Agentic Document Extraction

Dolphin

<p>Dolphin 是字节跳动开源的轻量级、高效的文档解析大模型。基于先解析结构后解析内容的两阶段方法，第一阶段生成文档布局元素序列，第二阶段用元素作为锚点并行解析内容。Dolphin在多种文档解析任务上表现出色，性能超越GPT-4.1、Mistral-OCR等模型。Dolphin 具有322M参数，体积小、速度快，支持多种文档元素解析，包括文本、表格、公式等。Dolphin的代码和预训练模型已公开，方便开发者使用和研究。</p> <h2 style="font-size: 20px;">Dolphin的主要功能</h2> <ul> <li>布局分析：识别文档中的各种元素（如标题、图表、表格、脚注等），按照自然阅读顺序生成元素序列。</li> <li>内容提取：将整个文档页面解析为结构化的JSON格式或Markdown格式，便于后续处理和展示。</li> <li>文本段落解析：准确识别和提取文档中的文本内容，支持多语言（如中文和英文）。</li> <li>公式识别：支持复杂公式的识别，包括行内公式和块级公式，输出LaTeX格式。</li> <li>表格解析：支持解析复杂的表格结构，提取单元格内容并生成HTML格式的表格。</li> <li>轻量级架构：模型参数量为322M，体积小，运行速度快，适合在资源受限的环境中使用。</li> <li>支持多种输入格式：支持处理多种类型的文档图像，包括学术论文、商业报告、技术文档等。</li> <li>多样化的输出格式：支持将解析结果输出为JSON、Markdown、HTML等多种格式，便于与不同系统集成。</li> </ul> <h2 style="font-size: 20px;">Dolphin的技术原理</h2> <ul> <li>页面级布局分析：用Swin Transformer对输入的文档图像进行编码，提取视觉特征。基于解码器生成文档元素序列，每个元素包含其类别（如标题、表格、图表等）和坐标位置。这一阶段的目标是按照自然阅读顺序生成结构化的布局信息。</li> <li>元素级内容解析：根据第一阶段生成的布局信息，从原始图像中裁剪出每个元素的局部视图。用特定的提示词（prompts），对每个元素进行并行内容解析。例如，表格用专门的提示词解析HTML格式，公式和文本段落共享提示词解析LaTeX格式。解码器根据裁剪后的元素图像和提示词，生成最终的解析内容。</li> </ul> <h2 style="font-size: 20px;">Dolphin的项目地址</h2> <ul> <li>GitHub仓库：<a class="external" href="https://github.com/bytedance/Dolphin" target="_blank" rel="noopener">https://github.com/bytedance/Dolphin</a></li> <li>HuggingFace模型库：<a class="external" href="https://huggingface.co/ByteDance/Dolphin" target="_blank" rel="noopener nofollow">https://huggingface.co/ByteDance/Dolphin</a></li> <li>arXiv技术论文：<a class="external" href="https://arxiv.org/pdf/2505.14059" target="_blank" rel="noopener nofollow">https://arxiv.org/pdf/2505.14059</a></li> <li>在线体验Demo：<a class="external" href="http://115.190.42.15:8888/dolphin/" target="_blank" rel="noopener nofollow">http://115.190.42.15:8888/dolphin/</a></li> </ul>

Pemo

<p>Pemo是AI驱动的文档管理工具。工具支持PDF、Epub、Word等多种格式文档的导入与管理，具备一键翻译、智能总结、思维导图生成等功能，帮助用户快速理解复杂文献，提升阅读效率。Pemo提供沉浸式阅读体验，用户自定义阅读模式、进行标注和笔记，方便记录灵感。Pemo支持文档格式转换，满足不同需求，是学生、科研人员和职场人士提升学习与工作效率的好帮手。</p> <p><img src="https://img.medsci.cn/aisite/img//vfRbHQKce6JgZFjoSVaeTxzkVmdAzNhHm1tu4Fno.png"></p> <h2 style="font-size: 20px;">Pemo的主要功能</h2> <ul> <li>导入与分类：支持PDF、Epub、Word等格式文档的导入，进行分类管理，方便查找。</li> <li>格式转换：将不同格式的文档相互转换，如PDF转Word、Epub转PDF等，满足多样化的阅读和编辑需求。</li> <li>AI翻译：实时翻译外文文档，帮助用户无障碍阅读多语言内容。</li> <li>语音朗读：将书籍和文献转换为语音，用户能随时随地收听。</li> <li>智能总结：AI自动生成文献摘要，帮助用户快速掌握核心内容，节省时间。</li> <li>思维导图：将复杂文献转化为直观的思维导图，助力理解和记忆。</li> <li>智能笔记：阅读时轻松做笔记，AI自动关联相关内容，提高学习效率。</li> <li>文档注释：为电子书和PDF文档添加高亮、笔记和书签，增强阅读体验。</li> </ul> <h2 style="font-size: 20px;">Pemo的官网地址</h2> <ul> <li>官网地址：<a href="https://pemo.ai/" target="_blank" rel="noopener">pemo.ai</a></li> </ul>

ContextGem

<p dir="auto"><a href="https://camo.githubusercontent.com/dcc762e8d3dc538b9e7dffbc07f3a0b3bfae2e4b56c8d5670075d156cd5d53b6/68747470733a2f2f636f6e7465787467656d2e6465762f5f7374617469632f636f6e7465787467656d5f726561646d655f6865616465722e706e67" target="_blank" rel="noopener noreferrer nofollow"><img title="ContextGem - 轻松从文档中提取 LLM" src="https://camo.githubusercontent.com/dcc762e8d3dc538b9e7dffbc07f3a0b3bfae2e4b56c8d5670075d156cd5d53b6/68747470733a2f2f636f6e7465787467656d2e6465762f5f7374617469632f636f6e7465787467656d5f726561646d655f6865616465722e706e67" alt="ContextGem" data-canonical-src="https://contextgem.dev/_static/contextgem_readme_header.png"></a></p> <div class="markdown-heading" dir="auto"> <h1 class="heading-element" dir="auto" tabindex="-1">ContextGem：轻松从文档中提取 