From 0ee73377897b969f1bebe4c64e8da4fc39d8d629 Mon Sep 17 00:00:00 2001
From: thinkgem <thinkgem@163.com>
Date: Thu, 20 Mar 2025 21:30:49 +0800
Subject: [PATCH] =?UTF-8?q?AI=20=E5=A2=9E=E5=8A=A0=20Chroma=20=E5=90=91?=
 =?UTF-8?q?=E9=87=8F=E5=BA=93?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 modules/cms-ai/README.md                      |  3 +-
 modules/cms-ai/pom.xml                        | 10 ++-
 .../main/resources/config/jeesite-cms-ai.yml  | 70 +++++++++++--------
 3 files changed, 49 insertions(+), 34 deletions(-)

diff --git a/modules/cms-ai/README.md b/modules/cms-ai/README.md
index 51bbec4e..ebbff64f 100644
--- a/modules/cms-ai/README.md
+++ b/modules/cms-ai/README.md
@@ -43,7 +43,7 @@
 特别适合处理复杂的企业知识库。
 
 此外该模块，支持云上大模型和本地部署的大模型，如：DeepSeek、通义千问，理论上支持所有 OpenAPI 标准接口的 AI 提供商。
-并能无缝集成多种嵌入式 AI 模型的向量数据库，如 PGVector、Elasticsearch、Milvus 等，实现高效的数据存储、检索及分析。
+并能无缝集成多种嵌入式 AI 模型的向量数据库，如 Chroma、PGVector、Elasticsearch、Milvus 等，实现高效的数据存储、检索及分析。
 无论是大规模数据集还是高度专业化的领域知识，JeeSite CMS + RAG + AI 都能提供定制化解决方案，满足企业多样化的业务需求和技术要求。
 企业可以轻松管理和访问复杂的信息资源，促进内部知识共享和创新，从而在竞争激烈的市场环境中保持领先地位。
 
@@ -65,6 +65,7 @@
 
 支持的向量库列表：<https://docs.spring.io/spring-ai/reference/1.0/api/vectordbs.html>
 
+* Chroma
 * PGVector
 * Elasticsearch
 * Milvus
diff --git a/modules/cms-ai/pom.xml b/modules/cms-ai/pom.xml
index 6e727534..dcda1e12 100644
--- a/modules/cms-ai/pom.xml
+++ b/modules/cms-ai/pom.xml
@@ -49,11 +49,17 @@
 		   <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
 		</dependency>
 
-		<!-- PG 向量数据库 -->
+		<!-- Chroma 向量数据库 -->
+		<dependency>
+			<groupId>org.springframework.ai</groupId>
+			<artifactId>spring-ai-chroma-store-spring-boot-starter</artifactId>
+		</dependency>
+
+		<!-- PG 向量数据库
         <dependency>
             <groupId>org.springframework.ai</groupId>
             <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
-        </dependency>
+        </dependency> -->
 
 		<!-- ES 向量数据库
 		<dependency>
diff --git a/modules/cms-ai/src/main/resources/config/jeesite-cms-ai.yml b/modules/cms-ai/src/main/resources/config/jeesite-cms-ai.yml
index e3b95e36..97c40c04 100644
--- a/modules/cms-ai/src/main/resources/config/jeesite-cms-ai.yml
+++ b/modules/cms-ai/src/main/resources/config/jeesite-cms-ai.yml
@@ -54,37 +54,45 @@ spring:
     # 向量数据库配置
     vectorstore:
 
-      # Postgresql 向量数据库（PG 连接配置，见下文，需要手动建表）
-      pgvector:
-        initialize-schema: false
-        id-type: TEXT
-        index-type: HNSW
-        distance-type: COSINE_DISTANCE
-        #table-name: vector_store_384
-        #dimensions: 384
-        #table-name: vector_store_786
-        #dimensions: 768
-        table-name: vector_store_1024
-        dimensions: 1024
-        batching-strategy: TOKEN_COUNT
-        max-document-batch-size: 10000
+      # Chroma 向量数据库
+      chroma:
+        client:
+          host: http://localhost
+          port: 8000
+        initialize-schema: true
+        collection-name: vector_store
+
+#      # Postgresql 向量数据库（PG 连接配置，见下文，需要手动建表）
+#      pgvector:
+#        id-type: TEXT
+#        index-type: HNSW
+#        distance-type: COSINE_DISTANCE
+#        initialize-schema: false
+#        #table-name: vector_store_384
+#        #dimensions: 384
+#        #table-name: vector_store_786
+#        #dimensions: 768
+#        table-name: vector_store_1024
+#        dimensions: 1024
+#        batching-strategy: TOKEN_COUNT
+#        max-document-batch-size: 10000
 
 #      # ES 向量数据库（ES 连接配置，见下文）
 #      elasticsearch:
-#        initialize-schema: true
 #        index-name: vector-index
+#        initialize-schema: true
 #        dimensions: 1024
 #        similarity: cosine
 #        batching-strategy: TOKEN_COUNT
 
 #      # Milvus 向量数据库（字符串长度不超过65535）
 #      milvus:
-#        initialize-schema: true
 #        client:
 #          host: "localhost"
 #          port: 19530
 #          username: "root"
 #          password: "milvus"
+#        initialize-schema: true
 #        database-name: "default2"
 #        collection-name: "vector_store2"
 #        embedding-dimension: 384
@@ -93,24 +101,24 @@ spring:
 
 # ========= Postgresql 向量数据库数据源 =========
 
-jdbc:
-  ds_pgvector:
-    type: postgresql
-    driver: org.postgresql.Driver
-    url: jdbc:postgresql://127.0.0.1:5433/jeesite-ai
-    username: postgres
-    password: postgres
-    testSql: SELECT 1
+#jdbc:
+#  ds_pgvector:
+#    type: postgresql
+#    driver: org.postgresql.Driver
+#    url: jdbc:postgresql://127.0.0.1:5433/jeesite-ai
+#    username: postgres
+#    password: postgres
+#    testSql: SELECT 1
 
 # ========= ES 向量数据库连接配置 =========
 
-spring.elasticsearch:
-    enabled: true
-    socket-timeout: 120s
-    connection-timeout: 120s
-    uris: http://127.0.0.1:9200
-    username: elastic
-    password: elastic
+#spring.elasticsearch:
+#    enabled: true
+#    socket-timeout: 120s
+#    connection-timeout: 120s
+#    uris: http://127.0.0.1:9200
+#    username: elastic
+#    password: elastic
 
 # 对话消息存缓存，可自定义存数据库
 j2cache: