diff --git a/README.md b/README.md index 4586a70..c34b356 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ + +> [!NOTE] +> 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。 +> [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) +> 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。 + # Local Deep Research
@@ -14,7 +20,7 @@ [![SimpleQA Accuracy](https://img.shields.io/badge/SimpleQA-~95%25_Accuracy-gold?style=for-the-badge)](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks) [![SQLCipher](https://img.shields.io/badge/Database-SQLCipher_Encrypted-red?style=for-the-badge&logo=sqlite&logoColor=white)](docs/SQLCIPHER_INSTALL.md) - + [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/LearningCircuit/local-deep-research/badge)](https://securityscorecards.dev/viewer/?uri=github.com/LearningCircuit/local-deep-research) [![CodeQL](https://github.com/LearningCircuit/local-deep-research/actions/workflows/codeql.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/security/code-scanning) [![Semgrep](https://github.com/LearningCircuit/local-deep-research/actions/workflows/semgrep.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/semgrep.yml) @@ -29,25 +35,25 @@ [![YouTube](https://img.shields.io/badge/YouTube-Channel-red?style=for-the-badge&logo=youtube)](https://www.youtube.com/@local-deep-research) -**AI-powered research assistant for deep, agentic research** +**由 AI 驱动的深度智能体（agentic）研究助手** -*Performs deep, agentic research using multiple LLMs and search engines with proper citations* +*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* -🧪 **First open-source project — fully-local on a single RTX 3090 (Qwen3.6-27B) — to report ~95% SimpleQA (n=500) and 77% xbench-DeepSearch (n=100) on local hardware.** See the [r/LocalLLaMA announcement](https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/) and the [benchmark dataset](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks). +🧪 **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). - ▶️ Watch Review by The Art Of The Terminal + ▶️ 观看 The Art Of The Terminal 评测
-## 🚀 What is Local Deep Research? +## 🚀 什么是 Local Deep Research？ -AI research assistant you control. Run locally for privacy, use any LLM and build your own searchable knowledge base. You own your data and see exactly how it works. +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。 -## ⚡ Quick Start +## ⚡ 快速开始 -**Option 1: Docker Run (Linux)** +**选项 1：Docker Run（Linux）** ```bash # Step 1: Pull and run Ollama docker run -d -p 11434:11434 --name ollama ollama/ollama @@ -64,52 +70,52 @@ docker run -d --network host \ localdeepresearch/local-deep-research ``` -> **Mac / Windows / WSL2 users:** `--network host` only works on native Linux. On Docker Desktop it silently fails to publish port 5000 *and* leaves `localhost` pointing at the LDR container itself (so it can't reach Ollama/SearXNG). Use **Option 2** below, or see the [Windows/WSL2 FAQ entry](docs/faq.md#port-5000-not-accessible-on-windows) for a working `docker run` recipe. +> **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。 -**Option 2: Docker Compose** +**选项 2：Docker Compose** -CPU-only (all platforms): +仅 CPU（全平台）： ```bash curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d ``` -With NVIDIA GPU (Linux): +使用 NVIDIA GPU（Linux）： ```bash curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && \ curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.gpu.override.yml && \ docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -d ``` -Open http://localhost:5000 after ~30 seconds. For GPU setup, environment variables, and more, see the [Docker Compose Guide](docs/docker-compose-guide.md). +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)。 -**Option 3: pip install** +**选项 3：pip install** ```bash pip install local-deep-research python -m local_deep_research.web.app # starts the web UI on http://localhost:5000 ``` -> You'll also need Ollama (or any OpenAI-compatible LLM endpoint) and SearXNG running — see the [pip install guide](docs/install-pip.md) for the full recipe. -> Works on Windows, macOS, and Linux. SQLCipher encryption is included via pre-built wheels — no compilation needed. -> PDF export on Windows requires Pango ([setup guide](https://doc.courtbouillon.org/weasyprint/stable/first_steps.html)). -> If you encounter issues with encryption, set `export LDR_BOOTSTRAP_ALLOW_UNENCRYPTED=true` to use standard SQLite instead. +> 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)。 +> 支持 Windows、macOS 和 Linux。SQLCipher 加密通过预编译 wheel 提供 — 无需编译。 +> Windows 上导出 PDF 需要 Pango（[配置指南](https://doc.courtbouillon.org/weasyprint/stable/first_steps.html)). +> 若遇到加密相关问题，可将 `export LDR_BOOTSTRAP_ALLOW_UNENCRYPTED=true` 设为使用标准 SQLite。 -**Detailed install guides:** [Docker](docs/installation.md#docker) · [Docker Compose](docs/docker-compose-guide.md) · [pip](docs/install-pip.md) · [Unraid](docs/deployment/unraid.md) · [full install reference](docs/installation.md) +**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) -> **Older CPU (x86-64)?** LDR needs an AVX-capable CPU — Intel Sandy Bridge / AMD Bulldozer (2011) or newer. Several scientific Python dependencies (pandas, scikit-learn) ship wheels that crash with `Illegal instruction` on older CPUs. ARM64 (aarch64) is fully supported. Every release is smoke-tested against this floor, including AVX-without-AVX2 CPUs ([#4480](https://github.com/LearningCircuit/local-deep-research/issues/4480)). +> **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)). -## 🏗️ How It Works +## 🏗️ 工作原理 -### Research +### 研究 -You ask a complex question. Local Deep Research (LDR): -- Does the research for you automatically -- Searches across web, academic papers, and your own documents -- Synthesizes everything into a report with proper citations +你提出一个复杂问题。Local Deep Research（LDR）会： +- 自动替你完成研究 +- 在网页、学术论文与你自己的文档中搜索 +- 将所有内容综合成附带规范引用的报告 -Choose the research strategy that fits: quick pipeline modes for fast facts, or fully agentic deep research for complex analysis and academic work. +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。 -**LangGraph Agent Strategy** — An autonomous agentic research mode where the LLM decides what to search, which specialized engines to use (arXiv, PubMed, Semantic Scholar, etc.), and when to synthesize. It adaptively switches between search engines based on what it finds and collects significantly more sources than pipeline-based strategies — this is the strategy behind the ~95% SimpleQA result above. Select `langgraph-agent` in Settings. +**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`。 -### Build Your Knowledge Base +### 构建你的知识库 ```mermaid flowchart LR @@ -120,28 +126,28 @@ flowchart LR S -.-> R ``` -Every research session finds valuable sources. Download them directly into your encrypted library — academic papers from ArXiv, PubMed articles, web pages. LDR extracts text, indexes everything, and makes it searchable. Next time you research, ask questions across your own documents and the live web together. Your knowledge compounds over time. +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。 -## 🛡️ Security +## 🛡️ 安全
- + [![DevSkim](https://github.com/LearningCircuit/local-deep-research/actions/workflows/devskim.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/devskim.yml) [![Bearer](https://github.com/LearningCircuit/local-deep-research/actions/workflows/bearer.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/bearer.yml) - + [![OSV-Scanner](https://github.com/LearningCircuit/local-deep-research/actions/workflows/osv-scanner.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/osv-scanner.yml) [![npm-audit](https://github.com/LearningCircuit/local-deep-research/actions/workflows/npm-audit.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/npm-audit.yml) [![Retire.js](https://github.com/LearningCircuit/local-deep-research/actions/workflows/retirejs.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/retirejs.yml) - + [![Container Security](https://github.com/LearningCircuit/local-deep-research/actions/workflows/container-security.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/container-security.yml) [![Dockle](https://github.com/LearningCircuit/local-deep-research/actions/workflows/dockle.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/dockle.yml) [![Hadolint](https://github.com/LearningCircuit/local-deep-research/actions/workflows/hadolint.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/hadolint.yml) [![Checkov](https://github.com/LearningCircuit/local-deep-research/actions/workflows/checkov.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/checkov.yml) - + [![Zizmor](https://github.com/LearningCircuit/local-deep-research/actions/workflows/zizmor-security.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/zizmor-security.yml) [![OWASP ZAP](https://github.com/LearningCircuit/local-deep-research/actions/workflows/owasp-zap-scan.