WeKnora
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Published
22 hours ago
•
Tencent
Tencent
[Bug]: 明明只上传了一个文档，没有删除也没有重复上传，但回溯里面会出现两次相同的文档
相关组件
前端界面
Bug 描述
初次部署，在空间中上传一个简单文档txt，名称为接线规范。使用对话问答，提问“接线规范是什么”，回溯中会出现两个文件，都是接线规范.txt。文档中一共只有一句话 “黑色 地 红色 电源 白色 SDA 绿色 SCL”。其他文档都是一样的问题
期望行为
溯源中应该只出现一次才对
相关日志
WeKnora-app | INFO [2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] session.go:115[CreateSession] | Processing session creation request, tenant ID: 10000, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417
WeKnora-app | DEBUG[2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] session.go:194[CreateSession] | Using default session strategy
WeKnora-app | INFO [2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] knowledgebase.go:78[GetKnowledgeBaseByID] | Retrieving knowledge base, ID: af87ff60-dbc5-467e-8387-6627e117d417
WeKnora-app | INFO [2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] knowledgebase.go:88[GetKnowledgeBaseByID] | Knowledge base retrieved successfully, ID: af87ff60-dbc5-467e-8387-6627e117d417, name: Default Knowledge Base
WeKnora-app | INFO [2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] session.go:213[CreateSession] | Calling session service to create session
WeKnora-app | INFO [2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] session.go:49[CreateSession] | Start creating session
WeKnora-app | INFO [2025-09-17 11:03:18.440] [request_id=egLBDuSUJS8p] session.go:57[CreateSession] | Creating session, tenant ID: 10000, model ID: 00986f06-94a2-4173-9723-14353efc7001, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417
WeKnora-app | INFO [2025-09-17 11:03:18.441] [request_id=egLBDuSUJS8p] session.go:66[CreateSession] | Session created successfully, ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.441] [request_id=egLBDuSUJS8p] session.go:222[CreateSession] | Session created successfully, ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:18.441] [] | [egLBDuSUJS8p] 201 | 2578 | 2.842013ms | 223.112.161.10 | POST /api/v1/sessions
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] tenant.go:84[GetTenantByID] | Start retrieving tenant
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] tenant.go:91[GetTenantByID] | Retrieving tenant, ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] tenant.go:101[GetTenantByID] | Tenant retrieved successfully, ID: 10000, name: licy's Workspace
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] initialization.go:141[CheckStatus] | Checking system initialization status
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] tenant.go:84[GetTenantByID] | Start retrieving tenant
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] tenant.go:91[GetTenantByID] | Retrieving tenant, ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] tenant.go:101[GetTenantByID] | Tenant retrieved successfully, ID: 10000, name: licy's Workspace
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] model.go:145[ListModels] | Start listing models
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] model.go:148[ListModels] | Listing models for tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] model.go:159[ListModels] | Retrieved 2 models successfully
WeKnora-app | INFO [2025-09-17 11:03:18.483] [request_id=e38pAII28j8l] initialization.go:187[CheckStatus] | System is already initialized
WeKnora-app | INFO [2025-09-17 11:03:18.483] [] | [e38pAII28j8l] 200 | 44 | 1.098285ms | 223.112.161.10 | GET /api/v1/initialization/status
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] tenant.go:84[GetTenantByID] | Start retrieving tenant
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] tenant.go:91[GetTenantByID] | Retrieving tenant, ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] tenant.go:101[GetTenantByID] | Tenant retrieved successfully, ID: 10000, name: licy's Workspace
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] session.go:694[KnowledgeQA] | Start processing knowledge QA request
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] session.go:728[KnowledgeQA] | Knowledge QA request, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, query: 接线规范是什么
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] message.go:42[CreateMessage] | Start creating message
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] message.go:43[CreateMessage] | Creating message for session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:18.518] [request_id=MK5kY3RTgGpu] message.go:47[CreateMessage] | Checking if session exists, tenant ID: 10000, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:18.519] [request_id=MK5kY3RTgGpu] message.go:55[CreateMessage] | Session exists, creating message
WeKnora-app | INFO [2025-09-17 11:03:18.520] [request_id=MK5kY3RTgGpu] message.go:64[CreateMessage] | Message created successfully, ID: f72b6e34-803f-4819-ac5e-d74c6ce6d192
