💡 WeKnora —— 通过 RAG、Agent 和 Auto-Wiki 将文档转化为动态知识

WeKnora 是一个开源、由 LLM 驱动的知识框架，面向企业级文档理解、语义检索和自主推理而构建。

视频/嵌入内容：WeKnora 讲解视频，时长 2:25，1080p，英文旁白与字幕。

2:25 · 1080p · 英文旁白与字幕。

它围绕三项核心能力构建：用于日常查询的、基于 RAG 的快速问答（Quick Q&A）；可自主编排检索、MCP 工具、租户技能目录、会话持久化的 Docker / E2B / Cube 沙箱以及网络搜索，以处理复杂多步骤任务的 ReAct Agent；以及全新的 Wiki Mode——Agent 可将原始文档提炼为可自我维护、相互链接的 Markdown 知识库，并提供交互式知识图谱、手动编辑、修订历史和一键回滚。跨会话长期记忆会记住用户身份及其持续关注的问题。知识管理同样支持深度操作：树状文件夹视图会保留上传内容的目录结构；支持带修订历史的分块编辑，可像编辑文档一样编辑、比较差异并回滚检索分块。

结合多源数据接入（飞书知识库 / 飞书云盘 / GitLab / 腾讯 IMA / Notion / 语雀 / RSS，且持续扩展）、可将 Agent 发布到外部网站的网页嵌入组件、用于程序化集成且采用主体模型的作用域 API Key、可在每个工作区配置多个存储实例以灵活部署数据的多实例存储后端、20 多家 LLM 提供商集成（包括 LiteLLM）、完整的 Langfuse 可观测性，以及带有运行时任务队列仪表盘与 Worker 池治理功能的系统、面向企业的多工作区 RBAC（四级角色矩阵 + 逐资源所有权 + 逐工作区审计日志），再加上可完全自托管的模块化架构，WeKnora 可将分散的文档转化为可查询、可推理且持续演进的知识资产。

该框架支持从飞书、GitLab、腾讯 IMA、Notion 和语雀自动同步知识（更多数据源即将推出），可处理 PDF、Word、图像、Excel、XMind 等 10 多种文档格式，并可通过企业微信、飞书、Slack、Telegram 等 IM 渠道直接提供问答服务。它兼容 OpenAI、DeepSeek、Qwen（阿里云）、智谱、混元、Gemini、MiniMax、NVIDIA、LiteLLM 和 Ollama 等主流 LLM 提供商。Office 文件可使用 anydoc 在进程内解析。其完全模块化的设计允许替换 LLM、向量数据库和存储后端，并支持本地及私有云部署，从而确保完整的数据主权。WeKnora 还集成 Langfuse，可全面观测 Agent 推理、Token 使用量和流水线追踪。

