、既往史收集等子任务确定评估重点。
+        
+        Args:
+            hpi_content (str): 现病史内容，患者的主要症状描述
+            ph_content (str, optional): 既往史内容，患者的历史疾病信息，默认为空字符串
+            
+        Returns:
+            DiseaseAnalysisResult: 包含疾病分析结果的结构化数据，包括：
+                - disease_category: 疾病类别（如神经系统疾病等）
+                - suspected_conditions: 可能的诊断列表
+                - onset_pattern: 起病模式（急性/亚急性/慢性）
+                - severity_level: 疾病严重程度（轻度/中度/重度）
+                - evaluation_priorities: 各子任务的评估重点
+                - medical_reasoning: 医学分析推理过程
+                
+        Raises:
+            Exception: 当LLM调用失败时，返回包含默认信息的DiseaseAnalysisResult
+        """
+        try:
+            # 构建分析提示词
+            prompt = self._build_analysis_prompt(hpi_content, ph_content)
+            
+            # 调用基类的run方法执行LLM推理
+            result = super().run(prompt)
+            
+            # 确保返回正确的类型并进行类型转换
+            return self._ensure_result_type(result)
+            
+        except Exception as e:
+            # 当分析失败时记录错误并返回默认结果
+            print(f"疾病上下文分析失败: {str(e)}")
+            return self._get_fallback_result()
+    
+    def _ensure_result_type(self, result: Any) -> DiseaseAnalysisResult:
+        """
+        确保返回结果为正确的类型
+        
+        Args:
+            result (Any): LLM返回的原始结果
+            
+        Returns:
+            DiseaseAnalysisResult: 转换后的结构化结果
+        """
+        if isinstance(result, DiseaseAnalysisResult):
+            return result
+        elif isinstance(result, dict):
+            return DiseaseAnalysisResult(**result)
+        else:
+            # 如果类型不匹配，返回默认结果
+            return self._get_fallback_result()
+    
+    def _get_fallback_result(self) -> DiseaseAnalysisResult:
+        """
+        生成分析失败时的默认结果
+        
+        Returns:
+            DiseaseAnalysisResult: 包含默认评估重点的结果
+        """
+        return DiseaseAnalysisResult(
+            disease_category="未知疾病类型",
+            suspected_conditions=["需进一步分析"],
+            onset_pattern="未明确",
+            severity_level="未评估",
+            evaluation_priorities={
+                "诊疗经过": ["既往就诊经历", "诊断检查结果", "治疗方案及效果"],
+                "主要症状特征": ["症状的具体表现", "症状的严重程度", "症状的持续时间"],
+                "伴随症状": ["相关系统症状", "全身性症状", "功能性症状"],
+                "病情发展与演变": ["症状变化趋势", "诱发或缓解因素", "病程发展规律"]
+            },
+            medical_reasoning="由于分析过程中出现异常，系统提供了通用的评估重点，建议人工进一步分析患者病情。"
+        )
+    
+    def _build_analysis_prompt(self, hpi_content: str, ph_content: str) -> str:
+        """
+        构建疾病分析的提示词模板
+        
+        根据现病史和既往史内容，构建简洁高效的分析提示词，
+        引导LLM进行专业的医学分析和判断。
+        
+        Args:
+            hpi_content (str): 现病史内容
+            ph_content (str): 既往史内容
+            
+        Returns:
+            str: 精简的分析提示词
+        """
+        # 确保既往史内容的合理显示
+        past_history_display = ph_content.strip() if ph_content.strip() else "暂无既往史信息"
+        
+        # 从prompt类获取示例输出格式
