feat: 支持多租户多模型对话及文档去重优化
This commit is contained in:
132
consts/model/config_type.go
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132
consts/model/config_type.go
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@@ -0,0 +1,132 @@
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package model
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import (
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"github.com/gogf/gf/v2/util/gconv"
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)
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var (
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ModelConfigTypeVectorArk = newModelConfigType(gconv.PtrString("ark"), "字节跳动火山引擎方舟大模型服务")
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ModelConfigTypeVectorOllama = newModelConfigType(gconv.PtrString("ollama"), "Ollama 本地大模型运行工具")
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ModelConfigTypeVectorOpenAI = newModelConfigType(gconv.PtrString("openAI"), "OpenAI 官方大模型服务")
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ModelConfigTypeVectorQianfan = newModelConfigType(gconv.PtrString("qianfan"), "百度文心一言千帆大模型平台")
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ModelConfigTypeVectorTencentCloud = newModelConfigType(gconv.PtrString("tencentCloud"), "腾讯云大模型服务")
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ModelConfigTypeVectorDashScope = newModelConfigType(gconv.PtrString("dashScope"), "阿里云通义千问 DashScope 平台")
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ModelConfigTypeChatArk = newModelConfigType(gconv.PtrString("ark"), "字节跳动火山引擎方舟大模型服务")
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ModelConfigTypeChatArkBot = newModelConfigType(gconv.PtrString("arkBot"), "火山引擎 ARK Bot 智能体服务")
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ModelConfigTypeChatClaude = newModelConfigType(gconv.PtrString("claude"), "Anthropic Claude 系列大模型")
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ModelConfigTypeChatDeepSeek = newModelConfigType(gconv.PtrString("deepSeek"), "DeepSeek 深度求索大模型")
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ModelConfigTypeChatOllama = newModelConfigType(gconv.PtrString("ollama"), "Ollama 本地大模型运行工具")
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ModelConfigTypeChatOpenAI = newModelConfigType(gconv.PtrString("openAI"), "OpenAI 官方大模型服务")
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ModelConfigTypeChatQianfan = newModelConfigType(gconv.PtrString("qianfan"), "百度文心一言千帆大模型平台")
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ModelConfigTypeChatQwen = newModelConfigType(gconv.PtrString("qwen"), "腾讯文心千问大模型平台")
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)
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type ModelConfigType *string
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type modelConfigType struct {
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code ModelConfigType
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desc string
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}
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func (s modelConfigType) Code() ModelConfigType {
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return s.code
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}
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func (s modelConfigType) Desc() string {
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return s.desc
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}
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func newModelConfigType(code ModelConfigType, desc string) modelConfigType {
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return modelConfigType{code: code, desc: desc}
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}
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func GetVectorDescByCode(code ModelConfigType) string {
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switch *code {
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case *ModelConfigTypeVectorArk.Code():
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return ModelConfigTypeVectorArk.Desc()
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case *ModelConfigTypeVectorOllama.Code():
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return ModelConfigTypeVectorOllama.Desc()
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case *ModelConfigTypeVectorOpenAI.Code():
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return ModelConfigTypeVectorOpenAI.Desc()
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case *ModelConfigTypeVectorQianfan.Code():
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return ModelConfigTypeVectorQianfan.Desc()
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case *ModelConfigTypeVectorTencentCloud.Code():
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return ModelConfigTypeVectorTencentCloud.Desc()
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case *ModelConfigTypeVectorDashScope.Code():
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return ModelConfigTypeVectorDashScope.Desc()
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}
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return "未知类型"
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}
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func GetChatDescByCode(code ModelConfigType) string {
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switch *code {
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case *ModelConfigTypeChatArk.Code():
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return ModelConfigTypeChatArk.Desc()
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case *ModelConfigTypeChatArkBot.Code():
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return ModelConfigTypeChatArkBot.Desc()
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case *ModelConfigTypeChatClaude.Code():
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return ModelConfigTypeChatClaude.Desc()
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case *ModelConfigTypeChatDeepSeek.Code():
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return ModelConfigTypeChatDeepSeek.Desc()
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case *ModelConfigTypeChatOllama.Code():
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return ModelConfigTypeChatOllama.Desc()
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case *ModelConfigTypeChatOpenAI.Code():
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return ModelConfigTypeChatOpenAI.Desc()
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case *ModelConfigTypeChatQianfan.Code():
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return ModelConfigTypeChatQianfan.Desc()
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case *ModelConfigTypeChatQwen.Code():
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return ModelConfigTypeChatQwen.Desc()
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}
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return "未知类型"
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}
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type GetModelConfigTypeEnumRes struct {
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Options []ConfigTypeKeyValue `json:"options"`
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}
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type ConfigTypeKeyValue struct {
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Key interface{} `json:"key"` // 对应原有常量值
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Value interface{} `json:"value"` // 对应描述信息
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}
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func GetAllModelConfigTypeEnums(modelType ModelType) *GetModelConfigTypeEnumRes {
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// 枚举列表
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var list []modelConfigType
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if *modelType == *ModelTypeVector.Code() {
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list = []modelConfigType{
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ModelConfigTypeVectorArk,
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ModelConfigTypeVectorOllama,
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ModelConfigTypeVectorOpenAI,
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ModelConfigTypeVectorQianfan,
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ModelConfigTypeVectorTencentCloud,
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ModelConfigTypeVectorDashScope,
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}
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}