• v0.8.0 —— 技能沙箱运行时（会话持久化的 Docker / E2B / Cube 后端，支持逐租户网络策略；移除本地主机进程后端；Docker 改为选择性启用）；租户技能目录（可从 ClawHub / SkillHub / git / zip 安装，提供逐沙箱快照、实时进度、文件浏览与编辑、个人及工作区环境变量）；跨会话长期记忆（配置文件 / 偏好 / 事实 / 任务 / 兴趣，可自动提取并确认，提供 search_memory）；进程内 anydoc Office 解析器；官方 DeepSeek Harness 插件 @wxg-prc-cpg/dsh-weknora；GitLab 和腾讯 IMA 数据源；LiteLLM；Exa 与 Metaso 网络搜索；XMind 解析；聊天产物、问题大纲和时间戳；上下文压缩与提供商 Prompt 缓存标记。此外还包括 OIDC JWKS 验证、可选的复杂密码、文档自动打标，以及广泛的沙箱与安全加固。详见 CHANGELOG.md。
• v0.7.2 —— 发布官方产品文档站点（VitePress；六个章节、约 50 个页面，覆盖约 360 个 API Endpoint 和约 150 个环境变量；支持独立 Docker/Nginx 部署、快速入门示例数据和本地 MCP 演示）；知识库文件夹树（将上传路径作为一等数据存储，可如文件管理器般浏览、重命名和重新归档文档）；带修订历史的分块编辑（在 UI 中编辑检索分块，支持逐版本 Diff 与回滚、自动重新索引及自定义文档元数据）；Wiki 页面修订历史（快照 + 行级 Diff + 一键回滚 + 浏览器内手动编辑）；通过 resource_urls=public / RESOURCE_URL_MODE 提供可直接加载的文件 URL（第三方应用无需第二次经身份验证的代理调用，即可渲染图像和文件）；飞书云盘数据源及通过 Blocks API 同步 docx；文档批量打标；MCP Server 1.1.x（迁移至 mcp 2.x 高层 API，发布官方 PyPI 包 tencent-weknora-mcp，新增 create_knowledge_from_text 和 list_shared_knowledge_bases，工具总数达 29 个）；AWS S3 默认凭据链（IAM Role / IRSA）；本地 HTML 上传解析；QQBot Markdown 回复；为 app / frontend / docreader / mcp-server 新增 PR CI 检查。此外还进行了大规模 router 和 modelcontext 重构、Rerank 与分块质量优化，以及广泛的稳定性修复。详见 CHANGELOG.md。
• v0.7.1 —— 新增云之家 IM 集成（WebSocket + 图像消息 + Markdown 回复）；火山引擎 Rerank 提供商（支持请求批处理）和智谱 AI 网络搜索提供商；用于控制平面自动化的平台级作用域 API Key（租户管理、系统设置、运行时队列、审计日志）；逐知识库活动审计轨迹；FAQ 管理增强（筛选、打标、导出、导入追踪）；迁移至 Langfuse OTLP/OTel 追踪，并支持 W3C traceparent 传播；聊天页头操作支持一键 Markdown 导出，Wiki 工具结果显示在引用抽屉中；Prompt 缓存可观测性；会话渠道治理（管理员作用域的 IM / 嵌入 / API 会话）；更稳健的飞书大型 Wiki 同步；移除旧版 Neo4j 对话记忆依赖。此外还进行了广泛的 slug 完整性、SSRF 传输和状态同步加固。详见 CHANGELOG.md。
• v0.7.0 —— 细粒度作用域 API Key 与主体模型（能力级授权 + 逐知识库限制 + API 集成 Playground）；运行时任务队列可观测性仪表盘与 Worker 池治理（逐阶段 Worker 池 + 逐模型并发治理器 + 失败任务检查与重试）；多实例存储后端（每个工作区可配置多个存储实例、逐知识库绑定、默认实例）；会话作用域临时附件（异步图像/文档解析 + 合并限额）；问题与追问建议；带 LLM 上下文别名压缩的稳定资源注册表；具有作用域 Agent 运行时的 @Skill / @MCP 提及；会话中途 MCP OAuth；QQBot 与 Lark（飞书国际版）IM 集成；Redis TLS；Requesty 模型提供商与 Keenable 网络搜索；无租户预配与受控自助工作区；管理员密码重置；知识库复制流程；weknora CLI v0.10。此外还进行了广泛的安全加固（SSRF、密钥脱敏、SQL 校验、IDOR）。详见 CHANGELOG.md。