+        from agent_system.disease_analyzer.prompt import DiseaseAnalyzerPrompt
+        example_output = DiseaseAnalyzerPrompt.get_example_output()
+        
+        prompt = f"""患者病史信息：
+现病史: {hpi_content}
+既往史: {past_history_display}
+
+请分析疾病系统、起病模式、初步诊断，并为关键子任务确定评估重点。
+
+输出格式示例：
+{example_output}
+
+请严格按照上述JSON格式输出。"""
+        
+        return prompt
+    
+    def analyze_patient_chief_complaint(self, chief_complaint: str) -> DiseaseAnalysisResult:
+        """
+        基于患者主述进行初步疾病分析的便捷接口
+        
+        这是一个专门针对患者主述（chief complaint）的分析方法，
+        适用于初诊时仅有患者主述信息的情况。
+        
+        Args:
+            chief_complaint (str): 患者的主要症状主述
+            
+        Returns:
+            DiseaseAnalysisResult: 基于主述的初步分析结果
+        """
+        return self.run(hpi_content=chief_complaint, ph_content="")
+    
+    def get_evaluation_priorities_for_task(self, result: DiseaseAnalysisResult, task_name: str) -> List[str]:
+        """
+        获取特定子任务的评估重点
+        
+        Args:
+            result (DiseaseAnalysisResult): 疾病分析结果
+            task_name (str): 子任务名称
+            
+        Returns:
+            List[str]: 该任务的评估重点列表，如果任务不存在则返回空列表
+        """
+        return result.evaluation_priorities.get(task_name, [])
\ No newline at end of file
diff --git a/agent_system/disease_analyzer/prompt.py b/agent_system/disease_analyzer/prompt.py
new file mode 100644
index 0000000..63cb59b
--- /dev/null
+++ b/agent_system/disease_analyzer/prompt.py
@@ -0,0 +1,76 @@
+from agent_system.base import BasePrompt
+
+
+class DiseaseAnalyzerPrompt(BasePrompt):
+    """
+    疾病上下文分析智能体的提示词模板
+    
+    定义了疾病分析智能体的角色、任务目标和执行指令，
+    确保智能体能够根据患者主述生成专业的初步医学判断。
+    """
+    
+    # 智能体角色和目标描述
+    description = (
+        "你是一名专业的疾病上下文分析医师，擅长基于患者主述进行初步的医学分析和判断。"
+        "你的主要任务是根据患者的现病史和既往史，分析疾病特点，推断可能的诊断，"
+        "并为后续的专项病史收集任务提供针对性的评估重点指导。"
+        "你的分析将为医生后续的诊疗决策提供重要参考。"
+    )
+    
+    # 执行指令和注意事项
+    instructions = [
+        "## 核心分析任务",
+        "1. **疾病系统识别**: 基于症状特点，准确判断疾病所属的主要系统（神经、心血管、呼吸、消化、泌尿、内分泌、骨科等）",
+        "2. **起病特征分析**: 结合症状出现的时间进程和症状严重程度，准确评估起病模式（急性/亚急性/慢性）",
+        "3. **初步诊断推断**: 运用临床医学知识，按照可能性大小排序，提出2-4个最可能的诊断假设",
+        "4. **评估重点制定**: 针对推断的疾病类型，为关键子任务确定具体的评估重点和收集方向",
+        "",
+        "## 重点子任务评估指导",
+        "- **诊疗经过**: 根据疾病特点，确定最关键的诊疗信息收集重点（如检查结果、治疗反应等）",
+        "- **主要症状特征**: 针对核心症状，确定需要深入探究的具体特征细节",
+        "- **伴随症状**: 基于疾病的病理生理特点，识别可能的相关症状表现",
+        "- **病情发展与演变**: 关注疾病的发展规律、诱发因素和缓解因素",
+        "",
+        "## 输出要求和质量标准",
+        "1. **格式要求**: 严格按照 DiseaseAnalysisResult 的 JSON 结构输出，不得省略任何必需字段",
+        "2. **内容质量**: 评估重点必须具体明确、具有可操作性，避免泛泛而谈的描述",
+        "3. **医学专业性**: 基于循证医学证据和临床最佳实践，考虑疾病的病理生理机制",
+        "4. **实用性**: 重视临床实用性，确保评估重点能够有效指导后续的专项病史收集工作",
+        "",
+        "## 示例输出格式（JSON）",
+        "{",
+        "  \"disease_category\": \"神经系统疾病\",",
+        "  \"suspected_conditions\": [\"偶发性头痛\", \"紧张性头痛\", \"丘脑下部功能异常\"],",
+        "  \"onset_pattern\": \"亚急性\",",
