From b43afe4b4a43c24503486106360989fc54d6b633 Mon Sep 17 00:00:00 2001
From: lzybetter <lzy178171973@163.com>
Date: Fri, 7 Aug 2026 17:13:43 +0800
Subject: [PATCH] =?UTF-8?q?=E5=BC=95=E5=85=A5rag?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .env_sample                                   |  10 +
 .gitignore                                    |   2 +
 README.md                                     |   3 +
 adata_test.py                                 |  92 +++-
 build_rag.py                                  | 147 ++++++
 knowledge/company_cases/nanda_optical.md      | 288 ++++++++++++
 knowledge/industry/semiconductor_material.md  | 137 ++++++
 knowledge/rules/pandas_chinese_field_rules.md |  90 ++++
 .../rules/semiconductor_material_rules.md     | 168 +++++++
 main.py                                       | 445 ++++++++++++++++--
 .../data_level0.bin                           | Bin 0 -> 218800 bytes
 .../header.bin                                | Bin 0 -> 100 bytes
 .../length.bin                                | Bin 0 -> 400 bytes
 .../link_lists.bin                            |   0
 rag_db/chroma.sqlite3                         | Bin 0 -> 385024 bytes
 15 files changed, 1330 insertions(+), 52 deletions(-)
 create mode 100644 .env_sample
 create mode 100644 build_rag.py
 create mode 100644 knowledge/company_cases/nanda_optical.md
 create mode 100644 knowledge/industry/semiconductor_material.md
 create mode 100644 knowledge/rules/pandas_chinese_field_rules.md
 create mode 100644 knowledge/rules/semiconductor_material_rules.md
 create mode 100644 rag_db/372f688c-7ce6-4638-b51f-32e4c9b8a6ca/data_level0.bin
 create mode 100644 rag_db/372f688c-7ce6-4638-b51f-32e4c9b8a6ca/header.bin
 create mode 100644 rag_db/372f688c-7ce6-4638-b51f-32e4c9b8a6ca/length.bin
 create mode 100644 rag_db/372f688c-7ce6-4638-b51f-32e4c9b8a6ca/link_lists.bin
 create mode 100644 rag_db/chroma.sqlite3

diff --git a/.env_sample b/.env_sample
new file mode 100644
index 0000000..65c0333
--- /dev/null
+++ b/.env_sample
@@ -0,0 +1,10 @@
+# 大模型接口地址
+OPENAI_BASE_URL=
+# 大模型 API 密钥
+OPENAI_API_KEY=
+# bot token
+TELEGRAM_BOT_TOKEN=
+# http代理
+HTTP_PROXY=
+HTTPS_PROXY=
+TELEGRAM_PROXY=
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index f5d312f..6688ccd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
+# 剔除环境文件
 .env
+# 剔除不需要的文件夹
 .venv/
 .idea/
 __pycache__/
\ No newline at end of file
diff --git a/README.md b/README.md
index e69de29..153e15b 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,3 @@
+main -- 主程序，所有逻辑都在这
+build_rag -- 构建rag库
+adata_test -- 用来才是adata库的
\ No newline at end of file
diff --git a/adata_test.py b/adata_test.py
index f51754b..36f0456 100644
--- a/adata_test.py
+++ b/adata_test.py
@@ -15,34 +15,82 @@ def get_fina_info(stock_code="600699"):
 
     df = stock.finance.get_core_index(stock_code=stock_code)
 
-    data = df[['report_date', 'report_type', 'total_rev', 'total_rev_yoy_gr','net_profit_attr_sh', 'net_profit_yoy_gr',
-               'non_gaap_net_profit', 'non_gaap_net_profit_yoy_gr', 'roe_wtd', 'gross_margin', 'net_margin', 'quick_ratio',
-               'inv_turn_rate', 'acct_recv_turn_rate']]
+    print(df.columns)
 
-    last_type = data['report_type'].to_list()[0]
+    last_type = df['report_type'].to_list()[0]
 
-    recent_df = data[data.report_type == last_type].iloc[:2,:].reset_index(drop=True)
+    last_data = df[df.report_type == last_type].iloc[:2,:]
 
