[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-weaviate--weaviate":3,"tool-weaviate--weaviate":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",160015,2,"2026-04-18T11:30:52",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":64,"owner_name":72,"owner_avatar_url":73,"owner_bio":74,"owner_company":75,"owner_location":75,"owner_email":76,"owner_twitter":77,"owner_website":78,"owner_url":79,"languages":80,"stars":116,"forks":117,"last_commit_at":118,"license":119,"difficulty_score":32,"env_os":120,"env_gpu":121,"env_ram":122,"env_deps":123,"category_tags":130,"github_topics":132,"view_count":32,"oss_zip_url":75,"oss_zip_packed_at":75,"status":17,"created_at":152,"updated_at":153,"faqs":154,"releases":183},9280,"weaviate\u002Fweaviate","weaviate","Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.","Weaviate 是一款开源的云原生向量数据库，专为处理大规模人工智能数据而设计。它不仅能存储传统的结构化对象，还能同时存储向量嵌入，从而将高效的向量相似度搜索与精确的结构化过滤完美结合。\n\n在构建基于大语言模型的应用时，开发者常面临如何快速检索相关知识以增强生成效果（即 RAG 技术）的挑战。Weaviate 有效解决了这一痛点，支持在单次查询中完成语义搜索、关键词过滤及重排序，广泛应用于智能问答机器人、语义搜索引擎、推荐系统及内容分类等场景。\n\n这款工具主要面向软件开发者和 AI 研究人员。其独特的技术亮点在于极高的易用性与灵活性：既支持在数据导入时调用 OpenAI、HuggingFace 等集成模型自动向量化，也允许直接导入预计算好的向量。此外，Weaviate 原生具备多租户管理、数据复制和细粒度权限控制等企业级特性，确保系统在云端环境下的稳定扩展。通过简单的 Docker 配置或 Python 客户端，用户即可快速搭建本地实例并启动语义搜索服务，是构建下一代智能应用的理想基础设施。","# Weaviate \u003Cimg alt='Weaviate logo' src='https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fweaviate_weaviate_readme_3450ac573e7a.png' width='148' align='right' \u002F>\n\n[![GitHub Repo stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fweaviate\u002Fweaviate?style=social)](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate)\n[![Go Reference](https:\u002F\u002Fpkg.go.dev\u002Fbadge\u002Fgithub.com\u002Fweaviate\u002Fweaviate.svg)](https:\u002F\u002Fpkg.go.dev\u002Fgithub.com\u002Fweaviate\u002Fweaviate)\n[![Build Status](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Factions\u002Fworkflows\u002F.github\u002Fworkflows\u002Fpull_requests.yaml\u002Fbadge.svg?branch=main)](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Factions\u002Fworkflows\u002F.github\u002Fworkflows\u002Fpull_requests.yaml)\n[![Go Report Card](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fweaviate_weaviate_readme_2b4a70945b89.png)](https:\u002F\u002Fgoreportcard.com\u002Freport\u002Fgithub.com\u002Fweaviate\u002Fweaviate)\n[![Coverage Status](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fweaviate\u002Fweaviate\u002Fbranch\u002Fmain\u002Fgraph\u002Fbadge.svg)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fweaviate\u002Fweaviate)\n\n**Weaviate** is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification.\n\nWeaviate supports two approaches to store vectors: automatic vectorization at import using [integrated models](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmodel-providers) (OpenAI, Cohere, HuggingFace, and others) or direct import of [pre-computed vector embeddings](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fstarter-guides\u002Fcustom-vectors). Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and [many other features](#weaviate-features).\n\nTo get started quickly, have a look at one of these tutorials:\n\n- [Quickstart - Weaviate Cloud](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fquickstart)\n- [Quickstart - local Docker instance](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fquickstart\u002Flocal)\n\n## Installation\n\nWeaviate offers multiple installation and deployment options:\n\n- [Docker](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Fdocker-installation)\n- [Kubernetes](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Fk8s-installation)\n- [Weaviate Cloud](https:\u002F\u002Fconsole.weaviate.cloud)\n\nSee the [installation docs](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy) for more deployment options, such as [AWS](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Faws-marketplace) and [GCP](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Fgcp-marketplace).\n\n## Getting started\n\nYou can easily start Weaviate and a local vector embedding model with [Docker](https:\u002F\u002Fdocs.docker.com\u002Fdesktop\u002F).\nCreate a `docker-compose.yml` file:\n\n```yml\nservices:\n  weaviate:\n    image: cr.weaviate.io\u002Fsemitechnologies\u002Fweaviate:1.36.0\n    ports:\n      - \"8080:8080\"\n      - \"50051:50051\"\n    environment:\n      ENABLE_MODULES: text2vec-model2vec\n      MODEL2VEC_INFERENCE_API: http:\u002F\u002Ftext2vec-model2vec:8080\n\n  # A lightweight embedding model that will generate vectors from objects during import\n  text2vec-model2vec:\n    image: cr.weaviate.io\u002Fsemitechnologies\u002Fmodel2vec-inference:minishlab-potion-base-32M\n```\n\nStart Weaviate and the embedding service with:\n\n```bash\ndocker compose up -d\n```\n\nInstall the Python client (or use another [client library](#client-libraries-and-apis)):\n\n```bash\npip install -U weaviate-client\n```\n\nThe following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:\n\n```python\nimport weaviate\nfrom weaviate.classes.config import Configure, DataType, Property\n\n# Connect to Weaviate\nclient = weaviate.connect_to_local()\n\n# Create a collection\nclient.collections.create(\n    name=\"Article\",\n    properties=[Property(name=\"content\", data_type=DataType.TEXT)],\n    vector_config=Configure.Vectors.text2vec_model2vec(),  # Use a vectorizer to generate embeddings during import\n    # vector_config=Configure.Vectors.self_provided()  # If you want to import your own pre-generated embeddings\n)\n\n# Insert objects and generate embeddings\narticles = client.collections.get(\"Article\")\narticles.data.insert_many(\n    [\n        {\"content\": \"Vector databases enable semantic search\"},\n        {\"content\": \"Machine learning models generate embeddings\"},\n        {\"content\": \"Weaviate supports hybrid search capabilities\"},\n    ]\n)\n\n# Perform semantic search\nresults = articles.query.near_text(query=\"Search objects by meaning\", limit=1)\nprint(results.objects[0])\n\nclient.close()\n```\n\nThis example uses the `Model2Vec` vectorizer, but you can choose any other [embedding model provider](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmodel-providers) or [bring your own pre-generated vectors](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fstarter-guides\u002Fcustom-vectors).\n\n## Client libraries and APIs\n\nWeaviate provides client libraries for several programming languages:\n\n- [Python](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fpython)\n- [JavaScript\u002FTypeScript](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Ftypescript)\n- [Java](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fjava)\n- [Go](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fgo)\n- [C#\u002F.NET](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fcsharp)\n\nThere are also additional [community-maintained libraries](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fcommunity).\n\nWeaviate exposes [REST API](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fapi\u002Frest), [gRPC API](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fapi\u002Fgrpc), and [GraphQL API](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fapi\u002Fgraphql) to communicate with the database server.\n\n## Weaviate features\n\nThese features enable you to build AI-powered applications:\n\n- **⚡ Fast Search Performance**: Perform complex semantic [searches](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fsimilarity) over billions of vectors in milliseconds. Weaviate's architecture is built in Go for speed and reliability, ensuring your AI applications are highly responsive even under heavy load. See our [ANN benchmarks](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fbenchmarks\u002Fann) for more info.\n\n- **🔌 Flexible Vectorization**: Seamlessly vectorize data at import time with [integrated vectorizers](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmodel-providers) from OpenAI, Cohere, HuggingFace, Google, and more. Or you can import [your own vector embeddings](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fstarter-guides\u002Fcustom-vectors).\n\n- **🔍 Advanced Hybrid & Image Search**: Combine the power of semantic search with traditional [keyword (BM25) search](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fbm25), [image search](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fimage) and [advanced filtering](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Ffilters) to get the best results with a single API call.