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588 lines
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588 lines
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Markdown
<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/LearningCircuit/local-deep-research) · [上游 README](https://github.com/LearningCircuit/local-deep-research/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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# Local Deep Research
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<div align="center">
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[](https://github.com/LearningCircuit/local-deep-research/stargazers)
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[](https://hub.docker.com/r/localdeepresearch/local-deep-research)
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[](https://pypi.org/project/local-deep-research/)
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[](https://trendshift.io/repositories/14116)
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[](https://github.com/LearningCircuit/local-deep-research/commits/main)
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[](https://github.com/LearningCircuit/local-deep-research/commits/main)
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[](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks)
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[](docs/SQLCIPHER_INSTALL.md)
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<!-- 访客能够识别的知名安全扫描器 -->
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[](https://securityscorecards.dev/viewer/?uri=github.com/LearningCircuit/local-deep-research)
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[](https://github.com/LearningCircuit/local-deep-research/security/code-scanning)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/semgrep.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/pre-commit.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/docker-publish.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/publish.yml)
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[](https://discord.gg/ttcqQeFcJ3)
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[](https://www.reddit.com/r/LocalDeepResearch/)
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[](https://www.youtube.com/@local-deep-research)
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**由 AI 驱动的深度智能体（agentic）研究助手**
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*使用多种 LLM 与搜索引擎进行深度智能体研究，并提供规范引用*
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🧪 **首个在单张 RTX 3090（Qwen3.6-27B）上完全本地运行、在本地硬件上报出约 95% SimpleQA（n=500）与 77% xbench-DeepSearch（n=100）的开源项目。** 参见 [r/LocalLLaMA 公告](https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/) 与 [基准数据集](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks).
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<a href="https://www.youtube.com/watch?v=pfxgLX-MxMY&t=1999">
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▶️ 观看 The Art Of The Terminal 评测
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</a>
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</div>
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## 🚀 什么是 Local Deep Research？
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由你掌控的 AI 研究助手。本地运行以保护隐私，可使用任意 LLM，并构建你自己的可搜索知识库。数据归你所有，工作原理完全透明。
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## ⚡ 快速开始
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**选项 1：Docker Run（Linux）**
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```bash
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# Step 1: Pull and run Ollama
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docker run -d -p 11434:11434 --name ollama ollama/ollama
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docker exec ollama ollama pull gpt-oss:20b
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# Step 2: Pull and run SearXNG for optimal search results
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docker run -d -p 8080:8080 --name searxng searxng/searxng
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# Step 3: Pull and run Local Deep Research
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docker run -d --network host \
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--name local-deep-research \
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--volume "deep-research:/data" \
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-e LDR_DATA_DIR=/data \
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localdeepresearch/local-deep-research
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```
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> **Mac / Windows / WSL2 用户：** `--network host` 仅在原生 Linux 上可用。在 Docker Desktop 上，它会静默失败，无法发布 5000 端口，*并且*会使 `localhost` 指向 LDR 容器自身（因而无法访问 Ollama/SearXNG）。请使用下方的 **选项 2**，或参阅 [Windows/WSL2 常见问题](docs/faq.md#port-5000-not-accessible-on-windows) 获取可用的 `docker run` 方案。
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**选项 2：Docker Compose**
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仅 CPU（全平台）：
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```bash
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curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
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```
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使用 NVIDIA GPU（Linux）：
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```bash
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curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && \
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curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.gpu.override.yml && \
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docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -d
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```
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约 30 秒后打开 http://localhost:5000。GPU 配置、环境变量等详见 [Docker Compose 指南](docs/docker-compose-guide.md)。
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**选项 3：pip install**
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```bash
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pip install local-deep-research
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python -m local_deep_research.web.app # starts the web UI on http://localhost:5000
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```
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> 你还需要运行 Ollama（或任何兼容 OpenAI 的 LLM 端点）和 SearXNG — 完整步骤见 [pip 安装指南](docs/install-pip.md)。
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> 支持 Windows、macOS 和 Linux。SQLCipher 加密通过预编译 wheel 提供 — 无需编译。
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> Windows 上导出 PDF 需要 Pango（[配置指南](https://doc.courtbouillon.org/weasyprint/stable/first_steps.html)).