-    recent_df.rename(columns={"report_date": "报告日期",
-                        "report_type": "报告类型",
-                        "total_rev":"营业总收入",
-                        "total_rev_yoy_gr":"营业总收入同比增长率",
-                        "net_profit_attr_sh":"归母净利润",
-                        "net_profit_yoy_gr":"归母净利润同比增长率",
-                        "non_gaap_net_profit":"扣非净利润",
-                        "non_gaap_net_profit_yoy_gr":"扣非净利润同比增长率",
-                        "roe_wtd":"加权净资产收益率",
-                        "gross_margin":"销售毛利率",
-                        "net_margin":"销售净利率",
-                        "quick_ratio":"速动比率",
-                        "inv_turn_rate":"存货周转率",
-                        "acct_recv_turn_rate":"应收账款周转率"
-                        }, inplace=True)
+    # 字典定义：英文指标名 -> 中文财务指标名
+    column_mapping = {
+        # 基础与披露信息
+        'stock_code': '股票代码',
+        'short_name': '股票简称',
+        'report_date': '报告期',
+        'report_type': '报表类型',
+        'notice_date': '公告日期',
+
+        # 每股指标
+        'basic_eps': '基本每股收益',
+        'diluted_eps': '稀释每股收益',
+        'non_gaap_eps': '扣非每股收益',
+        'net_asset_ps': '每股净资产',
+        'cap_reserve_ps': '每股公积金',
+        'undist_profit_ps': '每股未分配利润',
+        'oper_cf_ps': '每股经营现金流',
+
+        # 规模与利润指标 (元)
+        'total_rev': '营业总收入',
+        'gross_profit': '毛利润',
+        'net_profit_attr_sh': '归母净利润',
+        'non_gaap_net_profit': '扣非归母净利润',
+
+        # 增长率指标 (同比 YoY / 环比 QoQ)
+        'total_rev_yoy_gr': '营业总收入同比增长率',
+        'net_profit_yoy_gr': '归母净利润同比增长率',
+        'non_gaap_net_profit_yoy_gr': '扣非归母净利润同比增长率',
+        'total_rev_qoq_gr': '营业总收入环比增长率',
+        'net_profit_qoq_gr': '归母净利润环比增长率',
+        'non_gaap_net_profit_qoq_gr': '扣非归母净利润环比增长率',
+
+        # 盈利能力与收益率
+        'roe_wtd': '加权净资产收益率(ROE)',
+        'roe_non_gaap_wtd': '扣非加权净资产收益率(ROE)',
+        'roa_wtd': '加权总资产收益率(ROA)',
+        'gross_margin': '销售毛利率',
+        'net_margin': '销售净利率',
+
+        # 现金流与收入质量比率
+        'adv_receipts_to_rev': '预收款及合同负债占营收比',
+        'net_cf_sales_to_rev': '销售收现率(销售现金流/营收)',
+        'oper_cf_to_rev': '经营现金净流量占营收比',
+        'eff_tax_rate': '实际有效税率',
+
+        # 偿债能力与财务杠杆
+        'curr_ratio': '流动比率',
+        'quick_ratio': '速动比率',
+        'cash_flow_ratio': '现金流量比率',
+        'asset_liab_ratio': '资产负债率',
+        'equity_multiplier': '权益乘数',
+        'equity_ratio': '产权比率',
+
+        # 运营效率与周转率/天数
+        'total_asset_turn_days': '总资产周转天数',
+        'inv_turn_days': '存货周转天数',
+        'acct_recv_turn_days': '应收账款周转天数',
+        'total_asset_turn_rate': '总资产周转率',
+        'inv_turn_rate': '存货周转率',
+        'acct_recv_turn_rate': '应收账款周转率'
+    }
+
+    # 假设你的原始数据存储在 df 中，使用 rename 修改列名
+    # inplace=True 表示直接在原 DataFrame 上修改
+    last_data.rename(columns=column_mapping, inplace=True)
 