\n\n- **🤖 Integrated RAG & Reranking**: Go beyond simple retrieval with built-in [generative search (RAG)](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fgenerative) and [reranking](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Frerank) capabilities. Power sophisticated Q&A systems, chatbots, and summarizers directly from your database without additional tooling.\n\n- **📈 Production-Ready & Scalable**: Weaviate is built for mission-critical applications. Go from rapid prototyping to production at scale with native support for [horizontal scaling](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Fconfiguration\u002Fhorizontal-scaling), [multi-tenancy](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmanage-collections\u002Fmulti-tenancy), [replication](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Fconfiguration\u002Freplication), and fine-grained [role-based access control (RBAC)](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconfiguration\u002Frbac).\n\n- **💰 Cost-Efficient Operations**: Radically lower resource consumption and operational costs with built-in [vector compression](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconfiguration\u002Fcompression). Vector quantization and multi-vector encoding reduce memory usage with minimal impact on search performance.\n\n- **⏱️ Object TTL**: Automatically expire and remove stale data with configurable [time-to-live](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmanage-collections\u002Ftime-to-live) settings per collection, with full RBAC and multi-tenancy support.\n\nFor a complete list of all functionalities, visit the [official Weaviate documentation](https:\u002F\u002Fdocs.weaviate.io).\n\n## Useful resources\n\n### AI Agent Skills\n\n[Weaviate Agent Skills](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fagent-skills) is a collection of skills for AI coding agents (Claude Code, Cursor, GitHub Copilot, and others) that enable them to work with Weaviate more accurately and efficiently. Skills cover searching, querying, collection management, data import, and full application blueprints (RAG, agentic RAG, chatbots, and more).\n\nInstall with:\n\n```bash\nnpx skills add weaviate\u002Fagent-skills\n```\n\n### Demo projects & recipes\n\nThese demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub.\n\n- [Elysia](https:\u002F\u002Felysia.weaviate.io) ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Felysia)): Elysia is a decision tree based agentic system which intelligently decides what tools to use, what results have been obtained, whether it should continue the process or whether its goal has been completed.\n- [Verba](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fverba-open-source-rag-app) ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fverba)): A community-driven open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box.\n- [Healthsearch](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fhealthsearch-demo) ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fhealthsearch-demo)): An open-source project aimed at showcasing the potential of leveraging user-written reviews and queries to retrieve supplement products based on specific health effects.\n- Awesome-Moviate ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate-tutorials\u002Fawesome-moviate)): A movie search and recommendation engine that allows keyword-based (BM25), semantic, and hybrid searches.\n\nWe also maintain extensive repositories of **Jupyter Notebooks** and **TypeScript code snippets** that cover how to use Weaviate features and integrations:\n\n- [Weaviate Python Recipes](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Frecipes\u002F)\n- [Weaviate TypeScript Recipes](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Frecipes-ts\u002F)\n\n### Blog posts\n\n- [What is a Vector Database](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fwhat-is-a-vector-database)\n- [What is Vector Search](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fvector-search-explained)\n- [What is Hybrid Search](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fhybrid-search-explained)\n- [How to Choose an Embedding Model](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fhow-to-choose-an-embedding-model)\n- [What is RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fintroduction-to-rag)\n- [RAG Evaluation](https:\u002F\u002Fweaviate.io\u002Fblog\u002Frag-evaluation)\n- [Advanced RAG Techniques](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fadvanced-rag)\n- [What is Multimodal RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fmultimodal-rag)\n- [What is Agentic RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fwhat-is-agentic-rag)\n- [What is Graph RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fgraph-rag)\n- [Overview of Late Interaction Models](https:\u002F\u002Fweaviate.io\u002Fblog\u002Flate-interaction-overview)\n\n### Integrations\n\nWeaviate integrates with many external services:\n\n| Category                                                                                   | Description                                                | Integrations                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |\n| ------------------------------------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **[Cloud Hyperscalers](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcloud-hyperscalers)**         | Large-scale computing and storage                          | [AWS](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcloud-hyperscalers\u002Faws), [Google](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcloud-hyperscalers\u002Fgoogle)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |\n| **[Compute Infrastructure](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure)** | Run and scale containerized applications                   | [Modal](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure\u002Fmodal), [Replicate](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure\u002Freplicate), [Replicated](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure\u002Freplicated)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |\n| **[Data Platforms](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms)**                 | Data ingestion and web scraping                            | [Airbyte](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fairbyte), [Aryn](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Faryn), [Boomi](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fboomi), [Box](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fbox), [Confluent](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fconfluent), [Astronomer](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fastronomer), [Context Data](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fcontext-data), [Databricks](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fdatabricks), [Firecrawl](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Ffirecrawl), [IBM](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fibm), [Unstructured](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Funstructured)                |\n| **[LLM and Agent Frameworks](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks)** | Build agents and generative AI applications                | [Agno](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fagno), [Composio](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fcomposio), [CrewAI](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fcrewai), [DSPy](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fdspy), [Dynamiq](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fdynamiq), [Haystack](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fhaystack), [LangChain](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Flangchain), [LlamaIndex](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fllamaindex), [N8n](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fn8n), [Semantic Kernel](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fsemantic-kernel)                                   |\n| **[Operations](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations)**                         | Tools for monitoring and analyzing generative AI workflows | [AIMon](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Faimon), [Arize](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Farize), [Cleanlab](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fcleanlab), [Comet](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fcomet), [DeepEval](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fdeepeval), [Langtrace](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Flangtrace), [LangWatch](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Flangwatch), [Nomic](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fnomic), [Patronus AI](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fpatronus), [Ragas](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fragas), [TruLens](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Ftrulens), [Weights & Biases](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fwandb) |\n\n## Contributing\n\nWe welcome and appreciate contributions! Please see our [Contributor guide](https:\u002F\u002Fdocs.weaviate.io\u002Fcontributor-guide) for the development setup, code style guidelines, testing requirements and the pull request process.