| """Generate synthetic banking queries per intent with DeepSeek, seeded with real clean-pool examples.""" |
| import argparse, json, os, random, threading |
| from concurrent.futures import ThreadPoolExecutor |
| import numpy as np, pandas as pd |
| from llm import chat_json, usage_line |
|
|
| SYSTEM = """You write realistic customer messages sent to a retail bank's support chat. Given a target intent, real examples of it, and confusable OTHER intents, write NEW messages that clearly express ONLY the target intent. |
| Rules: |
| - Every message must be unmistakably about the target intent and must NOT be answerable as any of the confusable other intents. |
| - Do not copy or lightly paraphrase the examples; vary wording, structure, length, tone, typos and punctuation as instructed by the style hint. |
| - About 1 in 3 messages should include personal details, written ONLY as these placeholders (never real-looking values): {NAME} {AMOUNT} {LAST4} {MERCHANT} {CITY}. The rest must have no personal details and no placeholders. |
| - One message per string, no numbering, no quotes inside unless natural. |
| Return JSON: {"q": ["...", ...]}""" |
|
|
| STYLES = ["very short and terse (3-8 words)", "one casual sentence, lowercase, a typo or two", "polite and formal, 1-2 sentences", |
| "frustrated or worried, 2 sentences", "a question phrased indirectly", "detailed, gives the backstory, 2-3 sentences", |
| "non-native English speaker phrasing", "mix of short and medium messages"] |
|
|
| SYSTEM_NOISY = """You write realistic, MESSY customer messages sent to a retail bank's support chat, typed quickly on a phone. Given a target intent, real examples of it, and confusable OTHER intents, write NEW messages that clearly express ONLY the target intent. |
| Rules: |
| - Every message must still be unmistakably about the target intent and must NOT be answerable as any confusable other intent. |
| - Do NOT write polished, grammatically perfect sentences. Do not copy the examples. |
| - Follow the mess hint for the whole batch, but mix in variety: about 30% with typos or misspellings, about 20% very short (under 8 words), about 15% rambling run-ons, some with slang ("pls", "thx", "idk", "gonna", "bc"), some with no punctuation or "!!!" / "???", occasional mixed register. |
| - About 1 in 3 messages should include personal details, written ONLY as these placeholders (never real-looking values): {NAME} {AMOUNT} {LAST4} {MERCHANT} {CITY} {DATE} {REF}. Use a placeholder only where it fits the message naturally. The rest must have no placeholders. |
| - One message per string, no numbering. |
| Return JSON: {"q": ["...", ...]}""" |
|
|
| MESS = ["heavy typos and misspellings", "very short fragments, under 8 words", "rambling run-on sentences with backstory", |
| "texting slang and abbreviations, no punctuation", "frustrated, caps and !!! and ???", "mixed formal and rude register", |
| "odd or unusual word choices, non-native phrasing", "incomplete sentences and trailing off"] |
|
|
| def neighbors(intents): |
| seeds = pd.read_parquet("data/seeds_audited.parquet") |
| emb = np.load("data/seed_emb_bge.npy") |
| cent = {i: emb[(seeds.intent == i).values].mean(0) for i in seeds.intent.unique()} |
| for k in cent: cent[k] /= np.linalg.norm(cent[k]) |
| out = {} |
| for i in intents: |
| sims = sorted(((float(cent[i] @ cent[j]), j) for j in intents if j != i), reverse=True) |
| out[i] = [j for _, j in sims[:2]] |
| return out |
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--per-intent", type=int, default=195) |
| ap.add_argument("--batch", type=int, default=15) |
| ap.add_argument("--intents", type=int, default=0, help="pilot: only first N intents") |
| ap.add_argument("--workers", type=int, default=12) |
| ap.add_argument("--out", default="data/synth_raw.jsonl") |
| ap.add_argument("--noisy", action="store_true", help="messy-style prompt with extra PII slots") |
| a = ap.parse_args() |
|
|
| pool = pd.read_parquet("data/clean_pool.parquet") |
| intents = sorted(pool.intent.unique()) |
| nb = neighbors(intents) |
| by = {i: pool[pool.intent == i].text.tolist() for i in intents} |
| todo_intents = intents[: a.intents] if a.intents else intents |
| have = {i: 0 for i in intents} |
| if os.path.exists(a.out): |
| for l in open(a.out): have[json.loads(l)["intent"]] += 1 |
| jobs = [] |
| for i in todo_intents: |
| for _ in range(max(0, -(-(a.per_intent - have[i]) // a.batch))): jobs.append(i) |
| print(f"{len(jobs)} calls planned", flush=True) |
| lock, f = threading.Lock(), open(a.out, "a") |
|
|
| def run(intent): |
| rng = random.Random() |
| ex = rng.sample(by[intent], min(6, len(by[intent]))) |
| user = (f"Target intent: {intent}\nReal examples:\n" + "\n".join(f"- {s}" for s in ex) + |
| "\nConfusable OTHER intents (do NOT write these):\n" + |
| "\n".join(f"- {n}, e.g. \"{rng.choice(by[n])}\"" for n in nb[intent]) + |
| f"\n\n{'Mess hint' if a.noisy else 'Style hint'}: {rng.choice(MESS if a.noisy else STYLES)}\nWrite {a.batch} new messages.") |
| qs = chat_json("deepseek-flash", SYSTEM_NOISY if a.noisy else SYSTEM, user, max_tokens=1500).get("q", []) |
| with lock: |
| for q in qs: |
| if isinstance(q, str) and q.strip(): f.write(json.dumps({"intent": intent, "text": q.strip()}) + "\n") |
| f.flush() |
|
|
| with ThreadPoolExecutor(a.workers) as ex: list(ex.map(run, jobs)) |
| print(usage_line()) |
|
|
| if __name__ == "__main__": |
| main() |
|
|