• v0.6.3 —— 网站嵌入组件与 Integrations Center（安全模式 Token 交换 + 速率限制）；聊天体验重构（引用浮窗、RAG 流水线进度、流式 Markdown）；文档多标签与批量重新解析；Wiki 文件夹与层级导航；RSS 数据源；MCP OAuth2；EPUB / MHTML 解析；Agent 模型就绪检查；模型测试调试器；会话源筛选；工作区删除 UI。详见 CHANGELOG.md。
• v0.6.2 —— 针对每次上传的处理配置与上传确认对话框；使用 process_config 重新解析文档；weknora CLI v0.9（内置 Agent Skills、session stop、认证/配置文件统一）；知识库跑马灯式多选；面向 1024 维 pgvector Embedding 的 HNSW 索引；聊天资源存储重构；仅使用 Langfuse 追踪（移除 Jaeger）。详见 CHANGELOG.md。
• v0.6.1 —— 文档解析追踪时间线（Langfuse 风格 Span 树，提供逐阶段进度与停止解析功能）；OpenSearch 向量存储驱动；通过 YAML 声明式配置内置模型；系统管理员、统一平台设置与审计日志；新用户引导；设置 UI 重设计；weknora CLI v0.7 / v0.8（Agent 优先 Wire Contract、NDJSON、--dry-run）；OpenDataLoader 与 PaddleOCR-VL 解析器；MCP Server 多传输方式（stdio / SSE / HTTP）；逐模型思考模式配置；腾讯 LKEAP Rerank + 原生 Gemini Embedding + MiniMax-M3。详见 CHANGELOG.md。
• v0.6.0 —— 工作区 RBAC（四级角色矩阵 Owner / Admin / Contributor / Viewer + 逐知识库所有权 + 逐工作区审计日志）、工作区成员管理与多工作区 UX、自助工作区；weknora CLI v0.4 正式可用，提供 mcp serve；跨向量存储的知识库检索扇出；AES-256-GCM 凭据加密 + docreader gRPC TLS + Token；智谱 Embedding + 华为 OBS；服务端用户偏好设置；Go 1.26.0。参见 docs/RBAC说明.md 和 CHANGELOG.md。
• v0.5.2 —— Wiki 摄取可扩展至包含 4 万份文档的知识库（任务队列 + DLQ）；MCP 人在回路工具审批；Anthropic / Apache Doris / 腾讯 VectorDB / KS3 / SearXNG 后端；具备实时预览的自适应三级分块；全局 ⌘K 命令面板；语雀连接器 + 微信小程序；weknora CLI 预览版。
• v0.5.1 —— 知识库批量管理；工作区范围 IM 渠道总览；会话搜索 + 用户作用域置顶；统一的模型 / 网络搜索 / MCP 设置卡片；逐 Agent LLM 超时；桌面端工作区切换。
• v0.5.0 —— Wiki Mode 正式可用（GA）：Agent 自动生成结构化、相互链接的 Markdown Wiki 页面，并创建知识图谱；UI 提供 Wiki 浏览器与可视化图谱。
• v0.4.0 —— WeKnora Cloud（托管 LLM + 解析）；Chrome Extension；ClawHub Skill；微信 IM；附件处理；Azure OpenAI / 阿里云 OSS；Notion 连接器；百度 + Ollama 网络搜索；VectorStore 管理。
• v0.3.6 —— ASR（音频）；飞书数据源自动同步；OIDC；IM 引用回复上下文 + 基于线程的会话；文档摘要；Tavily 搜索；并行工具调用；Agent @ 提及范围限制。
• v0.3.5 —— Telegram / 钉钉 / Mattermost IM；IM 斜杠命令 + QA 队列；推荐问题；VLM 自动描述 MCP 工具图像；Novita AI；渠道追踪。
• v0.3.4 —— 企业微信 / 飞书 / Slack IM；多模态图像支持；NVIDIA 模型 API；Weaviate；AWS S3；AES-256-GCM API Key 加密；内置 MCP 服务；混合搜索优化；final_answer 工具。
• v0.3.3 —— 父子分块；知识库置顶；回退响应；用于 Rerank 的段落清理；存储自动创建；Milvus。
• v0.3.2 —— Knowledge Search 入口；逐数据源解析器与存储引擎配置；本地存储中的图像渲染；文档预览；火山引擎 TOS；Mermaid 渲染；批量会话管理；记忆图谱预览。
• v0.3.0 —— Shared Space；Agent Skills + 沙箱化执行；自定义 Agent；数据分析 Agent；思考模式；Bing / Google 网络搜索；API Key 认证；Helm Chart；韩语国际化；Qdrant。
• v0.2.0 —— Agent Mode（ReACT）；多类型知识库。

| English | 简体中文 | 日本語 | 한국어 |

💡 WeKnora — Turn Documents into Living Knowledge with RAG, Agents and Auto-Wiki

WeKnora is an open-source, LLM-powered knowledge framework built for enterprise-grade document understanding, semantic retrieval, and autonomous reasoning.

weknora-narrated.mp4

2:25 · 1080p · English narration & captions.

It is organized around three core capabilities: RAG-based Quick Q &A for everyday lookups, a ReAct Agent that autonomously orchestrates retrieval, MCP tools, a tenant skill catalog , session-persistent Docker / E2B / Cube sandboxes and web search to handle complex multi-step tasks, and a brand-new Wiki Mode in which agents distill raw documents into a self-maintaining, interlinked markdown knowledge base with an interactive knowledge graph, complete with manual editing, revision history and one-click rollback. Cross-session long-term memory remembers who you are and what you keep asking about. Knowledge curation is equally hands-on: a tree-structured folder view preserves the directory layout of uploads, and chunk editing with revision history lets retrieval chunks be edited, diffed and reverted like documents. Combined with multi-source ingestion (Feishu wiki / Feishu Drive / GitLab / Tencent IMA / Notion / Yuque / RSS, and growing), website embed widgets for publishing agents to external sites, scoped API keys with a principal model for programmatic integrations, multi-instance storage backends per workspace for flexible data placement, 20+ LLM provider integrations (including LiteLLM), full Langfuse observability plus a runtime task-queue dashboard with worker-pool governance , enterprise-ready multi-workspace RBAC (4-tier role matrix + per-resource ownership + per-workspace audit log), and a fully self-hostable modular architecture, WeKnora turns scattered documents into a queryable, reasoning-capable, continuously evolving knowledge asset.