LLM</h1> <a id="user-content-contextgem-effortless-llm-extraction-from-documents" class="anchor" href="https://github.com/shcherbak-ai/contextgem#contextgem-effortless-llm-extraction-from-documents" 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src="https://camo.githubusercontent.com/f8d782705bcb9ce83b5e04fc86504e194e5226b10cee5e6e9794cfc8b6101cba/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d4465657057696b69266d6573736167653d4368617425323077697468253230436f6465266c6162656c436f6c6f723d25323332383335393326636f6c6f723d253233374535374332267374796c653d666c61742d737175617265" alt="深度维基" data-canonical-src="https://img.shields.io/static/v1?label=DeepWiki&amp;message=Chat%20with%20Code&amp;labelColor=%23283593&amp;color=%237E57C2&amp;style=flat-square"></a></p> <p dir="auto">&nbsp;</p> <p dir="auto">ContextGem 是一个免费的开源 LLM 框架，它可以让您以最少的代码更轻松地从文档中提取结构化数据和见解。</p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">💎 为什么选择 ContextGem？</h2> <a id="user-content--why-contextgem" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-why-contextgem" aria-label="永久链接：💎 为什么选择 ContextGem？"></a></div> <p dir="auto">大多数流行的 LLM 框架用于从文档中提取结构化数据，即使是提取基本信息，也需要大量的样板代码。这大大增加了开发时间和复杂性。</p> <p dir="auto">ContextGem 通过提供灵活直观的框架来应对这一挑战，该框架能够以最小的投入从文档中提取结构化数据和洞察。复杂且耗时的部分由<strong>强大的抽象功能</strong>处理，从而消除了样板代码并降低了开发成本。</p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">⭐ 主要特点</h2> <a id="user-content--key-features" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-key-features" aria-label="永久链接：⭐ 主要特点"></a></div> <table> <thead> <tr> <th>内置抽象</th> <th><strong>ContextGem</strong></th> <th>其他 LLM 框架*</th> </tr> </thead> <tbody> <tr> <td>自动动态提示</td> <td>🟢</td> <td>◯</td> </tr> <tr> <td>自动化数据建模和验证器</td> <td>🟢</td> <td>◯</td> </tr> <tr> <td>精确的粒度参考映射（段落和句子）</td> <td>🟢</td> <td>◯</td> </tr> <tr> <td>理由（提取背后的推理）</td> <td>🟢</td> <td>◯</td> </tr> <tr> <td>神经分割（SaT）</td> <td>🟢</td> <td>◯</td> </tr> <tr> <td>多语言支持（无提示输入/输出）</td> <td>🟢</td> <td>◯</td> </tr> <tr> <td>单一、统一的提取管道（声明式、可重用、完全可序列化）</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>分组法学硕士课程，包含特定角色的任务</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>嵌套上下文提取</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>统一的、完全可序列化的结果存储模型（文档）</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>提取任务校准示例</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>内置并发 I/O 处理</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>自动使用和成本跟踪</td> <td>🟢</td> <td>🟡</td> </tr> <tr> <td>回退和重试逻辑</td> <td>🟢</td> <td>🟢</td> </tr> <tr> <td>多家 LLM 提供商</td> <td>🟢</td> <td>🟢</td> </tr> </tbody> </table> <p dir="auto">🟢 - 完全支持 - 无需额外设置<br>🟡 - 部分支持 - 需要额外设置<br>◯ - 不支持 - 需要自定义逻辑</p> <p dir="auto">* 查看ContextGem 抽象的<a href="https://contextgem.dev/motivation.html#the-contextgem-solution" rel="nofollow">描述</a>以及使用 ContextGem 和其他流行的开源 LLM 框架的具体实现示例的<a href="https://contextgem.dev/vs_other_frameworks.html" rel="nofollow">比较。