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> 若遇到加密相关问题，可将 `export LDR_BOOTSTRAP_ALLOW_UNENCRYPTED=true` 设为使用标准 SQLite。
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**详细安装指南：** [Docker](docs/installation.md#docker) · [Docker Compose](docs/docker-compose-guide.md) · [pip](docs/install-pip.md) · [Unraid](docs/deployment/unraid.md) · [完整安装参考](docs/installation.md)
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> **较旧的 CPU（x86-64）？** LDR 需要支持 AVX 的 CPU — Intel Sandy Bridge / AMD Bulldozer（2011）或更新型号。部分科学 Python 依赖（pandas、scikit-learn）提供的 wheel 在较旧 CPU 上会因 `Illegal instruction` 而崩溃。ARM64（aarch64）完全支持。每个发行版都会针对这一下限进行冒烟测试，包括仅 AVX、无 AVX2 的 CPU（[#4480](https://github.com/LearningCircuit/local-deep-research/issues/4480)).
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## 🏗️ 工作原理
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### 研究
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你提出一个复杂问题。Local Deep Research（LDR）会：
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- 自动替你完成研究
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- 在网页、学术论文与你自己的文档中搜索
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- 将所有内容综合成附带规范引用的报告
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选择适合的研究策略：快速 pipeline 模式用于快速查证事实，完全智能体（agentic）深度研究则适用于复杂分析与学术工作。
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**LangGraph Agent Strategy** — 一种自主智能体研究模式，由 LLM 决定搜索什么、使用哪些专用引擎（arXiv、PubMed、Semantic Scholar 等），以及何时进行综合归纳。它会根据检索结果自适应切换搜索引擎，并比基于 pipeline 的策略收集显著更多的来源 — 上文约 95% SimpleQA 成绩即由此策略达成。在 Settings 中选择 `langgraph-agent`。
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### 构建你的知识库
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```mermaid
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flowchart LR
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R[Research] --> D[Download Sources]
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D --> L[(Library)]
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L --> I[Index & Embed]
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I --> S[Search Your Docs]
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S -.-> R
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```
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每次研究会话都会发现宝贵来源。可直接下载到加密资料库 — ArXiv 学术论文、PubMed 文章、网页等。LDR 提取文本、建立索引并使一切可搜索。下次研究时，可同时对自己的文档与实时网页提问。你的知识会随时间不断累积。
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## 🛡️ 安全
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<div align="center">
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<!-- 静态分析（CodeQL/Semgrep 之外的额外扫描器） -->
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/devskim.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/bearer.yml)
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<!-- 依赖项与密钥扫描 -->
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/osv-scanner.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/npm-audit.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/retirejs.yml)
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<!-- 容器安全 -->
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/container-security.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/dockle.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/hadolint.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/checkov.yml)
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<!-- 工作流与运行时安全 -->
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/zizmor-security.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/owasp-zap-scan.yml)
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[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/security-tests.yml)
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</div>
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```mermaid
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flowchart LR
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U1[User A] --> D1[(Encrypted DB)]
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U2[User B] --> D2[(Encrypted DB)]
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```
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你的数据始终属于你。每位用户都拥有独立的 SQLCipher 数据库，采用 AES-256 加密，密钥由你的密码派生。你的密码绝不会被存储——登录通过尝试解密你的数据库来完成，因此数据库文件本身对任何获取到它们的人都无法使用。每位用户的 LLM API 密钥加密存储在同一个人数据库中，而非共享的服务器级存储。
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[Docker 部署](docker-compose.yml) 附带 `cap_drop: ALL`、`no-new-privileges` 以及非 root 运行时，捆绑的 Ollama 和 SearXNG 镜像均通过 digest 固定版本。或者完全在本地运行 Ollama + SearXNG，数据永不离开你的机器。
