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steps B.3/B.4/B.5 complete: bidirectional WS handler, _tool_loop verified, clean final frame

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roberto
2026-03-05 00:06:11 +01:00
parent 27c087d5d8
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AI_REFACTOR_PLAN.md
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@@ -158,7 +158,7 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
- **Outcome:** All 23 tools query real user data via WS. LLM sees actual rows, not action descriptors.
### Step B.3 — Bidirectional WebSocket handler
- [ ] Refactor `app/api/routes/chat.py` WS endpoint:
- [x] Refactor `app/api/routes/chat.py` WS endpoint:
- After auth + accept + receive `chat_request`:
1. Create `execute_on_client` callback closure capturing the websocket:
```python
@@ -192,13 +192,13 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
- **Outcome:** Full bidirectional WS. Tool calls and text streaming happen concurrently on the same connection.
### Step B.4 — `_tool_loop` — no changes needed
- [ ] Verify `app/core/agent_registry.py` works unchanged:
- [x] Verify `app/core/agent_registry.py` works unchanged:
- `_tool_loop` calls `tool_fn.ainvoke(args)` → tool awaits `execute_on_client()` (WS round-trip) → returns string → `ToolMessage(content=string)` → LLM sees real data
- The async WS round-trip happens inside each tool. `_tool_loop` just sees an awaited tool returning a string — same as before, different content.
- **No code changes.** Just verify + add a log line for tool execution times if desired.
### Step B.5 — Orchestrator cleanup
- [ ] Update `app/core/orchestrator.py`:
- [x] Update `app/core/orchestrator.py`:
- `orchestrate_stream()`: remove `"actions": []` from final frame. Final becomes: `{"done": true, "response": "..."}`
- No other changes — `classify_intent` → `call_agent` → chunk response → final frame
- **Files:** `app/core/orchestrator.py`