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if *modelType == *ModelTypeChat.Code() {
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list = []modelConfigType{
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ModelConfigTypeChatArk,
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ModelConfigTypeChatArkBot,
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ModelConfigTypeChatClaude,
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ModelConfigTypeChatDeepSeek,
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ModelConfigTypeChatOllama,
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ModelConfigTypeChatOpenAI,
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ModelConfigTypeChatQianfan,
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ModelConfigTypeChatQwen,
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}
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}
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// 组装返回格式
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options := make([]ConfigTypeKeyValue, 0, len(list))
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for _, item := range list {
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options = append(options, ConfigTypeKeyValue{
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Key: *item.Code(),
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Value: item.Desc(),
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})
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}
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return &GetModelConfigTypeEnumRes{
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Options: options,
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}
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}
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66
consts/model/type.go
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66
consts/model/type.go
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@@ -0,0 +1,66 @@
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package model
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import "github.com/gogf/gf/v2/util/gconv"
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var (
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ModelTypeVector = newModelType(gconv.PtrString("vector"), "向量")
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ModelTypeChat = newModelType(gconv.PtrString("chat"), "对话")
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)
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type ModelType *string
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type modelType struct {
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code ModelType
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desc string
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}
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func (s modelType) Code() ModelType {
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return s.code
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}
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func (s modelType) Desc() string {
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return s.desc
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}
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func newModelType(code ModelType, desc string) modelType {
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return modelType{code: code, desc: desc}
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}
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func GetModelTypeDescByCode(code ModelType) string {
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switch *code {
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case *ModelTypeVector.Code():
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return ModelTypeVector.Desc()
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case *ModelTypeChat.Code():
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return ModelTypeChat.Desc()
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}
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return "未知类型"
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}
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type GetModelTypeEnumRes struct {
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Options []TypeKeyValue `json:"options"`
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}
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type TypeKeyValue struct {
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Key interface{} `json:"key"` // 对应原有常量值
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Value interface{} `json:"value"` // 对应描述信息
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}
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func GetAllModelTypeEnums() *GetModelTypeEnumRes {
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// 枚举列表
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list := []modelType{
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//ModelTypeVector,
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ModelTypeChat,
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}
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// 组装返回格式
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options := make([]TypeKeyValue, 0, len(list))
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for _, item := range list {
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options = append(options, TypeKeyValue{
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Key: *item.Code(),
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Value: item.Desc(),
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})
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}
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return &GetModelTypeEnumRes{
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Options: options,
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}
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}
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@@ -2,17 +2,10 @@ package public
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const GmqMsgPluginsName = "gmq_msg"
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const KnowledgeLockEsKey = "rag_binary:knowledge:lock:knowledgeIdEs-%v"
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const KnowledgeLockSqlKey = "rag_binary:knowledge:lock:knowledgeIdSql-%v"
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const KnowledgeContentHashEsKey = "rag_binary:knowledge:knowledgeId:contentHashEs-%v"
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const KnowledgeContentHashSqlKey = "rag_binary:knowledge:knowledgeId:contentHashSql-%v"
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const (
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KnowledgeDocumentVectorStatusTopic = "knowledge:document:vector:status:stream"
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KnowledgeDocumentVectorStatusConsumer = "knowledge-document-vector-status-consumer"
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KnowledgeDocumentVectorStatusBatchSize = 1
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KnowledgeDocumentVectorStatusAutoAck = false
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)
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const KnowledgeLockEsKey = "knowledge:lock:knowledgeIdEs-%v"
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const KnowledgeLockSqlKey = "knowledge:lock:knowledgeIdSql-%v"
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const KnowledgeContentHashEsKey = "knowledge:knowledgeId:contentHashEs-%v"
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const KnowledgeContentHashSqlKey = "knowledge:knowledgeId:contentHashSql-%v"
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const (
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KnowledgeDocumentVectorTopic = "knowledge:document:vector:stream" // 请求 Stream 键名（与发消息的key一致）
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@@ -12,6 +12,7 @@ const (
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TableNameDataset = "dataset"
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TableNameKeyword = "keyword"
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TableNameTask = "task"
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TableNameModel = "model"
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TableNameDatasetIndex = "dataset_index"
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TableNameDocumentVector = "document_vector"
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)
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