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**内存中的凭据**：与任何在运行时使用密钥的应用一样，凭据在活跃会话期间保存在进程内存中——通过会话范围的凭据生命周期和核心转储排除（core dump exclusion）加以缓解。完整威胁模型请参阅[安全策略](SECURITY.md#in-memory-credentials)。
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**供应链安全**：Docker 镜像使用 [Cosign](https://github.com/sigstore/cosign) 通过 GitHub 的无密钥 OIDC 流程进行签名，包含 SLSA 来源证明（provenance attestations），并附带经认证的 SPDX SBOM。分步验证命令请参阅[验证镜像与 SBOM](SECURITY.md#verifying-images-and-sboms)。
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**安全透明度**：扫描器抑制项及其理由记录在 [Security Alerts Assessment](.github/SECURITY_ALERTS.md)、[Scorecard Compliance](.github/SECURITY_SCORECARD.md)、[Container CVE Suppressions](.trivyignore) 和 [SAST Rule Rationale](bearer.yml) 中。部分告警（Dependabot、代码扫描）只能在 [GitHub Security tab](https://docs.github.com/en/code-security/dependabot/dependabot-alerts/viewing-and-updating-dependabot-alerts), 中关闭，或在其之外极难抑制，因此上述文件并未涵盖每一条已关闭的发现。
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[详细架构 →](docs/architecture.md) | [安全策略 →](SECURITY.md) | [安全审查流程 →](docs/processes/security-review-process/)
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### 🔒 隐私与数据
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Local Deep Research **不包含遥测、分析或跟踪**。我们不会收集、传输或存储任何关于你或你使用情况的数据。没有分析 SDK、没有回传（phone-home）调用、没有崩溃报告、没有外部脚本。使用指标保留在你本地的加密数据库中。
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LDR 发出的唯一网络请求均由**你**发起：搜索查询（发往你配置的搜索引擎）、LLM API 调用（发往你选择的提供商），以及通知（仅在你配置了 Apprise 时）。
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由于我们不收集任何使用数据，我们依赖你告诉我们哪些好用、哪些有问题、以及你希望接下来看到什么——[错误报告](https://github.com/LearningCircuit/local-deep-research/issues), 功能建议，甚至你喜爱或从不使用的功能，都有助于我们改进 LDR。
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## 📊 基准测试
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来自[社区基准测试](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks) 的 headline 结果，采用 `langgraph-agent` 策略配合 Serper 搜索，通过 Ollama 完全在本地运行：
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| Model | SimpleQA | xbench-DeepSearch |
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|---|---|---|
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| Qwen3.6-27B | 95.7% (287/300) | 77.0% (77/100) |
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| Qwen3.5-9B | 91.2% (182/200) | 59.0% (59/100) |
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| gpt-oss-20B | 85.4% (295/346) | – |
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注意事项：样本量较小、LLM 评分器噪声，以及较新基础模型在 SimpleQA 上的污染风险。
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正在挑选本地模型？同一社区维护的数据集会跟踪各模型、搜索引擎和研究策略的准确率——这是你在下载数 GB 权重之前，快速了解哪些 Ollama / LM Studio / llama.cpp 模型真正适合深度研究的最快方式。**[在 Hugging Face 上浏览完整排行榜 →](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks)**
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[提交你自己的结果 →](https://github.com/LearningCircuit/ldr-benchmarks)（贡献者列于 [CONTRIBUTORS.md](https://github.com/LearningCircuit/ldr-benchmarks/blob/main/CONTRIBUTORS.md)), 或[在本地运行基准测试 →](docs/BENCHMARKING.md)。
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## ✨ 核心功能
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### 🔍 研究模式
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- **Quick Summary** - 30 秒至 3 分钟内获得带引用的答案
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- **Detailed Research** - 结构化发现的全面分析
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- **Report Generation** - 带章节与目录的专业报告
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- **Document Analysis** - 用 AI 搜索你的私有文档
|
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|
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### 🛠️ 高级能力
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||
- **[LangChain Integration](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)** - 将任意向量存储用作搜索引擎
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||
- **[REST API](docs/api-quickstart.md)** - 经身份验证的 HTTP 访问，每位用户独立数据库
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||
- **[Benchmarking](docs/BENCHMARKING.md)** - 测试并优化你的配置
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- **[Analytics Dashboard](docs/analytics-dashboard.md)** - 跟踪成本、性能与使用指标
|
||
- **[Journal Quality System](docs/journal-quality.md)** - 自动期刊声誉评分，索引 212K+ 来源，含掠夺性期刊检测与质量仪表板。由 [OpenAlex](https://openalex.org)（CC0）、[DOAJ](https://doaj.org)（CC0）和 [Stop Predatory Journals](https://predatoryjournals.org)（MIT）提供支持。参见 [v1.6.0 公告](https://www.reddit.com/r/LocalDeepResearch/comments/1svndjb/v160_highly_anticipated_journal_quality_filter/).
