1. 主动停止的响应的消息仍然存储对话数据；2. 对话消息框当手动向上滚动的时候，停止滚动到底部，方便阅读已生成的消息；3. 消息内容，对 JSON 格式的数据，进行格式化显示；4. 支持“深度思考”按钮，可展开和折叠深度思考的消息；对 AI 的用户消息进行转义，避免不会支持的消息报错；5. 当 AI 接口调用异常的时候，给于用户提示实际的接口返回内容；6. 新增自动更新对话标题；6. 加载消息过程中，避免再次发送新消息和切换对话。
This commit is contained in:
thinkgem
@@ -13,19 +13,17 @@ spring:
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#api-key: ${BAILIAN_APP_KEY}
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# 聊天对话模型
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chat:
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enabled: false
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options:
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model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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#model: DeepSeek-R1-Distill-Qwen-14B
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#model: deepseek-r1-distill-llama-8b
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max-tokens: 1024
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temperature: 0.6
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top-p: 0.7
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top-p: 0.9
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frequency-penalty: 0
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logprobs: true
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#logprobs: true
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# 向量库知识库模型（注意：不同的模型维度不同）
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embedding:
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enabled: false
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options:
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model: BAAI/bge-m3
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#model: bge-large-zh-v1.5
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@@ -33,12 +31,14 @@ spring:
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#model: text-embedding-v3
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#dimensions: 1024
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# 是否启用工具调用
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tool-calls: false
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# 本地大模型配置（使用该模型，请开启 enabled 参数）
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ollama:
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base-url: http://localhost:11434
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# 聊天对话模型
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chat:
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enabled: true
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options:
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model: qwen2.5
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#model: deepseek-r1:7b
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@@ -48,7 +48,6 @@ spring:
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frequency-penalty: 0
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# 向量库知识库模型（注意：不同的模型维度不同）
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embedding:
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enabled: true
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# 维度 dimensions 设置为 384
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#model: all-minilm:33m
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# 维度 dimensions 设置为 768
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@@ -68,67 +67,64 @@ spring:
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#collection-name: vector_store
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collection-name: vector_store_1024
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# # Postgresql 向量数据库（PG 连接配置，见下文，需要手动建表）
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# pgvector:
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# id-type: TEXT
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# index-type: HNSW
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# distance-type: COSINE_DISTANCE
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# initialize-schema: false
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# #table-name: vector_store_384
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# #dimensions: 384
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# #table-name: vector_store_786
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# #dimensions: 768
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# table-name: vector_store_1024
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# dimensions: 1024
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# batching-strategy: TOKEN_COUNT
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# max-document-batch-size: 10000
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# Postgresql 向量数据库（PG 连接配置，见下文，需要手动建表）
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pgvector:
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id-type: TEXT
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index-type: HNSW
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distance-type: COSINE_DISTANCE
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initialize-schema: false
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#table-name: vector_store_384
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#dimensions: 384
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#table-name: vector_store_786
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#dimensions: 768
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table-name: vector_store_1024
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dimensions: 1024
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max-document-batch-size: 10000
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# # ES 向量数据库（ES 连接配置，见下文）
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# elasticsearch:
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# index-name: vector-index
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# initialize-schema: true
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# dimensions: 1024
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# similarity: cosine
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# batching-strategy: TOKEN_COUNT
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# ES 向量数据库（ES 连接配置，见下文）
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elasticsearch:
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index-name: vector-index
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initialize-schema: true
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dimensions: 1024
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similarity: cosine
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# # Milvus 向量数据库（字符串长度不超过65535）
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# milvus:
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# client:
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# host: "localhost"
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# port: 19530
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# username: "root"
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# password: "milvus"
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# initialize-schema: true
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# database-name: "default2"
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# collection-name: "vector_store2"
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# embedding-dimension: 384
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# index-type: HNSW
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# metric-type: COSINE
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# Milvus 向量数据库
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milvus:
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client:
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host: "localhost"
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port: 19530
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username: "root"
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password: "milvus"
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initialize-schema: true
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database-name: "default"
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collection-name: "vector_store"
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embedding-dimension: 384
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index-type: HNSW
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metric-type: COSINE
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# ========= Postgresql 向量数据库数据源 =========
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#jdbc:
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# ds_pgvector:
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# type: postgresql
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# driver: org.postgresql.Driver
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# url: jdbc:postgresql://127.0.0.1:5433/jeesite-ai
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# username: postgres
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# password: postgres
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# testSql: SELECT 1
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# pool:
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# init: 0
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# minIdle: 0
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# breakAfterAcquireFailure: true
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jdbc:
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ds_pgvector:
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type: postgresql
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driver: org.postgresql.Driver
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url: jdbc:postgresql://127.0.0.1:5433/jeesite-ai
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username: postgres
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password: postgres
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testSql: SELECT 1
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pool:
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init: 0
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minIdle: 0
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breakAfterAcquireFailure: true
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# ========= ES 向量数据库连接配置 =========
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#spring.elasticsearch:
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# enabled: true
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# socket-timeout: 120s
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# connection-timeout: 120s
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# uris: http://127.0.0.1:9200
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# username: elastic
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# password: elastic
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spring.elasticsearch:
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socket-timeout: 120s
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connection-timeout: 120s
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uris: http://127.0.0.1:9200
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username: elastic
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password: elastic
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# 对话消息存缓存，可自定义存数据库
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j2cache:
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