The framework supports auto-syncing knowledge from Feishu, GitLab, Tencent IMA, Notion, and Yuque (more data sources coming soon), handles 10+ document formats including PDF, Word, images, Excel and XMind, and can serve Q&A directly through IM channels like WeCom, Feishu, Slack, and Telegram. It is compatible with major LLM providers including OpenAI, DeepSeek, Qwen (Alibaba Cloud), Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, LiteLLM, and Ollama. Office files can be parsed in-process with anydoc. Its fully modular design allows swapping LLMs, vector databases, and storage backends, with support for local and private cloud deployment ensuring complete data sovereignty. WeKnora also integrates with Langfuse for comprehensive observability into agent reasoning, token usage, and pipeline tracing.

• v0.8.0 — Skill sandbox runtime (session-persistent Docker / E2B / Cube backends with per-tenant network policy; Local host-process backend removed; Docker opt-in); tenant skill catalog (install from ClawHub / SkillHub / git / zip, per-sandbox snapshots, live progress, file browse/edit, personal and workspace env vars); cross-session long-term memory (profile / preference / fact / task / interest, auto-extract with confirm, search_memory); in-process anydoc office parser ; official DeepSeek Harness plugin @wxg-prc-cpg/dsh-weknora; GitLab and Tencent IMA data sources; LiteLLM; Exa and Metaso web search; XMind parsing; chat artifacts, question outline and timestamps; context compaction and provider prompt-cache markers. Plus OIDC JWKS verification, optional complex passwords, document auto-tagging, and broad sandbox/security hardening. See CHANGELOG.md.
• v0.7.2 — Launched the official product documentation site (VitePress; six sections, ~50 pages covering ~360 API endpoints and ~150 environment variables, with standalone Docker/Nginx deployment, quickstart sample data and a local MCP demo); knowledge base folder tree (upload paths stored as first-class data, browse/rename/re-file documents like a file manager); chunk editing with revision history (edit retrieval chunks in the UI, per-version diff and rollback, automatic reindexing, plus custom document metadata); Wiki page revision history (snapshots + line-level diff + one-click rollback + in-browser manual editing); directly loadable file URLs via resource_urls=public / RESOURCE_URL_MODE (third-party apps render images and files without a second authenticated proxy call); Feishu Drive data source and docx sync through the blocks API; batch document tagging; MCP Server 1.1.x (migrated to the mcp 2.x high-level API, official PyPI package tencent-weknora-mcp, new create_knowledge_from_text and list_shared_knowledge_bases for 29 tools total); AWS S3 default credential chain (IAM Role / IRSA); local HTML upload parsing; QQBot markdown replies; new PR CI checks for app / frontend / docreader / mcp-server. Plus large-scale router and modelcontext refactors, rerank and chunking quality work, and broad stability fixes. See CHANGELOG.md.
• v0.7.1 — New Yunzhijia (云之家) IM integration (WebSocket + image messages + markdown replies); Volcengine rerank provider (with request batching) and Zhipu AI web search provider; platform-scoped API keys for control-plane automation (tenant management, system settings, runtime queues, audit logs); per-KB activity audit trail ; FAQ management enhancements (filtering, tagging, export, import tracking); Langfuse OTLP/OTel tracing migration with W3C traceparent propagation; chat header actions with one-click Markdown export and wiki tool results in the references drawer; prompt-cache observability; session channel governance (admin-scoped IM/embed/API sessions); resilient Feishu large-wiki sync; and removal of the legacy Neo4j conversation-memory dependency. Plus broad slug-integrity, SSRF-transport, and state-sync hardening. See CHANGELOG.md.
• v0.7.0 — Fine-grained scoped API keys & principal model (capability-level grants + per-KB restriction + API integration playground); runtime task-queue observability dashboard & worker-pool governance (per-stage pools + per-model concurrency governors + failed-task inspection/retry); multi-instance storage backends (multiple storage instances per workspace, per-KB binding, default instance); session-scoped temporary attachments (async image/doc parsing + combined limits); question & follow-up suggestions; stable resource registry with LLM-context alias compaction; @Skill / @MCP mentions with scoped agent runtime; mid-conversation MCP OAuth; QQBot & Lark (Feishu International) IM integration; Redis TLS; Requesty model provider + Keenable web search; tenantless provisioning & gated self-service workspaces; admin password reset; knowledge base duplicate flow; weknora CLI v0.10. Plus broad security hardening (SSRF, secret redaction, SQL validation, IDOR). See CHANGELOG.md.
• v0.6.3 — Website embed widget & Integrations Center (secure-mode token exchange + rate limits); chat experience overhaul (citation popovers, RAG pipeline progress, streaming markdown); document multi-tag & batch reparse; Wiki folders & hierarchy navigation; RSS data source; MCP OAuth2; EPUB / MHTML parsing; agent model-readiness checks; model test debugger; session source filter; workspace deletion UI. See CHANGELOG.md.
• v0.6.2 — Per-upload process configuration with upload-confirm dialog; document reparse with process_config; weknora CLI v0.9 (bundled Agent Skills, session stop, auth/profile harmonization); KB marquee multi-select; HNSW index for 1024-dim pgvector embeddings; chat resources store refactor; Langfuse-only tracing (Jaeger removed). See CHANGELOG.md.
• v0.6.1 — Document parsing trace timeline (Langfuse-style span tree with stage-by-stage progress + stop-parse); OpenSearch vector store driver; declarative built-in models via YAML; system admin & consolidated platform settings + audit log; new-user onboarding guide; settings UI redesign; weknora CLI v0.7 / v0.8 (agent-first wire contract, NDJSON, --dry-run); OpenDataLoader + PaddleOCR-VL parsers; MCP server multi-transport (stdio / SSE / HTTP); per-model thinking-mode config; Tencent LKEAP rerank + native Gemini embeddings + MiniMax-M3. See CHANGELOG.md.
• v0.6.0 — Workspace RBAC (4-tier role matrix Owner / Admin / Contributor / Viewer + per-KB ownership + per-workspace audit log), workspace member management & multi-workspace UX, self-service workspaces; weknora CLI v0.4 GA with mcp serve; KB retrieval fan-out across vector stores; AES-256-GCM credential encryption + docreader gRPC TLS + Token; Zhipu embedder + Huawei OBS; server-side user preferences; Go 1.26.0. See docs/RBAC说明.md and CHANGELOG.md.
• v0.5.2 — Wiki ingest scales to 40k-document KBs (task queue + DLQ); MCP human-in-the-loop tool approval; Anthropic / Apache Doris / Tencent VectorDB / KS3 / SearXNG backends; adaptive 3-tier chunking with live preview; global ⌘K command palette; Yuque connector + WeChat Mini Program; weknora CLI preview.
• v0.5.1 — Knowledge-base batch management; workspace-wide IM channels overview; session search + user-scoped pinning; unified Model / Web Search / MCP settings cards; per-agent LLM timeout; desktop workspace switching.
• v0.5.0 — Wiki Mode GA — agents auto-generate structured, interlinked Markdown wiki pages with a knowledge graph; wiki browser + visual graph in the UI.
• v0.4.0 — WeKnora Cloud (hosted LLM + parsing); Chrome Extension; ClawHub Skill; WeChat IM; attachment processing; Azure OpenAI / Alibaba OSS; Notion connector; Baidu + Ollama web search; VectorStore management.
• v0.3.6 — ASR (audio); Feishu data-source auto-sync; OIDC; IM quote-reply context + thread-based sessions; document summarization; Tavily search; parallel tool calling; agent @mention scope restriction.
• v0.3.5 — Telegram / DingTalk / Mattermost IM; IM slash commands + QA queue; suggested questions; VLM auto-describe MCP tool images; Novita AI; channel tracking.
• v0.3.4 — WeCom / Feishu / Slack IM; multimodal image support; NVIDIA model API; Weaviate; AWS S3; AES-256-GCM API-key encryption; built-in MCP service; hybrid-search optimization; final_answer tool.
• v0.3.3 — Parent-child chunking; KB pinning; fallback response; passage cleaning for rerank; storage auto-creation; Milvus.
• v0.3.2 — Knowledge Search entry; per-source parser & storage engine config; image rendering in local storage; document preview; Volcengine TOS; Mermaid rendering; batch session management; memory graph preview.
• v0.3.0 — Shared Space; Agent Skills + sandboxed execution; custom agents; Data Analyst agent; thinking mode; Bing / Google web search; API Key auth; Helm chart; Korean i18n; Qdrant.
• v0.2.0 — Agent Mode (ReACT); multi-type knowledge bases (FAQ + document); conversation strategy config; DuckDuckGo web search; MCP tool integration; new UI with agent mode switching; MQ async task management.

