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The ultrathink + operator both found it: ~half the corpus is not cold prospecting (existing clients mid-booking, friends, vendors, spam) yet was forced into prospect moves -- the root cause of the 54% forward/backward disagreement. Taxonomy now leads with the prospect/not-prospect gate: existing_client | personal | vendor | spam as explicit classes, and collapses the noisy qualify/engage pair into 'pursue' (they route to the same action). Applied to both sweep (forward) and rationalize (backward) for the next re-sweep on the identity-gated corpus. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
78 lines
4.5 KiB
Python
78 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""Rationalize labeled conversations into CoT training rows (STaR / backward distill).
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Given (conversation context -> Quinn's ACTUAL next reply), infer the MOVE she ran
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and a one-sentence reasoning trace anchored to her real reply (not a forward
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guess). This is the high-quality way to manufacture the LoRA training set for
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move-classification: (context -> trace -> move), labeled by what she actually did.
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Input: a JSON list with `gold_reply` and either `context` (sweep) or `client_msg`
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(mined cluster). Default <DATA_DIR>/sweep_labels.json -> the full work-era corpus.
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Output: <DATA_DIR>/traincot_<input-stem>.json.
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Env: OSS_URL, DATA_DIR. Arg: input filename (default sweep_labels.json).
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"""
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import json, os, sys, urllib.request
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from collections import Counter
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from concurrent.futures import ThreadPoolExecutor, as_completed
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OSS_URL = os.environ.get("OSS_URL", "http://localhost:8800/v1/chat/completions")
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DATA = os.environ.get("DATA_DIR", os.path.join(os.path.dirname(__file__), ".data"))
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WORKERS = int(os.environ.get("WORKERS", "64"))
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INPUT = sys.argv[1] if len(sys.argv) > 1 else "sweep_labels.json"
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stem = os.path.splitext(os.path.basename(INPUT))[0]
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items = json.load(open(os.path.join(DATA, INPUT)))
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MOVES = ["opener", "pursue", "subhour", "address", "out_of_area", "of", "disengage", "escalate",
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"existing_client", "personal", "vendor", "spam"]
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SYSTEM = f"""You build training data by analyzing how Quinn (a touring companion, $1000/hr, incall williamsburg NYC, text only, OnlyFans @transquinnftw) handled a conversation. You are GIVEN her actual next reply, so infer her REAL reasoning -- do not invent a different reply.
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FIRST: was this even a COLD PROSPECT (new person evaluating/booking her), or NOT a prospect? Much traffic is not.
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NOT-A-PROSPECT (classify as these, NOT a prospect move):
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- existing_client: already her client -- mid-booking logistics, "see you soon", on-the-way, past-meeting references, ongoing relationship/sexting with someone she's met.
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- personal: a friend / family / non-work conversation.
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- vendor: someone selling HER a service.
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- spam: bot / automated / marketing / scam / wrong number.
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PROSPECT moves (one of: {', '.join(m for m in MOVES if m not in ('existing_client','personal','vendor','spam'))}):
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- opener: answered a new hello with her intro.
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- pursue: engaged a paying prospect / gave rate / answered a preference question / moved toward booking (even if crude or low, if she pursued).
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- subhour: gave the <1hr / half-hour rate stance.
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- address: withheld her address when asked before a locked time.
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- out_of_area: told him she's not in his city / offered outcall.
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- of: redirected to OnlyFans (harvester / free-content / out-of-budget).
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- disengage: brushed off a lowballer / hostile / someone offering his body.
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- escalate: a collab / photographer / business / opportunity she'd personally decide.
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Then a ONE-sentence trace: prospect or not, the subject, his pay-intent, why her move fits.
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Output ONLY JSON: {{"move":"<one of the classes>","trace":"<one sentence>"}}"""
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SCHEMA = {"type": "object",
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"properties": {"move": {"type": "string", "enum": MOVES}, "trace": {"type": "string"}},
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"required": ["move", "trace"], "additionalProperties": False}
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def rationalize(it):
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ctx = it.get("context") or ("CLIENT: " + it.get("client_msg", ""))
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gold = it.get("gold_reply") or it.get("quinn_reply_gold", "")
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user = f"{ctx}\nQUINN (actual reply): {gold}"
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body = json.dumps({"model": "quinn-oss",
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"messages": [{"role": "system", "content": SYSTEM}, {"role": "user", "content": user}],
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"temperature": 0.2, "max_tokens": 250,
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"response_format": {"type": "json_schema", "json_schema": {"name": "r", "schema": SCHEMA, "strict": True}}}).encode()
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req = urllib.request.Request(OSS_URL, data=body, headers={"Content-Type": "application/json"})
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d = json.loads(json.load(urllib.request.urlopen(req, timeout=120))["choices"][0]["message"]["content"])
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return {"context": ctx, "gold_reply": gold, "move": d["move"], "trace": d["trace"]}
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rows = []
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with ThreadPoolExecutor(max_workers=WORKERS) as ex:
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futs = [ex.submit(rationalize, it) for it in items if (it.get("gold_reply") or it.get("quinn_reply_gold"))]
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for f in as_completed(futs):
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try: rows.append(f.result())
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except Exception as e: print("ERR", e, flush=True)
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out = os.path.join(DATA, f"traincot_{stem}.json")
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json.dump(rows, open(out, "w"), ensure_ascii=False)
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print(f"rationalized {len(rows)} -> {out}")
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print("move dist:", dict(Counter(r["move"] for r in rows)))
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