|
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- **Real-time Updates** - WebSocket 支持，实时展示研究进度
|
||
- **[Chat Mode](docs/features.md#chat-mode)** - 多轮研究对话，支持流式进度与跨轮次累积上下文
|
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- **Export Options** - 将结果下载为 PDF 或 Markdown
|
||
- **Research History** - 保存、搜索并回顾过往研究
|
||
- **Adaptive Rate Limiting** - 智能重试系统，学习最优等待时间
|
||
- **Keyboard Shortcuts** - 高效导航（ESC、Ctrl+Shift+1-4）
|
||
|
||
### 📰 新闻与研究订阅
|
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- **自动化研究摘要** - 订阅主题或特定查询；AI 筛选并仅汇总最相关的发展动态
|
||
- **可定制投递** - 每日、每周或自定义计划，以 Markdown 报告或结构化摘要形式交付
|
||
|
||
### 🌐 搜索源
|
||
|
||
#### 免费搜索引擎
|
||
- **学术**：arXiv、PubMed、Semantic Scholar
|
||
- **通用**：Wikipedia、SearXNG
|
||
- **技术**：GitHub、Elasticsearch
|
||
- **历史**：Wayback Machine
|
||
- **新闻**：The Guardian、Wikinews
|
||
|
||
#### 付费搜索引擎
|
||
- **Tavily** - AI 驱动的搜索
|
||
- **Google** - 通过 SerpAPI 或 Programmable Search Engine
|
||
- **Brave Search** - 注重隐私的网页搜索
|
||
|
||
#### 自定义源
|
||
- **本地文档** - 用 AI 搜索你的文件
|
||
- **LangChain Retrievers** - 任意向量存储或数据库
|
||
- **元搜索（Meta Search）** - 智能组合多个搜索引擎
|
||
|
||
LDR 遵守 `robots.txt`，在抓取网页时会如实表明自身身份——不使用隐身或反检测技术。在少数情况下，这意味着会无法抓取阻止自动化访问的页面，我们认为这是正确的权衡。
|
||
|
||
[完整搜索引擎指南 →](docs/search-engines.md)
|
||
|
||
## 💻 使用示例
|
||
|
||
### Python API
|
||
```python
|
||
from local_deep_research.api import LDRClient, quick_query
|
||
|
||
# Option 1: Simplest - one line research
|
||
summary = quick_query("username", "password", "What is quantum computing?")
|
||
print(summary)
|
||
|
||
# Option 2: Client for multiple operations
|
||
client = LDRClient()
|
||
client.login("username", "password")
|
||
result = client.quick_research("What are the latest advances in quantum computing?")
|
||
print(result["summary"])
|
||
```
|
||
|
||
### HTTP API
|
||
|
||
*下方代码示例展示基本 API 结构——完整可运行示例请见下方链接*
|
||
|
||
```python
|
||
import requests
|
||
from bs4 import BeautifulSoup
|
||
|
||
# Create session and authenticate
|
||
session = requests.Session()
|
||
login_page = session.get("http://localhost:5000/auth/login")
|
||
soup = BeautifulSoup(login_page.text, "html.parser")
|
||
login_csrf = soup.find("input", {"name": "csrf_token"}).get("value")
|
||
|
||
# Login and get API CSRF token
|
||
session.post("http://localhost:5000/auth/login",
|
||
data={"username": "user", "password": "pass", "csrf_token": login_csrf})
|
||
csrf = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]
|
||
|
||
# Make API request
|
||
response = session.post("http://localhost:5000/api/start_research",
|
||
json={"query": "Your research question"},
|
||
headers={"X-CSRF-Token": csrf})
|
||
```
|
||
|
||
🚀 **[开箱即用的 HTTP API 示例 → examples/api_usage/http/](examples/api_usage/http/)**
|
||
- ✅ **自动创建用户** - 开箱即用
|
||
- ✅ **完整身份验证**，含 CSRF 处理
|
||
- ✅ **结果重试逻辑** - 等待研究完成
|
||
- ✅ **进度监控**与错误处理
|
||
|
||
### 命令行工具
|
||
|
||
```bash
|
||
# Run benchmarks from CLI
|
||
python -m local_deep_research.benchmarks.cli.benchmark_commands simpleqa --examples 50
|
||
|
||
# Manage rate limiting
|
||
python -m local_deep_research.web_search_engines.rate_limiting status
|
||
python -m local_deep_research.web_search_engines.rate_limiting reset --engine SearXNGSearchEngine
|
||
```
|
||
|
||
完整参考请见[命令行工具指南](docs/cli-tools.md)。
|
||
|
||
## 🔗 接入自有知识库
|
||
|
||
将 LDR 连接到你现有的知识库。与上文 HTTP 客户端不同，`quick_summary()` 在进程内运行 LDR——无需服务器——因此你可以传入实时 Python 对象，例如 LangChain retriever：
|
||
|
||
```python
|
||
from local_deep_research.api import quick_summary
|
||
|
||
# Use your existing LangChain retriever
|
||
result = quick_summary(
|
||
query="What are our deployment procedures?",
|
||
retrievers={"company_kb": your_retriever},
|
||
search_tool="company_kb"
|
||
)
|
||
```
|
||
|
||
支持：FAISS、Chroma、Pinecone、Weaviate、Elasticsearch，以及任意兼容 LangChain 的 retriever。
|
||
|
||
[集成指南 →](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)
|
||
|
||
## 🔌 MCP 服务器（Claude 集成）
|
||
|
||
LDR 提供 MCP（Model Context Protocol）服务器，使 Claude Desktop、Claude Code 等 AI 助手能够执行深度研究。完整配置说明见 [MCP 服务器指南](docs/mcp-server.md)。
|
||
|
||
> ⚠️ **安全提示**：此 MCP 服务器仅面向**本地使用**，通过 STDIO 传输（例如 Claude Desktop）。它没有内置身份验证或速率限制。若要在网络上暴露，必须先实施适当的安全控制。网络部署要求见 [MCP 安全最佳实践](https://modelcontextprotocol.io/docs/tutorials/security/security_best_practices)。
|
||
|
||
### 安装
|
||
|
||
```bash
|
||
# Install with MCP extras
|
||
pip install "local-deep-research[mcp]"
|
||
```
|
||
|
||
### Claude Desktop 配置
|
||
|
||
添加到你的 `claude_desktop_config.json`：
|
||
|
||
```json
|
||
{
|
||
"mcpServers": {
|
||
"local-deep-research": {
|
||
"command": "ldr-mcp",
|
||
"env": {
|
||
"LDR_LLM_PROVIDER": "openai",
|
||
"LDR_LLM_OPENAI_API_KEY": "sk-..."