🛠️ Skill Sandbox Chat · generate and preview a Word file

📦 Skill Catalog · install onto an E2B sandbox
| 🤖 Agent Mode · search, read a skill, write sandbox files

💬 Intelligent Q &A Conversation

📖 Wiki Browser
| 🕸️ Wiki Knowledge Graph

🕘 Wiki Page Revision History & Rollback
| ✂️ Chunk Editing & Revision History

📁 Folder Tree & Batch Operations
| 🔭 Observability · Langfuse Tracing

Fully modular pipeline from document parsing, vectorization, and retrieval to LLM inference — every component is swappable and extensible. Supports local / private cloud deployment with full data sovereignty and a zero-barrier Web UI for quick onboarding.

Intelligent Conversation

Capability	Details
Intelligent Reasoning	ReACT progressive multi-step reasoning, autonomously orchestrating knowledge retrieval, MCP tools, skill sandboxes, and web search
Quick Q&A	RAG-based Q&A over knowledge bases for fast and accurate answers
Wiki Mode	Agent-driven auto-generation of structured, interlinked markdown Wiki pages from raw documents; in-browser manual editing, page revision history, line-level diff and one-click rollback
Skill Catalog & Sandbox	Workspace skill catalog (ClawHub / SkillHub / git / zip) installed onto session-persistent Docker / E2B / Cube sandboxes; shell_exec, file tools, artifacts, per-config network policy; Local host-process backend removed
Long-term Memory	Cross-session memory (profile / preference / fact / task / interest) with auto-extract, user confirm, and on-demand search_memory
Tool Calling	Built-in tools, MCP tools (incl. OAuth2 remote services, mid-conversation OAuth), web search; @Skill / @MCP mentions to scope the agent runtime per turn
Conversation Strategy	Online Prompt editing, retrieval threshold tuning, multi-turn context awareness, per-agent citation output toggle
Suggested Questions	Auto-generated question suggestions and after-answer follow-ups based on knowledge base content
Temporary Attachments	Session-scoped image / document uploads with async parsing for one-off Q&A, with a combined image + attachment limit
Citations & RAG Progress	Inline citation popovers and a references drawer (web / KB source distinction), shared markdown rendering, and stage-by-stage RAG pipeline progress in chat
Session Management	Filter and group sidebar sessions by source (Web / IM / Embed), with inline session-title rename

Knowledge Management

Capability	Details
Knowledge Base Types	FAQ / Document / Wiki with folder import, URL import, multi-tag management, and online entry
Folder Tree	Folder uploads keep their original directory structure, with a sidebar tree for browsing, folder rename, and re-filing documents into another folder
Chunk Editing & Revisions	Edit retrieval chunks directly in the UI with per-version snapshots, diff and one-click rollback, and automatic reindexing after an edit; generated questions can be added, edited, deleted and regenerated; custom document metadata supported
Per-Upload Process Config	Override parser, chunking, multimodal (VLM / ASR), graph extraction, and question generation per upload batch via upload-confirm dialog or process_config API; reparse with new settings
Batch Reparse	Re-queue parsing for multiple documents at once with optional per-batch process_config
Data Source Import	Auto-sync from Feishu wiki / Feishu Drive / Lark / GitLab / Tencent IMA / Notion / Yuque / RSS feeds (more data sources coming soon); incremental and full sync
Document Formats	PDF / Word / Txt / Markdown / HTML / EPUB / MHTML / Images / CSV / Excel / PPT / JSON / XMind
Auto-Tagging	After parse, pick matching tags from the knowledge base's existing set without creating tags or overwriting manual ones
Retrieval Strategies	BM25 sparse / Dense retrieval / GraphRAG / parent-child chunking / HNSW-accelerated pgvector (1024-dim) / multi-dimensional indexing
Batch Selection & Tagging	Marquee drag-select multiple documents in the KB list for batch reparse and batch tagging (common tags pre-selected)
E2E Testing	Full-pipeline visualization with recall hit rate, BLEU / ROUGE metric evaluation

Integrations & Extensions

Capability	Details
LLMs	OpenAI / Azure OpenAI / Anthropic (Claude) / DeepSeek / Qwen (Alibaba Cloud) / Zhipu / Hunyuan / Doubao (Volcengine) / Gemini / MiniMax / NVIDIA / Novita AI / SiliconFlow / OpenRouter / Requesty / LiteLLM / Ollama
Embeddings	Ollama / BGE / GTE / Zhipu / OpenAI-compatible APIs
Vector DBs	PostgreSQL (pgvector) / Elasticsearch / OpenSearch / Milvus / Weaviate / Qdrant / Apache Doris / Tencent VectorDB
Object Storage	Local / MinIO / AWS S3 (IAM Role / IRSA default credential chain) / Volcengine TOS / Alibaba Cloud OSS / Kingsoft Cloud KS3 / Huawei Cloud OBS; multiple storage instances per workspace with per-KB binding and a default instance
IM Channels	WeCom / Feishu / Lark (Feishu International) / QQBot / Slack / Telegram / DingTalk / Mattermost / WeChat / Yunzhijia
Website Embed	Publish agents via embed widget with domain allowlists, rate limits, and secure-mode token exchange
Web Search	DuckDuckGo / Bing / Google / Tavily / Baidu / Ollama / SearXNG / Keenable / Zhipu AI / Exa / Metaso
API Integration	Scoped API keys (capability-level grants + per-KB restriction + throttled last-used tracking) with an API integration playground; MCP OAuth and embed sessions isolated per principal; resource_urls=public returns directly loadable file/image URLs, removing the second authenticated proxy call
MCP Server	Official PyPI package tencent-weknora-mcp with 29 tools over stdio / SSE / HTTP transports