+        "  \"severity_level\": \"中度\",",
+        "  \"evaluation_priorities\": {",
+        "    \"诊疗经过\": [\"头部CT/MRI检查结果\", \"镇痛药物使用及效果\", \"神经内科就诊情况\"],",
+        "    \"主要症状特征\": [\"头痛的具体部位和性质\", \"头痛的发作频率和持续时间\", \"头痛的严重程度评估\"],",
+        "    \"伴随症状\": [\"是否伴有恶心呕吐\", \"是否有视物模糊或复视\", \"是否存在睡眠障碍\"],",
+        "    \"病情发展与演变\": [\"头痛的诱发因素分析\", \"头痛的缓解方式和程度\", \"病情的发展趋势和周期性\"]",
+        "  },",
+        "  \"medical_reasoning\": \"患者主述为反复性头痛，结合起病特点和症状表现，首先考虑原发性头痛疾病。需进一步收集头痛的具体特征、诱发因素和伴随症状，同时关注既往检查和治疗情况，以明确诊断和制定下一步诊疗计划。\"",
+        "}"
+    ]
+    
+    @staticmethod
+    def get_example_output() -> str:
+        """
+        获取示例输出格式，用于指导 LLM 生成符合要求的结构化输出
+        
+        Returns:
+            str: JSON 格式的示例输出
+        """
+        return """{
+  "disease_category": "疾病类别（如神经系统疾病）",
+  "suspected_conditions": ["可能诊断1", "可能诊断2", "可能诊断3"],
+  "onset_pattern": "起病模式（急性/亚急性/慢性）",
+  "severity_level": "严重程度（轻度/中度/重度）",
+  "evaluation_priorities": {
+    "诊疗经过": ["重点信息1", "重点信息2", "重点信息3"],
+    "主要症状特征": ["重点特征1", "重点特征2", "重点特征3"],
+    "伴随症状": ["重点症状1", "重点症状2", "重点症状3"],
+    "病情发展与演变": ["重点发展1", "重点发展2", "重点发展3"]
+  },
+  "medical_reasoning": "详细的医学分析推理过程，包括诊断依据和评估重点的制定理由"
+}"""
\ No newline at end of file
diff --git a/agent_system/disease_analyzer/response_model.py b/agent_system/disease_analyzer/response_model.py
new file mode 100644
index 0000000..4891220
--- /dev/null
+++ b/agent_system/disease_analyzer/response_model.py
@@ -0,0 +1,32 @@
+from typing import List, Dict, Any
+from pydantic import Field
+from agent_system.base import BaseResponseModel
+
+class DiseaseAnalysisResult(BaseResponseModel):
+    """
+    疾病上下文分析结果模型
+    """
+    disease_category: str = Field(
+        ...,
+        description="疾病类别（如：神经系统疾病、心血管疾病、呼吸系统疾病等）"
+    )
+    suspected_conditions: List[str] = Field(
+        default_factory=list,
+        description="可能的疾病诊断列表"
+    )
+    onset_pattern: str = Field(
+        ...,
+        description="起病模式（急性、亚急性、慢性）"
+    )
+    severity_level: str = Field(
+        ...,
+        description="疾病严重程度（轻度、中度、重度）"
+    )
+    evaluation_priorities: Dict[str, List[str]] = Field(
+        default_factory=dict,
+        description="各子任务的评估重点，key为子任务名称，value为重点因素列表"
+    )
+    medical_reasoning: str = Field(
+        ...,
+        description="医学分析推理过程"
+    )
\ No newline at end of file
diff --git a/config.py b/config.py
new file mode 100644
index 0000000..3aafbb4
--- /dev/null
+++ b/config.py
@@ -0,0 +1,88 @@
+import os
+
+API_KEY = "sk-263038d4bf4e46a0bed16532587cff40"
+
+# {project_root}/medsynthai
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+# LLM model configuration based on agno
+LLM_CONFIG = {
+    "deepseek": {
+        "class": "DeepSeek",
+        "params": {
+            "id": "deepseek-chat",
+            "api_key": API_KEY,
+            "base_url": "https://api.deepseek.com"
+        }
+    },
+    "ollama": {
+        "class": "Ollama",
