增加代码知识库；修复文档处理内容；增加API设置

RAG-TEST-TOOLS/llm_processor.py

@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import logging
+import os
+
+import requests
+try:
+    from openai import OpenAI
+except ImportError:  # pragma: no cover - depends on deployment environment
+    OpenAI = None
+import time
+from typing import List
+
+try:
+    import numpy as np
+except ImportError:  # pragma: no cover - depends on deployment environment
+    np = None
+
+from code_parser import FUNCTION_CALL_GRAPH, CALLED_BY_GRAPH, FILE_DEPENDENCIES
+from config import QWEN_API_KEY, QWEN_API_URL, QWEN_CHAT_MODEL, QWEN_EMBEDDING_MODEL
+
+MAX_CODE_LENGTH = 800
+logger = logging.getLogger(__name__)
+
+
+def _get_qwen_api_key() -> str:
+    api_key = (
+        os.getenv("DASHSCOPE_API_KEY")
+        or os.getenv("DASH_SCOPE_API_KEY")
+        or os.getenv("QWEN_API_KEY")
+        or QWEN_API_KEY
+    )
+    if not api_key:
+        raise RuntimeError(
+            "Qwen/DashScope API key is not configured. Set DASHSCOPE_API_KEY, "
+            "DASH_SCOPE_API_KEY, or QWEN_API_KEY."
+        )
+    return api_key
+
+
+def _chat_completions_url() -> str:
+    return QWEN_API_URL.rstrip("/") + "/chat/completions"
+
+def generate_code_summary(func_name, comment, logic, code_snippet, file_path):
+    called_functions = FUNCTION_CALL_GRAPH.get(func_name, [])
+    caller_functions = CALLED_BY_GRAPH.get(func_name, [])
+    included_headers = FILE_DEPENDENCIES.get(file_path, [])
+
+    prompt = f"""
+你是一名资深的航天软件工程师，请总结以下C++函数的核心功能，**必须严格包含以下6点**：
+
+1. 函数流程与逻辑:
+{logic}
+
+2. 函数目的:
+→ 结合航天术语总结，必须根据上述“函数流程与逻辑”推导生成，不得依赖假设！
+
+3. 输入参数（名称、类型、作用，用括号列出）
+
+4. 返回值（类型和含义）
+
+5. 与其他函数的关联关系：
+   - 被调用的函数: {', '.join(called_functions) or '无'}
+   - 调用此函数的函数: {', '.join(caller_functions) or '无'}
+
+6. 与跨文件关联的函数（头文件）: {', '.join(included_headers) or '无'}
+
+---
+函数名: {func_name}
+注释: {comment or '无'}
+代码片段（截断至{MAX_CODE_LENGTH}字符）:
+{code_snippet[:MAX_CODE_LENGTH]}
+
+输出格式: "功能: [按上述6点组织的连贯段落，不要编号]"
+"""
+    headers = {"Authorization": f"Bearer {_get_qwen_api_key()}", "Content-Type": "application/json"}
+    payload = {
+        "model": QWEN_CHAT_MODEL,
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.1
+    }
+    response = requests.post(
+        _chat_completions_url(),
+        headers=headers,
+        json=payload
+    )
+    response.raise_for_status()
+    summary = response.json()['choices'][0]['message']['content'].strip()
+    return summary.replace("功能: ", "")
+
+
+def generate_code_logic(func_name, comment, code_snippet, file_path):
+    prompt = f"""
+    你是一名资深的航天软件工程师，请分析以下C++函数，并生成其markdown格式的**核心流程逻辑总结**。
+
+    **函数名**: {func_name}
+    **所在文件**: {file_path}
+    **前置注释**: {comment or '无'}
+    **函数代码片段**:{code_snippet}请严格按照以下步骤执行：
+1.  **步骤分解**：逐行或按代码块分析函数体，用简洁的步骤描述其执行流程。例如：“1. 检查输入参数x是否有效。2. 调用内部函数compute()进行计算。3. 对结果进行格式化处理。4. 返回格式化后的字符串。”
+2.  **逻辑归纳**：基于上述步骤，用1-3句话概括这个函数的核心功能与逻辑主线。
+
+**输出格式**：只需输出纯粹的、不带Markdown标题的流程逻辑描述文本。
+    """
+    headers = {
+        "Authorization": f"Bearer {_get_qwen_api_key()}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": QWEN_CHAT_MODEL,
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.1
+    }
+    response = requests.post(
+        _chat_completions_url(),
+        headers=headers,
+        json=payload
+    )
+    response.raise_for_status()
+    result = response.json()
+    logic = result['choices'][0]['message']['content'].strip()
+    return logic
+
+
+def get_qwen_embedding(text: str) -> np.ndarray:
+    try:
+        if OpenAI is None:
+            raise RuntimeError("openai package is not installed.")
+        client = OpenAI(
+            api_key=_get_qwen_api_key(),
+            base_url=QWEN_API_URL
+        )
+        response = client.embeddings.create(
+            model=QWEN_EMBEDDING_MODEL,
+            input=text
+        )
+        embedding = response.data[0].embedding
+        if np is not None:
+            return np.array(embedding, dtype='float32')
+        return [float(value) for value in embedding]
+    except Exception as e:
+        logger.error(f"获取嵌入失败: {e}")
+        raise RuntimeError(f"Failed to get embedding: {e}") from e
+
+
+def call_qwen_max(prompt: str, max_tokens: int = 1500, temperature: float = 0.1) -> str:
+    """
+    调用 Qwen-Max 进行推理
+
+    Args:
+        prompt: 输入的提示文本
+        max_tokens: 最大输出token数
+        temperature: 温度参数，控制随机性
+
+    Returns:
+        模型生成的文本
+    """
+    try:
+        # 使用 OpenAI 兼容格式调用通义千问
+        if OpenAI is None:
+            raise RuntimeError("openai package is not installed.")
+        client = OpenAI(
+            api_key=_get_qwen_api_key(),
+            base_url=QWEN_API_URL
+        )
+
+        response = client.chat.completions.create(
+            model=QWEN_CHAT_MODEL,
+            messages=[
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=max_tokens,
+            temperature=temperature
+        )
+
+        return response.choices[0].message.content.strip()
+
+    except Exception as e:
+        logger.error(f"调用Qwen-Max失败: {e}")
+        return f"API调用失败: {str(e)}"