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
### Claude Code 配置
|
||
|
||
添加到你的 `.mcp.json`（项目级）或 `~/.claude/mcp.json`（全局）：
|
||
|
||
```json
|
||
{
|
||
"mcpServers": {
|
||
"local-deep-research": {
|
||
"command": "ldr-mcp",
|
||
"env": {
|
||
"LDR_LLM_PROVIDER": "ollama",
|
||
"LDR_LLM_OLLAMA_URL": "http://localhost:11434"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
任意 LDR 设置均可通过 `env` 以 `LDR_*` 变量传入——完整列表见自动生成的[完整配置参考](docs/CONFIGURATION.md)。
|
||
|
||
### 可用工具
|
||
|
||
| Tool | Description | Duration | LLM Cost |
|
||
|------|-------------|----------|----------|
|
||
| `search` | 来自特定引擎（arxiv、pubmed、wikipedia 等）的原始结果 | 5-30s | None |
|
||
| `quick_research` | 快速研究摘要 | 1-5 min | Yes |
|
||
| `detailed_research` | 全面分析 | 5-15 min | Yes |
|
||
| `generate_report` | 完整 Markdown 报告 | 10-30 min | Yes |
|
||
| `analyze_documents` | 搜索本地集合 | 30s-2 min | Yes |
|
||
| `list_search_engines` | 列出可用搜索引擎 | instant | None |
|
||
| `list_strategies` | 列出研究策略 | instant | None |
|
||
| `get_configuration` | 获取当前配置 | instant | None |
|
||
|
||
### 单独搜索引擎
|
||
|
||
`search` 工具可让你直接查询特定搜索引擎并获取原始结果（标题、链接、摘要）——无 LLM 处理、无费用、速度快。这对需要**监控与订阅**、希望定期检查新内容而又不消耗 LLM token 的场景尤其有用。
|
||
|
||
```
|
||
# Search arXiv for recent papers
|
||
search(query="transformer architecture improvements", engine="arxiv")
|
||
|
||
# Search PubMed for medical literature
|
||
search(query="CRISPR clinical trials 2024", engine="pubmed")
|
||
|
||
# Search Wikipedia for quick facts
|
||
search(query="quantum error correction", engine="wikipedia")
|
||
|
||
# Search GitHub for code and repositories
|
||
search(query="agentic research frameworks", engine="github")
|
||
|
||
# Use list_search_engines() to see all available engines
|
||
```
|
||
|
||
### 使用示例
|
||
|
||
```
|
||
"Use quick_research to find information about quantum computing applications"
|
||
"Search arxiv for recent papers on diffusion models"
|
||
"Generate a detailed research report on renewable energy trends"
|
||
```
|
||
|
||
|
||
## 🤖 支持的 LLM
|
||
|
||
### 本地模型
|
||
- **Ollama** — 连接其原生 API（默认 `http://localhost:11434`）
|
||
- **LM Studio** — 连接其 OpenAI 兼容服务器（默认 `http://localhost:1234/v1`）
|
||
- **llama.cpp** — 连接 `llama-server` 的 OpenAI 兼容端点（默认 `http://localhost:8080/v1`）；使用 `llama-server -m <model.gguf>` 启动
|
||
- 常见模型：Llama、Mistral、Gemma、DeepSeek、Qwen
|
||
- LLM 处理保留在本地（搜索查询仍会访问网络）。无 API 费用。
|
||
|
||
> 💡 **该选哪个本地模型？** 参见[社区基准测试](#-benchmarks)——社区提交的本地与云端模型准确度数据，便于下载前对比。
|
||
|
||
### 云端模型
|
||
- OpenAI
|
||
- Anthropic Claude
|
||
- Google Gemini
|
||
- 通过 OpenRouter 支持 100+ 模型
|
||
|
||
### 自定义端点
|
||
- **OpenAI 兼容端点** — 任何支持 OpenAI chat-completions API 的服务（vLLM、llama.cpp、网关等）
|
||
- **Anthropic 兼容端点** — 自托管、支持 Anthropic Messages API 的服务（`/v1/messages`）；设置 `llm.anthropic_endpoint.url`
|
||
|
||
[模型配置 →](docs/env_configuration.md)
|
||
|
||
## 🔄 从早期版本升级
|
||
|
||
- **`llm.model` 不再有默认值。** 1.6.3 之前的安装在未配置模型时会自动填充 `gemma3:12b`（Ollama），这会默默下载一个数 GB 的二进制文件。该字段现在默认为空——请在 Settings → LLM 中选择模型，否则研究任务会明确报错并失败。
|
||
- **已移除 `auto` 和 `parallel` 元搜索引擎。** 默认的 langgraph-agent 策略会按查询动态选择引擎，以此替代它们。已保存的设置会自动迁移（被移除的值将变为 `searxng`）；请将任何显式使用 `search_tool="auto"` 的 API 调用或 `LDR_SEARCH_TOOL=auto` 环境变量覆盖更新为具体引擎，例如 `searxng`。
|
||
- **`llamacpp` 提供方现在使用 HTTP，而非进程内加载。** 如果你此前将 `llm.llamacpp_model_path` 设置为本地 `.gguf` 文件，该设置不再被读取。请改为运行 `llama-server -m <your-model.gguf>`（每个现代 llama.cpp 构建都自带），默认的 `llm.llamacpp.url`（`http://localhost:8080/v1`）会自动发现它。若你将 `llama-server` 置于认证代理之后，可通过 `llm.llamacpp.api_key` 可选地配置 API 密钥支持。
|
||
|
||
## 📚 文档
|
||
|
||
### 入门
|
||
- [安装指南](docs/installation.md)
|
||
- [常见问题](docs/faq.md)
|
||
- [API 快速入门](docs/api-quickstart.md)
|
||
- [配置指南](docs/env_configuration.md)
|
||
- [完整配置参考](docs/CONFIGURATION.md)
|
||
|
||
### 核心功能
|
||
- [全部功能指南](docs/features.md)
|
||
- [搜索引擎指南](docs/search-engines.md)
|
||
- [分析仪表盘](docs/analytics-dashboard.md)
|
||
|
||
### 高级功能
|
||
- [LangChain 集成](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)
|
||
- [基准测试系统](docs/BENCHMARKING.md)
|
||
- [Elasticsearch 配置](docs/elasticsearch_search_engine.md)
|
||
- [SearXNG 配置](docs/SearXNG-Setup.md)
|
||
|
||
### 开发
|
||
- [Docker Compose 指南](docs/docker-compose-guide.md)
|
||
- [开发指南](docs/developing.md)
|
||
- [CodeQL 指南](docs/security/CODEQL_GUIDE.md)
|
||
- [发布指南](docs/RELEASE_GUIDE.md)
|
||
- [CI/CD 基础设施](docs/CI_CD_INFRASTRUCTURE.md) — pre-commit 钩子、工作流、安全扫描
|
||
- [工作流状态仪表盘](docs/ci/workflow-status.md) — 各 GitHub Actions 工作流的实时运行状况
|
||
- [测试覆盖率报告](https://learningcircuit.github.io/local-deep-research/) — 发布至 GitHub Pages 的 pytest 覆盖率
|
||
|
||
### 示例与教程
|
||
- [API 示例](examples/api_usage/)
|
||
- [基准测试示例](examples/benchmarks/)
|
||
- [优化示例](examples/optimization/)
|
||
|
||
## 📰 精选报道
|
||
|
||
> "对于那些将隐私放在首位的用户，Local Deep Research **值得特别关注**……**专为使用可在消费级 GPU 甚至 CPU 上运行的开源 LLM 而调优**。记者、研究人员或处理敏感话题的企业可以展开调查，**查询永远不会触及外部服务器**。"
|
||
>
|
||
> — [Medium：开源深度研究 AI 助手](https://medium.com/@leucopsis/open-source-deep-research-ai-assistants-157462a59c14)
|
||
|
||
### 新闻与文章
|
||
- [Korben.info](https://korben.info/local-deep-research-alternative-gratuite-recherche-ia-sourcee.html) - 法国科技博客（"Sherlock Holmes numérique"）
|
||
- [Roboto.fr](https://www.roboto.fr/blog/local-deep-research-l-alternative-open-source-gratuite-deep-research-d-openai) - "L'alternative open-source gratuite à Deep Research d'OpenAI"
|
||
- [KDJingPai AI Tools](https://www.kdjingpai.com/en/local-deep-research/) - AI 效率工具报道
|
||
- [AI Sharing Circle](https://aisharenet.com/en/local-deep-research/) - AI 资源报道
|
||
|
||
### 社区讨论
|
||
- [r/LocalLLaMA: 95.7% SimpleQA on a single 3090, fully local](https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/) - Qwen3.6-27B 基准测试公告（161K 次浏览）
|
||
- [r/WebAfterAI: LDR as the agentic layer on top of Ollama / LM Studio / LocalAI](https://www.reddit.com/r/WebAfterAI/comments/1t18wr6/local_deep_research_opensource_ai_research/) - 技术栈定位说明
|
||
- [Hacker News](https://news.ycombinator.com/item?id=43330164) - 190+ 点，社区讨论
|
||
- [LangChain Twitter/X](https://x.com/LangChainAI/status/1901347759757902038) - LangChain 官方推广
|
||
- [LangChain LinkedIn](https://www.linkedin.com/posts/langchain_local-deep-research-an-ai-research-activity-7307113456095137792-cXRH) - 400+ 点赞
|
||
|
||
### 国际报道
|
||
|
||
#### 🇨🇳 中文
|
||
- [Juejin (掘金)](https://juejin.cn/post/7481604667589885991) - 开发者社区
|
||