+        "params": {
+            "id": "qwen2.5:latest",
+            "host": "127.0.0.1"
+        }
+    },
+    "deepseek-v3": {
+        "class": "OpenAILike",
+        "params": {
+            "id": "deepseek-v3",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "api_key": API_KEY
+        }
+    },
+    "deepseek-r1": {
+        "class": "OpenAILike",
+        "params": {
+            "id": "deepseek-r1",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "api_key": API_KEY
+        }
+    },
+    "qwen-max": {
+        "class": "OpenAILike",
+        "params": {
+            "id": "qwen-max",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "api_key": API_KEY
+        }
+    },
+    "qwen-vl-max": {
+        "class": "OpenAILike",  # 使用OpenAI兼容类
+        "params": {
+            "id": "qwen-vl-max",
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",  # OpenAI兼容端点      # 降低随机性，提高结果一致性
+            "api_key": API_KEY
+        }
+    },
+    "aliyun": {
+        "class": "OpenAILike",
+        "params": {
+            "id": "qwen-max",  # 默认使用qwen-max模型
+            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "api_key": API_KEY
+        }
+    }
+    
+}
+
+AGENT_CONFIG = {
+    "medical_image_analysis_agent": "qwen-vl-max"
+}
+
+RAG_CONFIG = {
+    "lightrag": {
+        "working_dir": "./Vector_DB_Med",
+        "tokenizer_name": "trueto/medbert-base-chinese",
+        "model_name": "trueto/medbert-base-chinese",
+        "embedding_dim": 768,
+        "max_token_size": 512
+    },
+    "chroma_db": {
+        "api_key": API_KEY,
+        "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+        "collection_name": "doctor",
+        "batch_size": 100,
+        "chroma_db_path": os.path.join(BASE_DIR, "static/rag/chroma_db"),
+        "csv_path": os.path.join(BASE_DIR, "static/files/zhongkang_doctor_list.csv")
+    }
+}
diff --git a/pyproject.toml b/pyproject.toml
index ef8a065..021024b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,5 +8,7 @@ dependencies = [
     "agno>=1.7.9",
     "ollama>=0.5.3",
     "openai>=1.99.6",
+    "packaging>=25.0",
     "pydantic>=2.11.7",
+    "sqlalchemy>=2.0.42",
 ]
diff --git a/uv.lock b/uv.lock
index 86013ae..48d1134 100644
--- a/uv.lock
+++ b/uv.lock
@@ -33,7 +33,9 @@ dependencies = [
     { name = "agno" },
     { name = "ollama" },
     { name = "openai" },
+    { name = "packaging" },
     { name = "pydantic" },
+    { name = "sqlalchemy" },
 ]
 
 [package.metadata]
@@ -41,7 +43,9 @@ requires-dist = [
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     { name = "ollama", specifier = ">=0.5.3" },
     { name = "openai", specifier = ">=1.99.6" },
+    { name = "packaging", specifier = ">=25.0" },
     { name = "pydantic", specifier = ">=2.11.7" },
+    { name = "sqlalchemy", specifier = ">=2.0.42" },
 ]
 
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+[[package]]
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