WeKnora Agent工作流
AI Skill Hub 强烈推荐：WeKnora Agent工作流 是一款优质的Agent工作流。在 GitHub 上收获超过 14.9k 颗 Star，AI 综合评分 8.2 分，在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案，这是一个值得深入了解的选择。
📚 深度解析
WeKnora Agent工作流 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。
在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.2 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
WeKnora Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
📖 中文文档
WeKnora Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
# 方式一：go install（推荐） go install github.com/Tencent/WeKnora@latest # 方式二：从源码编译 git clone https://github.com/Tencent/WeKnora cd WeKnora go build -o weknora . # 方式三：下载预编译二进制 # 访问 Releases 页面下载对应平台二进制文件 # https://github.com/Tencent/WeKnora/releases
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 查看帮助 weknora --help # 基本运行 weknora [options] <input> # 详细使用说明请查阅文档 # https://github.com/Tencent/WeKnora
# weknora 配置说明 # 查看配置选项 weknora --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export WEKNORA_CONFIG="/path/to/config.yml"
简介
<p align="center"> <picture> <img src="./docs/images/logo.png" alt="WeKnora Logo" height="120"/> </picture> </p>
<p align="center"> <picture> <a href="https://trendshift.io/repositories/15289" target="_blank"> <img src="https://trendshift.io/api/badge/repositories/15289" alt="Tencent/WeKnora | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/> </a> </picture> </p> <p align="center"> <a href="https://weknora.weixin.qq.com" target="_blank"> <img alt="Official Website" src="https://img.shields.io/badge/Official Website-WeKnora-4e6b99"> </a> <a href="https://chatbot.weixin.qq.com" target="_blank"> <img alt="WeChat Dialog Open Platform" src="https://img.shields.io/badge/WeChat Dialog Open Platform-5ac725"> </a> <a href="https://chromewebstore.google.com/detail/jpemjbopikggjlmikmclgbmkhhopjdgd" target="_blank"> <img alt="Chrome Extension" src="https://img.shields.io/badge/Chrome Extension-WeKnora-4285F4"> </a> <a href="https://clawhub.ai/lyingbug/weknora" target="_blank"> <img alt="ClawHub Skill" src="https://img.shields.io/badge/ClawHub Skill-WeKnora-ff6b35"> </a> <a href="https://www.npmjs.com/package/@wxg-prc-cpg/dsh-weknora" target="_blank"> <img alt="npm @wxg-prc-cpg/dsh-weknora" src="https://img.shields.io/npm/v/@wxg-prc-cpg/dsh-weknora?label=dsh-weknora"> </a> <a href="https://github.com/Tencent/WeKnora/blob/main/LICENSE"> <img src="https://img.shields.io/badge/License-MIT-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="License"> </a> <a href="./CHANGELOG.md"> <img alt="Version" src="https://img.shields.io/badge/version-0.8.0-2e6cc4?labelColor=d4eaf7"> </a> </p>
<p align="center"> | <b>English</b> | <a href="./README_CN.md"><b>简体中文</b></a> | <a href="./README_JA.md"><b>日本語</b></a> | <a href="./README_KO.md"><b>한국어</b></a> | </p>
<p align="center"> <h4 align="center">
Overview • Architecture • Key Features • Getting Started • API Reference • Developer Guide </h4> </p>
📌 Overview
WeKnora is an open-source, LLM-powered knowledge framework built for enterprise-grade document understanding, semantic retrieval, and autonomous reasoning.
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.
🧩 Feature Overview
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 |
🛠 Prerequisites
🚀 Getting Started
📦 Installation & Launch
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 servicesOnce started, visit http://localhost to get started.
To use a local Ollama model, run ollama serve > /dev/null 2>&1 & first.
🔧 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
🧭 Developer Guide
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.📱 Interface Showcase
🛠️ 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 |
⌨️ 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.
📘 API Reference
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
🧩 Chrome Extension
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.
