863cba4e4e
后端： 1. RAG embedding 接入修正，并兼容 Ark 多模态 embedding 链路 - 更新 backend/infra/rag/embed/eino_embedder.go：文本 embedding 继续走 Eino OpenAI 兼容链路；`doubao-embedding-vision-*` 模型切到 Ark 原生 `/embeddings/multimodal` - 增加 embedding baseURL 归一化：兼容把 `.../embeddings` 或 `.../embeddings/multimodal` 误填进配置的情况，统一回退到 `/api/v3` - 为第三方 embedding 调用增加 panic recover，避免向量检索/写入异常直接打崩主进程 2. RAG runtime / pipeline / store 稳定性加固，统一降级为 error 语义 - 更新 backend/infra/rag/runtime.go：runtime 对外入口增加 panic recover 与观测打点 - 更新 backend/infra/rag/core/pipeline.go：ingest / retrieve 编排边界增加 panic recover - 更新 backend/infra/rag/retrieve/vector_retriever.go：向量检索边界补充 panic recover - 更新 backend/infra/rag/store/milvus_store.go、backend/infra/rag/store/inmemory_store.go：补齐未初始化保护，避免 nil 依赖直接异常退出 3. RAG embedding 配置口径与普通 LLM 链路对齐 - 更新 backend/infra/rag/factory.go：RAG embedding API Key 不再走 `apiKeyEnv` 间接映射，统一直接读取 `ARK_API_KEY` - 更新 backend/infra/rag/config/config.go：删除 `rag.embed.apiKeyEnv` 配置字段，收敛配置分叉 - 更新 backend/config.example.yaml：示例配置切到当前联调口径，保持 `rag.enabled=true`、`memory.rag.enabled=true`，并对齐 Milvus / embed 配置 4. Memory + RAG 联调链路可运行态修正 - 当前已验证 memory 抽取写库、RAG ingest 写入 Milvus、后续语义召回链路可继续联调 - 检索失败场景已从“直接 panic”收敛为“记录日志并降级”，不再阻断主聊天链路 前端：无 仓库：无 undo： 1. 增删改查的 mysql 记忆去重没实现 2. 提取用户话为记忆的过滤机制不足，有点无脑 3. RAG 召回也有问题
114 lines
3.1 KiB
Go
114 lines
3.1 KiB
Go
package config
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import "github.com/spf13/viper"
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// Config 是 RAG Core 运行配置。
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type Config struct {
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Enabled bool
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Store string
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TopK int
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Threshold float64
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EmbedProvider string
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EmbedModel string
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EmbedBaseURL string
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EmbedTimeoutMS int
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EmbedDimension int
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RerankerEnabled bool
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RerankerProvider string
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RerankerTimeoutMS int
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ChunkSize int
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ChunkOverlap int
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RetrieveTimeoutMS int
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MilvusAddress string
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MilvusToken string
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MilvusDBName string
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MilvusCollectionName string
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MilvusMetricType string
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MilvusRequestTimeoutMS int
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}
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// LoadFromViper 读取 rag 配置并补默认值。
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func LoadFromViper() Config {
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cfg := Config{
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Enabled: viper.GetBool("rag.enabled"),
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Store: viper.GetString("rag.store"),
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TopK: viper.GetInt("rag.topK"),
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Threshold: viper.GetFloat64("rag.threshold"),
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EmbedProvider: viper.GetString("rag.embed.provider"),
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EmbedModel: viper.GetString("rag.embed.model"),
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EmbedBaseURL: viper.GetString("rag.embed.baseURL"),
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EmbedTimeoutMS: viper.GetInt("rag.embed.timeoutMs"),
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EmbedDimension: viper.GetInt("rag.embed.dimension"),
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RerankerEnabled: viper.GetBool("rag.reranker.enabled"),
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RerankerProvider: viper.GetString("rag.reranker.provider"),
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RerankerTimeoutMS: viper.GetInt("rag.reranker.timeoutMs"),
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ChunkSize: viper.GetInt("rag.ingest.chunkSize"),
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ChunkOverlap: viper.GetInt("rag.ingest.chunkOverlap"),
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RetrieveTimeoutMS: viper.GetInt("rag.retrieve.timeoutMs"),
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MilvusAddress: viper.GetString("rag.milvus.address"),
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MilvusToken: viper.GetString("rag.milvus.token"),
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MilvusDBName: viper.GetString("rag.milvus.dbName"),
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MilvusCollectionName: viper.GetString("rag.milvus.collectionName"),
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MilvusMetricType: viper.GetString("rag.milvus.metricType"),
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MilvusRequestTimeoutMS: viper.GetInt("rag.milvus.requestTimeoutMs"),
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}
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if cfg.Store == "" {
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cfg.Store = "inmemory"
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}
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if cfg.TopK <= 0 {
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cfg.TopK = 8
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}
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if cfg.Threshold < 0 {
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cfg.Threshold = 0
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}
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if cfg.EmbedProvider == "" {
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cfg.EmbedProvider = "mock"
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}
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if cfg.EmbedBaseURL == "" {
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cfg.EmbedBaseURL = viper.GetString("agent.baseURL")
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}
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if cfg.EmbedTimeoutMS <= 0 {
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cfg.EmbedTimeoutMS = 1200
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}
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if cfg.EmbedDimension <= 0 {
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cfg.EmbedDimension = 1024
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}
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if cfg.RerankerProvider == "" {
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cfg.RerankerProvider = "noop"
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}
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if cfg.RerankerTimeoutMS <= 0 {
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cfg.RerankerTimeoutMS = 1200
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}
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if cfg.ChunkSize <= 0 {
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cfg.ChunkSize = 400
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}
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if cfg.ChunkOverlap < 0 {
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cfg.ChunkOverlap = 80
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}
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if cfg.RetrieveTimeoutMS <= 0 {
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cfg.RetrieveTimeoutMS = 1500
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}
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if cfg.MilvusAddress == "" {
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cfg.MilvusAddress = "http://localhost:19530"
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}
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if cfg.MilvusToken == "" {
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cfg.MilvusToken = "root:Milvus"
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}
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if cfg.MilvusCollectionName == "" {
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cfg.MilvusCollectionName = "smartflow_rag_chunks"
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}
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if cfg.MilvusMetricType == "" {
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cfg.MilvusMetricType = "COSINE"
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}
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if cfg.MilvusRequestTimeoutMS <= 0 {
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cfg.MilvusRequestTimeoutMS = 1500
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}
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return cfg
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}
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