WeKnora-app | INFO [2025-09-17 11:03:18.520] [request_id=MK5kY3RTgGpu] message.go:42[CreateMessage] | Start creating message
WeKnora-app | INFO [2025-09-17 11:03:18.520] [request_id=MK5kY3RTgGpu] message.go:43[CreateMessage] | Creating message for session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:18.520] [request_id=MK5kY3RTgGpu] message.go:47[CreateMessage] | Checking if session exists, tenant ID: 10000, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:18.520] [request_id=MK5kY3RTgGpu] message.go:55[CreateMessage] | Session exists, creating message
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] message.go:64[CreateMessage] | Message created successfully, ID: e0880792-b367-4b00-b8bb-e83a1698b864
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:749[KnowledgeQA] | Calling knowledge QA service, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:312[KnowledgeQA] | Start knowledge base question answering
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:313[KnowledgeQA] | Knowledge base question answering parameters, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, query: 接线规范是什么
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:317[KnowledgeQA] | Getting session info, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:333[KnowledgeQA] | Creating chat manage object, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:364[KnowledgeQA] | Triggering knowledge base question answering event
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:385[KnowledgeQAByEvent] | Start processing knowledge base question answering through events
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:386[KnowledgeQAByEvent] | Knowledge base question answering parameters, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417, query: 接线规范是什么
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:396[KnowledgeQAByEvent] | Trigger event list: [rewrite_query preprocess_query chunk_search chunk_rerank chunk_merge filter_top_k into_chat_message chat_completion_stream stream_filter]
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: rewrite_query
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] message.go:153[GetRecentMessagesBySession] | Start getting recent messages by session
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] message.go:154[GetRecentMessagesBySession] | Getting recent messages for session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, limit: 20
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] message.go:158[GetRecentMessagesBySession] | Checking if session exists, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] message.go:166[GetRecentMessagesBySession] | Session exists, getting recent messages
WeKnora-app | INFO [2025-09-17 11:03:18.521] [request_id=MK5kY3RTgGpu] message.go:176[GetRecentMessagesBySession] | Retrieved 2 recent messages successfully
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] model.go:285[GetChatModel] | Start getting chat model
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] model.go:286[GetChatModel] | Getting chat model with ID: 00986f06-94a2-4173-9723-14353efc7001
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] model.go:289[GetChatModel] | Tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] model.go:306[GetChatModel] | Creating chat model instance
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] model.go:307[GetChatModel] | Model name: qwen2.5:14b, source: local
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] model.go:325[GetChatModel] | Chat model initialized successfully
WeKnora-app | INFO [2025-09-17 11:03:18.522] [request_id=MK5kY3RTgGpu] | 确保模型 qwen2.5:14b 可用
WeKnora-app | INFO [2025-09-17 11:03:18.542] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:18.547] [request_id=cyQma14EJYGk] tenant.go:84[GetTenantByID] | Start retrieving tenant
WeKnora-app | INFO [2025-09-17 11:03:18.547] [request_id=cyQma14EJYGk] tenant.go:91[GetTenantByID] | Retrieving tenant, ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:18.547] [request_id=cyQma14EJYGk] tenant.go:101[GetTenantByID] | Tenant retrieved successfully, ID: 10000, name: licy's Workspace
WeKnora-app | INFO [2025-09-17 11:03:18.547] [request_id=cyQma14EJYGk] knowledge.go:232[ListKnowledge] | Start retrieving knowledge list
WeKnora-app | INFO [2025-09-17 11:03:18.547] [request_id=cyQma14EJYGk] knowledge.go:250[ListKnowledge] | Retrieving knowledge list under knowledge base, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417, page: 1, page size: 35
WeKnora-app | INFO [2025-09-17 11:03:18.548] [request_id=cyQma14EJYGk] knowledge.go:261[ListKnowledge] | Knowledge list retrieved successfully, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417, total: 3
