{
  "id": "art_qJ6u7AFZAF-C",
  "slug": "llm-context-window-exceeded-text-truncation-strategies",
  "author": "goumang",
  "title": "LLM Context Window 超出错误的文本截断策略",
  "summary": "本文介绍 LLM 上下文窗口超出错误的处理策略，包括文本摘要压缩、滑动窗口、分块处理等方法，帮助开发者处理长文本场景。",
  "content": "# 概述\n\nLLM 有固定的上下文窗口限制（如 GPT-4 128K tokens），超出时会报错或截断。本文介绍处理长文本的策略。\n\n## 常见错误\n\n```python\nimport openai\n\ntry:\n    response = openai.ChatCompletion.create(\n        model=\"gpt-4\",\n        messages=long_messages  # 超出上下文限制\n    )\nexcept openai.error.InvalidRequestError as e:\n    if \"maximum context\" in str(e).lower():\n        print(\"超出上下文窗口限制\")\n```\n\n## 处理策略\n\n### 1. 文本摘要\n\n```python\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\n\ndef summarize_long_text(text: str, max_length: int = 4000) -> str:\n    \"\"\"将长文本摘要到指定长度\"\"\"\n    if len(text) <= max_length:\n        return text\n    \n    summary_prompt = f\"\"\"将以下文本摘要到 {max_length} 字符以内，保留关键信息：\n    \n    {text[:10000]}\"\"\"\n    \n    response = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=[{\"role\": \"user\", \"content\": summary_prompt}]\n    )\n    return response.choices[0].message.content\n```\n\n### 2. 滑动窗口\n\n```python\ndef sliding_window_search(\n    query: str,\n    document: str,\n    window_size: int = 2000,\n    step: int = 500\n) -> list[str]:\n    \"\"\"滑动窗口检索相关片段\"\"\"\n    chunks = []\n    for i in range(0, len(document), step):\n        chunk = document[i:i + window_size]\n        # 检查片段是否相关\n        if is_relevant(query, chunk):\n            chunks.append(chunk)\n        if len(chunks) >= 3:  # 最多取3个片段\n            break\n    return chunks\n\ndef is_relevant(query: str, chunk: str) -> bool:\n    \"\"\"简单相关性判断\"\"\"\n    query_words = set(query.lower().split())\n    chunk_words = set(chunk.lower().split())\n    overlap = query_words & chunk_words\n    return len(overlap) >= 2\n```\n\n### 3. 分块处理\n\n```python\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\n\ntext_splitter = RecursiveCharacterTextSplitter(\n    chunk_size=2000,\n    chunk_overlap=200,  # 重叠区域保持上下文\n    separators=[\"\\n\\n\", \"\\n\", \"。\", \" \"]\n)\n\ndef process_long_document(\n    document: str,\n    query: str,\n    llm\n) -> str:\n    # 1. 分块\n    chunks = text_splitter.split_text(document)\n    \n    # 2. 检索相关块\n    from langchain_openai import OpenAIEmbeddings\n    embeddings = OpenAIEmbeddings()\n    \n    relevant_chunks = []\n    for i, chunk in enumerate(chunks):\n        similarity = embeddings.embed_query(query)\n        chunk_emb = embeddings.embed_query(chunk)\n        # 简单相似度判断\n        if cosine_similarity(similarity, chunk_emb) > 0.5:\n            relevant_chunks.append((i, chunk))\n    \n    # 3. 按顺序组合\n    relevant_chunks.sort(key=lambda x: x[0])\n    combined = \"\\n\".join([c[1] for c in relevant_chunks[:3]])\n    \n    # 4. 生成答案\n    response = llm.invoke(f\"基于以下内容回答：\\n{combined}\\n\\n问题：{query}\")\n    return response\n```\n\n## 预防措施\n\n1. **输入检查**：发送前计算 token 数量\n2. **长度限制**：设置最大输入长度\n3. **自动截断**：超过阈值时自动摘要\n\n## 参考资料\n\n- [LangChain Text Splitters](https://docs.langchain.com/oss/python/langchain/overview)\n- [OpenAI Token Calculator](https://platform.openai.com/tokenizer)\n",
  "lang": "zh",
  "domain": "error_codes",
  "tags": [
    "context-window",
    "token-limit",
    "truncation",
    "chunking",
    "sliding-window",
    "llm"
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  "keywords": [
    "context window",
    "token limit",
    "text truncation",
    "chunking",
    "sliding window"
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      "notes": "策略说明准确",
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  "publishedAt": "2026-03-22T06:43:10.717Z",
  "updatedAt": "2026-05-11T18:24:15.607Z",
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