</a></p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">💡 使用<strong>最少的代码</strong>，您可以：</h2> <a id="user-content--with-minimal-code-you-can" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-with-minimal-code-you-can" aria-label="永久链接：💡 使用最少的代码，您可以："></a></div> <ul dir="auto"> <li>从文档（文本、图像）中<strong>提取结构化数据</strong></li> <li><strong>识别并分析</strong>文档中的关键方面（主题、主题、类别）</li> <li><strong>从文档中提取特定概念</strong>（实体、事实、结论、评估）</li> <li>通过简单、直观的 API<strong>构建复杂的提取工作流程</strong></li> <li><strong>创建多级提取管道</strong>（包含概念的方面、分层方面）</li> </ul> <p>&nbsp;</p> <p dir="auto"><a href="https://camo.githubusercontent.com/84c9fdd0aa6c0023582ec31ee75d304e1fc63abc15882a8092514ad4190ea616/68747470733a2f2f636f6e7465787467656d2e6465762f5f7374617469632f726561646d655f636f64655f736e69707065742e706e67" target="_blank" rel="noopener noreferrer nofollow"><img title="ContextGem 提取示例" src="https://camo.githubusercontent.com/84c9fdd0aa6c0023582ec31ee75d304e1fc63abc15882a8092514ad4190ea616/68747470733a2f2f636f6e7465787467656d2e6465762f5f7374617469632f726561646d655f636f64655f736e69707065742e706e67" alt="ContextGem 提取示例" data-canonical-src="https://contextgem.dev/_static/readme_code_snippet.png"></a></p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">📦安装</h2> <a id="user-content--installation" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-installation" aria-label="固定链接：📦安装"></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto"> <pre>pip install -U contextgem</pre> <div class="zeroclipboard-container">&nbsp;</div> </div> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">🚀 快速入门</h2> <a id="user-content--quick-start" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-quick-start" aria-label="永久链接：🚀 快速入门"></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto"> <pre><span class="pl-c"># Quick Start Example - Extracting anomalies from a document, with source references and justifications</span> <span class="pl-k">import</span> <span class="pl-s1">os</span> <span class="pl-k">from</span> <span class="pl-s1">contextgem</span> <span class="pl-k">import</span> <span class="pl-v">Document</span>, <span class="pl-v">DocumentLLM</span>, <span class="pl-v">StringConcept</span> <span class="pl-c"># Sample document text (shortened for brevity)</span> <span class="pl-s1">doc</span> <span class="pl-c1">=</span> <span class="pl-en">Document</span>( <span class="pl-s1">raw_text</span><span class="pl-c1">=</span>( <span class="pl-s">"Consultancy Agreement<span class="pl-cce">\n</span>"</span> <span class="pl-s">"This agreement between Company A (Supplier) and Company B (Customer)...<span class="pl-cce">\n</span>"</span> <span class="pl-s">"The term of the agreement is 1 year from the Effective Date...<span class="pl-cce">\n</span>"</span> <span class="pl-s">"The Supplier shall provide consultancy services as described in Annex 2...<span class="pl-cce">\n</span>"</span> <span class="pl-s">"The Customer shall pay the Supplier within 30 calendar days of receiving an invoice...<span class="pl-cce">\n</span>"</span> <span class="pl-s">"The purple elephant danced gracefully on the moon while eating ice cream.<span class="pl-cce">\n</span>"</span> <span class="pl-c"># 💎 anomaly</span> <span class="pl-s">"This agreement is governed by the laws of Norway...<span class="pl-cce">\n</span>"</span> ), ) <span class="pl-c"># Attach a document-level concept</span> <span class="pl-s1">doc</span>.