\n\nJoin our [Community forum](https:\u002F\u002Fforum.weaviate.io\u002F) to discuss ideas and get help.\n\n## License\n\nBSD 3-Clause License. See [LICENSE](.\u002FLICENSE) for details.\n","# Weaviate \u003Cimg alt='Weaviate logo' src='https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fweaviate_weaviate_readme_3450ac573e7a.png' width='148' align='right' \u002F>\n\n[![GitHub 仓库星级](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fweaviate\u002Fweaviate?style=social)](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate)\n[![Go 参考文档](https:\u002F\u002Fpkg.go.dev\u002Fbadge\u002Fgithub.com\u002Fweaviate\u002Fweaviate.svg)](https:\u002F\u002Fpkg.go.dev\u002Fgithub.com\u002Fweaviate\u002Fweaviate)\n[![构建状态](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Factions\u002Fworkflows\u002F.github\u002Fworkflows\u002Fpull_requests.yaml\u002Fbadge.svg?branch=main)](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Factions\u002Fworkflows\u002F.github\u002Fworkflows\u002Fpull_requests.yaml)\n[![Go Report Card](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fweaviate_weaviate_readme_2b4a70945b89.png)](https:\u002F\u002Fgoreportcard.com\u002Freport\u002Fgithub.com\u002Fweaviate\u002Fweaviate)\n[![覆盖率状态](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fweaviate\u002Fweaviate\u002Fbranch\u002Fmain\u002Fgraph\u002Fbadge.svg)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fweaviate\u002Fweaviate)\n\n**Weaviate** 是一款开源的云原生向量数据库，能够同时存储对象和向量，从而实现大规模的语义搜索。它将向量相似度搜索与关键词过滤、检索增强生成（RAG）以及重排序等功能整合到一个查询接口中。常见的应用场景包括 RAG 系统、语义搜索和图像搜索、推荐引擎、聊天机器人以及内容分类等。\n\nWeaviate 支持两种向量存储方式：一种是在导入时使用 [集成模型](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmodel-providers)（如 OpenAI、Cohere、HuggingFace 等）进行自动向量化；另一种则是直接导入 [预计算的向量嵌入](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fstarter-guides\u002Fcustom-vectors)。在生产环境中部署时，Weaviate 提供了内置的多租户支持、数据复制、基于角色的访问控制（RBAC）以及 [众多其他特性](#weaviate-features)。\n\n要快速入门，请参阅以下教程之一：\n\n- [快速入门 - Weaviate Cloud](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fquickstart)\n- [快速入门 - 本地 Docker 实例](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fquickstart\u002Flocal)\n\n## 安装\n\nWeaviate 提供了多种安装和部署选项：\n\n- [Docker](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Fdocker-installation)\n- [Kubernetes](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Fk8s-installation)\n- [Weaviate Cloud](https:\u002F\u002Fconsole.weaviate.cloud)\n\n更多部署选项，例如 [AWS](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Faws-marketplace) 和 [GCP](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Finstallation-guides\u002Fgcp-marketplace)，请参阅 [安装文档](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy)。\n\n## 开始使用\n\n您可以使用 [Docker](https:\u002F\u002Fdocs.docker.com\u002Fdesktop\u002F) 轻松启动 Weaviate 和本地向量嵌入模型。首先创建一个 `docker-compose.yml` 文件：\n\n```yml\nservices:\n  weaviate:\n    image: cr.weaviate.io\u002Fsemitechnologies\u002Fweaviate:1.36.0\n    ports:\n      - \"8080:8080\"\n      - \"50051:50051\"\n    environment:\n      ENABLE_MODULES: text2vec-model2vec\n      MODEL2VEC_INFERENCE_API: http:\u002F\u002Ftext2vec-model2vec:8080\n\n  # 一个轻量级的嵌入模型，可在导入时为对象生成向量\n  text2vec-model2vec:\n    image: cr.weaviate.io\u002Fsemitechnologies\u002Fmodel2vec-inference:minishlab-potion-base-32M\n```\n\n然后通过以下命令启动 Weaviate 和嵌入服务：\n\n```bash\ndocker compose up -d\n```\n\n接下来安装 Python 客户端（或使用其他 [客户端库](#client-libraries-and-apis)）：\n\n```bash\npip install -U weaviate-client\n```\n\n以下 Python 示例展示了如何轻松地将数据填充到 Weaviate 数据库中、创建向量嵌入并执行语义搜索：\n\n```python\nimport weaviate\nfrom weaviate.classes.config import Configure, DataType, Property\n\n# 连接到 Weaviate\nclient = weaviate.connect_to_local()\n\n# 创建集合\nclient.collections.create(\n    name=\"Article\",\n    properties=[Property(name=\"content\", data_type=DataType.TEXT)],\n    vector_config=Configure.Vectors.text2vec_model2vec(),  # 使用向量化器在导入时生成嵌入\n    # vector_config=Configure.Vectors.self_provided()  # 如果您希望导入自己预先生成的嵌入\n)\n\n# 插入对象并生成嵌入\narticles = client.collections.get(\"Article\")\narticles.data.insert_many(\n    [\n        {\"content\": \"向量数据库支持语义搜索\"},\n        {\"content\": \"机器学习模型可以生成嵌入\"},\n        {\"content\": \"Weaviate 支持混合搜索功能\"},\n    ]\n)\n\n# 执行语义搜索\nresults = articles.query.near_text(query=\"按语义搜索对象\", limit=1)\nprint(results.objects[0])\n\nclient.close()\n```\n\n此示例使用了 `Model2Vec` 向量化器，但您也可以选择其他 [嵌入模型提供商](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmodel-providers) 或者 [自行提供预生成的向量](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fstarter-guides\u002Fcustom-vectors)。\n\n## 客户端库和 API\n\nWeaviate 为多种编程语言提供了客户端库：\n\n- [Python](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fpython)\n- [JavaScript\u002FTypeScript](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Ftypescript)\n- [Java](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fjava)\n- [Go](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fgo)\n- [C#\u002F.NET](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fcsharp)\n\n此外，还有由社区维护的 [附加库](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fclient-libraries\u002Fcommunity)。\n\nWeaviate 暴露了 [REST API](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fapi\u002Frest)、[gRPC API](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fapi\u002Fgrpc) 和 [GraphQL API](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fapi\u002Fgraphql)，用于与数据库服务器进行通信。\n\n## Weaviate 的特性\n\n这些特性使您能够构建由 AI 驱动的应用程序：\n\n- **⚡ 快速的搜索性能**：在毫秒级内对数十亿个向量执行复杂的语义[搜索](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fsimilarity)。Weaviate 的架构采用 Go 语言构建，兼具速度与可靠性，确保您的 AI 应用程序即使在高负载下也能保持高度响应。更多信息请参阅我们的 [ANN 基准测试](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fbenchmarks\u002Fann)。\n\n- **🔌 灵活的向量化**：通过与 OpenAI、Cohere、HuggingFace、Google 等提供的[集成向量化器](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmodel-providers)，在数据导入时无缝完成向量化。您也可以导入[自定义向量嵌入](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fstarter-guides\u002Fcustom-vectors)。\n\n- **🔍 高级混合与图像搜索**：将语义搜索的强大功能与传统的[关键词（BM25）搜索](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fbm25)、[图像搜索](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fimage)以及[高级过滤](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Ffilters)相结合，只需一次 API 调用即可获得最佳结果。\n\n- **🤖 集成 RAG 与重排序**：借助内置的[生成式搜索（RAG）](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Fgenerative)和[重排序](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Frerank)功能，超越简单的检索。无需额外工具，即可直接从数据库中构建复杂的问答系统、聊天机器人和摘要生成器。\n\n- **📈 生产就绪且可扩展**：Weaviate 专为关键任务型应用而设计。通过原生支持[水平扩展](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Fconfiguration\u002Fhorizontal-scaling)、[多租户](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmanage-collections\u002Fmulti-tenancy)、[复制](https:\u002F\u002Fdocs.weaviate.io\u002Fdeploy\u002Fconfiguration\u002Freplication)以及细粒度的[基于角色的访问控制（RBAC）](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconfiguration\u002Frbac)，您可以轻松地从快速原型开发过渡到大规模生产环境。\n\n- **💰 低成本运营**：借助内置的[向量压缩](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fconfiguration\u002Fcompression)，大幅降低资源消耗和运营成本。向量量化和多向量编码可在几乎不影响搜索性能的情况下减少内存使用。\n\n- **⏱️ 对象 TTL**：通过为每个集合配置可调的[生存时间（TTL）](https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fmanage-collections\u002Ftime-to-live)设置，自动过期并移除过时数据，同时完全支持 RBAC 和多租户功能。\n\n如需查看所有功能的完整列表，请访问[Weaviate 官方文档](https:\u002F\u002Fdocs.weaviate.io)。\n\n## 有用资源\n\n### AI 代理技能\n\n[Weaviate Agent Skills](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fagent-skills) 是一套专为 AI 编码代理（Claude Code、Cursor、GitHub Copilot 等）设计的技能库，使它们能够更准确、更高效地与 Weaviate 集成。这些技能涵盖了搜索、查询、集合管理、数据导入，以及完整的应用蓝图（RAG、代理式 RAG、聊天机器人等）。