     formatted_lines = []
-    for _, row in recent_df.iterrows():
+    for _, row in last_data.iterrows():
         line_items = []
-        for col, val in zip(recent_df.columns, row):
+        for col, val in zip(last_data.columns, row):
             # 判断是否为数字类型
             if isinstance(val, (int, float)):
                 # 如果列名包含"率"，则格式化为保留两位小数并加上 %
diff --git a/build_rag.py b/build_rag.py
new file mode 100644
index 0000000..222d804
--- /dev/null
+++ b/build_rag.py
@@ -0,0 +1,147 @@
+import os
+
+from dotenv import load_dotenv
+from langchain_community.document_loaders import DirectoryLoader, TextLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+from langchain_huggingface import HuggingFaceEmbeddings
+
+from langchain_chroma import Chroma
+
+# ==========================
+# 配置
+# ==========================
+load_dotenv()
+
+# ======================
+# 配置
+# ======================
+
+KNOWLEDGE_PATH = "./knowledge"
+VECTOR_DB_PATH = "./rag_db"
+COLLECTION_NAME = "investment_knowledge"
+EMBEDDING_MODEL = "BAAI/bge-small-zh-v1.5"
+
+
+# ======================
+# Embedding
+# ======================
+
+print("加载Embedding模型")
+
+embeddings = HuggingFaceEmbeddings(
+
+    model_name=EMBEDDING_MODEL,
+    model_kwargs={
+        "device":"cpu"
+    }
+)
+
+
+
+# ======================
+# 加载Markdown
+# ======================
+
+print("加载知识文件")
+
+loader = DirectoryLoader(
+
+    KNOWLEDGE_PATH,
+    glob="**/*.md",
+    loader_cls=TextLoader,
+    loader_kwargs={
+        "encoding":"utf-8"
+    }
+
+)
+
+documents = loader.load()
+
+print(
+    f"加载 {len(documents)} 个文件"
+)
+
+# ======================
+# 自动生成metadata
+# ======================
+
+
+def build_metadata(doc):
+
+    path = doc.metadata["source"]
+
+    metadata={
+        "source":path
+    }
+
+
+    # rules
+    if "/rules/" in path:
+        metadata["category"]="rule"
+        metadata["knowledge_type"]="analysis_logic"
+    # industry
+    elif "/industry/" in path:
+        metadata["category"]="industry"
+        metadata["knowledge_type"]="industry_logic"
+    # company
+    elif "/company_cases/" in path:
+        metadata["category"]="company_case"
+        metadata["knowledge_type"]="company_fact"
+    # framework
+    elif "/investment_framework/" in path:
+        metadata["category"]="framework"
+        metadata["knowledge_type"]="workflow"
+    else:
+        metadata["category"]="unknown"
+
+    return metadata
+
+for doc in documents:
+
+    doc.metadata.update(
+        build_metadata(doc)
+    )
+
+# ======================
+# 文本切分
+# ======================
+
+splitter = RecursiveCharacterTextSplitter(
+
+    chunk_size=500,
+    chunk_overlap=80,
+    separators=[
+        "\n\n",
+        "\n",
+        "。",
+        "，"
+    ]
+
+)
+
+texts = splitter.split_documents(
+    documents
+)
+
+print(
+    f"生成 {len(texts)} 个chunk"
+)
+
+
+# ======================
+# 创建向量库
+# ======================
+
+print("生成Chroma")
+
+vector_store = Chroma.from_documents(
+
+    documents=texts,
+    embedding=embeddings,
+    persist_directory=VECTOR_DB_PATH,
+    collection_name=COLLECTION_NAME
+
+)
+
+print("完成")
\ No newline at end of file
diff --git a/knowledge/company_cases/nanda_optical.md b/knowledge/company_cases/nanda_optical.md
new file mode 100644
index 0000000..d8d0279
--- /dev/null
+++ b/knowledge/company_cases/nanda_optical.md
@@ -0,0 +1,288 @@
+# 南大光电（300346.SZ）投资案例
+
+## 基本信息
+
+公司：
+江苏南大光电材料股份有限公司
+
+股票代码：
+300346.SZ
+
+行业：
+半导体材料
+
+主营方向：
+
+- 半导体前驱体材料
+- 电子特气
+- 光刻胶及配套材料
+
+
+---
+
+# 财务数据
+
+## 2025年度
+
+营业收入：
+
+25.85亿元
+
+同比增长：
+
+9.93%
+
+
+归母净利润：
+
+3.20亿元
+
+同比增长：
+
+18%
+
+
+扣非净利润：
+
+2.54亿元
+
+同比增长：
+
+31.49%
+
+
+经营现金流：
+
+7.33亿元
+
+同比增长：
+
+16.76%
+
+
+
+## 2026年Q1
+
+营业收入：
+
+6.62亿元
+
+同比增长：
+
+5.45%
+
+
+归母净利润：
+
+1.24亿元
+
+同比增长：
+
+29.97%
+
+
+扣非净利润：
+
+同比增长：
+
+38.61%
+
+
+
+---
+
+# 业务结构
+
+
+## 1. 先进前驱体材料
+
+2025收入：
+
+7.14亿元
+
+
+占总收入：
+
+27.61%
+
+
+增长：
+
+23.46%
+
+
+毛利率：
+
+50.88%
+
+
+主要产品：
+
+- DIPAS
+- BDEAS
+- TDMAT
+- 三甲基镓
+- 三甲基铝
+
+
+应用：
+
+- ALD薄膜沉积
+- CVD薄膜沉积
+- LED
+- 5G射频芯片
+- 存储芯片
+
+
+
+---
+
+## 2. 电子特气
+
+2025收入：
+
+15.36亿元
+
+
+收入占比：
+
+59.39%
+
+
+毛利率：
+
+36.74%
+
+
+产品：
+
+- 高纯磷烷
+- 砷烷
+- 安全源
+- NF3
+- SF6
+
+
+应用：
+
+- IC制造
+- LED制造
+- 刻蚀
+- 腔体清洗
+
+
+
+---
+
+## 3. 光刻胶
+
+主要产品：
+
+ArF光刻胶
+
+
+技术：
+
+193nm
+
+
+应用：
+
+90nm-14nm先进制程
+
+
+2025进展：
+
+- 三款ArF光刻胶通过客户验证
+- 获得订单
+- 光刻胶收入突破2000万元
+
+
+---
+
+# 技术壁垒
+
+
+## 高纯材料能力
+
+MO源纯度：
+
+6N以上
+
+
+核心能力：
+
+- 合成
+- 纯化
+- 分析检测
+- 安全储运
+
+
+---
+
+## 光刻胶能力
+
+覆盖：
+
+- 单体
+- 树脂
+- 光敏剂
+- 成品
+
+
+形成完整研发体系。
+
+
+
+---
+
+# 重大事件
+
+
+2025年：
+
+现金2.30亿元收购全椒南大核心团队股份
+
+
+2026年：
+
+现金7760万元收购乌兰察布南大员工合伙股份
+
+
+公司参与：
+
+- 国家863计划
+- 集成电路02专项
+
+
+
+---
+
+# 核心竞争优势
+
+
+1. 半导体材料国产替代受益者
+
+2. 客户认证周期形成壁垒
+
+3. 高纯材料工艺积累
+
+4. 高端材料产品突破
+
+
+---
+
+# 后续跟踪指标
+
+
+重点关注：
+
+1. ArF光刻胶收入是否突破亿元级
+
+2. 前驱体毛利率变化
+
+3. 高K三甲基铝渗透情况
+
+4. 海外客户认证进展
+
+5. 安全生产情况
diff --git a/knowledge/industry/semiconductor_material.md b/knowledge/industry/semiconductor_material.md
new file mode 100644
index 0000000..d8b1093
--- /dev/null
+++ b/knowledge/industry/semiconductor_material.md
@@ -0,0 +1,137 @@
+# 半导体材料行业分析框架
+
+
+## 行业特点
+
+半导体材料属于高技术壁垒行业。