Platform

Capability	Details
Deployment	Local / Docker / Kubernetes (Helm) with private and offline support
UI	Web UI / RESTful API / CLI (weknora) / Chrome Extension / Website Embed Widget / WeChat Mini Program
Access Control	Workspace RBAC with 4-tier role matrix (Owner / Admin / Contributor / Viewer), per-KB resource ownership, per-workspace audit log, invite-only workspaces, tenantless provisioning & gated self-service workspace creation, admin password reset (session revocation), cross-workspace superuser, scoped API keys
Security	AES-256-GCM at-rest encryption for API keys and MCP / data-source credentials with graceful key rotation; gRPC TLS + Token between app and docreader; Redis TLS; SSRF-safe HTTP client (data sources, URL import, redirect chains); secret redaction in responses; skill sandbox isolation (Docker opt-in / E2B / Cube) with per-config network policy; OIDC ID-token JWKS verification; optional complex-password policy
Observability	Integrated Langfuse (sole tracing backend) for ReAct loops, token tracking, tool calls, and pipeline tracing; built-in Langfuse-style document parsing trace timeline with stage-by-stage progress; system-admin runtime task-queue dashboard (queue depth, per-model concurrency, failed-task inspection & manual retry)
Task Management	MQ async tasks with per-stage worker-pool governance (core / post-process / enrichment / maintenance + elastic shared pool, plus an independent Wiki pool) and per-model background concurrency governors; automatic database migration on version upgrade
Model Management	Centralized config, declarative built-in models via YAML, per-knowledge-base model selection, per-model thinking-mode and embedding-dimension overrides, interactive model test debugger, multi-workspace built-in model sharing, WeKnora Cloud hosted models and parsing

WeKnora Chrome Extension lets you capture web content directly into your WeKnora knowledge base. Select text, images, or entire pages in the browser and save them as knowledge entries with one click — no copy-paste or file upload needed.

The WeKnora Mini Program provides a lightweight mobile client for configuring WeKnora API access, selecting knowledge bases, importing URLs, and asking knowledge chat from WeChat.

WeKnora ClawHub Skill is a WeKnora skill published on the ClawHub platform. Once installed, it enables document import (file / URL / Markdown), hybrid search (vector + keyword) across knowledge bases, and knowledge entry management — all through the WeKnora REST API.

• Document Import — Upload files, import web pages, or write Markdown knowledge via the agent
• Hybrid Search — Search within or across knowledge bases with vector + keyword retrieval
• Knowledge Management — List, browse, edit, and delete knowledge entries programmatically

🐋 DeepSeek Harness Plugin

@wxg-prc-cpg/dsh-weknora is the official DeepSeek Harness (dsh) plugin (docs). The harness ships no retrieval, embedding or knowledge-base capability of its own, so the plugin gives a coding agent your documents: dsh plugin --profile web add @wxg-prc-cpg/dsh-weknora, point it at a deployment, and four read-only tools appear in the agent's tool set.

• weknora_search — hybrid retrieval returning source passages verbatim, each with a reusable knowledge_id
• weknora_read_document — one document's passages reassembled in order, with paging
• weknora_ask — WeKnora's own composed answer with citations, over the RAG or the ReAct pipeline
• weknora_list_knowledge_bases — knowledge base names and ids, so the agent can scope its own search

⌨️ Command-Line Interface

weknora is the official CLI for driving the API from a terminal or an AI agent. It is agent-first : every command emits a stable JSON envelope by default (with typed error codes mapped to exit codes), and --format text renders for humans. It also serves a curated MCP tool surface (weknora mcp serve) and ships bundled Agent Skills.

weknora profile add prod --host https://kb.example.com --use
weknora auth login
weknora kb list
weknora link --kb my-knowledge-base    # bind the current directory
weknora doc upload notes.md
weknora chat "summarise the design doc"

For headless / CI use, set WEKNORA_API_KEY + WEKNORA_HOST and skip auth login entirely — no credentials written to disk.

See cli/README.md for install + 5-minute quickstart and cli/AGENTS.md for the operational contract AI agents rely on.

git clone https://github.com/Tencent/WeKnora.git
cd WeKnora
cp .env.example .env   # Edit .env as needed, see comments in the file
docker compose pull     # Pull the latest images
docker compose up -d    # Start core services

Once started, visit http://localhost to get started.