\n\n安装方法如下：\n\n```bash\nnpx skills add weaviate\u002Fagent-skills\n```\n\n### 演示项目与示例代码\n\n这些演示项目是实际运行的应用程序，展示了 Weaviate 的部分强大功能。其源代码可在 GitHub 上获取。\n\n- [Elysia](https:\u002F\u002Felysia.weaviate.io) ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Felysia))：Elysia 是一个基于决策树的代理系统，能够智能地决定使用哪些工具、已获得哪些结果，以及是否需要继续处理或目标是否已完成。\n- [Verba](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fverba-open-source-rag-app) ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fverba))：这是一款由社区驱动的开源应用，旨在提供开箱即用的端到端、简化且用户友好的界面，用于增强检索的生成式模型（RAG）。\n- [Healthsearch](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fhealthsearch-demo) ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fhealthsearch-demo))：这是一个开源项目，旨在展示如何利用用户撰写的评论和查询，根据特定的健康效果检索补充剂产品。\n- Awesome-Moviate ([GitHub](https:\u002F\u002Fgithub.com\u002Fweaviate-tutorials\u002Fawesome-moviate))：这是一款电影搜索与推荐引擎，支持基于关键词（BM25）、语义以及混合搜索。\n\n我们还维护着大量的 **Jupyter Notebook** 和 **TypeScript 代码片段** 仓库，涵盖如何使用 Weaviate 的各项功能及集成：\n\n- [Weaviate Python 示例代码](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Frecipes\u002F)\n- [Weaviate TypeScript 示例代码](https:\u002F\u002Fgithub.com\u002Fweaviate\u002Frecipes-ts\u002F)\n\n### 博客文章\n\n- [什么是向量数据库](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fwhat-is-a-vector-database)\n- [什么是向量搜索](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fvector-search-explained)\n- [什么是混合搜索](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fhybrid-search-explained)\n- [如何选择嵌入模型](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fhow-to-choose-an-embedding-model)\n- [什么是 RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fintroduction-to-rag)\n- [RAG 评估](https:\u002F\u002Fweaviate.io\u002Fblog\u002Frag-evaluation)\n- [高级 RAG 技术](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fadvanced-rag)\n- [什么是多模态 RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fmultimodal-rag)\n- [什么是代理式 RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fwhat-is-agentic-rag)\n- [什么是图结构 RAG](https:\u002F\u002Fweaviate.io\u002Fblog\u002Fgraph-rag)\n- [晚期交互模型概述](https:\u002F\u002Fweaviate.io\u002Fblog\u002Flate-interaction-overview)\n\n### 集成\n\nWeaviate 与众多外部服务集成：\n\n| 类别                                                                                   | 描述                                                | 集成                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |\n| ------------------------------------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **[云超大规模服务商](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcloud-hyperscalers)**         | 大规模计算和存储                          | [AWS](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcloud-hyperscalers\u002Faws), [Google](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcloud-hyperscalers\u002Fgoogle)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |\n| **[计算基础设施](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure)**           | 运行和扩展容器化应用                   | [Modal](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure\u002Fmodal), [Replicate](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure\u002Freplicate), [Replicated](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fcompute-infrastructure\u002Freplicated)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |\n| **[数据平台](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms)**                     | 数据摄取和网页抓取                            | [Airbyte](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fairbyte), [Aryn](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Faryn), [Boomi](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fboomi), [Box](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fbox), [Confluent](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fconfluent), [Astronomer](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fastronomer), [Context Data](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fcontext-data), [Databricks](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fdatabricks), [Firecrawl](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Ffirecrawl), [IBM](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Fibm), [Unstructured](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fdata-platforms\u002Funstructured)                |\n| **[LLM 和智能体框架](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks)**           | 构建智能体和生成式 AI 应用                | [Agno](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fagno), [Composio](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fcomposio), [CrewAI](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fcrewai), [DSPy](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fdspy), [Dynamiq](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fdynamiq), [Haystack](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fhaystack), [LangChain](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Flangchain), [LlamaIndex](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fllamaindex), [N8n](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fn8n), [Semantic Kernel](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Fllm-agent-frameworks\u002Fsemantic-kernel)                                   |\n| **[运维工具](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations)**                         | 用于监控和分析生成式 AI 工作流的工具 | [AIMon](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Faimon), [Arize](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Farize), [Cleanlab](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fcleanlab), [Comet](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fcomet), [DeepEval](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fdeepeval), [Langtrace](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Flangtrace), [LangWatch](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Flangwatch), [Nomic](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fnomic), [Patronus AI](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fpatronus), [Ragas](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fragas), [TruLens](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Ftrulens), [Weights & Biases](https:\u002F\u002Fdocs.weaviate.io\u002Fintegrations\u002Foperations\u002Fwandb) |\n\n## 贡献\n\n我们欢迎并感谢您的贡献！请参阅我们的[贡献者指南](https:\u002F\u002Fdocs.weaviate.io\u002Fcontributor-guide)，了解开发环境搭建、代码风格规范、测试要求以及拉取请求的提交流程。\n\n加入我们的[社区论坛](https:\u002F\u002Fforum.weaviate.io\u002F)，讨论想法并获取帮助。\n\n## 许可证\n\nBSD 3-Clause 许可证。详情请参阅[LICENSE](.\u002FLICENSE)。","# Weaviate 快速上手指南\n\nWeaviate 是一个开源的云原生向量数据库，支持存储对象与向量数据，适用于语义搜索、RAG（检索增强生成）、推荐系统及聊天机器人等 AI 应用场景。它支持导入时自动向量化（集成 OpenAI、Cohere、HuggingFace 等模型）或直接导入预计算向量。\n\n## 环境准备\n\n*   **操作系统**：Linux、macOS 或 Windows（需安装 WSL2 或 Docker Desktop）。\n*   **核心依赖**：\n    *   [Docker](https:\u002F\u002Fdocs.docker.com\u002Fget-docker\u002F) 及 Docker Compose（推荐使用 v2 版本）。\n    *   Python 3.8+（用于运行客户端示例）。\n*   **网络建议**：由于拉取镜像可能涉及国外源，建议配置 Docker 镜像加速器（如阿里云、腾讯云等国内加速地址）以提升下载速度。\n\n## 安装步骤\n\n### 1. 创建配置文件\n在项目目录下创建 `docker-compose.yml` 文件，内容如下。该配置将启动 Weaviate 服务及一个轻量级的本地向量化模型（Model2Vec）。\n\n```yml\nservices:\n  weaviate:\n    image: cr.weaviate.io\u002Fsemitechnologies\u002Fweaviate:1.36.0\n    ports:\n      - \"8080:8080\"\n      - \"50051:50051\"\n    environment:\n      ENABLE_MODULES: text2vec-model2vec\n      MODEL2VEC_INFERENCE_API: http:\u002F\u002Ftext2vec-model2vec:8080\n\n  # A lightweight embedding model that will generate vectors from objects during import\n  text2vec-model2vec:\n    image: cr.weaviate.io\u002Fsemitechnologies\u002Fmodel2vec-inference:minishlab-potion-base-32M\n```\n\n> **提示**：如果在国内拉取 `cr.weaviate.io` 镜像较慢，可尝试在 Docker Daemon 配置中注册镜像加速，或手动拉取后重命名标签。\n\n### 2. 启动服务\n在终端执行以下命令启动 Weaviate 和嵌入模型服务：\n\n```bash\ndocker compose up -d\n```\n\n### 3. 安装 Python 客户端\n安装官方提供的 Python 客户端库：\n\n```bash\npip install -U weaviate-client\n```\n\n## 基本使用\n\n以下示例演示了如何连接本地 Weaviate、创建集合（Collection）、插入数据（自动生成向量）并执行语义搜索。\n\n```python\nimport weaviate\nfrom weaviate.classes.config import Configure, DataType, Property\n\n# 连接到本地 Weaviate 实例\nclient = weaviate.connect_to_local()\n\n# 创建一个名为 \"Article\" 的集合\n# 配置属性：content (文本类型)\n# 配置向量化：使用 text2vec-model2vec 在导入时自动生成向量\nclient.collections.create(\n    name=\"Article\",\n    properties=[Property(name=\"content\", data_type=DataType.TEXT)],\n    vector_config=Configure.Vectors.text2vec_model2vec(),  \n    # 若需导入自有预生成向量，可使用：vector_config=Configure.Vectors.self_provided()\n)\n\n# 获取集合并插入数据\narticles = client.collections.get(\"Article\")\narticles.data.insert_many(\n    [\n        {\"content\": \"Vector databases enable semantic search\"},\n        {\"content\": \"Machine learning models generate embeddings\"},\n        {\"content\": \"Weaviate supports hybrid search capabilities\"},\n    ]\n)\n\n# 执行语义搜索\n# 查询含义为 \"Search objects by meaning\" 的最相似对象\nresults = articles.query.near_text(query=\"Search objects by meaning\", limit=1)\nprint(results.objects[0])\n\n# 关闭连接\nclient.close()\n```\n\n### 下一步\n*   **更换模型**：示例使用了内置的 `Model2Vec`，生产环境中可替换为 OpenAI、Cohere 或其他支持的模型提供商。