- [Cnblogs (博客园)](https://www.cnblogs.com/qife122/p/18955032) - 开发者博客
|
||
- [GitHubDaily (Twitter/X)](https://x.com/GitHub_Daily/status/1900169979313741846) - 有影响力的科技账号
|
||
- [Zhihu (知乎)](https://zhuanlan.zhihu.com/p/30886269290) - 科技社区
|
||
- [A姐分享](https://www.ahhhhfs.com/68713/) - AI 资源
|
||
- [CSDN](https://blog.csdn.net/gitblog_01198/article/details/147061415) - 安装指南
|
||
- [NetEase (网易)](https://www.163.com/dy/article/JQKAS50205567BLV.html) - 科技新闻门户
|
||
|
||
#### 🇯🇵 日文
|
||
- [note.com: 調査革命：Local Deep Research徹底活用法](https://note.com/r7038xx/n/nb3b74debbb30) - 全面教程
|
||
- [Qiita: Local Deep Researchを試す](https://qiita.com/orca13/items/635f943287c45388d48f) - Docker 配置指南
|
||
- [LangChainJP (Twitter/X)](https://x.com/LangChainJP/status/1902918110073807073) - 日本 LangChain 社区
|
||
|
||
#### 🇰🇷 韩文
|
||
- [PyTorch Korea Forum](https://discuss.pytorch.kr/t/local-deep-research/6476) - 韩国机器学习社区
|
||
- [GeekNews (Hada.io)](https://news.hada.io/topic?id=19707) - 韩国科技新闻
|
||
|
||
### 评测与分析
|
||
- [BSAIL Lab: How useful is Deep Research in Academia?](https://uflbsail.net/uncategorized/how-useful-is-deep-research-in-academia/) - 贡献者 [@djpetti](https://github.com/djpetti) 的学术评测
|
||
- [The Art Of The Terminal: Use Local LLMs Already!](https://youtu.be/pfxgLX-MxMY?t=1999) - 本地 AI 工具全面评测，重点介绍 LDR 的研究能力
|
||
- [Fahd Mirza: Local Deep Research + Ollama — Free AI Research Assistant You Control](https://youtu.be/Q6kygd04sFI) - 安装演练（349K 订阅者的频道）。视频中展示的 SearXNG 启动问题已在 [#3881](https://github.com/LearningCircuit/local-deep-research/pull/3881) 中修复——干净的 Docker 安装现已开箱即用。
|
||
- [BC Adventure Tech: Run ChatGPT Deep Research Locally (Unraid AI Agent Setup)](https://youtu.be/bhy5TqmoLYo) - Unraid 配置与展示
|
||
|
||
### 相关项目
|
||
- [SearXNG LDR-Academic](https://github.com/porespellar/searxng-LDR-academic) - 面向学术的 SearXNG 分支，内置 12 个研究引擎（arXiv、Google Scholar、PubMed 等），专为 LDR 设计
|
||
- [DeepWiki Documentation](https://deepwiki.com/LearningCircuit/local-deep-research) - 第三方文档与指南
|
||
|
||
> **注意：** 第三方项目与文章由各维护方独立维护。我们提供链接作为有用资源，但无法保证其代码质量或安全性。
|
||
|
||
## 🤝 社区与支持
|
||
|
||
- [Discord](https://discord.gg/ttcqQeFcJ3) - 获取帮助并分享研究技巧
|
||
- [Reddit](https://www.reddit.com/r/LocalDeepResearch/) - 更新与展示
|
||
- [GitHub Issues](https://github.com/LearningCircuit/local-deep-research/issues) - 错误报告
|
||
|
||
## 🧑💻 贡献
|
||
|
||
我们欢迎各种规模的贡献——从错字修复到新功能。关键原则：**保持 PR 小而原子化**（每个 PR 只做一项变更）。对于较大改动，请先开一个 issue 或发起讨论——我们希望保护你的时间，并确保你的努力导向一次成功合并，而非方向不对的 PR。请参阅我们的[贡献指南](CONTRIBUTING.md)开始参与。
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## 致谢
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Local Deep Research 建立在众多开放获取计划、学术数据库和开源项目的基础之上。我们感谢：
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### 学术与研究数据
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| 来源 | 提供内容 | 许可证 |
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|--------|-----------------|---------|
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| [OpenAlex](https://openalex.org) | 约 28 万个来源和约 12 万个机构的学术元数据，含 DOAJ 状态 | CC0 |
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| [DOAJ](https://doaj.org) | 开放获取期刊目录——开放获取验证（通过 OpenAlex） | CC0 |
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| [arXiv](https://arxiv.org) | 物理学、数学、计算机科学等领域的预印本 | 多种（见 arXiv 许可证） |