<span class="pl-c1">concepts</span> <span class="pl-c1">=</span> [ <span class="pl-en">StringConcept</span>( <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"Anomalies"</span>, <span class="pl-c"># in longer contexts, this concept is hard to capture with RAG</span> <span class="pl-s1">description</span><span class="pl-c1">=</span><span class="pl-s">"Anomalies in the document"</span>, <span class="pl-s1">add_references</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">reference_depth</span><span class="pl-c1">=</span><span class="pl-s">"sentences"</span>, <span class="pl-s1">add_justifications</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">justification_depth</span><span class="pl-c1">=</span><span class="pl-s">"brief"</span>, <span class="pl-c"># see the docs for more configuration options</span> ) <span class="pl-c"># add more concepts to the document, if needed</span> <span class="pl-c"># see the docs for available concepts: StringConcept, JsonObjectConcept, etc.</span> ] <span class="pl-c"># Or use `doc.add_concepts([...])`</span> <span class="pl-c"># Define an LLM for extracting information from the document</span> <span class="pl-s1">llm</span> <span class="pl-c1">=</span> <span class="pl-en">DocumentLLM</span>( <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s">"openai/gpt-4o-mini"</span>, <span class="pl-c"># or another provider/LLM</span> <span class="pl-s1">api_key</span><span class="pl-c1">=</span><span class="pl-s1">os</span>.<span class="pl-c1">environ</span>.<span class="pl-c1">get</span>( <span class="pl-s">"CONTEXTGEM_OPENAI_API_KEY"</span> ), <span class="pl-c"># your API key for the LLM provider</span> <span class="pl-c"># see the docs for more configuration options</span> ) <span class="pl-c"># Extract information from the document</span> <span class="pl-s1">doc</span> <span class="pl-c1">=</span> <span class="pl-s1">llm</span>.<span class="pl-c1">extract_all</span>(<span class="pl-s1">doc</span>) <span class="pl-c"># or use async version `await llm.extract_all_async(doc)`</span> <span class="pl-c"># Access extracted information in the document object</span> <span class="pl-en">print</span>( <span class="pl-s1">doc</span>.<span class="pl-c1">concepts</span>[<span class="pl-c1">0</span>].<span class="pl-c1">extracted_items</span> ) <span class="pl-c"># extracted items with references &amp; justifications</span> <span class="pl-c"># or `doc.get_concept_by_name("Anomalies").extracted_items`</span></pre> <div class="zeroclipboard-container">&nbsp;</div> </div> <p dir="auto"><a href="https://colab.research.google.com/github/shcherbak-ai/contextgem/blob/main/dev/notebooks/readme/quickstart_concept.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="在 Colab 中打开" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a></p> <hr> <p dir="auto">请参阅文档中的更多示例：</p> <div class="markdown-heading" dir="auto"> <h3 class="heading-element" dir="auto" tabindex="-1">基本使用示例</h3> <a id="user-content-basic-usage-examples" class="anchor" href="https://github.com/shcherbak-ai/contextgem#basic-usage-examples" aria-label="永久链接：基本用法示例"></a></div> <ul dir="auto"> <li><a href="https://contextgem.dev/quickstart.html#aspect-extraction-from-document" rel="nofollow">从文档中提取方面</a></li> <li><a href="https://contextgem.dev/quickstart.html#extracting-aspect-with-sub-aspects" rel="nofollow">使用子方面提取方面</a></li> <li><a href="https://contextgem.dev/quickstart.html#concept-extraction-from-aspect" rel="nofollow">从方面提取概念</a></li> <li><a href="https://contextgem.dev/quickstart.html#concept-extraction-from-document-text" rel="nofollow">从文档（文本）中提取概念</a></li> <li><a href="https://contextgem.dev/quickstart.html#concept-extraction-from-document-vision" rel="nofollow">从文档中提取概念（视觉）</a></li> <li><a href="https://contextgem.dev/quickstart.html#lightweight-llm-chat-interface" rel="nofollow">LLM聊天界面</a></li> </ul> <div