{"database": "data", "table": "requestDetails", "rows": [["1784628761140-5nx8msuyc", "2026-07-21T10:12:52.745Z", "anthropic-compatible-6f67ec10-c8c2-4d13-ae77-48f0747753e2", "gpt-5.6-sol", "090f052c-ffd2-40da-968e-5d7ae8b4175a", "success", "{\"id\":\"1784628761140-5nx8msuyc\",\"provider\":\"anthropic-compatible-6f67ec10-c8c2-4d13-ae77-48f0747753e2\",\"model\":\"gpt-5.6-sol\",\"connectionId\":\"090f052c-ffd2-40da-968e-5d7ae8b4175a\",\"timestamp\":\"2026-07-21T10:12:52.745Z\",\"status\":\"success\",\"latency\":{\"ttft\":3309,\"total\":12256},\"tokens\":{\"prompt_tokens\":174412,\"completion_tokens\":485,\"total_tokens\":174897,\"input_tokens\":2892,\"output_tokens\":485,\"cache_read_input_tokens\":171520},\"request\":{\"_truncated\":true,\"_originalSize\":618089,\"_preview\":\"{\\\"messages\\\":[{\\\"role\\\":\\\"system\\\",\\\"content\\\":\\\"You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of t\"},\"providerRequest\":{\"_truncated\":true,\"_originalSize\":607063,\"_preview\":\"{\\\"model\\\":\\\"gpt-5.6-sol\\\",\\\"max_tokens\\\":65536,\\\"stream\\\":true,\\\"messages\\\":[{\\\"role\\\":\\\"user\\\",\\\"content\\\":[{\\\"type\\\":\\\"text\\\",\\\"text\\\":\\\"t\u00ecm hi\u1ec3u nh\u1eefng \u0111i\u1ec3m hay c\u1ee7a repo n\u00e0y, xem tham kh\u1ea3o b\u1ed9 skill hi\u1ec7n t\u1ea1i c\u00f3 c\u1ea7n c\u1ea3i th\"},\"providerResponse\":\"RAG n\u1eb1m \u1edf hai n\u01a1i:\\n\\n## 1. Code c\u1ee7a RAG\\n\\n```text\\n/Users/macbook/.hermes/skills/creative/story-management/scripts/story_rag.py\\n```\\n\\n\u0110\u00e2y l\u00e0 c\u00f4ng c\u1ee5 t\u00ecm ki\u1ebfm local d\u00f9ng:\\n\\n```text\\nSQLite FTS5 + BM25\\n```\\n\\nHi\u1ec7n l\u00e0 **lexical RAG**, ch\u01b0a c\u00f3 embedding/vector semantic search.\\n\\n## 2. Database c\u1ee7a truy\u1ec7n\\n\\n```text\\n/Users/macbook/Library/CloudStorage/OneDrive-Personal/2_Daffodil_Street/stories/ben-kia-buc-man/.story-rag/index.db\\n```\\n\\nDatabase hi\u1ec7n ch\u1ee9a:\\n\\n```text\\n32 ch\u01b0\u01a1ng canon\\n324 chunks\\n```\\n\\n## C\u00e1ch g\u1ecdi\\n\\n```bash\\nSCRIPT=\\\"$HOME/.hermes/skills/creative/story-management/scripts/story_rag.py\\\"\\nSTORY=\\\"$HOME/Library/CloudStorage/OneDrive-Personal/2_Daffodil_Street/stories/ben-kia-buc-man\\\"\\n```\\n\\n### Rebuild index\\n\\n```bash\\npython3 \\\"$SCRIPT\\\" index \\\"$STORY\\\"\\n```\\n\\n### T\u00ecm ki\u1ebfm\\n\\n```bash\\npython3 \\\"$SCRIPT\\\" query \\\"$STORY\\\" \\\\\\n  \\\"v\u00f2ng tay t\u00edn hi\u1ec7u v\u1ecb tr\u00ed th\u1ee9 m\u01b0\u1eddi b\u1ea3y\\\" \\\\\\n  --limit 8\\n```\\n\\n### T\u1ea1o context cho ch\u01b0\u01a1ng 33\\n\\n```bash\\npython3 \\\"$SCRIPT\\\" context \\\"$STORY\\\" 33 \\\\\\n  \\\"Nh\u00e3 Minh v\u00f2ng tay t\u00edn hi\u1ec7u v\u1ecb tr\u00ed th\u1ee9 m\u01b0\u1eddi b\u1ea3y\\\" \\\\\\n  --limit 8\\n```\\n\\n## T\u00e0i li\u1ec7u workflow\\n\\n```text\\n/Users/macbook/.hermes/skills/creative/story-management/references/canon-rag-workflow.md\\n```\\n\\nL\u01b0u \u00fd: \u0111\u00e2y m\u1edbi l\u00e0 **giai \u0111o\u1ea1n Local RAG b\u1eb1ng BM25**, kh\u00f4ng ph\u1ea3i hybrid/vector RAG nh\u01b0 repo `webnovel-writer`. Ch\u01b0a c\u00f3:\\n\\n- Embedding.\\n- Vector database.