To use a local Ollama model, run ollama serve > /dev/null 2>&1 & first.

If you already have WeKnora running and downloaded a newer release:

# Set WEKNORA_VERSION in .env to the target release (e.g. 0.7.0), or keep latest
docker compose pull     # Pull images matching WEKNORA_VERSION
docker compose up -d    # Recreate containers with new images

docker compose up -d alone reuses locally cached images and may leave the UI version out of sync with the release you downloaded.

🔧 Optional Services (Docker Compose Profiles)

Add --profile flags to enable additional components. Multiple profiles can be combined:

Profile	Description	Command
(default)	Core services	docker compose pull && docker compose up -d
full	All features	docker compose --profile full pull && docker compose --profile full up -d
neo4j	Knowledge Graph (Neo4j)	docker compose --profile neo4j pull && docker compose --profile neo4j up -d
minio	Object Storage (MinIO)	docker compose --profile minio pull && docker compose --profile minio up -d
langfuse	Tracing (Langfuse)	docker compose --profile langfuse pull && docker compose --profile langfuse up -d

Combine profiles: docker compose --profile neo4j --profile minio pull && docker compose --profile neo4j --profile minio up -d

Stop services: docker compose down

Service	URL
Web UI	http://localhost
Backend API	http://localhost:8080
Langfuse Tracing	http://localhost:3000

Please refer to the MCP Configuration Guide for the necessary setup.

🔌 Using WeChat Dialog Open Platform

WeKnora serves as the core technology framework for the WeChat Dialog Open Platform, providing a more convenient usage approach:

• Zero-code Deployment : Simply upload knowledge to quickly deploy intelligent Q&A services within the WeChat ecosystem, achieving an "ask and answer" experience
• Efficient Question Management : Support for categorized management of high-frequency questions, with rich data tools to ensure accurate, reliable, and easily maintainable answers
• WeChat Ecosystem Integration : Through the WeChat Dialog Open Platform, WeKnora's intelligent Q&A capabilities can be seamlessly integrated into WeChat Official Accounts, Mini Programs, and other WeChat scenarios, enhancing user interaction experiences

Official product documentation : website-docs/ — the complete documentation set organized as Getting Started → Architecture → Features → API → Clients → Development, covering ~360 API endpoints, ~150 environment variables, and 9 extension points. The directory is also a VitePress site: run cd website-docs && npm install && npm run dev to preview locally, or deploy it standalone with the Dockerfile inside.

Troubleshooting FAQ: Troubleshooting FAQ

Detailed API documentation is available at: API Docs

Product plans and upcoming features: Roadmap

⚡ Fast Development Mode (Recommended)

If you need to frequently modify code, you don't need to rebuild Docker images every time! Use fast development mode:

# Start infrastructure
make dev-start

# Start backend (new terminal)
make dev-app

# Start frontend (new terminal)
make dev-frontend

Development Advantages:

• ✅ Frontend modifications auto hot-reload (no restart needed)
• ✅ Backend modifications quick restart (5-10 seconds, supports Air hot-reload)
• ✅ No need to rebuild Docker images
• ✅ Support IDE breakpoint debugging

Detailed Documentation: Development Environment Quick Start

Welcome to submit Issues or Pull Requests.

Process: Fork → Create branch → Commit changes → Open PR

Standards: Format code with gofmt, follow Conventional Commits (feat: / fix: / docs: / test: / refactor:)

For a focused PR, validate the changed scope first:

git fetch origin main
git diff --check origin/main...HEAD
golangci-lint run --new-from-rev=origin/main ./...
go test ./path/to/changed/package -count=1

Run gofmt on changed Go files before committing. For frontend changes, run the relevant tests from frontend/ and use npm run type-check when the change affects TypeScript or Vue components.

The full maintainer gate remains:

make fmt
make lint
make test

make fmt formats the entire Go repository, so run it only with a clean worktree and review the resulting diff. Some full-suite tests require local infrastructure or service configuration. If a full check fails for an unrelated baseline or environment reason, include the exact command and failure in the PR while still providing passing targeted tests for your change.

Important: Starting from v0.1.3, WeKnora includes login authentication functionality to enhance system security. For production deployments, we strongly recommend:

• Deploy WeKnora services in internal/private network environments rather than public internet
• Avoid exposing the service directly to public networks to prevent potential information leakage
• Configure proper firewall rules and access controls for your deployment environment
• Regularly update to the latest version for security patches and improvements

Thanks to these excellent contributors:

This project is licensed under the MIT License. You are free to use, modify, and distribute the code with proper attribution.