requestDetails: 1784636445691-ge79hhesi
This data as json
| id | timestamp | provider | model | connectionId | status | data |
|---|---|---|---|---|---|---|
| 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":"Đang sửa lỗi BLOB SQLite, incremental reuse và doctor.","response":{"content":"Đang sửa lỗi BLOB SQLite, incremental reuse và 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`—but 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"}} |