\\n- Semantic similarity.\\n- Hybrid BM25 + vector.\\n- RRF/reranker.\\n\\nT\u00f4i g\u1ecdi n\u00f3 l\u00e0 RAG v\u00ec n\u00f3 th\u1ef1c hi\u1ec7n \u0111\u1ee7 hai b\u01b0\u1edbc:\\n\\n```text\\nRetrieve: t\u00ecm c\u00e1c chunk li\u00ean quan t\u1eeb index\\nAugment: \u0111\u01b0a ch\u00fang v\u00e0o Context Pack \u0111\u1ec3 vi\u1ebft ch\u01b0\u01a1ng\\n```\\n\\nNh\u01b0ng n\u00f3i ch\u00ednh x\u00e1c h\u01a1n, \u0111\u00e2y l\u00e0 **BM25 retrieval pipeline**, ch\u01b0a ph\u1ea3i semantic RAG \u0111\u1ea7y \u0111\u1ee7.\",\"response\":{\"content\":\"RAG n\u1eb1m \u1edf hai n\u01a1i:\\n\\n## 1. Code c\u1ee7a RAG\\n\\n```text\\n/Users/macbook/.hermes/skills/creative/story-management/scripts/story_rag.py\\n```\\n\\n\u0110\u00e2y l\u00e0 c\u00f4ng c\u1ee5 t\u00ecm ki\u1ebfm local d\u00f9ng:\\n\\n```text\\nSQLite FTS5 + BM25\\n```\\n\\nHi\u1ec7n l\u00e0 **lexical RAG**, ch\u01b0a c\u00f3 embedding/vector semantic search.\\n\\n## 2. Database c\u1ee7a truy\u1ec7n\\n\\n```text\\n/Users/macbook/Library/CloudStorage/OneDrive-Personal/2_Daffodil_Street/stories/ben-kia-buc-man/.story-rag/index.db\\n```\\n\\nDatabase hi\u1ec7n ch\u1ee9a:\\n\\n```text\\n32 ch\u01b0\u01a1ng canon\\n324 chunks\\n```\\n\\n## C\u00e1ch g\u1ecdi\\n\\n```bash\\nSCRIPT=\\\"$HOME/.hermes/skills/creative/story-management/scripts/story_rag.py\\\"\\nSTORY=\\\"$HOME/Library/CloudStorage/OneDrive-Personal/2_Daffodil_Street/stories/ben-kia-buc-man\\\"\\n```\\n\\n### Rebuild index\\n\\n```bash\\npython3 \\\"$SCRIPT\\\" index \\\"$STORY\\\"\\n```\\n\\n### T\u00ecm ki\u1ebfm\\n\\n```bash\\npython3 \\\"$SCRIPT\\\" query \\\"$STORY\\\" \\\\\\n  \\\"v\u00f2ng tay t\u00edn hi\u1ec7u v\u1ecb tr\u00ed th\u1ee9 m\u01b0\u1eddi b\u1ea3y\\\" \\\\\\n  --limit 8\\n```\\n\\n### T\u1ea1o context cho ch\u01b0\u01a1ng 33\\n\\n```bash\\npython3 \\\"$SCRIPT\\\" context \\\"$STORY\\\" 33 \\\\\\n  \\\"Nh\u00e3 Minh v\u00f2ng tay t\u00edn hi\u1ec7u v\u1ecb tr\u00ed th\u1ee9 m\u01b0\u1eddi b\u1ea3y\\\" \\\\\\n  --limit 8\\n```\\n\\n## T\u00e0i li\u1ec7u workflow\\n\\n```text\\n/Users/macbook/.hermes/skills/creative/story-management/references/canon-rag-workflow.md\\n```\\n\\nL\u01b0u \u00fd: \u0111\u00e2y m\u1edbi l\u00e0 **giai \u0111o\u1ea1n Local RAG b\u1eb1ng BM25**, kh\u00f4ng ph\u1ea3i hybrid/vector RAG nh\u01b0 repo `webnovel-writer`. Ch\u01b0a c\u00f3:\\n\\n- Embedding.\\n- Vector database.\\n- Semantic similarity.\\n- Hybrid BM25 + vector.\\n- RRF/reranker.\\n\\nT\u00f4i g\u1ecdi n\u00f3 l\u00e0 RAG v\u00ec n\u00f3 th\u1ef1c hi\u1ec7n \u0111\u1ee7 hai b\u01b0\u1edbc:\\n\\n```text\\nRetrieve: t\u00ecm c\u00e1c chunk li\u00ean quan t\u1eeb index\\nAugment: \u0111\u01b0a ch\u00fang v\u00e0o Context Pack \u0111\u1ec3 vi\u1ebft ch\u01b0\u01a1ng\\n```\\n\\nNh\u01b0ng n\u00f3i ch\u00ednh x\u00e1c h\u01a1n, \u0111\u00e2y l\u00e0 **BM25 retrieval pipeline**, ch\u01b0a ph\u1ea3i semantic RAG \u0111\u1ea7y \u0111\u1ee7.\",\"thinking\":null,\"type\":\"streaming\"}}"]], "columns": ["id", "timestamp", "provider", "model", "connectionId", "status", "data"], "primary_keys": ["id"], "primary_key_values": ["1784628761140-5nx8msuyc"], "units": {}, "query_ms": 204.25505749881268}