c762564d40
- Fix 71 invalid-syntax files (class-body newline-broken assignments) - Add from/None chain to 307 B904 raise-without-from sites - Add B008 ignore to ruff.toml (already in pyproject.toml) - Noqa F401 on __init__.py re-exports (137 sites) - Noqa E402 on deferred imports (63 sites) - Bulk-add stdlib/FastAPI/project imports for F821 (127 sites) - Replace ×→x, –→-, …→... in docstrings (4093 chars) - Manual refactor of 5 SIM103/SIM116 patterns Tests: 791 passed (66 deselected due to pre-existing Redis issues in test_rag.py) Co-authored-by: opencode <opencode@rugmunch.io>
293 lines
10 KiB
Python
293 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Domain Fine-Tuned Embeddings - MarginMSE with hard negative mining.
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Trains scam-specific embeddings that outperform general-purpose models
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for crypto security tasks. Uses cross-encoder as teacher, hard negative
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mining from retrieval errors, MarginMSE loss for optimization.
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Output: Fine-tuned sentence-transformer model saved to /app/data/models/
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Usage:
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python3 scripts/finetune_embeddings.py --epochs 3 --batch-size 16
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Requirements:
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- Redis with existing RAG documents (for hard negative mining)
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- Cross-encoder model (for teacher scores)
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- Training data: scam queries + positive/negative document pairs
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"""
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import argparse
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import asyncio
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import logging
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import os
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import random
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import sys
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# ── Training data: query → [positive_doc, negative_docs...] ─────
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SCAM_QUERIES = [
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(
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"What are rug pull indicators?",
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[
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"Rug pulls involve liquidity removal, disabled transfers, and anonymous teams. Look for: large LP withdrawals, deployer selling tokens, and social media going dark.",
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],
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),
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(
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"How to detect honeypot tokens?",
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[
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"Honeypot tokens prevent selling. Check for: transfer restrictions, max transaction limits, and sell taxes above 50%. Use honeypot.is or tokensniffer for automated checks.",
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],
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),
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(
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"What is a flash loan attack?",
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[
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"Flash loan attacks exploit uncollateralized lending. Attackers borrow large amounts, manipulate oracle prices, drain funds, and repay within one transaction. Crema Finance lost $8.8M to this.",
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],
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),
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(
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"Phishing scam detection",
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[
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"Phishing scams use fake websites mimicking DEXs. Signs: slightly different URLs, requests for seed phrases, fake airdrop claims. Always verify contract addresses on official sources.",
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],
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),
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(
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"Wash trading identification",
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[
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"Wash trading creates fake volume. Indicators: circular token flows, self-trading between linked wallets, volume spikes without holder growth, and near-identical buy/sell timestamps.",
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],
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),
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(
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"Pump and dump patterns",
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[
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"Pump and dump groups coordinate buying to inflate prices, then dump on new buyers. Look for: Telegram/Discord 'signal' groups, rapid price spikes followed by 90%+ drops, and concentrated holder distribution.",
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],
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),
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(
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"MEV sandwich attack explained",
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[
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"Sandwich attacks insert transactions before and after a victim's trade. The attacker buys before the victim (raising price) and sells after (profiting from slippage). Common on Uniswap, PancakeSwap.",
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],
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),
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(
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"Smart contract vulnerability patterns",
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[
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"Common vulnerabilities: reentrancy (DAO hack), integer overflow, unchecked external calls, tx.origin authentication, and delegatecall to untrusted contracts. Use Slither or Mythril for analysis.",
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],
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),
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(
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"Token impersonation scams",
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[
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"Impersonation tokens copy legitimate token names, symbols, and logos. They trick users into buying the wrong token. Check: contract address matches official sources, token age, and holder count vs. legitimate token.",
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],
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),
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(
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"Liquidity pool manipulation",
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[
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"LP manipulation involves adding/removing liquidity to manipulate price. Signs: sudden LP changes, imbalanced pools, and flash loan-funded attacks. Monitor LP health ratio.",
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],
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),
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]
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# Hard negative candidates (similar-sounding but different concepts)
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HARD_NEGATIVES = [
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"Legitimate token launches with locked liquidity and audited contracts are safe.",
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"Airdrops to active community members are a normal marketing strategy.",
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"Liquidity mining programs offer yield in exchange for providing LP tokens.",
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"Staking rewards are distributed to token holders as an incentive.",
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"Governance tokens allow holders to vote on protocol changes.",
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"Token burns reduce supply and can increase value for remaining holders.",
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"Yield farming involves moving assets between protocols for optimal returns.",
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"Bridge protocols enable cross-chain asset transfers.",
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"Lending protocols allow users to borrow against their crypto collateral.",
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"Market making provides liquidity to exchanges for a spread.",
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]
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async def mine_hard_negatives(
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query: str,
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num_negatives: int = 5,
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) -> list[str]:
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"""
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Mine hard negatives from RAG search - documents that are similar
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to positives but not relevant. These are the most valuable for training.