+
+核心竞争因素：
+
+- 纯度
+- 一致性
+- 良率
+- 客户认证
+
+
+---
+
+# 核心商业模式
+
+
+半导体材料企业成长路径：
+
+研发
+
+↓
+
+客户验证
+
+↓
+
+小批量供应
+
+↓
+
+进入量产线
+
+↓
+
+扩大份额
+
+
+
+---
+
+# 主要材料类型
+
+
+## 前驱体材料
+
+应用：
+
+ALD/CVD薄膜沉积
+
+
+评价指标：
+
+- 纯度等级
+- 金属杂质
+- 稳定性
+- 客户认证
+
+
+
+## 电子特气
+
+应用：
+
+- 刻蚀
+- 清洗
+- 外延
+
+
+关键指标：
+
+- 气体纯度
+- 供应稳定性
+- 安全体系
+
+
+
+## 光刻胶
+
+评价：
+
+- 分辨率
+- 缺陷率
+- 工艺匹配度
+
+
+进入先进制程需要长期验证。
+
+
+
+---
+
+# 行业护城河
+
+
+半导体材料不是普通化工。
+
+竞争优势来自：
+
+1. 长期工艺积累
+
+2. 客户认证周期
+
+3. 高纯制造能力
+
+4. 安全生产体系
+
+
+
+---
+
+# 行业风险
+
+
+## 技术路线变化
+
+新材料可能替代旧材料。
+
+
+## 安全风险
+
+部分材料：
+
+- 易燃
+- 易爆
+- 有毒
+
+
+事故可能导致：
+
+- 停产
+- 客户流失
+- 估值下降
+
diff --git a/knowledge/rules/pandas_chinese_field_rules.md b/knowledge/rules/pandas_chinese_field_rules.md
new file mode 100644
index 0000000..c713d88
--- /dev/null
+++ b/knowledge/rules/pandas_chinese_field_rules.md
@@ -0,0 +1,90 @@
+## 一、 盈利质量与现金流验证（现金流与利润质量）
+
+### 规则编号：RULE_P01_CFO_QUALITY
+
+#### 分析主题：净利润现金含量与“虚盈实亏”诊断
+
+- **触发指标**：`经营现金净流量占营收比`、`销售收现率(销售现金流/营收)`、`归母净利润`、`每股经营现金流`、`每股净资产`、`归母净利润同比增长率`
+- **诊断逻辑与阈值**：
+  1. **警告（虚盈实亏/纸面富贵）**：
+     - **条件**：若 `归母净利润同比增长率 > 0`（账面净利润增长），但 `经营现金净流量占营收比 < 0.08`（低于 8%）或 `销售收现率(销售现金流/营收) < 0.8`（低于 80%）。
+     - **风险判定**：公司账面利润看似增长，但实际现金回款极差，大量利润沦为“应收账款”或纸面数字，流动性变现能力低下。
+  2. **优质（现金奶牛/议价力强）**：
+     - **条件**：若 `销售收现率(销售现金流/营收) > 1.0` 且 `经营现金净流量占营收比 > 0.15`。
+     - **判定**：公司销售回款能力极强，盈利质量高，对下游具备较强的商业话语权。
+
+## 二、 运营效率与存货/应收防雷（周转率与周转天数风险）
+
+### 规则编号：RULE_T01_AR_RISK
+
+#### 分析主题：应收账款回款危机与“赊销冲业绩”风险
+
+- **触发指标**：`应收账款周转天数`、`应收账款周转率`、`营业总收入同比增长率`、`归母净利润同比增长率`
+- **诊断逻辑与阈值**：
+  1. **周转恶化预警**：
+     - **条件**：若 `应收账款周转天数` 同比显著增加（如较上年同期增加 $> 30$ 天），或 `应收账款周转率` 持续下降。
+     - **风险判定**：公司下游客户打款周期拉长，需密切关注未来大额坏账准备计提的爆发风险。
+  2. **赊销暴雷风险**：
+     - **条件**：若 `营业总收入同比增长率 > 15%`，但 `应收账款周转天数` 同比拉长 $> 40\%$。
+     - **风险判定**：公司可能在通过大幅放宽信用期限（赊销）来强行刺激销量拉动营收增长，业绩含金量极低。
+
+### 规则编号：RULE_T02_INV_RISK
+
+#### 分析主题：存货滞销与毛利率背离诊断（隐蔽成本）
+
+- **触发指标**：`存货周转天数`、`存货周转率`、`销售毛利率`
+- **诊断逻辑与阈值**：
+  1. **滞销与跌价减值风险**：
+     - **条件**：若 `存货周转天数` 呈连续上升趋势（或大幅高于同行业均值）。
+     - **判定**：产品面临滞销或更新换代淘汰风险，后续需密切关注存货跌价准备对利润的侵蚀。
+  2. **经典财务造假背离项**：
+     - **条件**：若 `存货周转天数` 显著增加（周转变慢，产品滞销），但 `销售毛利率` 却逆势大幅上升。
+     - **高风险预警**：严重违背商业逻辑！公司极有可能少转了生产成本（将本应计入损益表的营业成本留在存货资产中），以此虚增毛利和存货资产。
+
+## 三、 偿债能力与杠杆风险（流动性与负债风险）
+
+### 规则编号：RULE_S01_DEBT_RISK
+
+#### 分析主题：资产负债率与流动性压迫风险
+
+- **触发指标**：`资产负债率`、`流动比率`、`速动比率`、`权益乘数`、`现金流量比率`
+- **诊断逻辑与阈值**：
+  1. **流动性断裂预警**：
+     - **条件**：若 `流动比率 < 1.0` 且 `速动比率 < 0.7`。
+     - **风险判定**：公司短期变现偿债能力极弱，若无新的融资打入，面临短期债务违约或流动性断裂风险。
+  2. **高杠杆压迫**：
+     - **条件**：若 `资产负债率 > 70%`（或 `权益乘数 > 3.5`）且 `现金流量比率 < 0.1`。
+     - **风险判定**：公司债务负担沉重，且经营活动产生的现金流无法有效覆盖负债，抗风险能力差。
+
+## 四、 ROE 杜邦分析与盈利驱动力（DuPont Analysis）
+
+### 规则编号：RULE_D01_ROE_DRIVERS
+
+#### 分析主题：ROE 质量与三要素驱动力拆解
+
+- **触发指标**：`加权净资产收益率(ROE)`、`扣非加权净资产收益率(ROE)`、`销售净利率`、`总资产周转率`、`权益乘数`
+- **诊断逻辑与阈值**：
+  1. **扣非背离风险（依赖非经常损益）**：
+     - **条件**：若 `加权净资产收益率(ROE)` 较高，但 `扣非加权净资产收益率(ROE)` 极低（两者差值 $> 3\%$）。
+     - **判定**：公司的 ROE 主要依赖“非经常性损益”（如卖资产、政府补贴、投资收益），核心主营业务盈利能力低下。
+  2. **高风险杠杆驱动型**：
+     - **条件**：若 ROE 上升，但 `销售净利率` 和 `总资产周转率` 持平或下降，仅靠 `权益乘数` 升高驱动。
+     - **风险判定**：高杠杆举债驱动的 ROE 扩张，不具备可持续性，财务风险剧增。
+  3. **优质效率/议价驱动型**：
+     - **条件**：若 ROE 上升主要由 `总资产周转率` 或 `销售净利率` 驱动。
+     - **判定**：公司通过提高运营效率或提升产品附加值/议价权来实现 ROE 增长，属于高质量增长。
+
+## 五、 收入增长与合同负债（成长可持续性）
+
+### 规则编号：RULE_G01_REVENUE_QUALITY
+
+#### 分析主题：业绩增长含金量与预收款（蓄水池）检验
+
+- **触发指标**：`营业总收入同比增长率`、`归母净利润同比增长率`、`扣非归母净利润同比增长率`、`预收款及合同负债占营收比`
+- **诊断逻辑与阈值**：
+  1. **利润增速质量校验**：
+     - **条件**：若 `归母净利润同比增长率 > 营业总收入同比增长率`。
+     - **分析**：利润增速快于收入增速，需结合 `扣非归母净利润同比增长率` 交叉验证是“降本增效/毛利提升（优质）”还是“少计提费用/依赖非经常损益（劣质）”。
+  2. **蓄水池池水干涸预警**：
+     - **条件**：若 `营业总收入同比增长率` 保持增长，但 `预收款及合同负债占营收比` 同比连续下滑 $> 30\%$。
+     - **预警**：公司“业绩蓄水池”（未确认的订单或预收款）正在枯竭，未来几个季度的营收增速大概率会出现失速或下滑。
\ No newline at end of file
diff --git a/knowledge/rules/semiconductor_material_rules.md b/knowledge/rules/semiconductor_material_rules.md
new file mode 100644
index 0000000..3de6b98
--- /dev/null
+++ b/knowledge/rules/semiconductor_material_rules.md
@@ -0,0 +1,168 @@
+# 半导体材料企业分析规则
+
+
+## 规则1：利润质量判断
+
+
+条件：
+
+如果：
+
+净利润增速 > 营收增速
+
+
+结论：
+
+说明企业可能存在：
+
+- 产品结构升级
+- 高毛利产品占比提升
+- 成本改善
+
+
+验证：
+
+检查：
+
+- 毛利率趋势
+- 高端产品收入占比
+- 经营现金流
+
+
+
+---
+
+# 规则2：判断国产替代真实性
+
+
+条件：
+
+企业宣称国产替代。
+
+
+不能直接认为成功。
+
+
+需要满足：
+
+研发
+
+↓
+
+客户认证
+
+↓
+
+订单
+
+↓
+
+收入贡献
+
+
+
+验证：
+
+观察：
+
+- 客户验证情况
+- 销售收入
+- 市占率
+
+
+
+---
+
+# 规则3：判断半导体材料护城河
+
+
+条件：
+
+企业产品需要：
+
+- 高纯度
+- 高一致性
+- 长认证周期
+
+
+结论：
+
+具备较强竞争壁垒。
+
+
+验证：
+
+查看：
+
+- 产品纯度等级
+- 龙头客户
+- 认证周期
+- 良率
+
+
+
+---
+
+# 规则4：危险材料企业风险评估
+
+
+条件：
+
+产品属于：
+
+危险化学品。
+
+
+结论：
+
+需要给予安全风险折价。
+
+
+验证：
+
+检查：
+
+- 安全事故
+- 环保处罚
+- 自动化水平
+- 安全投入
+
+
+
+---
+
+# 规则5：成长空间判断
+
+
+条件：
+
+新产品收入占比较低。
+
+
+不能简单认为没有价值。
+
+
+需要判断：
+
+是否存在：
+
+- 技术突破
+- 客户验证
+- 放量可能
+
+
+验证：
+
+观察：
+
+收入是否：
+
+百万级
+
+↓
+
+千万级
+
+↓
+
+亿元级
diff --git a/main.py b/main.py
index c71866e..9f8e5c5 100644
--- a/main.py
+++ b/main.py
@@ -8,50 +8,224 @@ from adata import stock
 from langchain_core.tools import tool
 from telegram import Update
 from telegram.ext import ApplicationBuilder, ContextTypes, CommandHandler, MessageHandler, filters
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
 