WeKnora-app | INFO [2025-09-17 11:03:18.548] [] | [cyQma14EJYGk] 200 | 2726 | 1.100794ms | 223.112.161.10 | GET /api/v1/knowledge-bases/af87ff60-dbc5-467e-8387-6627e117d417/knowledge?page=1&page_size=35
WeKnora-app | INFO [2025-09-17 11:03:18.560] [request_id=MK5kY3RTgGpu] | 发送聊天请求到模型 qwen2.5:14b
WeKnora-app | INFO [2025-09-17 11:03:18.580] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-postgres | 2025-09-17 03:03:18.799 UTC [3567] FATAL: database "dxuser" does not exist
WeKnora-app | INFO [2025-09-17 11:03:21.905] [request_id=MK5kY3RTgGpu] | Rewritten query, session_id: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, rewrite_query: 接线规范是什么样的标准或要求？
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event rewrite_query triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: preprocess_query
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] | Starting query preprocessing, original query: 接线规范是什么样的标准或要求？
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] | Query preprocessing complete, processed query: 接线 规范 什么样 标准 或 要求
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event preprocess_query triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: chunk_search
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] search.go:50[OnEvent] | Search parameters: {接线规范是什么样的标准或要求？ 0.5 0.3 10}
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] knowledgebase.go:266[HybridSearch] | Hybrid search parameters, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417, query text: 接线规范是什么样的标准或要求？
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] knowledgebase.go:269[HybridSearch] | Creating composite retrieval engine, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] knowledgebase.go:284[HybridSearch] | Vector retrieval supported, preparing vector retrieval parameters
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] knowledgebase.go:294[HybridSearch] | Getting embedding model, model ID: eb6f1a12-1a38-4d90-99e3-b09cef78baa2
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] model.go:207[GetEmbeddingModel] | Start getting embedding model
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] model.go:208[GetEmbeddingModel] | Getting embedding model with ID: eb6f1a12-1a38-4d90-99e3-b09cef78baa2
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] model.go:99[GetModelByID] | Start getting model by ID
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] model.go:100[GetModelByID] | Getting model with ID: eb6f1a12-1a38-4d90-99e3-b09cef78baa2
WeKnora-app | INFO [2025-09-17 11:03:21.906] [request_id=MK5kY3RTgGpu] model.go:103[GetModelByID] | Tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] model.go:121[GetModelByID] | Model found, name: qwen2.5:14b, status: active
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] model.go:125[GetModelByID] | Model is active and ready to use
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] model.go:219[GetEmbeddingModel] | Creating embedder instance
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] model.go:220[GetEmbeddingModel] | Model name: qwen2.5:14b, source: local
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] model.go:240[GetEmbeddingModel] | Embedding model initialized successfully
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] knowledgebase.go:300[HybridSearch] | Embedding model retrieved: &{qwen2.5:14b 511 0xc000232810 5120 eb6f1a12-1a38-4d90-99e3-b09cef78baa2 0xc00006a6d0}
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] knowledgebase.go:303[HybridSearch] | Starting to generate query embedding
WeKnora-app | INFO [2025-09-17 11:03:21.907] [request_id=MK5kY3RTgGpu] | Ensuring model qwen2.5:14b is available
WeKnora-app | INFO [2025-09-17 11:03:21.920] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:21.959] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | DEBUG[2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | Embedding vector retrieval took: 162.256862ms
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] knowledgebase.go:309[HybridSearch] | Query embedding generated successfully, embedding vector length: 5120
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] knowledgebase.go:319[HybridSearch] | Vector retrieval parameters setup completed
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] knowledgebase.go:324[HybridSearch] | Keyword retrieval supported, preparing keyword retrieval parameters
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] knowledgebase.go:332[HybridSearch] | Keyword retrieval parameters setup completed
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] knowledgebase.go:341[HybridSearch] | Starting retrieval, parameter count: 2
WeKnora-app | DEBUG[2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | [Postgres] Processing retrieval request of type: keywords