class="markdown-heading" dir="auto"> <h3 class="heading-element" dir="auto" tabindex="-1">高级用法示例</h3> <a id="user-content-advanced-usage-examples" class="anchor" href="https://github.com/shcherbak-ai/contextgem#advanced-usage-examples" aria-label="永久链接：高级用法示例"></a></div> <ul dir="auto"> <li><a href="https://contextgem.dev/advanced_usage.html#extracting-aspects-with-concepts" rel="nofollow">提取包含概念的方面</a></li> <li><a href="https://contextgem.dev/advanced_usage.html#extracting-aspects-and-concepts-from-a-document" rel="nofollow">从文档中提取方面和概念</a></li> <li><a href="https://contextgem.dev/advanced_usage.html#using-a-multi-llm-pipeline-to-extract-data-from-several-documents" rel="nofollow">使用多 LLM 管道从多个文档中提取数据</a></li> </ul> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">🔄 文档转换器</h2> <a id="user-content--document-converters" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-document-converters" aria-label="永久链接：🔄 文档转换器"></a></div> <p dir="auto">要创建用于 LLM 分析的 ContextGem 文档，您可以直接传递原始文本，也可以使用处理各种文件格式的内置转换器。</p> <div class="markdown-heading" dir="auto"> <h3 class="heading-element" dir="auto" tabindex="-1">📄 DOCX 转换器</h3> <a id="user-content--docx-converter" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-docx-converter" aria-label="永久链接：📄 DOCX 转换器"></a></div> <p dir="auto">ContextGem 提供内置转换器，可轻松将 DOCX 文件转换为 LLM 就绪数据。</p> <ul dir="auto"> <li>提取其他开源工具通常无法捕获的信息：未对齐的表格、注释、脚注、文本框、页眉/页脚和嵌入图像</li> <li>保留具有丰富元数据的文档结构，以改进 LLM 分析</li> </ul> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto"> <pre><span class="pl-c"># Using ContextGem's DocxConverter</span> <span class="pl-k">from</span> <span class="pl-s1">contextgem</span> <span class="pl-k">import</span> <span class="pl-v">DocxConverter</span> <span class="pl-s1">converter</span> <span class="pl-c1">=</span> <span class="pl-en">DocxConverter</span>() <span class="pl-c"># Convert a DOCX file to an LLM-ready ContextGem Document</span> <span class="pl-c"># from path</span> <span class="pl-s1">document</span> <span class="pl-c1">=</span> <span class="pl-s1">converter</span>.<span class="pl-c1">convert</span>(<span class="pl-s">"path/to/document.docx"</span>) <span class="pl-c"># or from file object</span> <span class="pl-k">with</span> <span class="pl-en">open</span>(<span class="pl-s">"path/to/document.docx"</span>, <span class="pl-s">"rb"</span>) <span class="pl-k">as</span> <span class="pl-s1">docx_file_object</span>: <span class="pl-s1">document</span> <span class="pl-c1">=</span> <span class="pl-s1">converter</span>.<span class="pl-c1">convert</span>(<span class="pl-s1">docx_file_object</span>) <span class="pl-c"># You can also use it as a standalone text extractor</span> <span class="pl-s1">docx_text</span> <span class="pl-c1">=</span> <span class="pl-s1">converter</span>.<span class="pl-c1">convert_to_text_format</span>( <span class="pl-s">"path/to/document.docx"</span>, <span class="pl-s1">output_format</span><span class="pl-c1">=</span><span class="pl-s">"markdown"</span>, <span class="pl-c"># or "raw"</span> )</pre> <div class="zeroclipboard-container">&nbsp;</div> </div> <p dir="auto">在文档中了解有关<a href="https://contextgem.dev/converters/docx.html" rel="nofollow">DOCX 转换器功能的更多信息。</a></p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">🎯 重点文档分析</h2> <a id="user-content--focused-document-analysis" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-focused-document-analysis" aria-label="永久链接：🎯 重点文档分析"></a></div> <p dir="auto">ContextGem 利用 LLM 的长上下文窗口，从单个文档中提取出卓越的准确率。