AI Skill Hub 推荐使用:SQL数据库向量搜索RAG工具 是一款优质的AI工具。AI 综合评分 7.8 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。
基于Blazor和Minimal API的开源RAG应用,集成Azure OpenAI和SQL数据库,支持向量化检索和智能问答。
SQL数据库向量搜索RAG工具 是一款基于 C# 开发的开源工具,专注于 RAG、Azure OpenAI、Blazor 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
基于Blazor和Minimal API的开源RAG应用,集成Azure OpenAI和SQL数据库,支持向量化检索和智能问答。
SQL数据库向量搜索RAG工具 是一款基于 C# 开发的开源工具,专注于 RAG、Azure OpenAI、Blazor 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 克隆仓库 git clone https://github.com/marcominerva/SqlDatabaseVectorSearch cd SqlDatabaseVectorSearch # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 sqldatabasevectorsearch --help # 基本运行 sqldatabasevectorsearch [options] <input> # 详细使用说明请查阅文档 # https://github.com/marcominerva/SqlDatabaseVectorSearch
# sqldatabasevectorsearch 配置说明 # 查看配置选项 sqldatabasevectorsearch --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 SQLDATABASEVECTORSEARCH_CONFIG="/path/to/config.yml"
This application allows you to: - Load documents (PDF, DOCX, TXT, MD) - Generate embeddings and save them as vectors in Azure SQL Database - Perform semantic search and RAG using Azure OpenAI - Interact via a Blazor Web App or programmatically via Minimal API
Embeddings and chat completion are powered by Semantic Kernel.
git clone https://github.com/marcominerva/SqlDatabaseVectorSearch.git
2. Configure the database and OpenAI settings - Edit SqlDatabaseVectorSearch/appsettings.json and set your Azure SQL connection string and OpenAI settings. - Important: The ModelId values for both ChatCompletion and Embedding are used for token counting via Microsoft.ML.Tokenizers. These values must be valid model identifiers supported by the tokenizer library (e.g., gpt-4o, gpt-4, gpt-3.5-turbo, text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002). The ModelId may differ from the actual deployment name you're using in Azure OpenAI. For example, for gpt-4.1 and gpt-5 models set the ModelId to gpt-4o for proper token counting. - If using embedding models with shortening (e.g., text-embedding-3-small or text-embedding-3-large), set the Dimensions property accordingly. For text-embedding-3-large, you must specify a value <= 1998. - If you change the VECTOR size, update both the ApplicationDbContext and the Initial Migration.
dotnet run --project SqlDatabaseVectorSearch/SqlDatabaseVectorSearch.csproj
5. Access the Web App - Navigate to https://localhost:5001 (or the port shown in the console)
POST /api/documents and ask questions via POST /api/ask or POST /api/ask-streaming.#### Example API Request
POST /api/ask
Content-Type: application/json
{
"conversationId": "3d0bd178-499d-433a-b2bc-c35e488d9e2c"
"text": "Why is Mars called the red planet?"
}
{
"originalQuestion": "why is mars called the red planet?",
"reformulatedQuestion": "Why is the planet Mars called the red planet?",
"answer": "Mars is called the Red Planet because its surface has an orange-red color due to being covered in iron(III) oxide dust, also known as rust. This iron oxide gives Mars its distinctive reddish appearance when observed from Earth and is the origin of its well-known nickname",
"streamState": "End",
"tokenUsage": {
"reformulation": {
"promptTokens": 812,
"completionTokens": 11,
"totalTokens": 823
},
"embeddingTokenCount": 10,
"question": {
"promptTokens": 31708,
"completionTokens": 227,
"totalTokens": 31935
}
},
"citations": [
{
"documentId": "b1870ad7-4685-42a3-576a-08ddb01159d5",
"chunkId": "749aba1e-0db5-4033-cfa6-08ddb0115da3",
"fileName": "Mars.pdf",
"quote": "surface of Mars is orange-red because it is covered in iron(III) oxide",
"pageNumber": 1,
"indexOnPage": 0
},
{
"documentId": "b1870ad7-4685-42a3-576a-08ddb01159d5",
"chunkId": "215e7197-513f-4fbe-cfa8-08ddb0115da3",
"fileName": "Mars.pdf",
"quote": "Martian surface is caused by ferric oxide, or rust",
"pageNumber": 3,
"indexOnPage": 0
}
]
}
A Blazor Web App and Minimal API for performing RAG (Retrieval Augmented Generation) and vector search using the native VECTOR type in Azure SQL Database and Azure OpenAI.

text-embedding-3-large, set Dimensions <= 1998.完整的RAG解决方案实现,技术栈现代,集成度高。Stars数量中等,社区活跃度有待提升,适合企业级应用参考。
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。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
总体来看,SQL数据库向量搜索RAG工具 是一款质量良好的AI工具,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 | SqlDatabaseVectorSearch |
| 原始描述 | 开源AI工具:A Blazor Web App and Minimal API for performing RAG (Retrieval Augmented Generat。⭐142 · C# |
| Topics | RAGAzure OpenAIBlazor向量搜索C#.NET |
| GitHub | https://github.com/marcominerva/SqlDatabaseVectorSearch |
| License | MIT |
| 语言 | C# |
收录时间:2026-06-15 · 更新时间:2026-06-15 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。