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"knowledge/_chunks.jsonl", "rows": 617, "keys": [ "chapter", "chars", "collection", "content", "doc_id", "doc_no", "effective_date", "expire_date", "reviewer", "section", "source_file", "source_url", "tags", "title", "version", "visibility" ], "collection_dist": { "fin_policy_collection": 281, "fin_product_collection": 211, "fin_faq_collection": 125 }, "visibility_dist": { "public": 617 }, "brand_occurrences": { "南方基金": 0, "南方科技": 441, "奶龙": 0, "南方财富": 0 }, "brand_marker_zero": { "南方基金": true, "奶龙": true } }, "milvus_vs_source_gap": { "fin_faq_collection": { "milvus_rows": 15, "source_rows": 125 }, "fin_policy_collection": { "milvus_rows": 11, "source_rows": 281 }, "fin_product_collection": { "milvus_rows": 26, "source_rows": 211 } }, "collection_schema_variants": { "official_setup_tool": [ "knowledge_id(64,PK)", "title(256)", "snippet(4000)", "tags(512)", "version(16)", "intent(32,nullable)", "embedding(1024)" ], "temp_loader_script": [ "doc_id(64,PK)", "title", "content", "chapter", "section", "tags", "doc_no", "version", "source_file", "visibility", "embedding(1024)" ], "live_milvus": [ "knowledge_id(64,PK)", "title(256)", "snippet(1024)", "tags(1024)", "version(64)", "embedding(1024)" ] }, "embedding_endpoint_probe": { "chosen_model": "text-embedding-v3", "chosen_dim": 1024, "dim_contract": "app/core/knowledge_contracts.py VECTOR_DIM=1024", "doc_basis": "D1.2-南方基金业务事实基座与虚构数据规范 §(embedding 维度 1024 / 通义千问 text-embedding-v3)", "model_probe": { "text-embedding-v3": { "status": 200, "dim": 1024 }, "qwen3.7-text-embedding-flash": { "status": 200, "dim": 1024 } }, "env_fix_applied": "已向 group_fqcd_jr/.env 补 QWEN_API_KEY(与 QWEN_EMBEDDING_API_KEY/DASHSCOPE_API_KEY 同一把千问 key,115 字符)", "finding": "实测两个模型名在 DashScope 均可用且均输出 1024 维:text-embedding-v3(现库+文档口径)与 qwen3.7-text-embedding-flash(tools 脚本口径)。故真正导致向量化失败的**只有** .env 缺 env:QWEN_API_KEY 这一个变量名;模型名不一致属口径分裂,不是致命错误,但必须收敛为一种(建议 text-embedding-v3)。", "inconsistency_list": [ "tools/configure_embedding_endpoint.py:89,91 -> model=qwen3.7-text-embedding-flash, secret_ref=env:QWEN_EMBEDDING_API_KEY", "tools/load_knowledge_milvus.py:36-38 -> EMBED_MODEL=qwen3.7-text-embedding-flash, EMBED_KEY=QWEN_EMBEDDING_API_KEY", "app/core/knowledge_contracts.py:27 VECTOR_DIM=1024 注释写 text-embedding-v3 输出维度", "现库 model_endpoint_config.id=1 -> model=text-embedding-v3, secret_ref=env:QWEN_API_KEY, context_window=8192, allowed_data_levels=[public]" ], "post_fix_verification": { "command": "get_memory_embedding_service().embed(resolve(agent_type='customer_service', task_type='embedding'), '基金赎回多久到账')", "endpoint": "knowledge-embedding-qwen-v3 / text-embedding-v3 / env:QWEN_API_KEY", "before_fix": "RecoverableAgentError: 所有已批准向量端点调用失败", "after_fix": "EMBED_OK dim=1024", "conclusion": "补齐 env:QWEN_API_KEY 后,知识检索向量化全链路恢复;这是一条独立于词表的『强制转人工』根因,已消除。" } } }