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords retrieval: query=接线规范是什么样的标准或要求？, topK=10
WeKnora-app | DEBUG[2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | [Postgres] Filtering by knowledge base IDs: [af87ff60-dbc5-467e-8387-6627e117d417]
WeKnora-app | DEBUG[2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | [Postgres] Processing retrieval request of type: vector
WeKnora-app | INFO [2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | [Postgres] Vector retrieval: dim=5120, topK=10, threshold=0.5000
WeKnora-app | DEBUG[2025-09-17 11:03:22.101] [request_id=MK5kY3RTgGpu] | [Postgres] Filtering vector search by knowledge base IDs: [af87ff60-dbc5-467e-8387-6627e117d417]
WeKnora-app | INFO [2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector retrieval found 6 results
WeKnora-app | DEBUG[2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 0: chunk_id 6eae34ae-e1a2-41d5-9ab2-e0c5e5aa237a, score 0.7951
WeKnora-app | DEBUG[2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 1: chunk_id 7d43f2f8-468b-4ad0-bd1e-961920143cb5, score 0.7768
WeKnora-app | DEBUG[2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 2: chunk_id be6a6df5-beb4-4b6c-87ec-2c204953080b, score 0.7709
WeKnora-app | DEBUG[2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 3: chunk_id 64d4773b-c5b0-4a41-9f4d-7bec595e7c0d, score 0.7515
WeKnora-app | DEBUG[2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 4: chunk_id 0a99c236-2912-4cd3-a959-c1b815f774df, score 0.6864
WeKnora-app | DEBUG[2025-09-17 11:03:22.106] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 5: chunk_id 6f13ea26-81c4-4f2e-8b93-902b3f5a8843, score 0.6275
WeKnora-app |
WeKnora-app | 2025/09/17 11:03:22 /app/internal/application/repository/retriever/postgres/repository.go:179
WeKnora-app | [84.529ms] [rows:6] SELECT paradedb.score(id) as score,"id","content","source_id","source_type","chunk_id","knowledge_id","knowledge_base_id" FROM "embeddings" WHERE knowledge_base_id @@@ 'in ("af87ff60-dbc5-467e-8387-6627e117d417")' AND id @@@ paradedb.match(field => 'content', value => '接线规范是什么样的标准或要求？', distance => 1) ORDER BY "score" DESC LIMIT 10
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords retrieval found 6 results
WeKnora-app | DEBUG[2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 0: chunk=7d43f2f8-468b-4ad0-bd1e-961920143cb5, score=93.870537
WeKnora-app | DEBUG[2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 1: chunk=be6a6df5-beb4-4b6c-87ec-2c204953080b, score=39.370541
WeKnora-app | DEBUG[2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 2: chunk=64d4773b-c5b0-4a41-9f4d-7bec595e7c0d, score=30.370541
WeKnora-app | DEBUG[2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 3: chunk=6eae34ae-e1a2-41d5-9ab2-e0c5e5aa237a, score=24.370541
WeKnora-app | DEBUG[2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 4: chunk=0a99c236-2912-4cd3-a959-c1b815f774df, score=14.370541
WeKnora-app | DEBUG[2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 5: chunk=6f13ea26-81c4-4f2e-8b93-902b3f5a8843, score=6.370540
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] knowledgebase.go:352[HybridSearch] | Processing retrieval results
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] knowledgebase.go:355[HybridSearch] | Retrieval results, engine: postgres, retriever: vector, count: 6
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] knowledgebase.go:355[HybridSearch] | Retrieval results, engine: postgres, retriever: keywords, count: 6
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] knowledgebase.go:370[HybridSearch] | Result count before deduplication: 12
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] knowledgebase.go:372[HybridSearch] | Result count after deduplication: 6
WeKnora-app | INFO [2025-09-17 11:03:22.186] [request_id=MK5kY3RTgGpu] knowledgebase.go:407[processSearchResults] | Fetching knowledge data for 3 IDs
WeKnora-app |
WeKnora-app | 2025/09/17 11:03:22 /app/internal/application/repository/knowledge.go:107
WeKnora-app | [0.618ms] [rows:3] SELECT * FROM "knowledges" WHERE (tenant_id = 10000 AND id IN ('813cbf73-3ff0-4a30-8778-f7f403d89624','ff1a62f0-6c06-4bb6-9782-9e305840e087','e2e802ca-d216-481c-9798-ca4a4ba6b86d')) AND "knowledges"."deleted_at" IS NULL
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] knowledgebase.go:414[processSearchResults] | Fetching chunk data for 6 IDs
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] knowledgebase.go:504[processSearchResults] | Search results processed, total: 6
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] search.go:54[OnEvent] | Search results count: 6, error: <nil>
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] search.go:59[OnEvent] | Search result count: 6
