{"database": "data", "table": "requestDetails", "rows": [["1784636445691-ge79hhesi", "2026-07-21T12:21:26.568Z", "xai", "grok-4.5", "cf346328-65b0-4859-bfc6-7898b42dd13e", "success", "{\"id\":\"1784636445691-ge79hhesi\",\"provider\":\"xai\",\"model\":\"grok-4.5\",\"connectionId\":\"cf346328-65b0-4859-bfc6-7898b42dd13e\",\"timestamp\":\"2026-07-21T12:21:26.568Z\",\"status\":\"success\",\"latency\":{\"ttft\":1881,\"total\":41831},\"tokens\":{\"prompt_tokens\":207264,\"completion_tokens\":2676,\"total_tokens\":181028,\"estimated\":true,\"cached_tokens\":202624,\"reasoning_tokens\":1561,\"prompt_tokens_details\":{\"text_tokens\":207264,\"audio_tokens\":0,\"image_tokens\":0,\"cached_tokens\":202624},\"completion_tokens_details\":{\"reasoning_tokens\":1561,\"audio_tokens\":0,\"accepted_prediction_tokens\":0,\"rejected_prediction_tokens\":0}},\"request\":{\"_truncated\":true,\"_originalSize\":714646,\"_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\":714712,\"_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\"},\"providerResponse\":\"\u0110ang s\u1eeda l\u1ed7i BLOB SQLite, incremental reuse v\u00e0 doctor.\",\"response\":{\"content\":\"\u0110ang s\u1eeda l\u1ed7i BLOB SQLite, incremental reuse v\u00e0 doctor.\",\"thinking\":\"There are several issues:\\nThe issue you're seeing with `reused=0` is likely because `_load_existing_vectors` is failing to find any rows after the `DELETE FROM vectors`\u2014but that shouldn't happen if the table still has data from the first build. Let me walk through why this might be happening and how to fix it.\\n\\nFirst - the `DELETE FROM vectors` is happening *after* `_load_existing_vectors`, so if the query in `_load_existing_vectors` is returning rows, they should be cached. But if the embedding column is coming back as `str` instead of `bytes`, then when you try to re-insert it later, SQLite 3 might be treating it as text and corrupting the BLOB.\\n\\nThe fix is to force proper BLOB handling:\\n\\n```python\\n# In _load_existing_vectors\\nrows = conn.execute(\\n    \\\"SELECT content_hash, embed_model, embedding, embed_dim, embed_version FROM vectors\\\"\\n).fetchall()\\n\\nfor row in rows:\\n    content_hash, model, embedding, dim, version = row\\n    embedding = bytes(embedding)  # ensure bytes\\n    out[content_hash] = (content_hash, embedding, model, dim, version)\\n```\\n\\nAnd when inserting:\\n\\n```python\\ndef _insert_vector(conn, content_hash # ...):\\n    blob = _pack_vector(embedding)\\n    conn.execute(\\n        \\\"INSERT INTO vectors (content_hash, embed_model, embedding, embed_dim, embed_version) VALUES (?, ?, ?, ?, ?)\\\",\\n        (content_hash, model, sqlite3.\",\"type\":\"streaming\"}}"]], "columns": ["id", "timestamp", "provider", "model", "connectionId", "status", "data"], "primary_keys": ["id"], "primary_key_values": ["1784636445691-ge79hhesi"], "units": {}, "query_ms": 23.574368096888065}