 load_dotenv()
 
+# VectorDB 存在 ./rules_data 目录中
+# ======================
+# Embedding模型
+# ======================
+
+embeddings = HuggingFaceEmbeddings(
+    model_name="BAAI/bge-small-zh-v1.5",
+    model_kwargs={
+        "device":"cpu"
+    }
+)
+
+# ======================
+# 加载向量库
+# ======================
+
+vector_store = Chroma(
+    persist_directory="./rag_db",
+    collection_name="investment_knowledge",
+    embedding_function=embeddings
+)
+
+# 公司事实 Retriever
+company_retriever = vector_store.as_retriever(
+    search_kwargs={
+        "k":5,
+        "filter":{
+            "category":"company_case"
+        }
+    }
+)
+
+# 行业 Retriever
+industry_retriever = vector_store.as_retriever(
+    search_kwargs={
+        "k":3,
+        "filter":{
+            "category":"industry"
+        }
+    }
+)
+
+# 规则 Retriever
+rule_retriever = vector_store.as_retriever(
+    search_kwargs={
+        "k":5,
+        "filter":{
+            "category":"rule"
+        }
+    }
+)
+
+def research_retrieve(question):
+
+    result = {}
+    # 公司事实
+    company_docs = company_retriever.invoke(question)
+    result["company"] = "\n\n".join(
+        [
+            doc.page_content
+            for doc in company_docs
+        ]
+    )
+    # 行业知识
+    industry_docs = industry_retriever.invoke(question)
+    result["industry"] = "\n\n".join(
+        [
+            doc.page_content
+            for doc in industry_docs
+        ]
+    )
+    # 分析规则
+    rule_docs = rule_retriever.invoke(question)
+    result["rules"] = "\n\n".join(
+        [
+            doc.page_content
+            for doc in rule_docs
+        ]
+    )
+    return result
 