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/owasp-zap-scan.yml) [![Security Tests](https://github.com/LearningCircuit/local-deep-research/actions/workflows/security-tests.yml/badge.svg?branch=main)](https://github.com/LearningCircuit/local-deep-research/actions/workflows/security-tests.yml) @@ -154,29 +160,29 @@ flowchart LR U2[User B] --> D2[(Encrypted DB)] ``` -Your data stays yours. Each user gets their own isolated SQLCipher database encrypted with AES-256, with the key derived from your password. Your password is never stored — login works by attempting to decrypt your database, so the database files on their own are unusable to anyone who obtains them. Per-user LLM API keys live encrypted inside the same personal database rather than in a shared server-level store. +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。 -The [Docker setup](docker-compose.yml) ships with `cap_drop: ALL`, `no-new-privileges`, and a non-root runtime, with the bundled Ollama and SearXNG images pinned by digest. Or run fully local with Ollama + SearXNG and nothing ever leaves your machine. +[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。 -**In-memory credentials**: Like any application that uses secrets at runtime, credentials are held in process memory during active sessions — mitigated with session-scoped credential lifetimes and core dump exclusion. See the [Security Policy](SECURITY.md#in-memory-credentials) for the full threat model. +**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)。 -**Supply Chain Security**: Docker images are signed with [Cosign](https://github.com/sigstore/cosign) using GitHub's keyless OIDC flow, include SLSA provenance attestations, and ship with attested SPDX SBOMs. See [Verifying images and SBOMs](SECURITY.md#verifying-images-and-sboms) for the step-by-step verification commands. +**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)。 -**Security Transparency**: Scanner suppressions are documented with justifications in [Security Alerts Assessment](.github/SECURITY_ALERTS.md), [Scorecard Compliance](.github/SECURITY_SCORECARD.md), [Container CVE Suppressions](.trivyignore), and [SAST Rule Rationale](bearer.yml). Some alerts (Dependabot, code scanning) can only be dismissed or are very difficult to suppress outside the [GitHub Security tab](https://docs.github.com/en/code-security/dependabot/dependabot-alerts/viewing-and-updating-dependabot-alerts), so the files above do not cover every dismissed finding. +**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。 -[Detailed Architecture →](docs/architecture.md) | [Security Policy →](SECURITY.md) | [Security Review Process →](docs/processes/security-review-process/) +[详细架构 →](docs/architecture.md) | [安全策略 →](SECURITY.md) | [安全审查流程 →](docs/processes/security-review-process/) -### 🔒 Privacy & Data +### 🔒 隐私与数据 -Local Deep Research contains **no telemetry, no analytics, and no tracking**. We do not collect, transmit, or store any data about you or your usage. No analytics SDKs, no phone-home calls, no crash reporting, no external scripts. Usage metrics stay in your local encrypted database. +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。 -The only network calls LDR makes are ones **you** initiate: search queries (to engines you configure), LLM API calls (to your chosen provider), and notifications (only if you set up Apprise). +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）。 -Since we don't collect any usage data, we rely on you to tell us what works, what's broken, and what you'd like to see next — [bug reports](https://github.com/LearningCircuit/local-deep-research/issues), feature ideas, and even which features you love or never use all help us improve LDR. +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。 -## 📊 Benchmarks +## 📊 基准测试 -Headline results from the [community benchmarks](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks) using the `langgraph-agent` strategy with Serper search, fully local via Ollama: +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： | Model | SimpleQA | xbench-DeepSearch | |---|---|---| @@ -184,61 +190,61 @@ Headline results from the [community benchmarks](https://huggingface.co/datasets | Qwen3.5-9B | 91.2% (182/200) | 59.0% (59/100) | | gpt-oss-20B | 85.4% (295/346) | – | -Caveats: small samples, LLM-grader noise, and SimpleQA contamination risk on newer base models. +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。 -Picking a local model? The same community-maintained dataset tracks accuracy across models, search engines, and research strategies — the fastest way to see which Ollama / LM Studio / llama.cpp models actually work well for deep research before you download multi-GB weights. **[Browse the full leaderboard on Hugging Face →](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks)** +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)** -[Submit your own results →](https://github.com/LearningCircuit/ldr-benchmarks) (contributors are listed in [CONTRIBUTORS.md](https://github.com/LearningCircuit/ldr-benchmarks/blob/main/CONTRIBUTORS.md)), or [run benchmarks locally →](docs/BENCHMARKING.md). +[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)。 -## ✨ Key Features +## ✨ 核心功能 -### 🔍 Research Modes -- **Quick Summary** - Get answers in 30 seconds to 3 minutes with citations -- **Detailed Research** - Comprehensive analysis with structured findings -- **Report Generation** - Professional reports with sections and table of contents -- **Document Analysis** - Search your private documents with AI +### 🔍 研究模式 +- **Quick Summary** - 30 秒至 3 分钟内获得带引用的答案 +- **Detailed Research** - 结构化发现的全面分析 +- **Report Generation** - 带章节与目录的专业报告 +- **Document Analysis** - 用 AI 搜索你的私有文档 -### 🛠️ Advanced Capabilities -- **[LangChain Integration](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)** - Use any vector store as a search engine -- **[REST API](docs/api-quickstart.md)** - Authenticated HTTP access with per-user databases -- **[Benchmarking](docs/BENCHMARKING.md)** - Test and optimize your configuration -- **[Analytics Dashboard](docs/analytics-dashboard.md)** - Track costs, performance, and usage metrics -- **[Journal Quality System](docs/journal-quality.md)** - Automatic journal reputation scoring with 212K+ indexed sources, predatory detection, and quality dashboard. Powered by [OpenAlex](https://openalex.org) (CC0), [DOAJ](https://doaj.org) (CC0), and [Stop Predatory Journals](https://predatoryjournals.org) (MIT). See the [v1.6.0 announcement](https://www.reddit.com/r/LocalDeepResearch/comments/1svndjb/v160_highly_anticipated_journal_quality_filter/). -- **Real-time Updates** - WebSocket support for live research progress -- **[Chat Mode](docs/features.md#chat-mode)** - Multi-turn research conversations with streaming progress and accumulated context across turns -- **Export Options** - Download results as PDF or Markdown -- **Research History** - Save, search, and revisit past research -- **Adaptive Rate Limiting** - Intelligent retry system that learns optimal wait times -- **Keyboard Shortcuts** - Navigate efficiently (ESC, Ctrl+Shift+1-4) +### 🛠️ 高级能力 +- **[LangChain Integration](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md)** - 将任意向量存储用作搜索引擎 +- **[REST API](docs/api-quickstart.md)** - 经身份验证的 HTTP 访问，每位用户独立数据库 +- **[Benchmarking](docs/BENCHMARKING.md)** - 测试并优化你的配置 +- **[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/). +- **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 +- **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） -### 📰 News & Research Subscriptions -- **Automated Research Digests** - Subscribe to topics or specific queries; AI filters and summarizes only the most relevant developments -- **Customizable Delivery** - Daily, weekly, or custom schedules, as markdown reports or structured summaries +### 📰 新闻与研究订阅 +- **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 -### 🌐 Search Sources +### 🌐 搜索源 -#### Free Search Engines -- **Academic**: arXiv, PubMed, Semantic Scholar -- **General**: Wikipedia, SearXNG -- **Technical**: GitHub, Elasticsearch -- **Historical**: Wayback Machine -- **News**: The Guardian, Wikinews +#### 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 -#### Premium Search Engines -- **Tavily** - AI-powered search -- **Google** - Via SerpAPI or Programmable Search Engine -- **Brave Search** - Privacy-focused web search +#### 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 -#### Custom Sources -- **Local Documents** - Search your files with AI -- **LangChain Retrievers** - Any vector store or database -- **Meta Search** - Combine multiple engines intelligently +#### 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 respects `robots.txt` and identifies itself honestly when fetching web pages — no stealth or anti-detection techniques. In rare cases this means a page that blocks automated access won't be fetched, which we consider the right trade-off. +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。 -[Full Search Engines Guide →](docs/search-engines.md) +[完整搜索引擎指南 →](docs/search-engines.md) -## 💻 Usage Examples +## 💻 使用示例 ### Python API ```python @@ -257,7 +263,7 @@ print(result["summary"]) ### HTTP API -*The code example below shows the basic API structure - for working examples, see the link below* +*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 @@ -280,13 +286,13 @@ response = session.post("http://localhost:5000/api/start_research", headers={"X-CSRF-Token": csrf}) ``` -🚀 **[Ready-to-use HTTP API Examples → examples/api_usage/http/](examples/api_usage/http/)** -- ✅ **Automatic user creation** - works out of the box -- ✅ **Complete authentication** with CSRF handling -- ✅ **Result retry logic** - waits until research completes -- ✅ **Progress monitoring** and error handling +🚀 **[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 -### Command Line Tools +### 命令行工具 ```bash # Run benchmarks from CLI @@ -297,11 +303,11 @@ python -m local_deep_research.web_search_engines.rate_limiting status python -m local_deep_research.web_search_engines.rate_limiting reset --engine SearXNGSearchEngine ``` -See the [Command Line Tools guide](docs/cli-tools.md) for the full reference. +完整参考请见[命令行工具指南](docs/cli-tools.md)。 -## 🔗 Bring Your Own Knowledge Base +## 🔗 接入自有知识库 -Connect LDR to your existing knowledge base. Unlike the HTTP client above, `quick_summary()` runs LDR in-process — no server needed — so you can pass it live Python objects such as LangChain retrievers: +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 @@ -314,26 +320,26 @@ result = quick_summary( ) ``` -Works with: FAISS, Chroma, Pinecone, Weaviate, Elasticsearch, and any LangChain-compatible retriever. +支持：FAISS、Chroma、Pinecone、Weaviate、Elasticsearch，以及任意兼容 LangChain 的 retriever。 -[Integration Guide →](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md) +[集成指南 →](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md) -## 🔌 MCP Server (Claude Integration) +## 🔌 MCP 服务器（Claude 集成） -LDR provides an MCP (Model Context Protocol) server that allows AI assistants like Claude Desktop and Claude Code to perform deep research. Full setup details in the [MCP Server guide](docs/mcp-server.md). +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)。 -> ⚠️ **Security Note**: This MCP server is designed for **local use only** via STDIO transport (e.g., Claude Desktop). It has no built-in authentication or rate limiting. Do not expose over a network without implementing proper security controls. See the [MCP Security Best Practices](https://modelcontextprotocol.io/docs/tutorials/security/security_best_practices) for network deployment requirements. +> ⚠️ **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)。 -### Installation +### 安装 ```bash # Install with MCP extras pip install "local-deep-research[mcp]" ``` -### Claude Desktop Configuration +### Claude Desktop 配置 -Add to your `claude_desktop_config.json`: +添加到你的 `claude_desktop_config.json`： ```json { @@ -349,9 +355,9 @@ Add to your `claude_desktop_config.json`: } ``` -### Claude Code Configuration +### Claude Code 配置 -Add to your `.mcp.json` (project-level) or `~/.claude/mcp.json` (global): +添加到你的 `.mcp.json`（项目级）或 `~/.claude/mcp.json`（全局）： ```json { @@ -367,24 +373,24 @@ Add to your `.mcp.json` (project-level) or `~/.claude/mcp.json` (global): } ``` -Any LDR setting can be passed in `env` as an `LDR_*` variable — see the auto-generated [Full Configuration Reference](docs/CONFIGURATION.md) for the complete list. +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)。 -### Available Tools +### 可用工具 | Tool | Description | Duration | LLM Cost | |------|-------------|----------|----------| -| `search` | Raw results from a specific engine (arxiv, pubmed, wikipedia, ...) | 5-30s | None | -| `quick_research` | Fast research summary | 1-5 min | Yes | -| `detailed_research` | Comprehensive analysis | 5-15 min | Yes | -| `generate_report` | Full markdown report | 10-30 min | Yes | -| `analyze_documents` | Search local collections | 30s-2 min | Yes | -| `list_search_engines` | List available search engines | instant | None | -| `list_strategies` | List research strategies | instant | None | -| `get_configuration` | Get current config | instant | None | +| `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 | -### Individual Search Engines +### 单独搜索引擎 -The `search` tool lets you query specific search engines directly and get raw results (title, link, snippet) — no LLM processing, no cost, fast. This is especially useful for **monitoring and subscriptions** where you want to check for new content regularly without burning LLM tokens. +`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 @@ -402,7 +408,7 @@ search(query="agentic research frameworks", engine="github") # Use list_search_engines() to see all available engines ``` -### Example Usage +### 使用示例 ``` "Use quick_research to find information about quantum computing applications" @@ -411,171 +417,171 @@ search(query="agentic research frameworks", engine="github") ``` -## 🤖 Supported LLMs +## 🤖 支持的 LLM -### Local Models -- **Ollama** — connect to its native API (default `http://localhost:11434`) -- **LM Studio** — connect to its OpenAI-compatible server (default `http://localhost:1234/v1`) -- **llama.cpp** — connect to `llama-server`'s OpenAI-compatible endpoint (default `http://localhost:8080/v1`); start with `llama-server -m ` -- Common models: Llama, Mistral, Gemma, DeepSeek, Qwen -- LLM processing stays local (search queries still go to web). No API costs. +### 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 ` 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。 -> 💡 **Which local model should I pick?** See the [community benchmarks](#-benchmarks) — community-submitted accuracy numbers across local and cloud models, so you can compare before downloading. +> 💡 **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。 -### Cloud Models +### 云端模型 - OpenAI - Anthropic Claude - Google Gemini -- 100+ models via OpenRouter +- 通过 OpenRouter 支持 100+ 模型 -### Custom Endpoints -- **OpenAI-Compatible Endpoint** — any service speaking the OpenAI chat-completions API (vLLM, llama.cpp, gateways) -- **Anthropic-Compatible Endpoint** — a self-hosted service speaking the Anthropic Messages API (`/v1/messages`); set `llm.anthropic_endpoint.url` +### 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` -[Model Setup →](docs/env_configuration.md) +[模型配置 →](docs/env_configuration.md) -## 🔄 Upgrading from Earlier Versions +## 🔄 从早期版本升级 -- **`llm.model` no longer has a default.** Pre-1.6.3 installs auto-filled `gemma3:12b` (Ollama) when no model was configured, which silently downloaded a multi-GB binary. The field is now empty by default — pick a model in Settings → LLM, or research will fail loudly with a clear error. -- **The `auto` and `parallel` meta search engines were removed.** The default langgraph-agent strategy selects engines dynamically per query, which replaces them. Stored settings are migrated automatically (removed values become `searxng`); update any explicit `search_tool="auto"` API calls or `LDR_SEARCH_TOOL=auto` env overrides to a concrete engine such as `searxng`. -- **The `llamacpp` provider now uses HTTP instead of in-process loading.** If you previously set `llm.llamacpp_model_path` to a local `.gguf` file, that setting is no longer read. Instead, run `llama-server -m ` (it ships with every modern llama.cpp build) and the default `llm.llamacpp.url` of `http://localhost:8080/v1` will pick it up. Optional API key support is available via `llm.llamacpp.api_key` if you put `llama-server` behind an auth proxy. +- **`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 `（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。 -## 📚 Documentation +## 📚 文档 -### Getting Started -- [Installation Guide](docs/installation.md) -- [Frequently Asked Questions](docs/faq.md) -- [API Quickstart](docs/api-quickstart.md) -- [Configuration Guide](docs/env_configuration.md) -- [Full Configuration Reference](docs/CONFIGURATION.md) +### 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) -### Core Features -- [All Features Guide](docs/features.md) -- [Search Engines Guide](docs/search-engines.md) -- [Analytics Dashboard](docs/analytics-dashboard.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) -### Advanced Features -- [LangChain Integration](docs/LANGCHAIN_RETRIEVER_INTEGRATION.md) -- [Benchmarking System](docs/BENCHMARKING.md) -- [Elasticsearch Setup](docs/elasticsearch_search_engine.md) -- [SearXNG Setup](docs/SearXNG-Setup.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) -### Development -- [Docker Compose Guide](docs/docker-compose-guide.md) -- [Development Guide](docs/developing.md) -- [CodeQL Guide](docs/security/CODEQL_GUIDE.md) -- [Release Guide](docs/RELEASE_GUIDE.md) -- [CI/CD Infrastructure](docs/CI_CD_INFRASTRUCTURE.md) — pre-commit hooks, workflows, security scanning -- [Workflow Status Dashboard](docs/ci/workflow-status.md) — live health of every GitHub Actions workflow -- [Test Coverage Report](https://learningcircuit.github.io/local-deep-research/) — pytest coverage published to GitHub Pages +### 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 -### Examples & Tutorials -- [API Examples](examples/api_usage/) -- [Benchmark Examples](examples/benchmarks/) -- [Optimization Examples](examples/optimization/) +### 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/) -## 📰 Featured In +## 📰 精选报道 -> "Local Deep Research **deserves special mention** for those who prioritize privacy... **tuned to use open-source LLMs** that can run on consumer GPUs or even CPUs. Journalists, researchers, or companies with sensitive topics can investigate information **without queries ever hitting an external server**." +> "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: Open-Source Deep Research AI Assistants](https://medium.com/@leucopsis/open-source-deep-research-ai-assistants-157462a59c14) +> — [Medium：开源深度研究 