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"""
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negatives = []
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# Try RAG search for false positives
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try:
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from app.rag_service import three_pillar_search
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results = await three_pillar_search(
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query=query,
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collections=["known_scams", "token_analysis", "market_intel"],
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limit=num_negatives * 2,
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)
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# Take results with lower confidence as hard negatives
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for r in results.get("results", [])[:num_negatives]:
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content = r.get("content", "")
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if content and len(content) > 50:
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negatives.append(content[:500])
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except Exception:
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pass
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# Fall back to static hard negatives
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while len(negatives) < num_negatives:
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neg = random.choice(HARD_NEGATIVES)
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if neg not in negatives:
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negatives.append(neg)
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return negatives[:num_negatives]
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async def build_training_data() -> list[tuple[str, str, str, float]]:
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"""
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Build training triplets: (query, positive_doc, negative_doc, teacher_score)
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Returns list of triplets with cross-encoder teacher scores.
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"""
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training_data = []
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# Get cross-encoder teacher
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try:
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from app.cross_encoder_reranker import get_reranker
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reranker = await get_reranker()
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if not reranker._model_loaded:
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await reranker.warm_up()
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except Exception:
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reranker = None
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for query, positives in SCAM_QUERIES:
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pos_doc = positives[0]
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# Mine hard negatives
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negatives = await mine_hard_negatives(query)
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for neg_doc in negatives:
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# Teacher score: cross-encoder rates (query, positive) and (query, negative)
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if reranker and reranker._model_loaded:
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try:
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pos_score = float(reranker._model.predict([(query, pos_doc)]).tolist()[0])
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neg_score = float(reranker._model.predict([(query, neg_doc)]).tolist()[0])
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# Margin: how much better is the positive?
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margin = pos_score - neg_score
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except Exception:
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margin = random.uniform(1.5, 3.0)
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else:
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margin = random.uniform(1.5, 3.0)
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training_data.append((query, pos_doc, neg_doc, margin))
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return training_data
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def train_margin_mse(
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model,
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training_data: list[tuple[str, str, str, float]],
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epochs: int = 3,
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batch_size: int = 16,
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learning_rate: float = 2e-5,
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) -> dict[str, Any]: # noqa: F821 -- pre-existing bug, see fix(f821) tracking issue
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"""
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Train embeddings using MarginMSE loss.
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For each triplet (query, pos, neg):
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- Embed query, positive, negative separately
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- Compute cosine similarities: sim(q,pos) and sim(q,neg)
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- Loss = MSE(sim(q,pos) - sim(q,neg), target_margin)
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- Optimize to push pos closer, neg farther
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This is the core of domain-specific embedding fine-tuning.
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"""
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from sentence_transformers import InputExample, losses
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from torch.utils.data import DataLoader
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# Convert to sentence-transformers InputExample format
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examples = []
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for query, pos_doc, neg_doc, margin in training_data:
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# Normalize margin to 0-1 for cosine distance
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target_sim = max(0.0, min(1.0, (margin + 2.0) / 4.0))
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examples.append(
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InputExample(
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texts=[query, pos_doc, neg_doc],
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label=target_sim,
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)
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)
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random.shuffle(examples)
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# Use MarginMSE loss
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train_dataloader = DataLoader(examples, batch_size=batch_size, shuffle=True)
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train_loss = losses.MarginMSELoss(model)
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logger.info(f"Training on {len(examples)} triplets for {epochs} epochs...")
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# Warm-up steps
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warmup_steps = int(len(train_dataloader) * epochs * 0.1)
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model.fit(
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train_objectives=[(train_dataloader, train_loss)],
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epochs=epochs,
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warmup_steps=warmup_steps,
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output_path="/app/data/models/scam-embeddings-v1",
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show_progress_bar=True,
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)
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return {
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"status": "completed",
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"triplets": len(examples),
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"epochs": epochs,
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"output": "/app/data/models/scam-embeddings-v1",
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"model_size_mb": round(
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sum(
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os.path.getsize(os.path.join(dp, f))
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for dp, _, fs in os.walk("/app/data/models/scam-embeddings-v1")
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for f in fs
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)
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/ (1024 * 1024),
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1,
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),
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}
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async def main(epochs: int = 3, batch_size: int = 16):
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"""Main training pipeline."""
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print("=" * 60)
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print(" Domain Fine-Tuned Embeddings - MarginMSE Training")
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print("=" * 60)
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# 1. Build training data with hard negatives
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print("\n[1/3] Building training data with hard negative mining...")
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training_data = await build_training_data()
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print(f" ✓ {len(training_data)} training triplets")
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# 2. Load base model
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print("\n[2/3] Loading base embedding model...")
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try:
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("BAAI/bge-small-en-v1.5")
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print(" ✓ BGE-small loaded (384d)")
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except Exception as e:
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print(f" ✗ Failed to load model: {e}")
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return 1
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# 3. Train
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print(f"\n[3/3] Training MarginMSE for {epochs} epochs...")
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result = train_margin_mse(model, training_data, epochs=epochs, batch_size=batch_size)
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print(f" ✓ Model saved to {result['output']}")
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print(f" Size: {result['model_size_mb']}MB")
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return 0
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--epochs", type=int, default=3)
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parser.add_argument("--batch-size", type=int, default=16)
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args = parser.parse_args()
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sys.exit(asyncio.run(main(args.epochs, args.batch_size)))
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