🐋 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 reusableknowledge_idweknora_read_document— one document's passages reassembled in order, with pagingweknora_ask— WeKnora's own composed answer with citations, over the RAG or the ReAct pipelineweknora_list_knowledge_bases— knowledge base names and ids, so the agent can scope its own search
WeKnora 是一个开源的、由大语言模型（LLM）驱动的知识框架，专为企业级文档理解、语义检索及自主推理而设计。它集成了三种核心能力：基于 RAG 的快速问答（Quick Q&A）用于日常信息查询；具备 ReAct Agent 能力的智能体，能够自主编排检索、MCP 工具及 Web Search 来处理复杂的跨步骤任务；以及能够自动生成结构化、互联 Markdown 文档的 Wiki 模式。
WeKnora 提供多样化的智能交互能力：通过 Intelligent Reasoning 实现基于 ReAct 框架的多步推理，支持自定义 Agent 并自主调用 MCP 工具与 Web Search；Quick Q&A 模式利用 RAG 技术实现对知识库的高效、准确问答；Wiki Mode 则能通过 Agent 驱动，自动将碎片化信息转化为结构化的知识百科。
在开始部署 WeKnora 之前，请确保您的开发环境已安装 Git 以及 Docker 与 Docker Compose，以便通过容器化技术快速构建运行环境。
您可以通过 Git 克隆仓库并使用 Docker Compose 进行部署。首先执行 `git clone` 获取源码，复制 `.env.example` 为 `.env` 并根据注释修改配置。随后运行 `docker compose up -d` 启动核心服务。若需使用本地 Ollama 模型，请先运行 `ollama serve`。此外，您可以通过 `--profile` 参数启用 full、neo4j 或 minio 等可选服务组件。
项目启动后，请访问 http://localhost 进入 Web 界面进行交互。开发者可以通过官方提供的 `weknora` CLI 工具在终端或 AI Agent 中驱动 API，其命令设计遵循 `gh` CLI 的 `<noun> <verb>` 规范，并支持通过 `--json` 参数输出稳定的 JSON 格式数据。
WeKnora 提供直观的智能问答界面与 Wiki 浏览器功能。开发者可以通过官方 API 进行深度集成，详细的 API 文档请参考 `./docs/api/README.md`。此外，项目还提供了命令行接口（CLI）以支持自动化操作与 AI Agent 的调用。
WeKnora 配备了专用的 Chrome Extension 插件，允许用户直接将网页内容捕获到 WeKnora 知识库中。通过在浏览器中选择文本、图片或整个页面，用户可以实现一键保存为知识条目，彻底告别繁琐的复制粘贴操作。
Go语言实现，性能优异。RAG工作流完整，文档知识库转换能力强。生态活跃，14.9k星标表明社区认可度高。
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 跨境业务、多语言内容运营团队
- 做语音类 AI 产品的开发者
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Docker：WeKnora 提供官方镜像，docker compose up 一键启动
- CLI：直接 npm install -g / pip install，命令行调用
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 可视化 Agent 工作流编排，无需编写复杂代码
- 支持多步骤自动化任务链，实现全流程无人值守
- 与外部 API、数据库和第三方服务无缝集成
- 内置错误处理与自动重试机制，保障稳定运行
- 提供可复用的自动化模板，快速在同类场景部署
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 跨境业务、多语言内容运营团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 生产部署优先使用 Docker Compose 隔离依赖，并挂载 volume 持久化数据
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- 容器内无法访问宿主机 localhost — 使用 host.docker.internal
- embedding 模型与查询模型不一致导致检索失效
👥 适合人群
🎯 使用场景
- 自动化日常重复性工作，将精力集中于创造性任务
- 构建数据采集 → 处理 → 输出的完整自动化管线
- 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
- +GitHub 14.9k Star，社区高度认可
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
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❓ 常见问题 FAQ
总体来看，WeKnora Agent工作流 是一款质量优秀的Agent工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | WeKnora |
| 原始描述 | 开源AI工作流：Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an 。⭐14.9k · Go |
| Topics | RAG知识库LLM工作流Go开发 |
| GitHub | https://github.com/Tencent/WeKnora |
| License | NOASSERTION |
| 语言 | Go |
收录时间：2026-05-14 · 更新时间：2026-05-16 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。