-# ================= 你的原有逻辑（完全保留） =================
 @tool
 def get_fina_info(stock_code: str) -> str:
     """获取指定股票代码的财务数据。当需要获取财务数据的时使用此工具"""
+
+    # 字典定义：英文指标名 -> 中文财务指标名
+    column_mapping = {
+        # 基础与披露信息
+        'stock_code': '股票代码',
+        'short_name': '股票简称',
+        'report_date': '报告期',
+        'report_type': '报表类型',
+        'notice_date': '公告日期',
+
+        # 每股指标
+        'basic_eps': '基本每股收益',
+        'diluted_eps': '稀释每股收益',
+        'non_gaap_eps': '扣非每股收益',
+        'net_asset_ps': '每股净资产',
+        'cap_reserve_ps': '每股公积金',
+        'undist_profit_ps': '每股未分配利润',
+        'oper_cf_ps': '每股经营现金流',
+
+        # 规模与利润指标 (元)
+        'total_rev': '营业总收入',
+        'gross_profit': '毛利润',
+        'net_profit_attr_sh': '归母净利润',
+        'non_gaap_net_profit': '扣非归母净利润',
+
+        # 增长率指标 (同比 YoY / 环比 QoQ)
+        'total_rev_yoy_gr': '营业总收入同比增长率',
+        'net_profit_yoy_gr': '归母净利润同比增长率',
+        'non_gaap_net_profit_yoy_gr': '扣非归母净利润同比增长率',
+        'total_rev_qoq_gr': '营业总收入环比增长率',
+        'net_profit_qoq_gr': '归母净利润环比增长率',
+        'non_gaap_net_profit_qoq_gr': '扣非归母净利润环比增长率',
+
+        # 盈利能力与收益率
+        'roe_wtd': '加权净资产收益率(ROE)',
+        'roe_non_gaap_wtd': '扣非加权净资产收益率(ROE)',
+        'roa_wtd': '加权总资产收益率(ROA)',
+        'gross_margin': '销售毛利率',
+        'net_margin': '销售净利率',
+
+        # 现金流与收入质量比率
+        'adv_receipts_to_rev': '预收款及合同负债占营收比',
+        'net_cf_sales_to_rev': '销售收现率(销售现金流/营收)',
+        'oper_cf_to_rev': '经营现金净流量占营收比',
+        'eff_tax_rate': '实际有效税率',
+
+        # 偿债能力与财务杠杆
+        'curr_ratio': '流动比率',
+        'quick_ratio': '速动比率',
+        'cash_flow_ratio': '现金流量比率',
+        'asset_liab_ratio': '资产负债率',
+        'equity_multiplier': '权益乘数',
+        'equity_ratio': '产权比率',
+
+        # 运营效率与周转率/天数
+        'total_asset_turn_days': '总资产周转天数',
+        'inv_turn_days': '存货周转天数',
+        'acct_recv_turn_days': '应收账款周转天数',
+        'total_asset_turn_rate': '总资产周转率',
+        'inv_turn_rate': '存货周转率',
+        'acct_recv_turn_rate': '应收账款周转率'
+    }
+
     df = stock.finance.get_core_index(stock_code=stock_code)
 
-    data = df[['report_date', 'report_type', 'total_rev', 'total_rev_yoy_gr', 'net_profit_attr_sh', 'net_profit_yoy_gr',
-               'non_gaap_net_profit', 'non_gaap_net_profit_yoy_gr', 'roe_wtd', 'gross_margin', 'net_margin',
-               'quick_ratio',
-               'inv_turn_rate', 'acct_recv_turn_rate']]
+    year_data = df[df.report_type == "年报"].iloc[:5, :]  # 取5年的年报数据
+    year_data.rename(columns=column_mapping, inplace=True)
 
-    last_type = data['report_type'].to_list()[0]
-    recent_df = data[data.report_type == last_type].iloc[:2, :].reset_index(drop=True)
-
-    recent_df.rename(columns={"report_date": "报告日期", "report_type": "报告类型", "total_rev": "营业总收入",
-                              "total_rev_yoy_gr": "营业总收入同比增长率", "net_profit_attr_sh": "归母净利润",
-                              "net_profit_yoy_gr": "归母净利润同比增长率", "non_gaap_net_profit": "扣非净利润",
-                              "non_gaap_net_profit_yoy_gr": "扣非净利润同比增长率", "roe_wtd": "加权净资产收益率",
-                              "gross_margin": "销售毛利率", "net_margin": "销售净利率", "quick_ratio": "速动比率",
-                              "inv_turn_rate": "存货周转率", "acct_recv_turn_rate": "应收账款周转率"
-                              }, inplace=True)
-
-    formatted_lines = []
-    for _, row in recent_df.iterrows():
+    year_formatted_lines = []
+    for _, row in year_data.iterrows():
         line_items = []
-        for col, val in zip(recent_df.columns, row):
+        for col, val in zip(year_data.columns, row):
+            # 判断是否为数字类型
             if isinstance(val, (int, float)):
+                # 如果列名包含"率"，则格式化为保留两位小数并加上 %
                 if "率" in col:
-                    formatted_val = f"{val:.2f}%"
+                    # 如果原始数值本身就是小数（如 0.35 表示 35%），需要乘以 100
+                    val_pct = val * 100 if abs(val) <= 1.0 else val
+                    formatted_val = f"{val_pct:.2f}%"
                 else:
                     formatted_val = f"{val:,.2f}"
             else:
+                # 非数字类型（如日期、文本）直接转为字符串
                 formatted_val = str(val)
+
             line_items.append(f"{col}：{formatted_val}")
-        formatted_lines.append("\n".join(line_items))
 
