Init: 第一个版本

app/generator.py

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+import logging
+
+import httpx
+
+from app.config import LOW_SCORE, llm_config
+
+logger = logging.getLogger("learn_rag")
+
+SYSTEM_PROMPT = """你是「知迹」的知识库问答助手。只能根据给出的知识切片回答，不要补充切片之外的信息，也不要编造。
+如果切片不足以回答，就说明知识库里没有这项信息。
+用自然的中文直接回答问题，不要使用 Markdown，不要写标题。
+引用事实时在相关句末标注编号，格式必须是〔来源1〕这种全角括号。编号只能使用给定切片的编号。"""
+
+
+def build_context(chunks: list[dict]) -> str:
+    blocks = []
+    for chunk in chunks:
+        blocks.append(
+            f"〔来源{chunk['n']}〕《{chunk['title']}》相关度 {chunk['score']:.0%}\n{chunk['text']}"
+        )
+    return "\n\n".join(blocks)
+
+
+def extractive_answer(chunks: list[dict], low_confidence: bool) -> str:
+    if not chunks:
+        return "知识库里还没有可以检索的内容。"
+    lead = ""
+    if low_confidence:
+        lead = "最接近的切片和问题只是部分相关，下面按原文摘出，请以右侧切片为准。\n\n"
+    lines = [lead + chunks[0]["text"].strip() + f"〔来源{chunks[0]['n']}〕"]
+    for chunk in chunks[1:2]:
+        if chunks[0]["score"] - chunk["score"] <= 0.08:
+            lines.append("\n" + chunk["text"].strip() + f"〔来源{chunk['n']}〕")
+    return "\n".join(lines).strip()
+
+
+def generate(question: str, chunks: list[dict]) -> tuple[str, str, str | None, bool]:
+    low_confidence = not chunks or chunks[0]["score"] < LOW_SCORE
+    if not chunks:
+        return "知识库里还没有可以检索的内容。", "extractive", None, True
+
+    settings = llm_config()
+    if settings is None:
+        notice = "还没有配置大模型，回答直接摘自相关度最高的切片。在项目目录的 .env 中填入 OPENAI_API_KEY 后重启，即可生成连贯回答。"
+        return extractive_answer(chunks, low_confidence), "extractive", notice, low_confidence
+
+    caution = ""
+    if low_confidence:
+        caution = "注意：最高相关度偏低。如果切片不能直接支持答案，请明确说知识库里没有这项信息。\n\n"
+    user_prompt = f"{caution}知识切片如下：\n\n{build_context(chunks)}\n\n问题：{question}"
+    try:
+        answer = call_llm(settings, user_prompt)
+    except Exception:
+        logger.exception("大模型调用失败")
+        notice = "大模型暂时不可用，已改为直接摘录检索到的切片。"
+        return extractive_answer(chunks, low_confidence), "extractive", notice, low_confidence
+    return answer, "llm", None, low_confidence
+
+
+def call_llm(settings: dict, user_prompt: str) -> str:
+    response = httpx.post(
+        f"{settings['base']}/chat/completions",
+        headers={"Authorization": f"Bearer {settings['key']}"},
+        json={
+            "model": settings["model"],
+            "temperature": 0.2,
+            "max_tokens": 800,
+            "messages": [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": user_prompt},
+            ],
+        },
+        timeout=httpx.Timeout(60.0, connect=10.0),
+    )
+    response.raise_for_status()
+    message = response.json()["choices"][0]["message"]
+    content = message.get("content")
+    if isinstance(content, list):
+        content = "".join(
+            part.get("text", "") if isinstance(part, dict) else str(part) for part in content
+        )
+    text = (content or "").strip()
+    if not text:
+        raise ValueError("模型返回了空回答")
+    return text