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] knowledgebase.go:266[HybridSearch] | Hybrid search parameters, knowledge base ID: af87ff60-dbc5-467e-8387-6627e117d417, query text: 接线 规范 什么样 标准 或 要求
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] knowledgebase.go:269[HybridSearch] | Creating composite retrieval engine, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] knowledgebase.go:284[HybridSearch] | Vector retrieval supported, preparing vector retrieval parameters
WeKnora-app | INFO [2025-09-17 11:03:22.187] [request_id=MK5kY3RTgGpu] knowledgebase.go:294[HybridSearch] | Getting embedding model, model ID: eb6f1a12-1a38-4d90-99e3-b09cef78baa2
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:207[GetEmbeddingModel] | Start getting embedding model
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:208[GetEmbeddingModel] | Getting embedding model with ID: eb6f1a12-1a38-4d90-99e3-b09cef78baa2
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:99[GetModelByID] | Start getting model by ID
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:100[GetModelByID] | Getting model with ID: eb6f1a12-1a38-4d90-99e3-b09cef78baa2
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:103[GetModelByID] | Tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:121[GetModelByID] | Model found, name: qwen2.5:14b, status: active
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:125[GetModelByID] | Model is active and ready to use
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:219[GetEmbeddingModel] | Creating embedder instance
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:220[GetEmbeddingModel] | Model name: qwen2.5:14b, source: local
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] model.go:240[GetEmbeddingModel] | Embedding model initialized successfully
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] knowledgebase.go:300[HybridSearch] | Embedding model retrieved: &{qwen2.5:14b 511 0xc000232810 5120 eb6f1a12-1a38-4d90-99e3-b09cef78baa2 0xc00006a6d0}
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] knowledgebase.go:303[HybridSearch] | Starting to generate query embedding
WeKnora-app | INFO [2025-09-17 11:03:22.188] [request_id=MK5kY3RTgGpu] | Ensuring model qwen2.5:14b is available
WeKnora-app | INFO [2025-09-17 11:03:22.205] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:22.253] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | DEBUG[2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | Embedding vector retrieval took: 131.122972ms
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] knowledgebase.go:309[HybridSearch] | Query embedding generated successfully, embedding vector length: 5120
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] knowledgebase.go:319[HybridSearch] | Vector retrieval parameters setup completed
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] knowledgebase.go:324[HybridSearch] | Keyword retrieval supported, preparing keyword retrieval parameters
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] knowledgebase.go:332[HybridSearch] | Keyword retrieval parameters setup completed
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] knowledgebase.go:341[HybridSearch] | Starting retrieval, parameter count: 2
WeKnora-app | DEBUG[2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | [Postgres] Processing retrieval request of type: keywords
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords retrieval: query=接线 规范 什么样 标准 或 要求, topK=10
WeKnora-app | DEBUG[2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | [Postgres] Filtering by knowledge base IDs: [af87ff60-dbc5-467e-8387-6627e117d417]
WeKnora-app | DEBUG[2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | [Postgres] Processing retrieval request of type: vector
WeKnora-app | INFO [2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | [Postgres] Vector retrieval: dim=5120, topK=10, threshold=0.5000
WeKnora-app | DEBUG[2025-09-17 11:03:22.370] [request_id=MK5kY3RTgGpu] | [Postgres] Filtering vector search by knowledge base IDs: [af87ff60-dbc5-467e-8387-6627e117d417]
WeKnora-app | INFO [2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector retrieval found 6 results
WeKnora-app | DEBUG[2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 0: chunk_id 6eae34ae-e1a2-41d5-9ab2-e0c5e5aa237a, score 0.7940
WeKnora-app | DEBUG[2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 1: chunk_id 0a99c236-2912-4cd3-a959-c1b815f774df, score 0.7768
WeKnora-app | DEBUG[2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 2: chunk_id 64d4773b-c5b0-4a41-9f4d-7bec595e7c0d, score 0.7206
WeKnora-app | DEBUG[2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 3: chunk_id 6f13ea26-81c4-4f2e-8b93-902b3f5a8843, score 0.7111
WeKnora-app | DEBUG[2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 4: chunk_id 7d43f2f8-468b-4ad0-bd1e-961920143cb5, score 0.7059