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| [PubMed / NCBI](https://pubmed.ncbi.nlm.nih.gov) | 生物医学与生命科学文献 | 公有领域（美国政府） |
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| [Semantic Scholar](https://www.semanticscholar.org) | 跨学科学术搜索与引文数据 | [Terms](https://www.semanticscholar.org/product/api/license) |
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| [NASA ADS](https://ui.adsabs.harvard.edu) | 天体物理学、物理学与天文学论文 | [Terms](https://ui.adsabs.harvard.edu/help/terms/) |
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| [Zenodo](https://zenodo.org) | 开放研究数据、数据集与软件 | 因记录而异 |
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| [PubChem](https://pubchem.ncbi.nlm.nih.gov) | 化学与生物化学数据库 | 公有领域（美国政府） |
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| [Stop Predatory Journals](https://predatoryjournals.org) | 掠夺性期刊/出版商黑名单 | MIT |
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| [JabRef](https://github.com/JabRef/abbrv.jabref.org) | 期刊缩写数据库 | CC0 |
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### 知识与内容来源
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[Wikipedia](https://www.wikipedia.org) • [OpenLibrary](https://openlibrary.org) • [Project Gutenberg](https://www.gutenberg.org) • [GitHub](https://github.com) • [Stack Exchange](https://stackexchange.com) • [The Guardian](https://www.theguardian.com) • [Wayback Machine](https://web.archive.org)
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### 基础设施与框架
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[LangChain](https://github.com/hwchase17/langchain) • [Ollama](https://ollama.ai) • [SearXNG](https://searxng.org/) • [FAISS](https://github.com/facebookresearch/faiss)
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### 支持开放获取
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这些项目依靠捐赠和资助运营，而非付费墙。如果 Local Deep Research 对你有帮助，请考虑回馈使其得以实现的开放获取生态：
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- [arXiv](https://arxiv.org/about/give) — 物理、数学、计算机科学等领域的免费预印本
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- [PubMed / NLM](https://www.nlm.nih.gov/pubs/donations/donations.html) — 开放的生物医学文献
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- [Wikipedia / Wikimedia](https://donate.wikimedia.org) — 免费百科全书
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- [Internet Archive](https://archive.org/donate) — Wayback Machine 与开放数字图书馆
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- [DOAJ](https://doaj.org/support) — 全球开放获取期刊的策展与认证
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- [OpenAlex](https://openalex.org) — 开放的学术元数据（由 [OurResearch](https://ourresearch.org)) 赞助
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- [Project Gutenberg](https://www.gutenberg.org/donate/) — 自 1971 年以来的免费电子书
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## 📄 许可证
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MIT License - 详见 [LICENSE](LICENSE) 文件。
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**依赖项：** 所有第三方软件包均采用宽松许可证（MIT、Apache-2.0、BSD 等）- 详见 [allowlist](.github/workflows/dependency-review.yml)
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