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《动手学SpringAI》包含SSE流/Agent智能体/知识图谱RAG/FunctionCall/历史消息/图片生成/图片理解/Embedding/VectorDatabase/RAG
| Date | Stars |
|---|---|
| 2026-07-31 | 420 |
| 2026-08-06 | 420 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 项目介绍
本项目使用SpringAI教学，包含了SSE流/Agent智能体/FunctionCall/Embedding/VectorDatabase/RAG/Graph RAG/历史消息/图片生成/图片理解
> **起凡 API 中转：Codex / Claude**
>
> 提供 Codex 与 Claude API 中转服务，兼容 OpenAI 协议，可直接集成到项目中使用。注册即赠送 25 刀额度。
>
> [立即注册](http://ai.jarcheng.top/)
[文档地址](https://www.jarcheng.top/blog/project/spring-ai/intro.html)
[视频地址](https://www.bilibili.com/video/BV14y411q7RN/)
## 运行环境
- Java 17
- Node.js 18+
- MySQL 8
- DashScope API KEY（或者其他）。如果使用 OpenAI 协议模型，可以前往 [起凡 AI 中转站](http://ai.jarcheng.top/) 注册，赠送25刀额度，支持 OpenAI 协议。
- Redis-Stack
redis基础上拓展向量查询功能
```shell
docker run -d \
--name redis-stack \
--restart=always \
-v redis-data:/data \
-p 6379:6379 \
-p 8001:8001 \
-e REDIS_ARGS="--requirepass 123456" redis/redis-stack:latest
```
- neo4j 5+
安装完neo4j访问`localhost:7474`, 默认的账号密码都是`neo4j`和`neo4j`。
```shell
docker run \
-d \
-p 7474:7474 -p 7687:7687 \
-v neo4j-data:/data -v neo4j-data:/plugins \
--name neo4j \
-e NEO4J_apoc_export_file_enabled=true \
-e NEO4J_apoc_import_file_enabled=true \
-e NEO4J_apoc_import_file_use__neo4j__config=true \
-e NEO4JLABS_PLUGINS=\[\"apoc\"\] \
-e NEO4J_dbms_security_procedures_unrestricted=apoc.\\\* \
neo4j
```
## 运行步骤
### 1.clone代码
```shell
git clone https://github.com/qifan777/dive-into-spring-ai
```
### 2. idea打开项目
### 3. 修改配置文件
修改 `application.yml` 中的 API-KEY、MySQL、Redis-Stack、Neo4j 配置。
如果使用 OpenAI 协议模型，可以在 `spring.ai.openai` 中配置：
- `base-url`：填写 `http://ai.jarcheng.top`（不带 `/v1`）
- `api-key`：填写中转站控制台创建的 API Key
### 4. 运行项目
后端运行
1. 运行ServerApplication.java
2. target/generated-sources/annotations右键mark directory as/generated source root
前端运行，在front-end目录下
- npm run install
- npm run api （先运行后端）
- npm run dev
## 联系方式
付费远程运行/安装/定制开发联系微信：ljc666max
其他关于程序运行安装报错请加QQ群：
- 416765656（满）
- 632067985
- 1102705673
Excerpt of 1,967 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:44108187435d2a3d, llm:description: contains SSE stream / Agent 智能体 / 知识图谱 RAG / FunctionCall / history messages / image generation/understanding / Embedding / VectorDatabase / RAG; topics: agent, aigc, functioncall, graph-rag, knowlege-base, prompt, rag, spring-ai, sse
matched fp:44108187435d2a3d, llm:description: contains SSE stream / Agent 智能体 / 知识图谱 RAG / FunctionCall / history messages / image generation/understanding / Embedding / VectorDatabase / RAG; topics: agent, aigc, functioncall, graph-rag, knowlege-base, prompt, rag, spring-ai, sse
matched fp:44108187435d2a3d, llm:description: contains SSE stream / Agent 智能体 / 知识图谱 RAG / FunctionCall / history messages / image generation/understanding / Embedding / VectorDatabase / RAG; topics: agent, aigc, functioncall, graph-rag, knowlege-base, prompt, rag, spring-ai, sse
matched fp:44108187435d2a3d, llm:description: contains SSE stream / Agent 智能体 / 知识图谱 RAG / FunctionCall / history messages / image generation/understanding / Embedding / VectorDatabase / RAG; topics: agent, aigc, functioncall, graph-rag, knowlege-base, prompt, rag, spring-ai, sse