\n*   **多语言支持**：Weaviate 还提供 JavaScript\u002FTypeScript、Java、Go 和 C# 等客户端库。\n*   **更多功能**：探索混合搜索（BM25 + 向量）、多租户管理、RBAC 权限控制及 RAG 生成式搜索等高级特性。","某电商平台的客服团队正在构建一个智能问答系统，旨在让用户能通过自然语言描述快速找到对应的商品售后政策和技术文档。\n\n### 没有 weaviate 时\n- **搜索精度低**：传统关键词匹配无法理解“屏幕碎了怎么办”与“显示屏破裂维修政策”之间的语义关联，导致大量相关文档被遗漏。\n- **架构复杂冗余**：需要分别维护关系型数据库存储文档元数据（如分类、日期）和独立的向量服务进行相似度计算，开发和维护成本高昂。\n- **过滤功能缺失**：难以在语义搜索的同时结合结构化条件（如“仅限 2024 年发布的手机型号”），往往需要先全量搜索再在代码层二次过滤，效率极低。\n- **扩展性差**：随着文档量激增，自建向量索引在并发查询时延迟飙升，且缺乏原生的多租户隔离机制，难以支撑不同品牌商家的独立数据需求。\n\n### 使用 weaviate 后\n- **语义理解精准**：weaviate 内置向量模型自动将文本转化为嵌入向量，用户即使使用口语化表达也能精准命中核心政策文档。\n- **架构统一简化**：weaviate 同时存储对象属性和向量数据，在一个数据库中即可完成混合检索，大幅降低了技术栈复杂度。\n- **原生混合过滤**：支持在单次查询中结合向量相似度与结构化过滤（如按时间、品类筛选），直接返回符合所有条件的精确结果。\n- **云原生高可用**：凭借内置的多租户、复制和 RBAC 权限控制，系统轻松应对海量数据增长，并为不同商家提供安全隔离的数据空间。\n\nweaviate 通过将向量搜索与结构化过滤无缝融合，让开发者能以极简架构构建出既懂语义又具备精细控制能力的高性能检索应用。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fweaviate_weaviate_3450ac57.png","Weaviate","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fweaviate_0b25af12.png","Weaviate creates database software like the Weaviate vector search engine",null,"hello@weaviate.io","weaviate_io","https:\u002F\u002Fweaviate.io","https:\u002F\u002Fgithub.com\u002Fweaviate",[81,85,89,93,97,101,105,108,111,113],{"name":82,"color":83,"percentage":84},"Go","#00ADD8",96.8,{"name":86,"color":87,"percentage":88},"Python","#3572A5",1.2,{"name":90,"color":91,"percentage":92},"Assembly","#6E4C13",1.1,{"name":94,"color":95,"percentage":96},"Shell","#89e051",0.6,{"name":98,"color":99,"percentage":100},"C","#555555",0.3,{"name":102,"color":103,"percentage":104},"Makefile","#427819",0,{"name":106,"color":107,"percentage":104},"Jinja","#a52a22",{"name":109,"color":110,"percentage":104},"Dockerfile","#384d54",{"name":112,"color":83,"percentage":104},"Go Template",{"name":114,"color":115,"percentage":104},"JavaScript","#f1e05a",16027,1256,"2026-04-18T14:14:48","BSD-3-Clause","Linux, macOS, Windows","非必需（可选用于加速嵌入模型推理，具体取决于所选模型提供商）","最低未说明，推荐 8GB+（生产环境需根据数据量和并发调整）",{"notes":124,"python":125,"dependencies":126},"Weaviate 核心服务通常通过 Docker 或 Kubernetes 部署，不直接依赖本地 Python 环境运行。Python 仅用于安装客户端库（weaviate-client）进行交互。支持多种嵌入模型提供商（如 OpenAI, Cohere, HuggingFace 等），若使用本地轻量级模型（如示例中的 Model2Vec），需额外运行对应的容器服务。生产环境支持水平扩展、多租户和复制功能。","3.8+ (客户端库要求)",[127,128,129],"weaviate-client","docker","docker-compose",[15,131,16,14],"其他",[133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,64,151],"search-engine","semantic-search","semantic-search-engine","vector-search","vector-search-engine","vector-database","approximate-nearest-neighbor-search","image-search","hnsw","information-retrieval","mlops","nearest-neighbor-search","neural-search","recommender-system","similarity-search","vectors","generative-search","hybrid-search","grpc","2026-03-27T02:49:30.150509","2026-04-19T06:02:50.067919",[155,160,165,170,175,179],{"id":156,"question_zh":157,"answer_zh":158,"source_url":159},41657,"如何在 Weaviate 中实现“非”（Not）或“不包含”（Not Like）的过滤条件？","该功能实际上已经存在，可以通过组合多个条件来实现。用户无需等待新的 'Not' 操作符，应参考官方文档中关于“多条件过滤”（filter with multiple conditions）的部分。如果在评论中看到建议添加新操作符的提案，通常是因为忽略了现有的组合过滤功能。请查阅文档：https:\u002F\u002Fdocs.weaviate.io\u002Fweaviate\u002Fsearch\u002Ffilters#filter-with-multiple-conditions","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fissues\u002F3683",{"id":161,"question_zh":162,"answer_zh":163,"source_url":164},41658,"如何检查一个字段是否包含数组中的任意值（类似 SQL 的 IN 操作）？","Weaviate 已支持 `ContainsAny` 和 `ContainsAll` 过滤操作符。在使用 Python 客户端（版本 3.23.2+）和 Weaviate（版本 1.21.1+）时，可以直接在过滤器中使用这些操作符。常见错误是在过滤数组值时错误地添加了引号，确保传递的是真正的数组对象而不是字符串表示形式。可以参考相关的 Colab 笔记本查看具体用法示例。","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fissues\u002F2387",{"id":166,"question_zh":167,"answer_zh":168,"source_url":169},41659,"如何在生成向量时排除类名（Class Name）或属性名（Property Name）的影响？","可以在定义 Schema 时通过配置参数来控制向量化行为。具体设置如下：\n1. 设置 `vectorizeClassName: false` 以排除类名对向量的影响（默认为 true）。\n2. 在具体属性中设置 `vectorizePropertyName: false` 以排除属性名的影响（默认为 false）。\n这样向量将仅基于属性的实际值生成，适用于元数据不应干扰语义搜索的场景。","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fissues\u002F1047",{"id":171,"question_zh":172,"answer_zh":173,"source_url":174},41660,"混合搜索（Hybrid Search）是否支持过滤条件（Filters）？","在早期版本（如 v1.17）中，BM25 稀疏搜索尚未完全支持过滤条件，因此混合搜索（结合向量和 BM25）也暂时无法在查询中直接使用过滤器。维护者确认这是一个已知差距，并计划在后续版本中添加对 BM25 过滤的支持，从而实现混合搜索中的完整过滤功能。建议关注版本更新日志以获取此功能的上线时间。","https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fissues\u002F2134",{"id":176,"question_zh":177,"answer_zh":178,"source_url":164},41661,"为什么我的数组过滤查询没有返回预期结果？","最常见的原因是在过滤器中错误地将数组值用引号包裹成了字符串。例如，应该传递 `['x', 'y']` 而不是 `\"['x', 'y']\"`。请检查代码中传递给 `ContainsAny` 或 `ContainsAll` 操作符的值是否为合法的数组格式。修正引号问题后，过滤功能通常即可正常工作。",{"id":180,"question_zh":181,"answer_zh":182,"source_url":169},41662,"如何优化向量搜索以忽略无关的元数据字段（如列名、索引等）？","通过在 Schema 配置中显式禁用特定字段的向量化。对于不需要参与语义理解的元数据字段（如 'index', 'dataType' 等属性名），将其 `vectorizePropertyName` 设为 `false`；如果整个类名也不应影响向量空间，将类的 `vectorizeClassName` 设为 `false`。这将确保向量空间仅由核心内容值（如文本内容）决定，提高搜索的相关性。",[184,189,194,199,204,209,214,219,224,229,234,239,244,249,254,259,264,269,274,279],{"id":185,"version":186,"summary_zh":187,"released_at":188},333708,"v1.35.18","## 重大变更\n*无*\n\n## 新功能\n*无*\n\n## 修复\n* 修复：由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11060 中实现，处理不同键数量下二级索引大小的累积问题。\n* 启动优化：由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11064 中实现，通过更频繁地检查数据库状态来加快启动速度。\n* 修复：由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11067 中实现，重构了桶的创建逻辑，以使用二级索引选项。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.35.17...v1.35.18","2026-04-17T14:21:29",{"id":190,"version":191,"summary_zh":192,"released_at":193},333709,"v1.37.1","## 重大变更\n*无*\n\n## 新功能\n*无*\n\n## 修复\n* 修复：由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11060 中实现，处理不同键数量下二级索引大小的累积问题。\n* 启动优化：通过更频繁地检查数据库状态来加快启动速度，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11064 中实现。\n* 修复：重构桶创建逻辑，使用选项来配置二级索引，由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11067 中实现。\n* [MCP] 权限重构，由 @g-despot 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11037 中实现。\n* 将 `export default` 路径默认设置为空，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11068 中实现。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.37.0...v1.37.1","2026-04-17T10:28:16",{"id":195,"version":196,"summary_zh":197,"released_at":198},333710,"v1.36.12","## 重大变更\n*无*\n\n## 新功能\n*无*\n\n## 修复\n* 修复：由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11060 中实现，处理不同键数量下二级索引大小的累积问题。\n* 启动优化：通过更频繁地检查数据库状态来加快启动速度，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11064 中实现。\n* 修复：重构桶创建逻辑，改为使用选项来配置二级索引，由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11067 中实现。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.11...v1.36.12","2026-04-17T10:27:24",{"id":200,"version":201,"summary_zh":202,"released_at":203},333711,"v1.37.0","## 破坏性变更\n\n*无*\n\n## HFresh（预览版）\n*进行了大量改进，包括多项性能优化，有效降低了内存占用、磁盘写入次数和内存分配。*\n\n* HFresh：@trengrj 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10714 中提升了最大发布大小的下限。\n* hfresh \u002F geo：@asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10815 中实现了备份期间持续出队。\n* 队列：@asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10927 中将 storagestate.ErrStatusReadOnly 视为临时性错误。\n* hfresh：@asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10930 中优化了初始化逻辑。\n* 队列：@asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10966 中添加了缺失的 Pause 调用。\n* 为 HFresh 添加自动分类功能：@trengrj 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10972 中完成。\n* hfresh：@asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10999 中修复了指标报告问题。\n\n## 安全的 MCP 服务器（预览版）\n*提供安全的内置模型上下文协议（MCP）服务器接口。这使得 AI 代理（如 Claude、IDE 等）无需自定义代码即可原生读写 Weaviate 数据库。该实现将 Weaviate 从被动检索引擎转变为面向代理工作流的“长期记忆”系统，开箱即用地支持混合搜索、RAG 和多租户场景。*\n\n* 引入原生 MCP API：@tsmith023 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F8245 中完成。\n* MCP：重构文件结构并添加自定义参数与响应描述：@g-despot 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10948 中完成。\n\n## BlobHash 属性类型\n*新增 BlobHash 属性类型，可自动将 Blob 数据以哈希形式存储在数据库中，从而大幅减少处理 Blob 数据所需的磁盘空间。*\n\n* 功能：添加 BlobHash 属性类型：@antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10763 中完成。