-    final_report = f"近两期{last_type}主要财务数据如下：\n" + "\n\n".join(formatted_lines)
+        # 将这一期的所有字段用换行符拼接
+        year_formatted_lines.append("\n".join(line_items))
+
+    final_report = f"近五期年报主要财务数据如下：\n" + "\n\n".join(year_formatted_lines)
+
+    last_type = df['report_type'].to_list()[0]
+
+    if last_type != "年报":
+
+        last_data = df[df.report_type == last_type].iloc[:2,:]
+
+        # 假设你的原始数据存储在 df 中，使用 rename 修改列名
+        # inplace=True 表示直接在原 DataFrame 上修改
+        last_data.rename(columns=column_mapping, inplace=True)
+
+        formatted_lines = []
+        for _, row in last_data.iterrows():
+            line_items = []
+            for col, val in zip(last_data.columns, row):
+                # 判断是否为数字类型
+                if isinstance(val, (int, float)):
+                    # 如果列名包含"率"，则格式化为保留两位小数并加上 %
+                    if "率" in col:
+                        # 如果原始数值本身就是小数（如 0.35 表示 35%），需要乘以 100
+                        val_pct = val * 100 if abs(val) <= 1.0 else val
+                        formatted_val = f"{val_pct:.2f}%"
+                    else:
+                        formatted_val = f"{val:,.2f}"
+                else:
+                    # 非数字类型（如日期、文本）直接转为字符串
+                    formatted_val = str(val)
+
+                line_items.append(f"{col}：{formatted_val}")
+
+            # 将这一期的所有字段用换行符拼接
+            formatted_lines.append("\n".join(line_items))
+
+        # 3. 组装最终报告文本
+        final_report = final_report + "\n\n" + f"近两期{last_type}主要财务数据如下：\n" + "\n\n".join(formatted_lines)
+
     return final_report
 