WeKnora-app | DEBUG[2025-09-17 11:03:22.374] [request_id=MK5kY3RTgGpu] | [Postgres] Vector search result 5: chunk_id be6a6df5-beb4-4b6c-87ec-2c204953080b, score 0.6834
WeKnora-app |
WeKnora-app | 2025/09/17 11:03:22 /app/internal/application/repository/retriever/postgres/repository.go:179
WeKnora-app | [82.829ms] [rows:6] SELECT paradedb.score(id) as score,"id","content","source_id","source_type","chunk_id","knowledge_id","knowledge_base_id" FROM "embeddings" WHERE knowledge_base_id @@@ 'in ("af87ff60-dbc5-467e-8387-6627e117d417")' AND id @@@ paradedb.match(field => 'content', value => '接线 规范 什么样 标准 或 要求', distance => 1) ORDER BY "score" DESC LIMIT 10
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords retrieval found 6 results
WeKnora-app | DEBUG[2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 0: chunk=7d43f2f8-468b-4ad0-bd1e-961920143cb5, score=141.370544
WeKnora-app | DEBUG[2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 1: chunk=be6a6df5-beb4-4b6c-87ec-2c204953080b, score=59.870541
WeKnora-app | DEBUG[2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 2: chunk=64d4773b-c5b0-4a41-9f4d-7bec595e7c0d, score=47.870541
WeKnora-app | DEBUG[2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 3: chunk=6eae34ae-e1a2-41d5-9ab2-e0c5e5aa237a, score=36.370541
WeKnora-app | DEBUG[2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 4: chunk=0a99c236-2912-4cd3-a959-c1b815f774df, score=22.870541
WeKnora-app | DEBUG[2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] | [Postgres] Keywords result 5: chunk=6f13ea26-81c4-4f2e-8b93-902b3f5a8843, score=9.370541
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] knowledgebase.go:352[HybridSearch] | Processing retrieval results
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] knowledgebase.go:355[HybridSearch] | Retrieval results, engine: postgres, retriever: vector, count: 6
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] knowledgebase.go:355[HybridSearch] | Retrieval results, engine: postgres, retriever: keywords, count: 6
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] knowledgebase.go:370[HybridSearch] | Result count before deduplication: 12
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] knowledgebase.go:372[HybridSearch] | Result count after deduplication: 6
WeKnora-app | INFO [2025-09-17 11:03:22.453] [request_id=MK5kY3RTgGpu] knowledgebase.go:407[processSearchResults] | Fetching knowledge data for 3 IDs
WeKnora-app |
WeKnora-app | 2025/09/17 11:03:22 /app/internal/application/repository/knowledge.go:107
WeKnora-app | [0.481ms] [rows:3] SELECT * FROM "knowledges" WHERE (tenant_id = 10000 AND id IN ('813cbf73-3ff0-4a30-8778-f7f403d89624','e2e802ca-d216-481c-9798-ca4a4ba6b86d','ff1a62f0-6c06-4bb6-9782-9e305840e087')) AND "knowledges"."deleted_at" IS NULL
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] knowledgebase.go:414[processSearchResults] | Fetching chunk data for 6 IDs
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] knowledgebase.go:504[processSearchResults] | Search results processed, total: 6
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] search.go:72[OnEvent] | Search by processed query: 接线 规范 什么样 标准 或 要求, results count: 6, error: <nil>
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] search.go:85[OnEvent] | Get search results, count: 6, session_id: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event chunk_search triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: chunk_rerank
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] rerank.go:39[OnEvent] | Starting reranking process
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] rerank.go:40[OnEvent] | Getting rerank model, model ID:
WeKnora-app | WARNING[2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] rerank.go:46[OnEvent] | Rerank model ID is empty, skipping reranking
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event chunk_rerank triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: chunk_merge
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:31[OnEvent] | Starting chunk merge process
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:36[OnEvent] | No rerank results available, using search results
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:40[OnEvent] | Processing 6 chunks for merging
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:53[OnEvent] | Grouped chunks by knowledge ID, 3 knowledge sources
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:58[OnEvent] | Processing knowledge ID: e2e802ca-d216-481c-9798-ca4a4ba6b86d with 2 chunks
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:103[OnEvent] | Merged 1 chunks into 2 chunks for knowledge ID: e2e802ca-d216-481c-9798-ca4a4ba6b86d
