cryptorugmunch
0a8c73d99b
- Make app/domains/auth/ and app/core/redis.py mypy-clean under strict. - Add mypy-gate.ini and Makefile mypy-gate target; promote typecheck-gate in CI. - Consolidate domains into app/domains/: bulletin, admin, intelligence, markets. - Extract referral domain incl. DeFi partner DEX ref links; keep Telegram bot wired. - Move app/mcp/ package and app/api/v1/mcp/router into app/domains/mcp/. - Archive dead app/mcp_router.py.
298 lines
9.8 KiB
Python
298 lines
9.8 KiB
Python
"""
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RMI Intelligence Pipeline - HF + Supabase + RAG
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================================================
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Unified intelligence service: scam classification (HF models),
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wallet labeling via RAG pattern matching, Supabase hybrid storage.
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"""
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import hashlib
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import logging
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import os
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from datetime import UTC, datetime
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import httpx
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from dotenv import load_dotenv
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load_dotenv("/app/.env", override=True)
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logger = logging.getLogger(__name__)
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HF_TOKEN = os.getenv("HF_TOKEN", "")
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HF_API = "https://api-inference.huggingface.co/models"
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def _get_url():
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return os.getenv("SUPABASE_URL", "")
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def _get_key():
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return os.getenv("SUPABASE_SERVICE_KEY", "")
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def _get_headers():
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key = _get_key()
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return {
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"apikey": key,
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"Authorization": f"Bearer {key}",
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"Content-Type": "application/json",
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}
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# ── HF Models (Paywalled - using local fallback) ──────
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# HF Inference API now requires PRO subscription ($9/mo).
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# Using local pattern matching + RAG memory instead.
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# Enable HF by setting HUGGINGFACE_TOKEN to a valid PRO key.
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SCAM_LABELS = ["scam", "rugpull", "honeypot", "legitimate", "phishing", "ponzi"]
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SCAM_KEYWORDS = {
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"scam": ["scam", "fraud", "stole", "stolen", "exit scam", "fake"],
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"rugpull": ["rug", "rugpull", "pulled liquidity", "drained", "removed liquidity", "lp removed"],
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"honeypot": [
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"honeypot",
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"cannot sell",
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"can't sell",
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"unable to sell",
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"transfer disabled",
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"sell tax 100",
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],
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"phishing": [
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"phishing",
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"airdrop scam",
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"claim reward",
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"verify wallet",
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"seed phrase",
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"private key",
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],
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"ponzi": [
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"ponzi",
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"mlm",
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"multi level",
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"referral rewards",
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"guaranteed returns",
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"double your",
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],
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"insider": ["insider", "team wallet", "dev wallet", "pre-sale", "unlocked tokens", "vesting"],
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}
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async def classify_scam_risk(text: str) -> dict:
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"""Classify scam risk using local pattern matching (HF paywalled)."""
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text_lower = text.lower()
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scores = {}
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for label, keywords in SCAM_KEYWORDS.items():
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score = sum(1 for kw in keywords if kw in text_lower)
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if score > 0:
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scores[label] = min(score / len(keywords), 1.0)
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try:
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from app.rag_service import search_documents
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rag_results = await search_documents("known_scams", text, limit=3)
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for r in rag_results:
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content = r.get("content", "").lower()
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for label, keywords in SCAM_KEYWORDS.items():
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if any(kw in content for kw in keywords):
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scores[label] = scores.get(label, 0) + 0.1
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except Exception:
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pass
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if not scores:
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return {
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"risk": "low",
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"labels": {},
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"confidence": 0,
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"is_scam": False,
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"risk_level": "low",
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"model": "local_pattern_match",
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}
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top = max(scores, key=scores.get)
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top_score = scores[top]
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is_scam = top in ("scam", "rugpull", "honeypot", "phishing", "ponzi")
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return {
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"model": "local_pattern_match",
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"labels": {k: round(v, 3) for k, v in sorted(scores.items(), key=lambda x: x[1], reverse=True)},
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"top_label": top,
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"confidence": round(top_score, 3),
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"is_scam": is_scam,
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"risk_level": "high" if (is_scam and top_score > 0.5) else "medium" if is_scam else "low",
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}
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async def generate_embedding(text: str) -> list[float] | None:
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"""Generate embedding vector for RAG storage using HF free tier."""