-
 class MyOutputParser(StrOutputParser):
     def parse(self, text) -> str:
         return text.strip()
@@ -60,7 +234,179 @@ class MyOutputParser(StrOutputParser):
 llm = ChatOpenAI(model="deepseek-v4-flash")
 tools = [get_fina_info]
 template = """
-你是一个专业的股票分析助手。用户会输入一个股票代码，你可以使用工具来获取其财务数据，之后根据获取到的财务数据来分析这家公司的基本面情况。
+你是一位专业的股票基本面分析与投资研究专家。
+你的任务是结合：
+1. 实时获取的数据
+2. 公司历史资料
+3. 行业商业逻辑
+4. 专业投资分析规则
+对上市公司进行客观、严谨、深度的基本面诊断。
+你的目标不是预测股价，而是分析公司的商业质量、竞争优势、成长逻辑以及潜在风险。
+=============================
+【信息来源优先级】
+==============================
+进行分析时，请遵循以下信息优先级：
+第一优先级：
+用户提供的数据、工具返回的数据、最新财务数据。
+第二优先级：
+公司事实资料（company_facts）。
+第三优先级：
+行业知识与商业逻辑（industry_knowledge）。
+第四优先级：
+通用分析规则（analysis_rules）。
+如果不同来源之间存在冲突：
+- 以最新、可验证的数据为准。
+- 明确指出信息冲突。
+- 不得自行编造缺失数据。
+==============================
+【决策与工具使用原则】
+==============================
+1. 按需调用工具：
+根据用户问题类型，自主选择：
+- 财务数据查询工具
+
+不要为了调用工具而调用工具。
+2. 数据驱动：
+所有涉及以下内容：
+- 收入
+- 利润
+- 毛利率
+- 现金流
+- 估值
+- 市占率
+- 产品规模
+必须基于真实数据。
+禁止：
+- 编造财务数字
+- 推测不存在的订单
+- 将行业趋势当作公司事实
+==============================
+【知识库参考信息】
+==============================
+以下为公司事实资料：
+{company_facts}
+说明：
+- 这些内容代表已经整理的公司历史信息。
+- 可以用于理解公司的业务结构、产品、技术和历史事件。
+- 不得擅自修改其中的数据。
+------------------------------
+以下为行业背景与商业逻辑：
+{industry_knowledge}
+说明：
+- 用于判断行业空间、竞争格局、商业模式。
+- 不代表当前公司的实际情况。
+- 必须结合公司数据进行分析。
+------------------------------
+以下为基本面分析规则：
+{analysis_rules}
+说明：
+- 这些规则用于辅助判断公司质量。
+- 不应机械套用，需要结合行业特点分析。
+==============================
+【分析方法要求】
+==============================
+请按照以下逻辑进行分析：
+一、数据与规则交叉验证
+将公司的实际数据与分析规则进行匹配：
+例如：
+- 收入增长是否符合成长标准？
+- 利润增速是否超过收入增速？
+- 毛利率变化是否体现产品升级？
+- 现金流是否支持利润真实性？
+- 资产负债结构是否存在风险？
+明确指出：
+- 触发的积极信号
+- 触发的风险信号
+- 尚未验证的关键因素
+------------------------------
+二、商业模式分析
+分析：
+- 公司主要产品和收入来源
+- 产品竞争壁垒
+- 客户粘性
+- 行业地位
+- 上下游关系
+重点回答：
+“公司为什么能够赚钱？”
+以及：
+“这种赚钱能力是否可持续？”
+------------------------------
+三、成长逻辑分析
+不要只关注收入增长。
+需要分析：
+- 新产品放量
+- 市场空间
+- 技术升级
+- 国产替代
+- 市占率变化
+- 客户拓展
+区分：
+- 已经兑现的成长
+- 预期中的成长
+- 尚未验证的成长
+------------------------------
+四、风险评估
+重点关注：
+- 盈利质量下降
+- 毛利率持续下滑
+- 现金流恶化
+- 资本开支压力
+- 行业竞争加剧
+- 技术路线变化
+- 客户集中风险
+- 安全生产风险
+- 政策与国际环境风险
+
+================================================================================
+【 Telegram 消息专属输出排版规范 (CRITICAL FORMATTING RULES) 】
+================================================================================
+
+由于本诊断报告最终将在 Telegram 移动端客户端展示，为保证最佳的阅读体验与视觉质感，你必须严格遵循以下 Telegram 专属排版规则：
+
+1. 禁用标准 Markdown 表格 (STRICTLY FORBIDDEN)：
+   - 严禁使用 | 列分隔符 | 的 Markdown 表格语法！Telegram 移动端屏幕狭窄，多列表格会导致严重的强行折行与对齐错乱。
+   - 所有多维财务数据、年度对比数据，必须拆解为【卡片式结构】或【树状分支结构】。
+
+2. 树状分支与数据对齐 (Tree Structure)：
+   - 涉及多项财务指标（如营收、归母、扣非）的平行对比时，使用 ├ 和 └ 字符构建清晰的树状层级：
+     * 2025年
+       ├ 营收：`25.85亿`（+9.93%） │ 归母：`3.20亿`（+18.00%）
+       └ 扣非：`2.54亿`（+31.49%）
+   - 单指标多年趋势，采用带箭头的平铺演进线表示：
+     * 毛利率走势：`43.42%` (21) ➔ `41.16%` (24) ➔ `39.62%` (25)
+
+3. 视觉高亮与数据卡片 (Inline Code Highlighting)：
+   - 所有具体的【财务数值】、【百分比】、【比率】以及【核心定性结论】，必须用单反引号包围（即 Inline Code 格式，例如 `22.97%`、`中性偏正面`）。
+   - 这会在 Telegram 界面中形成高亮灰色背景卡片，提升视觉重点的提炼效率。
+
+4. 标题与符号锚点 (Visual Anchors)：
+   - 每一个大章节标题前必须配有明确的 Emoji 视觉锚点（如 📊、💡、🔍、⚠️、🎯）。
+   - 结论与诊断部分，优先采用带色彩倾向的标识符：
+     * 正向/优质：✅ 或 🚀 或 `[良好]`
+     * 中性/提示：📌 或 💡
+     * 隐忧/警报：⚠️ 或 🔴 或 🟠
+
+5. 控制行宽与分段 (Mobile Readability)：
+   - 单段分析文字切忌过长，移动端每段尽量保持在 2-4 行以内。
+   - 章节之间使用全划线 `---` 或空行进行视觉隔离，避免形成“文本大山”。
+
+6. Telegram 模版输出参照：
+   大纲结构示例：
+   # 📊 [股票名称（代码）] 基本面深度诊断
+   > **综合评级**：`[评级结果]`
+
+   ---
+   ### 💡 一、核心结论
+   * **优质面**
+     ├ **[维度]**：[简短分析]
+     └ **[维度]**：[简短分析]
+
+   ### 🔍 二、基本面深度诊断
+   #### 1️⃣ 成长性：[一句话小结]
+   * **[年份]**
+     ├ 营收：`[数据]`（[增速]） │ 归母：`[数据]`（[增速]）
+     └ 扣非：`[数据]`（[增速]）
+   📌 **关键判断**：[分析文字]
 """
 chat_prompt = ChatPromptTemplate.from_messages([
     ("system", template),
@@ -71,13 +417,39 @@ chat_prompt = ChatPromptTemplate.from_messages([
 agent = create_tool_calling_agent(llm, tools, chat_prompt)
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
+def run_fundamental_agent(user_input: str):
+
+    # ======================
+    # 多知识源检索
+    # ======================
+    rag_context = research_retrieve(
+        user_input
+    )
+
+    # ======================
+    # 注入Agent
+    # ======================
+
+    result = agent_executor.invoke(
+        {
+            "input": user_input,
+            "company_facts":
+                rag_context["company"],
+            "industry_knowledge":
+                rag_context["industry"],
+            "analysis_rules":
+                rag_context["rules"]
+        }
+    )
+
+
+    return result["output"]
 
 # ================= 新增：Telegram 机器人交互逻辑 =================
 async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
     """处理 /start 命令"""
     await update.message.reply_text("你好！我是你的股票分析助手，请直接发送类似“分析一下600699的基本面”的消息。")
 
-
 async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
     """处理普通文本消息，调用你的 Agent"""
     user_input = update.message.text
@@ -86,8 +458,7 @@ async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
 
     try:
         # 调用你原有的 Agent 逻辑
-        res = agent_executor.invoke({"input": user_input})
-        output = res["output"]
+        output = run_fundamental_agent(user_input)
     except Exception as e:
         output = f"分析出错：{str(e)}"
 
@@ -100,10 +471,24 @@ if __name__ == "__main__":
     if not token:
         raise ValueError("未在 .env 文件中找到 TELEGRAM_BOT_TOKEN")
 
-    proxy_url = "http://127.0.0.1:8889"
+    proxy_url = os.getenv(
+        "TELEGRAM_PROXY"
+    )
+
+    builder = (
+        ApplicationBuilder()
+        .token(token)
+    )
+
+    if proxy_url:
+        builder = (
+            builder
+            .proxy(proxy_url)
+            .get_updates_proxy(proxy_url)
+        )
+
+    application = builder.build()
 
-    # 启动机器人
-    application = ApplicationBuilder().token(token).proxy(proxy_url).get_updates_proxy(proxy_url).build()
     application.add_handler(CommandHandler("start", start))
     application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
 
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