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:58[OnEvent] | Processing knowledge ID: ff1a62f0-6c06-4bb6-9782-9e305840e087 with 2 chunks
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:103[OnEvent] | Merged 1 chunks into 2 chunks for knowledge ID: ff1a62f0-6c06-4bb6-9782-9e305840e087
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:58[OnEvent] | Processing knowledge ID: 813cbf73-3ff0-4a30-8778-f7f403d89624 with 2 chunks
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:103[OnEvent] | Merged 1 chunks into 2 chunks for knowledge ID: 813cbf73-3ff0-4a30-8778-f7f403d89624
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] merge.go:114[OnEvent] | Final merged result: 6 chunks, sorted by score
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event chunk_merge triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: filter_top_k
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] filter_top_k.go:30[OnEvent] | Starting filter top-K process
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] filter_top_k.go:31[OnEvent] | Filter configuration: top-K = 5
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] filter_top_k.go:35[func1] | Filtering results: before=6, after=5
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] filter_top_k.go:51[OnEvent] | Filter top-K process completed
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event filter_top_k triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.454] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: into_chat_message
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event into_chat_message triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: chat_completion_stream
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] chat_completion_stream.go:39[OnEvent] | Starting chat completion stream
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] common.go:16[prepareChatModel] | Getting chat model, model ID: 00986f06-94a2-4173-9723-14353efc7001
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] model.go:285[GetChatModel] | Start getting chat model
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] model.go:286[GetChatModel] | Getting chat model with ID: 00986f06-94a2-4173-9723-14353efc7001
WeKnora-app | INFO [2025-09-17 11:03:22.455] [request_id=MK5kY3RTgGpu] model.go:289[GetChatModel] | Tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] model.go:306[GetChatModel] | Creating chat model instance
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] model.go:307[GetChatModel] | Model name: qwen2.5:14b, source: local
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] model.go:325[GetChatModel] | Chat model initialized successfully
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] common.go:24[prepareChatModel] | Setting up chat options
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] chat_completion_stream.go:48[OnEvent] | Preparing chat messages
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] chat_completion_stream.go:52[OnEvent] | Calling chat stream model
WeKnora-app | INFO [2025-09-17 11:03:22.456] [request_id=MK5kY3RTgGpu] | 确保模型 qwen2.5:14b 可用
WeKnora-app | INFO [2025-09-17 11:03:22.469] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] | 发送流式聊天请求到模型 qwen2.5:14b
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] chat_completion_stream.go:59[OnEvent] | Chat stream initiated successfully
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event chat_completion_stream triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] session.go:405[KnowledgeQAByEvent] | Starting to trigger event: stream_filter
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] stream_filter.go:30[OnEvent] | Starting stream filter
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] stream_filter.go:31[OnEvent] | Creating new stream channel
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] stream_filter.go:43[OnEvent] | Using no match prefix filter: <think>
WeKnora-app | </think>
WeKnora-app | NO_MATCH
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] stream_filter.go:82[OnEvent] | Stream filter initialized
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] session.go:425[KnowledgeQAByEvent] | Event stream_filter triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] session.go:428[KnowledgeQAByEvent] | All events triggered successfully
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] session.go:374[KnowledgeQA] | Knowledge base question answering completed
WeKnora-app | INFO [2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] stream_filter.go:48[func1] | Starting stream filter goroutine
WeKnora-app | DEBUG[2025-09-17 11:03:22.487] [request_id=MK5kY3RTgGpu] session.go:768[KnowledgeQA] | Sending reference content, total 5
