From 90dad24a9e946b10af656a2c1fc3d6f144486036 Mon Sep 17 00:00:00 2001
From: Andrew Park <andysampark@gmail.com>
Date: Sun, 12 Jul 2026 14:12:43 -0700
Subject: [PATCH] orchestrator: drop the chat REPL

Nothing imports it and it isn't part of the pipeline. Recoverable from
history if the manual routing-inspection loop is wanted again.
---
 scripts/orchestrator/chat_orchestrator.py | 133 ----------------------
 1 file changed, 133 deletions(-)
 delete mode 100644 scripts/orchestrator/chat_orchestrator.py

diff --git a/scripts/orchestrator/chat_orchestrator.py b/scripts/orchestrator/chat_orchestrator.py
deleted file mode 100644
index 42dbf83d..00000000
--- a/scripts/orchestrator/chat_orchestrator.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env python
-"""Interactive REPL to chat with a trained orchestrator checkpoint.
-
-Reuses the SAME routing loop the eval uses (OrchestratorBackend.generate_full),
-so what you see here is exactly how it behaves on the benchmarks: it reads your
-question, decides whether to answer itself / call a tool / delegate to an expert
-model, runs that rollout, and prints the final answer + a routing summary.
-
-Prereqs:
-  1. Serve the checkpoint you want on a vLLM port (see --endpoint). e.g. 8k:
-       PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True CUDA_VISIBLE_DEVICES=0 \
-       .venv/bin/python -m vllm.entrypoints.openai.api_server \
-           --model "$OJ_WORK/qwen_eval_ckpts/8k_served" \
-           --served-model-name sft-qwen-8k --port 8020 \
-           --gpu-memory-utilization 0.88 --trust-remote-code --max-model-len 32768 \
-           --enforce-eager --enable-auto-tool-choice --tool-call-parser qwen3_xml
-  2. source .env         (cloud experts GPT-5.5 / Opus need the API keys)
-
-Usage:
-  .venv/bin/python scripts/orchestrator/chat_orchestrator.py \
-      --endpoint http://localhost:8020/v1 --model sft-qwen-8k
-
-  # point local expert tiers at their own vLLMs if you have them up (optional):
-  #   --local-endpoint Qwen/Qwen3.6-27B=http://localhost:8002/v1
-"""
-
-import argparse
-import sys
-import time
-
-
-def _parse_args(argv=None):
-    p = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-    p.add_argument(
-        "--endpoint",
-        default="http://localhost:8020/v1",
-        help="vLLM endpoint serving the orchestrator checkpoint.",
-    )
-    p.add_argument(
-        "--model", default="sft-qwen-8k", help="Served-model-name of the checkpoint."
-    )
-    p.add_argument(
-        "--api-key",
-        default="EMPTY",
-        help="API key for the endpoint (local vLLM = EMPTY).",
-    )
-    p.add_argument("--temperature", type=float, default=0.0)
-    p.add_argument("--max-turns", type=int, default=8, help="Same default as the eval.")
-    p.add_argument(
-        "--max-tokens",
-        type=int,
-        default=3000,
-        help="Keep high: the model reasons a lot before it emits the delegation.",
-    )
-    p.add_argument(
-        "--local-endpoint",
-        action="append",
-        default=[],
-        help="MODEL_ID=URL for a local expert tier. Repeatable.",
-    )
-    return p.parse_args(argv)
-
-
-def main(argv=None):
-    args = _parse_args(argv)
-
-    local_endpoints = {}
-    for pair in args.local_endpoint:
-        if "=" not in pair:
-            raise SystemExit(f"--local-endpoint expects MODEL_ID=URL, got: {pair!r}")
-        mid, url = pair.split("=", 1)
-        local_endpoints[mid.strip()] = url.strip()
-
-    from openjarvis.learning.intelligence.orchestrator.eval_backend import (
-        OrchestratorBackend,
-    )
-
-    backend = OrchestratorBackend(
-        orchestrator_endpoint=args.endpoint,
-        orchestrator_model=args.model,
-        api_key=args.api_key,
-        local_endpoints=local_endpoints,
-        max_turns=args.max_turns,
-        temperature=args.temperature,
-    )
-
-    print(
-        f"orchestrator: {args.model} @ {args.endpoint}  "
-        f"(temp={args.temperature}, max_turns={args.max_turns})"
-    )
-    print("type a question and hit enter. Ctrl-D or 'quit' to exit.\n")
-
-    try:
-        while True:
-            try:
-                prompt = input("you> ").strip()
-            except EOFError:
-                print()
-                break
-            if not prompt:
-                continue
-            if prompt.lower() in {"quit", "exit"}:
-                break
-
-            started = time.time()
-            full = backend.generate_full(
-                prompt,
-                model=args.model,
-                temperature=args.temperature,
-                max_tokens=args.max_tokens,
-            )
-            elapsed = time.time() - started
-
-            if full.get("error"):
-                print(f"\n[ERROR] {full['error']}\n", file=sys.stderr)
-                continue
-
-            print(f"\norch> {full.get('content', '').strip()}\n")
-            print(
-                f"  [turns={full.get('turn_count', '?')} "
-                f"tool_calls={full.get('tool_calls', '?')} "
-                f"cost=${full.get('cost_usd', 0.0):.4f} "
-                f"{elapsed:.1f}s]\n"
-            )
-    finally:
-        backend.close()
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())