\n\n## 可扩展的分词器（第一阶段）\n*将 Weaviate 的分词支持从以英语为中心且受操作限制的功能，演进为自助式、可观测且可扩展的基础，适用于代理型和多语言工作负载。此次改进主要针对拉丁语系语言，而对 CJK 语言的支持将在第二阶段进一步完善。*\n\n* 功能：添加分词器端点及中间件集成：@amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10863 中完成。\n* 重构：更新 TestPropertyTokenize 以使用 collection_factory 和 weaviate_client：@amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10902 中完成。\n* 功能：为文本属性添加不区分重音的处理选项：@amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10860 中完成。\n* 功能：自定义停用词预设：@amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10931 中完成。\n* 修复：改进可选分词器初始化中的错误处理：@amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11035 中完成。\n\n## 备份可靠性提升\n*提升了大型集合的备份可靠性，支持非活跃租户，避免中断压缩操作，并引入增量备份功能。*\n\n* 文件 ba","2026-04-16T13:58:51",{"id":205,"version":206,"summary_zh":207,"released_at":208},333712,"v1.35.17","## 中断性变更\n*无*\n\n## 新特性\n*无*\n\n## 修复\n* 功能：添加 baseURL 验证支持，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10878 中实现。\n* 功能（向后兼容性）：如果模块不可用，在降级路径上显示描述性错误，由 @moogacs 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10876 中实现。\n* 测试：改进崩溃恢复测试中的异步删除验证，由 @jeroiraz 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10886 中实现。\n* 队列备份，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10868 中实现。\n* 依赖项（安全）：将 github.com\u002Fbuger\u002Fjsonparser 升级至 v1.1.2，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10898 中实现。\n* 在 S3 客户端中添加缺失的关闭操作，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10903 中实现。\n* 队列：将指标所有权转移给调度器，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10916 中实现。\n* 使用 SHA 哈希而非标签固定依赖项版本，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10907 中实现。\n* 为 PR 添加检查隐藏字符的 linter 和工作流，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10909 中实现。\n* 仅针对 HNSW 的稀疏访问列表，由 @abdelr 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10893 中实现。\n* 修复隐藏字符 linter 中的两个潜在问题，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10922 中实现。\n* 修复（卸载）：在 FREEZING\u002FUNFREEZING 过渡状态下拒绝租户更新，由 @moogacs 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10951 中实现。\n* 修复：在压缩预加载循环中使用长度检查代替空值检查，由 @rlmanrique 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10950 中实现。\n* 修复（Raft 应用程序）：允许在部分租户更新时继续执行 updateStore 操作，由 @moogacs 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10900 中实现。\n* 当尝试在整数属性上使用浮点数进行过滤时返回错误，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10978 中实现。\n* 更新易受攻击的 black 版本，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10982 中实现。\n* 更新所有存在安全问题的依赖项，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10988 中实现。\n* HNSW：添加对 compactv2 降级的支持，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10992 中实现。\n* 将基础备份 ID 的验证方式与普通 ID 一致，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10997 中实现。\n* 实现 `INACTIVE` 租户的备份和恢复功能，由 @tsmith023 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10851 中实现。\n* 安全（Dockerfile）：将 libcrypto3、libssl3 和 openssl 升级到最新版本，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11003 中实现。\n* 对 INACTIVE 租户备份代码库进行小幅改进，由 @tsmith023 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11001 中实现。\n* 修复：在预加载向量后持久化压缩元数据，以避免重启时数据丢失，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11025 中实现。\n* 修复 S3 备份客户端：解决 HomeDir 路径重复以及使用路径覆盖时初始化清理的问题，由 @jfrancoa 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fp","2026-04-15T17:42:00",{"id":210,"version":211,"summary_zh":212,"released_at":213},333713,"v1.36.11","## 中断性变更\n*无*\n\n## 新特性\n*无*\n\n## 修复\n* 修复：在预加载向量后持久化压缩元数据，以避免重启时数据丢失，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11025 中实现。\n* 修复 S3 备份客户端问题：HomeDir 路径重复以及使用路径覆盖时的初始化清理，由 @jfrancoa 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11033 中实现。\n* 安全性：升级 musl、musl-utils 和 zlib 的 Alpine 软件包，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11044 中完成。\n* 修复：改进可选分词器初始化中的错误处理，由 @amourao 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11035 中完成。\n* 修复（multi2vec-google）：添加对 Google AI Studio API 密钥头的支持，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11045 中实现。\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.10...v1.36.11","2026-04-17T10:28:58",{"id":215,"version":216,"summary_zh":217,"released_at":218},333714,"v1.37.0-rc.1","这是即将发布的 v1.37.0 版本的候选发布版。\n\n候选发布版（RC）意味着该版本的功能已完整，并且已完成 Beta 测试。在 RC 阶段发现的任何问题都可能导致新的 RC 版本发布。最终的 RC 版本将成为稳定版。我们非常欢迎您对这个预发布版本提出反馈。\n\n此预发布版本包含以下内容：\n\n- 可扩展的分词器（第一阶段）\n- 增量备份（GA）\n- 内部集群通信：gRPC 与安全加固\n- 异步复制生产就绪性（GA）\n- 模式变更——删除向量索引（GA）\n- 安全的 MCP 服务器（预览）","2026-04-13T07:21:02",{"id":220,"version":221,"summary_zh":222,"released_at":223},333715,"v1.36.10","## 重大变更\n*无*\n\n## 新功能\n*无*\n\n## 修复\n* 修复（卸载）：在 FREEZING\u002FUNFREEZING 过渡状态下拒绝租户更新，由 @moogacs 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10951 中完成\n* 修复：在压缩预加载循环中使用长度检查代替空值检查，由 @rlmanrique 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10950 中完成\n* 修复（Raft 应用）：允许在部分租户更新时继续执行 updateStore 操作，由 @moogacs 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10900 中完成\n* 当尝试对整数属性使用浮点数进行过滤时返回错误，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10978 中完成\n* 更新易受攻击的 black 版本，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10982 中完成\n* 更新所有存在安全问题的依赖项，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10988 中完成\n* 为 HFresh 添加自动分类，由 @trengrj 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10972 中完成\n* HNSW：添加对 compactv2 降级的支持，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10992 中完成\n* 修复：为 GroupBy 查询返回查询性能数据，由 @byronvoorbach 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10980 中完成\n* 对基础备份 ID 的验证方式与普通 ID 一致，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10997 中完成\n* 实现 `INACTIVE` 租户的备份与恢复功能，由 @tsmith023 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10851 中完成\n* 安全性（Dockerfile）：将 libcrypto3、libssl3 和 openssl 升级到最新版本，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11003 中完成\n* 对 INACTIVE 租户备份代码库的细微改进，由 @tsmith023 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F11001 中完成\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.9...v1.36.10","2026-04-11T11:31:01",{"id":225,"version":226,"summary_zh":227,"released_at":228},333716,"v1.37.0-rc.0","这是即将发布的 v1.37.0 版本的候选发布版。\n\n候选发布版（RC）意味着该版本的功能已完整，并已完成 beta 测试。在 RC 阶段发现的任何问题都可能导致新的 RC 版本发布。最终的 RC 版本将成为稳定版。我们非常欢迎您对这个预发布版本提出反馈。\n\n此预发布版本包含以下内容：\n\n- 可扩展的分词器（第一阶段）\n- 增量备份（GA）\n- 内部集群通信：gRPC 与安全加固\n- 异步复制生产就绪性（GA）\n- 模式变更——删除向量索引（GA）","2026-04-03T07:37:51",{"id":230,"version":231,"summary_zh":232,"released_at":233},333717,"v1.36.9","## 破坏性变更\n*无*\n\n## 新特性\n*无*\n\n## 修复\n* 测试：在崩溃恢复测试中改进异步删除验证，由 @jeroiraz 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10886 中完成\n* 实现 AUTHENTICATION_OIDC_INSECURE_SKIP_TLS_VERIFY，由 @dudanogueira 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10813 中完成\n* 队列备份，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10868 中完成\n* 依赖（安全）：将 github.com\u002Fbuger\u002Fjsonparser 升级至 v1.1.2，由 @antas-marcin 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10898 中完成\n* 功能：添加按需查询剖析支持，由 @byronvoorbach 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10696 中完成\n* 在 S3 客户端中添加缺失的关闭操作，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10903 中完成\n* 队列：将指标所有权转移给调度器，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10916 中完成\n* 使用 SHA 哈希而非标签固定依赖版本，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10907 中完成\n* 为 PR 添加用于检测隐藏字符的 linter 和工作流，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10909 中完成\n* 仅针对 HNSW 的稀疏访问列表，由 @abdelr 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10893 中完成\n* 修复隐藏字符 linter 中的两个潜在问题，由 @dirkkul 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10922 中完成\n* hfresh：改进初始化逻辑，由 @asdine 在 https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10930 中完成\n\n\n**完整变更日志**：https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.8...v1.36.9","2026-04-03T06:56:04",{"id":235,"version":236,"summary_zh":237,"released_at":238},333718,"v1.36.8","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* Use hardlinks in test to mirror production by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10866\r\n* [Compression] Catch target vector error by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10806\r\n* [fix] Read Link by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10865\r\n* feat: add baseURL validation support by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10878\r\n* feat(backward-compatibility): descriptive error on downgrade path if module is not avaliable by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10876\r\n* refact(dynamic lazy load shards) backward compatibility if DISABLE_LAZY_LOAD_SHARDS=true by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10877\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.7...v1.36.8","2026-03-30T05:34:54",{"id":240,"version":241,"summary_zh":242,"released_at":243},333719,"v1.34.20","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* Fix