AI 助手](https://medium.com/@leucopsis/open-source-deep-research-ai-assistants-157462a59c14) -### News & Articles -- [Korben.info](https://korben.info/local-deep-research-alternative-gratuite-recherche-ia-sourcee.html) - French tech blog ("Sherlock Holmes numérique") +### 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 productivity tools coverage -- [AI Sharing Circle](https://aisharenet.com/en/local-deep-research/) - AI resources coverage +- [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 -### Community Discussions -- [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 benchmark announcement (161K views) -- [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/) - Stack-positioning explainer -- [Hacker News](https://news.ycombinator.com/item?id=43330164) - 190+ points, community discussion -- [LangChain Twitter/X](https://x.com/LangChainAI/status/1901347759757902038) - Official LangChain promotion -- [LangChain LinkedIn](https://www.linkedin.com/posts/langchain_local-deep-research-an-ai-research-activity-7307113456095137792-cXRH) - 400+ likes +### 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 -### International Coverage +### 国际报道 -#### 🇨🇳 Chinese -- [Juejin (掘金)](https://juejin.cn/post/7481604667589885991) - Developer community -- [Cnblogs (博客园)](https://www.cnblogs.com/qife122/p/18955032) - Developer blogs -- [GitHubDaily (Twitter/X)](https://x.com/GitHub_Daily/status/1900169979313741846) - Influential tech account -- [Zhihu (知乎)](https://zhuanlan.zhihu.com/p/30886269290) - Tech community -- [A姐分享](https://www.ahhhhfs.com/68713/) - AI resources -- [CSDN](https://blog.csdn.net/gitblog_01198/article/details/147061415) - Installation guide -- [NetEase (网易)](https://www.163.com/dy/article/JQKAS50205567BLV.html) - Tech news portal +#### 🇨🇳 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 -#### 🇯🇵 Japanese -- [note.com: 調査革命：Local Deep Research徹底活用法](https://note.com/r7038xx/n/nb3b74debbb30) - Comprehensive tutorial -- [Qiita: Local Deep Researchを試す](https://qiita.com/orca13/items/635f943287c45388d48f) - Docker setup guide -- [LangChainJP (Twitter/X)](https://x.com/LangChainJP/status/1902918110073807073) - Japanese LangChain community +#### 🇯🇵 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 -#### 🇰🇷 Korean -- [PyTorch Korea Forum](https://discuss.pytorch.kr/t/local-deep-research/6476) - Korean ML community -- [GeekNews (Hada.io)](https://news.hada.io/topic?id=19707) - Korean tech news +#### 🇰🇷 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 -### Reviews & Analysis -- [BSAIL Lab: How useful is Deep Research in Academia?](https://uflbsail.net/uncategorized/how-useful-is-deep-research-in-academia/) - Academic review by contributor [@djpetti](https://github.com/djpetti) -- [The Art Of The Terminal: Use Local LLMs Already!](https://youtu.be/pfxgLX-MxMY?t=1999) - Comprehensive review of local AI tools, featuring LDR's research capabilities -- [Fahd Mirza: Local Deep Research + Ollama — Free AI Research Assistant You Control](https://youtu.be/Q6kygd04sFI) - Installation walkthrough (349K-subscriber channel). The SearXNG startup issue shown in the video has since been fixed in [#3881](https://github.com/LearningCircuit/local-deep-research/pull/3881) — clean Docker installs now start out of the box. -- [BC Adventure Tech: Run ChatGPT Deep Research Locally (Unraid AI Agent Setup)](https://youtu.be/bhy5TqmoLYo) - Unraid setup and showcase +### 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 -### Related Projects -- [SearXNG LDR-Academic](https://github.com/porespellar/searxng-LDR-academic) - Academic-focused SearXNG fork with 12 research engines (arXiv, Google Scholar, PubMed, etc.) designed for LDR -- [DeepWiki Documentation](https://deepwiki.com/LearningCircuit/local-deep-research) - Third-party documentation and guides +### 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 -> **Note:** Third-party projects and articles are independently maintained. We link to them as useful resources but cannot guarantee their code quality or security. +> **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。 -## 🤝 Community & Support +## 🤝 社区与支持 -- [Discord](https://discord.gg/ttcqQeFcJ3) - Get help and share research techniques -- [Reddit](https://www.reddit.com/r/LocalDeepResearch/) - Updates and showcases -- [GitHub Issues](https://github.com/LearningCircuit/local-deep-research/issues) - Bug reports +- [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 -## 🧑‍💻 Contributing +## 🧑‍💻 贡献 -We welcome contributions of all sizes — from typo fixes to new features. The key rule: **keep PRs small and atomic** (one change per PR). For larger changes, please open an issue or start a discussion first — we want to protect your time and make sure your effort leads to a successful merge rather than a misaligned PR. See our [Contributing Guide](CONTRIBUTING.md) to get started. +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。 -## Acknowledgements +## 致谢 -Local Deep Research is built on the work of many open-access initiatives, academic databases, and open-source projects. We are grateful to: +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： -### Academic & Research Data +### 学术与研究数据 -| Source | What It Provides | License | +| 来源 | 提供内容 | 许可证 | |--------|-----------------|---------| -| [OpenAlex](https://openalex.org) | Academic metadata for ~280K sources and ~120K institutions, including DOAJ status | CC0 | -| [DOAJ](https://doaj.org) | Directory of Open Access Journals — open-access verification (via OpenAlex) | CC0 | -| [arXiv](https://arxiv.org) | Preprints in physics, mathematics, CS, and more | Various (see arXiv license) | -| [PubMed / NCBI](https://pubmed.ncbi.nlm.nih.gov) | Biomedical and life sciences literature | Public domain (US Gov) | -| [Semantic Scholar](https://www.semanticscholar.org) | Cross-discipline academic search with citation data | [Terms](https://www.semanticscholar.org/product/api/license) | -| [NASA ADS](https://ui.adsabs.harvard.edu) | Astrophysics, physics, and astronomy papers | [Terms](https://ui.adsabs.harvard.edu/help/terms/) | -| [Zenodo](https://zenodo.org) | Open research data, datasets, and software | Various per record | -| [PubChem](https://pubchem.ncbi.nlm.nih.gov) | Chemistry and biochemistry database | Public domain (US Gov) | -| [Stop Predatory Journals](https://predatoryjournals.org) | Predatory journal/publisher blacklist | MIT | -| [JabRef](https://github.com/JabRef/abbrv.jabref.org) | Journal abbreviation database | CC0 | +| [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 | +| [DOAJ](https://doaj.org) | 开放获取期刊目录——开放获取验证（通过 OpenAlex） | CC0 | +| [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） | +| [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） | +| [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) | +| [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/) | +| [Zenodo](https://zenodo.org) | 开放研究数据、数据集与软件 | 因记录而异 | +| [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） | +| [Stop Predatory Journals](https://predatoryjournals.org) | 掠夺性期刊/出版商黑名单 | MIT | +| [JabRef](https://github.com/JabRef/abbrv.jabref.org) | 期刊缩写数据库 | CC0 | -### Knowledge & Content Sources +### 知识与内容来源 [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) -### Infrastructure & Frameworks +### 基础设施与框架 [LangChain](https://github.com/hwchase17/langchain) • [Ollama](https://ollama.ai) • [SearXNG](https://searxng.org/) • [FAISS](https://github.com/facebookresearch/faiss) -### Support Open Access +### 支持开放获取 -These projects run on donations and grants, not paywalls. If Local Deep Research is useful to you, consider giving back to the open-access ecosystem that makes it possible: +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： -- [arXiv](https://arxiv.org/about/give) — free preprints for physics, math, CS, and more -- [PubMed / NLM](https://www.nlm.nih.gov/pubs/donations/donations.html) — open biomedical literature -- [Wikipedia / Wikimedia](https://donate.wikimedia.org) — the free encyclopedia -- [Internet Archive](https://archive.org/donate) — the Wayback Machine and open digital library -- [DOAJ](https://doaj.org/support) — curating and verifying open-access journals worldwide -- [OpenAlex](https://openalex.org) — open scholarly metadata (sponsored by [OurResearch](https://ourresearch.org)) -- [Project Gutenberg](https://www.gutenberg.org/donate/) — free ebooks since 1971 +- [arXiv](https://arxiv.org/about/give) — 物理、数学、计算机科学等领域的免费预印本 +- [PubMed / NLM](https://www.nlm.nih.gov/pubs/donations/donations.html) — 开放的生物医学文献 +- [Wikipedia / Wikimedia](https://donate.wikimedia.org) — 免费百科全书 +- [Internet Archive](https://archive.org/donate) — Wayback Machine 与开放数字图书馆 +- [DOAJ](https://doaj.org/support) — 全球开放获取期刊的策展与认证 +- [OpenAlex](https://openalex.org) — 开放的学术元数据（由 [OurResearch](https://ourresearch.org)) 赞助 +- [Project Gutenberg](https://www.gutenberg.org/donate/) — 自 1971 年以来的免费电子书 -## 📄 License +## 📄 许可证 -MIT License - see [LICENSE](LICENSE) file. +MIT License - 详见 [LICENSE](LICENSE) 文件。 -**Dependencies:** All third-party packages use permissive licenses (MIT, Apache-2.0, BSD, etc.) - see [allowlist](.github/workflows/dependency-review.yml) +**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)