与 RAG 方法（通常<a href="https://www.linkedin.com/pulse/raging-contracts-pitfalls-rag-contract-review-shcherbak-ai-ptg3f" rel="nofollow">难以处理复杂概念和细微洞察）</a>不同，ContextGem 充分利用了<a href="https://arxiv.org/abs/2502.12962" rel="nofollow">持续扩展的上下文容量</a>、不断改进的 LLM 功能以及降低的成本。这种专注的方法能够直接从完整文档中提取信息，消除检索不一致，同时针对深入的单文档分析进行优化。虽然这可以提高单个文档的准确率，但 ContextGem 目前不支持跨文档查询或全语料库检索&mdash;&mdash;对于这些用例，现代 RAG 系统（例如 LlamaIndex、Haystack）仍然更为合适。</p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">🤖 支持</h2> <a id="user-content--supported-llms" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-supported-llms" aria-label="永久链接：🤖 支持的法学硕士"></a></div> <p dir="auto"><a href="https://github.com/BerriAI/litellm">ContextGem 通过LiteLLM</a>集成支持基于云和本地的 LLM ：</p> <ul dir="auto"> <li><strong>云端法学硕士</strong>：OpenAI、Anthropic、Google、Azure OpenAI 等</li> <li><strong>本地 LLM</strong>：使用 Ollama、LM Studio 等提供商在本地运行模型。</li> <li><strong>模型架构</strong>：适用于推理/CoT 功能（例如 o4-mini）和非推理模型（例如 gpt-4.1）</li> <li><strong>简单的 API</strong>：所有 LLM 的统一接口，可轻松切换提供商</li> </ul> <p dir="auto">在文档中了解<a href="https://contextgem.dev/llms/supported_llms.html" rel="nofollow">有关支持的 LLM 提供程序和模型</a>以及如何<a href="https://contextgem.dev/llms/llm_config.html" rel="nofollow">配置 LLM 的更多信息。</a></p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">⚡ 优化</h2> <a id="user-content--optimizations" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-optimizations" aria-label="永久链接：⚡ 优化"></a></div> <p dir="auto">ContextGem 文档提供了有关优化策略的指导，以最大限度地提高性能、最大限度地降低成本并提高提取准确性：</p> <ul dir="auto"> <li><a href="https://contextgem.dev/optimizations/optimization_accuracy.html" rel="nofollow">优化准确性</a></li> <li><a href="https://contextgem.dev/optimizations/optimization_speed.html" rel="nofollow">优化速度</a></li> <li><a href="https://contextgem.dev/optimizations/optimization_cost.html" rel="nofollow">优化成本</a></li> <li><a href="https://contextgem.dev/optimizations/optimization_long_docs.html" rel="nofollow">处理长文档</a></li> <li><a href="https://contextgem.dev/optimizations/optimization_choosing_llm.html" rel="nofollow">选择合适的法学硕士</a></li> </ul> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">💾 序列化结果</h2> <a id="user-content--serializing-results" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-serializing-results" aria-label="永久链接：💾 序列化结果"></a></div> <p dir="auto">ContextGem 允许您使用内置序列化方法保存和加载 Document 对象、管道和 LLM 配置：</p> <ul dir="auto"> <li>保存已处理的文档以避免重复昂贵的 LLM 调用</li> <li>在系统之间传输提取结果</li> <li>保留管道和 LLM 配置以供以后重用</li> </ul> <p dir="auto">在文档中了解有关<a href="https://contextgem.dev/serialization.html" rel="nofollow">序列化选项的更多信息。</a></p> <div class="markdown-heading" dir="auto"> <h2 class="heading-element" dir="auto" tabindex="-1">📚 文档</h2> <a id="user-content--documentation" class="anchor" href="https://github.com/shcherbak-ai/contextgem#-documentation" aria-label="永久链接：📚 文档"></a></div> <p dir="auto">完整文档可在<a href="https://contextgem.dev/" rel="nofollow">contextgem.dev</a>上找到。</p> <p dir="auto">完整文档的原始文本版本可在处获取<a href="https://github.com/shcherbak-ai/contextgem/blob/main/docs/docs-raw-for-llm.txt"><code>docs/docs-raw-for-llm.txt</code></a>。此文件自动生成，包含所有文档，其格式已针对 LLM 导入进行了优化（例如，用于问答）。</p>

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暴躁的教授读论文（mad-professor）

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