edge case of file being deleted between stat and open by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10716\r\n* Fix potential race during RBAC restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10706\r\n* modules(multi2vec-google): add support for Google AI Studio models by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10719\r\n* test(multi2vec-google): add Google AI Studio tests by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10725\r\n* feat: add audio support to multi2vec-google module by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10733\r\n* Fix race between empty cache prefill and compression by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10715\r\n* Fix race in RBAC restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10736\r\n* [fix] Checks on cache by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10673\r\n* Fix flaky TestNetworkIsolationSplitBrain with periodic memberlist rejoin by @etiennedi in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10698\r\n* optimization: object digest with partial unmarshalling by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10741\r\n* chore(log): decrease log level to DEBUG by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10757\r\n* fix-test(offload): Test_UploadS3Journey simulate network down on cloud provider by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10713\r\n* test: reduce MaxWorkers in async replication config from 20 to 10 by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10759\r\n* Let claude prefer table driven tests by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10766\r\n* feat: add DisableDimensionMetrics configuration to control reporting by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10800\r\n* fix: ensure original objects are preserved during read-repair in CheckConsistency by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10788\r\n* chore: include switching to binary encoding for digest responses by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10791\r\n* fix: avoid double metric registration by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10808\r\n* security: bump google.golang.org\u002Fgrpc to v1.79.3 by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10818\r\n* security: add .claude folder to CODEOWNERS by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10827\r\n* fix(ci): add Orca scans to PR docker image builds by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10686\r\n* feat: introduce token source authentication for backups-gcs and usage-gcs by @gkampitakis in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10540\r\n* [fix] Add checks on quantized distance bag by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10755\r\n* fix: nil pointer panic in UpdateLastUsedTimestamp for unknown users by @jfrancoa in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10833\r\n* Fix cycle callbacks to include dynamic index by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10846\r\n* feat: add name to CycleManager with start\u002Fstop logging by @aliszka in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10845\r\n* perf: compaction replace use less heap by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10678\r\n* Improve parsing of property length in BMW segments by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10844\r\n* [Compression] Catch target vector error by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10806\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.34.19...v1.34.20","2026-03-26T07:21:37",{"id":245,"version":246,"summary_zh":247,"released_at":248},333720,"v1.35.16","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* security: add .claude folder to CODEOWNERS by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10827\r\n* fix(ci): add Orca scans to PR docker image builds by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10686\r\n* feat: introduce token source authentication for backups-gcs and usage-gcs by @gkampitakis in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10540\r\n* [fix] Add checks on quantized distance bag by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10755\r\n* fix: nil pointer panic in UpdateLastUsedTimestamp for unknown users by @jfrancoa in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10833\r\n* Improve UX when mistakenly sending \"2\" or \"2.0\" when filtering on int\u002Fnumber props by @tsmith023 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10843\r\n* Improve list backup speed by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10804\r\n* Fix cycle callbacks to include dynamic index by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10846\r\n* feat: add name to CycleManager with start\u002Fstop logging by @aliszka in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10845\r\n* perf: compaction replace use less heap by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10678\r\n* Improve parsing of property length in BMW segments by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10844\r\n* Use hardlinks in test to mirror production by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10866\r\n* [Compression] Catch target vector error by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10806\r\n* [fix] Read Link by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10865\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.35.15...v1.35.16","2026-03-26T07:22:37",{"id":250,"version":251,"summary_zh":252,"released_at":253},333721,"v1.36.7","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* security: add .claude folder to CODEOWNERS by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10827\r\n* Achieve true BC and server-side default behaviour (#10783) by @tsmith023 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10828\r\n* fix(ci): add Orca scans to PR docker image builds by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10686\r\n* feat: introduce token source authentication for backups-gcs and usage-gcs by @gkampitakis in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10540\r\n* [fix] Add checks on quantized distance bag by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10755\r\n* fix: nil pointer panic in UpdateLastUsedTimestamp for unknown users by @jfrancoa in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10833\r\n* Improve UX when mistakenly sending \"2\" or \"2.0\" when filtering on int\u002Fnumber props by @tsmith023 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10843\r\n* Improve list backup speed by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10804\r\n* Fix cycle callbacks to include dynamic index by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10846\r\n* feat: add name to CycleManager with start\u002Fstop logging by @aliszka in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10845\r\n* perf: compaction replace use less heap by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10678\r\n* Improve parsing of property length in BMW segments by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10844\r\n* chore(log): update HNSW_STARTUP_WAIT_FOR_VECTOR_CACHE warn log line by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10855\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.6...v1.36.7","2026-03-25T13:56:14",{"id":255,"version":256,"summary_zh":257,"released_at":258},333722,"v1.36.6","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* feat: add audio support to multi2vec-google module by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10733\r\n* Fix race between empty cache prefill and compression by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10715\r\n* Fix race in RBAC restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10736\r\n* [fix] Checks on cache by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10673\r\n* fix: Add IPv6 support for clustering (#10722) by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10737\r\n* !feat(shards): dynamic lazy load shards by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10414\r\n* Fix flaky TestNetworkIsolationSplitBrain with periodic memberlist rejoin by @etiennedi in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10698\r\n* optimization: object digest with partial unmarshalling by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10741\r\n* chore(log): decrease log level to DEBUG