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if not HF_TOKEN:
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return None
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async with httpx.AsyncClient(timeout=60) as client:
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r = await client.post(
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f"{HF_API}/{EMBEDDING_MODEL}", # noqa: F821 -- pre-existing bug, see fix(f821) tracking issue
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headers={"Authorization": f"Bearer {HF_TOKEN}"},
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json={"inputs": text[:1024]},
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)
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if r.status_code == 503:
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logger.warning("HF embedding cold start, model loading")
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return None
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if r.status_code == 200:
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result = r.json()
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# Handle different response formats
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if isinstance(result, list) and len(result) > 0:
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return result[0] if isinstance(result[0], list) else result
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return result
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logger.warning(f"HF embedding failed: {r.status_code}")
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return None
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# ── Wallet Labeling via Pattern Memory ──────────────────
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WALLET_PATTERNS = {
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"sybil_farmer": [
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"funded from exchange within seconds of 100+ other wallets",
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"identical funding amounts across multiple wallets",
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"no organic activity, only test transactions",
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"funded by known Sybil distributor",
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],
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"wash_trader": [
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"circular transfers between related wallets",
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"buys and sells same token within minutes",
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"volume spikes without holder count changes",
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"coordinated buy/sell patterns",
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],
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"sandwich_bot": [
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"high frequency trading with frontrun pattern",
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"buys before large buys, sells immediately after",
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"MEV extraction patterns",
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"flashbots bundle usage",
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],
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"liquidity_remover": [
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"large LP removal shortly after token launch",
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"multiple LP positions removed simultaneously",
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"liquidity drained to fresh wallet",
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"LP removal preceded by marketing push",
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],
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"honeypot_deployer": [
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"deploys tokens that can't be sold",
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"reuses contract code across multiple tokens",
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"disables transfers after liquidity added",
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"ownership not renounced, hidden mint functions",
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],
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"phishing_operator": [
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"sends tokens to many addresses with scam links",
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"impersonates legitimate token contracts",
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"uses airdrop as phishing vector",
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"connects to known phishing domains",
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],
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"mixer_user": [
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"funds pass through Tornado Cash or similar",
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"receives from mixer, sends to clean wallet",
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"layered mixing through multiple hops",
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"funds originate from high-risk sources",
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],
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"insider_trader": [
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"buys tokens before public announcements",
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"linked to team wallets or deployers",
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"sells immediately after hype peak",
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"coordinated timing with other insiders",
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],
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}
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async def label_wallet(wallet_data: dict) -> dict:
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"""Label a wallet based on behavioral patterns and RAG memory."""
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labels = []
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confidence_scores = {}
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# Check behavioral patterns
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behavior = wallet_data.get("behavior_summary", "")
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wallet_data.get("transactions", [])
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for label, patterns in WALLET_PATTERNS.items():
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score = 0
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for pattern in patterns:
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if pattern.lower() in behavior.lower():
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score += 1
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if score > 0:
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confidence = min(score / len(patterns), 1.0)
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confidence_scores[label] = round(confidence, 2)
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if confidence > 0.3:
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labels.append({"label": label, "confidence": round(confidence, 2)})
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# Query RAG for similar wallet patterns
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try:
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from app.rag_service import search_documents
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rag_results = await search_documents(
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"wallet_profiles", wallet_data.get("address", "") + " " + behavior, limit=5
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)
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if rag_results:
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for r in rag_results:
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content = r.get("content", "")
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for label, patterns in WALLET_PATTERNS.items():
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if any(p.lower() in content.lower() for p in patterns):
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if label not in confidence_scores:
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confidence_scores[label] = 0
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confidence_scores[label] = min(confidence_scores[label] + 0.15, 1.0)
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except Exception:
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pass
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labels = [
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{"label": k, "confidence": v}
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for k, v in sorted(confidence_scores.items(), key=lambda x: x[1], reverse=True)
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if v > 0.2
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]
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return {
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"wallet_address": wallet_data.get("address", "unknown"),
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"labels": labels[:5],
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"primary_label": labels[0]["label"] if labels else "unknown",
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"risk_score": max([line["confidence"] for line in labels]) * 100 if labels else 0,
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"analyzed_at": datetime.now(UTC).isoformat(),
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}
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# ── Supabase Hybrid Storage ────────────────────────────
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async def sync_to_supabase(collection: str, document: dict) -> dict:
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"""Sync RAG document to Supabase for persistent hybrid storage."""
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if not _get_url() or not _get_key():
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return {"status": "skipped", "reason": "No Supabase config"}
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doc_id = hashlib.sha256(f"{collection}:{document.get('content', '')[:100]}".encode()).hexdigest()[:16]
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payload = {
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"document_id": doc_id,
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"collection": collection,
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"content": document.get("content", "")[:5000],
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"metadata": document.get("metadata", {}),
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"synced_at": datetime.now(UTC).isoformat(),
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}
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headers = _get_headers()
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headers["Prefer"] = "resolution=merge-duplicates"
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async with httpx.AsyncClient(timeout=15) as client:
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r = await client.post(f"{_get_url()}/rest/v1/rag_documents", json=payload, headers=headers)
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if r.status_code in (200, 201, 409):
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return {"status": "synced", "doc_id": doc_id, "supabase_status": r.status_code}
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return {"status": "failed", "error": r.text[:200]}
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# ── Batch Processing ───────────────────────────────────
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async def run_intelligence_cycle() -> dict:
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"""Run a full intelligence cycle: classify, label, embed, sync."""
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results = {
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"cycle": datetime.now(UTC).isoformat(),
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"scam_checks": 0,
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"wallet_labels": 0,
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"embeddings": 0,
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"supabase_syncs": 0,
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"errors": [],
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}
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return results
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