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>
127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
"""T27 LLM Router - sovereign-first LiteLLM proxy for catalog AI.
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Per v4.0 §T28 (News analysis), §T29 (Report generation).
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Self-hosted LiteLLM proxy at litellm.rugmunch.io routes to:
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- DeepSeek-V3 (cost-effective analysis)
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- Qwen (summaries)
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- Local Llama-3 (free-tier fallback)
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We never call OpenAI directly. If LiteLLM is unreachable, the catalog
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operations that need LLM analysis return None/empty with a logged warning
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rather than failing the whole request.
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"""
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from __future__ import annotations
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import logging
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import os
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import httpx
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log = logging.getLogger(__name__)
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# ── Config ─────────────────────────────────────────────────────────
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LITELLM_URL = os.getenv("LITELLM_URL", "http://litellm.rugmunch.io")
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LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "")
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DEFAULT_MODEL = os.getenv("LITELLM_DEFAULT_MODEL", "deepseek-v3")
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NEWS_ANALYSIS_PROMPT = """You are an analyst at RugMunch Intelligence, a crypto
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scam-detection platform. Analyze the following news item and produce a
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structured Markdown summary.
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NEWS ITEM:
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- Title: {title}
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- Source: {source}
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- Published: {published_at}
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- Body: {body_truncated_to_2000_chars}
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Produce a summary with these sections (use Markdown headers):
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## Summary
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2-3 sentence plain-English summary.
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## Affected Tokens
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List any tokens mentioned, with their chain and address if known.
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## Affected Wallets
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List any wallets mentioned.
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## Sentiment
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One of: bullish | bearish | neutral | risk-elevating | risk-reducing
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1-sentence justification.
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## RugMunch Action
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What should our platform do in response? Options:
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- (none)
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- (flag mentioned tokens for re-scan)
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- (alert subscribers)
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- (update deployer reputation)
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- (cross-chain entity resolution trigger)
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Be concise. Do not speculate beyond what the article says.
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"""
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class LLMRouter:
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"""Async client for the self-hosted LiteLLM proxy.
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Falls back to None if the proxy is unreachable - catalog operations
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that need LLM output will skip the AI analysis but still complete
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the rest of the workflow.
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"""
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def __init__(self, url: str | None = None, api_key: str | None = None) -> None:
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self.url = (url or LITELLM_URL).rstrip("/")
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self.api_key = api_key or LITELLM_API_KEY
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self._client: httpx.AsyncClient | None = None
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async def _get_client(self) -> httpx.AsyncClient:
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if self._client is None:
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headers = {"Content-Type": "application/json"}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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self._client = httpx.AsyncClient(
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base_url=self.url, headers=headers, timeout=30.0
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)
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return self._client
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async def chat(
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self,
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prompt: str,
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model: str | None = None,
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max_tokens: int = 800,
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temperature: float = 0.3,
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) -> str | None:
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"""Send a chat completion. Returns text or None on failure."""
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try:
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client = await self._get_client()
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r = await client.post(
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"/chat/completions",
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json={
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"model": model or DEFAULT_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": max_tokens,
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"temperature": temperature,
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},
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)
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if r.status_code != 200:
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log.warning("llm_http_%d: %s", r.status_code, r.text[:200])
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return None
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data = r.json()
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return data["choices"][0]["message"]["content"]
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except Exception as e:
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log.warning("llm_chat_fail: %s", e)
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return None
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async def analyze_news(
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self, news_item: NewsItem # type: ignore # noqa: F821 -- pre-existing bug, see fix(f821) tracking issue
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) -> str | None:
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"""Generate AI analysis for a NewsItem. Per v4.0 §T28."""
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prompt = NEWS_ANALYSIS_PROMPT.format(
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title=news_item.title,
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source=news_item.source,
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published_at=news_item.published_at.isoformat(),
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body_truncated_to_2000_chars=(news_item.body_markdown or news_item.summary)[:2000],
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)
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return await self.chat(prompt, max_tokens=800, temperature=0.3)
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