by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10757\r\n* fix-test(offload): Test_UploadS3Journey simulate network down on cloud provider by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10713\r\n* fix: set secondary key on Bucket::Delete calls by @aliszka in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10687\r\n* test: reduce MaxWorkers in async replication config from 20 to 10 by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10759\r\n* Remove immutability checks for skipDefaultQuantization\u002FtrackDefaultQuantization by @bevzzz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10751\r\n* Fix logic bug creating a timer by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10765\r\n* Fix flaky test by waiting for terminal states by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10764\r\n* Let claude prefer table driven tests by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10766\r\n* Fix race in backup test by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10772\r\n* Add mock of replicator commit to avoid flakes in `TestReplicatedIndicesShutdown` by @tsmith023 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10780\r\n* fix: sets FullParser as cron schedule's parser by @aliszka in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10798\r\n* Avoid halting compactions during backup by @tsmith023 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10677\r\n* Add DEFAULT_SHARDING_COUNT env var to override default shard count by @etiennedi in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10797\r\n* feat: add DisableDimensionMetrics configuration to control reporting by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10800\r\n* fix: ensure original objects are preserved during read-repair in CheckConsistency by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10788\r\n* chore: include switching to binary encoding for digest responses by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10791\r\n* fix: avoid double metric registration by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10808\r\n* security: bump google.golang.org\u002Fgrpc to v1.79.3 by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10818\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.5...v1.36.6","2026-03-19T18:42:52",{"id":260,"version":261,"summary_zh":262,"released_at":263},333723,"v1.35.15","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* Backup chunk size split files by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10633\r\n* Fix edge case of file being deleted between stat and open by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10716\r\n* Fix potential race during RBAC restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10706\r\n* modules(multi2vec-google): add support for Google AI Studio models by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10719\r\n* test(multi2vec-google): add Google AI Studio tests by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10725\r\n* Close reader after finishing to avoid deadlock in restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10729\r\n* feat: add audio support to multi2vec-google module by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10733\r\n* Fix race between empty cache prefill and compression by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10715\r\n* Fix race in RBAC restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10736\r\n* [fix] Checks on cache by @robbespo00 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10673\r\n* Fix flaky TestNetworkIsolationSplitBrain with periodic memberlist rejoin by @etiennedi in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10698\r\n* optimization: object digest with partial unmarshalling by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10741\r\n* chore(log): decrease log level to DEBUG by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10757\r\n* fix-test(offload): Test_UploadS3Journey simulate network down on cloud provider by @moogacs in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10713\r\n* fix: set secondary key on Bucket::Delete calls by @aliszka in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10687\r\n* test: reduce MaxWorkers in async replication config from 20 to 10 by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10759\r\n* Remove immutability checks for skipDefaultQuantization\u002FtrackDefaultQuantization by @bevzzz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10751\r\n* Fix logic bug creating a timer by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10765\r\n* Fix flaky test by waiting for terminal states by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10764\r\n* Let claude prefer table driven tests by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10766\r\n* Fix race in backup test by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10772\r\n* Avoid halting compactions during backup by @tsmith023 in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10677\r\n* Add DEFAULT_SHARDING_COUNT env var to override default shard count by @etiennedi in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10797\r\n* feat: add DisableDimensionMetrics configuration to control reporting by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10800\r\n* fix: ensure original objects are preserved during read-repair in CheckConsistency by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10788\r\n* chore: include switching to binary encoding for digest responses by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10791\r\n* fix: avoid double metric registration by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10808\r\n* security: bump google.golang.org\u002Fgrpc to v1.79.3 by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10818\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.35.14...v1.35.15","2026-03-19T13:20:39",{"id":265,"version":266,"summary_zh":267,"released_at":268},333724,"v1.36.5","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* Add client version to audit log by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10709\r\n* Backup chunk size split files by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10633\r\n* HFresh increase max posting size floor by @trengrj in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10714\r\n* Fix edge case of file being deleted between stat and open by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10716\r\n* Fix potential race during RBAC restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10706\r\n* modules(multi2vec-google): add support for Google AI Studio models by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10719\r\n* test(multi2vec-google): add Google AI Studio tests by @antas-marcin in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10725\r\n* Close reader after finishing to avoid deadlock in restore by @dirkkul in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10729\r\n\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.4...v1.36.5","2026-03-12T09:19:30",{"id":270,"version":271,"summary_zh":272,"released_at":273},333725,"v1.36.4","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* chore: update default propagation settings for async replication by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10689\r\n* fix: don't strip nils for bm25 search by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10702\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.36.3...v1.36.4","2026-03-10T08:44:42",{"id":275,"version":276,"summary_zh":277,"released_at":278},333726,"v1.35.14","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* chore: update default propagation settings for async replication by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10689\r\n* fix: don't strip nils for bm25 search by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10702\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.35.13...v1.35.14","2026-03-09T14:15:03",{"id":280,"version":281,"summary_zh":282,"released_at":283},333727,"v1.34.19","## Breaking Changes \r\n*none*\r\n\r\n## New Features\r\n*none*\r\n\r\n## Fixes\r\n* chore: update default propagation settings for async replication by @jeroiraz in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10689\r\n* fix: don't strip nils for bm25 search by @amourao in https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fpull\u002F10702\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fweaviate\u002Fweaviate\u002Fcompare\u002Fv1.34.18...v1.34.19","2026-03-09T14:41:26"]