WeKnora-app | INFO [2025-09-17 11:03:22.500] [request_id=MK5kY3RTgGpu] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:22.868] [request_id=MK5kY3RTgGpu] stream_filter.go:65[func1] | Content does not match no-match prefix, passing through, content: 根据
WeKnora-app | INFO [2025-09-17 11:03:24.071] [request_id=MK5kY3RTgGpu] stream_filter.go:78[func1] | Stream filter completed, closing new stream
WeKnora-app | INFO [2025-09-17 11:03:24.071] [request_id=MK5kY3RTgGpu] message.go:227[UpdateMessage] | Start updating message
WeKnora-app | INFO [2025-09-17 11:03:24.071] [request_id=MK5kY3RTgGpu] message.go:228[UpdateMessage] | Updating message, ID: e0880792-b367-4b00-b8bb-e83a1698b864, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:24.071] [request_id=MK5kY3RTgGpu] message.go:232[UpdateMessage] | Checking if session exists, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:24.072] [request_id=MK5kY3RTgGpu] message.go:240[UpdateMessage] | Session exists, updating message
WeKnora-app | INFO [2025-09-17 11:03:24.073] [request_id=MK5kY3RTgGpu] message.go:250[UpdateMessage] | Message updated successfully
WeKnora-app | INFO [2025-09-17 11:03:24.073] [] | [MK5kY3RTgGpu] 200 | 16632 | 5.555313155s | 223.112.161.10 | POST /api/v1/knowledge-chat/9b3d390a-a7d8-4a5c-92e7-1b3387bc5961
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] tenant.go:84[GetTenantByID] | Start retrieving tenant
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] tenant.go:91[GetTenantByID] | Retrieving tenant, ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] tenant.go:101[GetTenantByID] | Tenant retrieved successfully, ID: 10000, name: licy's Workspace
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] session.go:399[GenerateTitle] | Start generating session title
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] session.go:418[GenerateTitle] | Generating session title, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, message count: 1
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] session.go:205[GenerateTitle] | Start generating session title
WeKnora-app | INFO [2025-09-17 11:03:24.102] [request_id=NkgV3AgmvgTL] session.go:215[GenerateTitle] | Getting session info, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] session.go:245[GenerateTitle] | Searching for user message in message list
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] session.go:261[GenerateTitle] | Getting chat model, model ID: 00986f06-94a2-4173-9723-14353efc7001
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] model.go:285[GetChatModel] | Start getting chat model
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] model.go:286[GetChatModel] | Getting chat model with ID: 00986f06-94a2-4173-9723-14353efc7001
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] model.go:289[GetChatModel] | Tenant ID: 10000
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] model.go:306[GetChatModel] | Creating chat model instance
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] model.go:307[GetChatModel] | Model name: qwen2.5:14b, source: local
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] model.go:325[GetChatModel] | Chat model initialized successfully
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] session.go:271[GenerateTitle] | Preparing to generate session title
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] session.go:282[GenerateTitle] | Calling model to generate title
WeKnora-app | INFO [2025-09-17 11:03:24.103] [request_id=NkgV3AgmvgTL] | 确保模型 qwen2.5:14b 可用
WeKnora-app | INFO [2025-09-17 11:03:24.124] [request_id=NkgV3AgmvgTL] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:24.142] [request_id=NkgV3AgmvgTL] | 发送聊天请求到模型 qwen2.5:14b
WeKnora-app | INFO [2025-09-17 11:03:24.157] [request_id=NkgV3AgmvgTL] | Ollama service ready
WeKnora-app | INFO [2025-09-17 11:03:24.341] [request_id=NkgV3AgmvgTL] session.go:294[GenerateTitle] | Title generated successfully: 接线规范要求
WeKnora-app | INFO [2025-09-17 11:03:24.341] [request_id=NkgV3AgmvgTL] session.go:297[GenerateTitle] | Updating session title
WeKnora-app | INFO [2025-09-17 11:03:24.342] [request_id=NkgV3AgmvgTL] session.go:304[GenerateTitle] | Session title updated successfully, ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, title: 接线规范要求
WeKnora-app | INFO [2025-09-17 11:03:24.342] [request_id=NkgV3AgmvgTL] session.go:427[GenerateTitle] | Session title generated successfully, session ID: 9b3d390a-a7d8-4a5c-92e7-1b3387bc5961, title: 接线规范要求
WeKnora-app | INFO [2025-09-17 11:03:24.342] [] | [NkgV3AgmvgTL] 200 | 44 | 240.736172ms | 223.112.161.10 | POST /api/v1/sessions/9b3d390a-a7d8-4a5c-92e7-1b3387bc5961/generate_title
WeKnora-postgres | 2025-09-17 03:03:28.850 UTC [3580] FATAL: database "dxuser" does not exist
WeKnora-postgres | 2025-09-17 03:03:38.903 UTC [3588] FATAL: database "dxuser" does not exist
操作系统
ubuntu 22.04
确认事项
- [x] 我已经搜索了现有的 issues，确认这是一个新问题
