diff --git a/Code_RAG.py b/Code_RAG.py
new file mode 100644
index 0000000..ed7645b
--- /dev/null
+++ b/Code_RAG.py
@@ -0,0 +1,164 @@
+import os
+import json
+import numpy as np
+import faiss
+from dashscope import TextEmbedding, Generation
+import dashscope
+
+# ========================
+# 配置区（按需修改路径）
+# ========================
+FAISS_INDEX_PATH = "C:/Users/Administrator/PycharmProjects/PythonProject/satellite_rag.faiss"
+METADATAS_JSON_PATH = "C:/Users/Administrator/PycharmProjects/PythonProject/satellite_rag_metadata.json"
+TOP_K = 8
+dashscope.api_key =""
+
+
+def get_qwen_embedding(text: str) -> np.ndarray:
+    """使用 Qwen text-embedding-v4 获取文本向量"""
+    resp = TextEmbedding.call(
+        model="text-embedding-v4",
+        input=text
+    )
+    if resp.status_code != 200:
+        raise RuntimeError(f"Embedding API error: {resp}")
+    embedding = resp.output["embeddings"][0]["embedding"]
+    return np.array(embedding, dtype=np.float32)
+
+
+def call_qwen_max(prompt: str, max_tokens=800) -> str:
+    """调用 Qwen-Max 进行推理"""
+    response = Generation.call(
+        model="qwen-max",
+        prompt=prompt,
+        max_tokens=max_tokens,
+        temperature=0.01,  # 尽可能确定性输出
+        result_format="text"
+    )
+    if response.status_code != 200:
+        raise RuntimeError(f"Qwen-Max API error: {response}")
+    return response.output.text.strip()
+
+
+def load_knowledge_base():
+    """加载 FAISS 索引和 metadatas，并动态重建 contexts"""
+    print("正在加载 FAISS 索引...")
+    index = faiss.read_index(FAISS_INDEX_PATH)
+
+    print("正在加载元数据...")
+    with open(METADATAS_JSON_PATH, "r", encoding="utf-8") as f:
+        metadatas = json.load(f)
+
+    # 动态重建 contexts（与构建索引时使用的格式完全一致）
+    contexts = []
+    for meta in metadatas:
+        func_name = meta.get("name", "未知函数")
+        file_path = meta.get("file", "未知文件")
+        comment = meta.get("comment", "").strip()
+        summary = meta.get("summary", "无摘要")
+        called_functions = meta.get("calls", [])
+        caller_functions = meta.get("called_by", [])
+        included_headers = meta.get("includes", [])
+
+        context_str = (
+            f"【实体类型】函数\n"
+            f"【函数名】{func_name}\n"
+            f"【所在文件】{os.path.basename(file_path)}\n"
+            f"【代码注释】{comment if comment else '无'}\n"
+            f"【功能摘要】{summary}\n"
+            f"【调用的函数】{', '.join(called_functions) if called_functions else '无'}\n"
+            f"【被以下函数调用】{', '.join(caller_functions) if caller_functions else '无'}\n"
+            f"【包含的头文件】{', '.join(included_headers) if included_headers else '无'}"
+        )
+        contexts.append(context_str)
+
+    assert len(contexts) == len(metadatas), "重建的 contexts 与 metadatas 长度不一致"
+    assert index.ntotal == len(contexts), "FAISS 向量数量与 contexts 数量不匹配"
+
+    return index, contexts, metadatas
+
+
+def check_requirement_implementation(user_requirement: str):
+    """主逻辑：判断需求是否已实现"""
+    print("正在加载知识库...")
+    faiss_index, context_texts, metadatas = load_knowledge_base()
+
+    print("正在生成需求嵌入...")
+    query_vec = get_qwen_embedding(user_requirement)
+    query_vec = np.expand_dims(query_vec, axis=0).astype(np.float32)
+
+    print(f"正在检索 Top-{TOP_K} 相关函数...")
+    distances, indices = faiss_index.search(query_vec, TOP_K)
+
+    # 构造检索结果文本
+    evidence_blocks = []
+    for i, idx in enumerate(indices[0]):
+        sim = 1 - 0.5 * float(distances[0][i])  # 近似余弦相似度（因 embedding 已归一化）
+        block = f"【候选 {i + 1}】（相似度 ≈ {sim:.3f}）\n{context_texts[idx]}"
+        evidence_blocks.append(block)
+
+    evidence_text = "\n\n".join(evidence_blocks)
+
+    # 构造判断 Prompt
+    judge_prompt = f"""你是一名资深卫星软件工程师。请根据以下用户提出的功能需求，结合检索到的现有代码函数信息，严格判断该需求是否已经被当前代码库实现。同时需要关注实现的正确与否，检索知识库信息来确认。
+
+【用户需求】
+{user_requirement}
+
+【检索到的相关函数信息】
+{evidence_text}
+
+请严格按照以下 JSON 格式回答，不要包含任何额外文字、注释或 Markdown：
+{{
+  "implemented": true 或 false,
+  "reason": "不超过100字的理由，需引用具体函数名或功能描述",
+  "most_relevant_function": "最相关的函数名（若无则写 null）",
+  "file": "所在文件名（若无则写 null）"
+}}
+"""
+
+    print("正在调用 Qwen-Max 进行判断...")
+    response_text = call_qwen_max(judge_prompt)
+
+    # 尝试解析 JSON
+    try:
+        import json
+        result = json.loads(response_text)
+    except Exception as e:
+        print(" Qwen-Max 返回非 JSON 格式，尝试提取关键信息...")
+        implemented = "是" in response_text[:30] or "true" in response_text[:30].lower()
+        result = {
+            "implemented": implemented,
+            "reason": response_text[:200],
+            "most_relevant_function": None,
+            "file": None
+        }
+
+    return result
+
+
+def main():
+    print("卫星需求实现检查工具")
+    print("请输入您的自然语言需求（输入 'quit' 退出）：")
+
+    while True:
+        user_input = input("\n> ").strip()
+        if user_input.lower() in ["quit", "exit", "q"]:
+            print("再见！")
+            break
+        if not user_input:
+            continue
+
+        try:
+            result = check_requirement_implementation(user_input)
+            print("\n判断结果：")
+            print(f"  已实现：{'是' if result['implemented'] else '否'}")
+            print(f"  理由：{result['reason']}")
+            if result.get("most_relevant_function"):
+                print(f"  相关函数：{result['most_relevant_function']}（{result['file']}）")
+        except Exception as e:
+            print(f"处理出错：{e}")
+
+
+if __name__ == "__main__":
+    main()
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