tcpsyn
79e6bc79be
- 30% of callers now call about topics (prestige TV, science, poker, astrophotography, physics, tech, US news) instead of personal problems - 86 curated interests weighted toward shows like Severance, Breaking Bad, The Wire, LOST, Westworld, etc. Removed reality TV/celebrity gossip - 32-town knowledge base with real facts so callers don't invent landmarks - Smart topic detection for news enrichment (keyword->search query mapping) - Enrichment now summarizes articles naturally via LLM instead of quoting headlines - Prompt rewrite for varied response lengths and no rehashing - Extra weight for Animas and Lordsburg callers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
193 lines
7.6 KiB
Python
193 lines
7.6 KiB
Python
"""News service using local SearXNG for current events awareness in AI callers"""
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import asyncio
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import time
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import re
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from dataclasses import dataclass
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import httpx
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SEARXNG_URL = "http://localhost:8888"
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@dataclass
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class NewsItem:
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title: str
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source: str
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published: str
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content: str = ""
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class NewsService:
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def __init__(self):
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self._client: httpx.AsyncClient | None = None
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self._headlines_cache: list[NewsItem] = []
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self._headlines_ts: float = 0
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self._search_cache: dict[str, tuple[float, list[NewsItem]]] = {}
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@property
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def client(self) -> httpx.AsyncClient:
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if self._client is None or self._client.is_closed:
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self._client = httpx.AsyncClient(timeout=5.0)
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return self._client
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async def get_headlines(self) -> list[NewsItem]:
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# Cache for 30min
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if self._headlines_cache and time.time() - self._headlines_ts < 1800:
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return self._headlines_cache
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try:
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resp = await self.client.get(
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f"{SEARXNG_URL}/search",
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params={"q": "news", "format": "json", "categories": "news"},
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)
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resp.raise_for_status()
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items = self._parse_searxng(resp.json(), max_items=10)
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self._headlines_cache = items
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self._headlines_ts = time.time()
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return items
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except Exception as e:
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print(f"[News] Headlines fetch failed: {e}")
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self._headlines_ts = time.time()
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return self._headlines_cache
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async def search_topic(self, query: str) -> list[NewsItem]:
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cache_key = query.lower()
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if cache_key in self._search_cache:
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ts, items = self._search_cache[cache_key]
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if time.time() - ts < 600:
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return items
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# Evict oldest when cache too large
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if len(self._search_cache) > 50:
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oldest_key = min(self._search_cache, key=lambda k: self._search_cache[k][0])
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del self._search_cache[oldest_key]
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try:
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resp = await self.client.get(
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f"{SEARXNG_URL}/search",
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params={"q": query, "format": "json", "categories": "news"},
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)
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resp.raise_for_status()
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items = self._parse_searxng(resp.json(), max_items=5)
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self._search_cache[cache_key] = (time.time(), items)
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return items
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except Exception as e:
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print(f"[News] Search failed for '{query}': {e}")
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if cache_key in self._search_cache:
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return self._search_cache[cache_key][1]
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return []
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def _parse_searxng(self, data: dict, max_items: int = 10) -> list[NewsItem]:
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items = []
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for result in data.get("results", [])[:max_items]:
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title = result.get("title", "").strip()
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if not title:
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continue
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# Extract source from engines list or metadata
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engines = result.get("engines", [])
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source = engines[0] if engines else ""
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published = result.get("publishedDate", "")
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content = result.get("content", "").strip()
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items.append(NewsItem(title=title, source=source, published=published, content=content))
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return items
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def format_headlines_for_prompt(self, items: list[NewsItem]) -> str:
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lines = []
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for item in items:
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if item.source:
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lines.append(f"- {item.title} ({item.source})")
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else:
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lines.append(f"- {item.title}")
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return "\n".join(lines)
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async def close(self):
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if self._client and not self._client.is_closed:
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await self._client.aclose()
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STOP_WORDS = {
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"the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
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"have", "has", "had", "do", "does", "did", "will", "would", "could",
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"should", "may", "might", "shall", "can", "need", "dare", "ought",
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"used", "to", "of", "in", "for", "on", "with", "at", "by", "from",
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"as", "into", "through", "during", "before", "after", "above", "below",
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"between", "out", "off", "over", "under", "again", "further", "then",
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"once", "here", "there", "when", "where", "why", "how", "all", "both",
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"each", "few", "more", "most", "other", "some", "such", "no", "nor",
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"not", "only", "own", "same", "so", "than", "too", "very", "just",
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"but", "and", "or", "if", "while", "because", "until", "about",
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"that", "this", "these", "those", "what", "which", "who", "whom",
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"it", "its", "he", "him", "his", "she", "her", "they", "them",
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"their", "we", "us", "our", "you", "your", "me", "my", "i",
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# Casual speech fillers
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"yeah", "well", "like", "man", "dude", "okay", "right", "know",
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"think", "mean", "really", "actually", "honestly", "basically",
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"literally", "stuff", "thing", "things", "something", "anything",
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"nothing", "everything", "someone", "anyone", "everyone", "nobody",
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"gonna", "wanna", "gotta", "kinda", "sorta", "dunno",
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"look", "see", "say", "said", "tell", "told", "talk", "talking",
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"feel", "felt", "guess", "sure", "maybe", "probably", "never",
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"always", "still", "even", "much", "many", "also", "got", "get",
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"getting", "going", "come", "came", "make", "made", "take", "took",
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"give", "gave", "want", "keep", "kept", "let", "put", "went",
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"been", "being", "doing", "having", "call", "called", "calling",
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"tonight", "today", "night", "time", "long", "good", "bad",
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"first", "last", "back", "down", "ever", "away", "cant", "dont",
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"didnt", "doesnt", "isnt", "wasnt", "wont", "wouldnt", "couldnt",
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"shouldnt", "aint", "stop", "start", "started", "help",
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# Radio show filler
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"welcome", "thanks", "thank", "show", "roost", "luke", "whats",
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"youre", "thats", "heres", "theyre", "ive", "youve", "weve",
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"sounds", "listen", "hear", "heard", "happen", "happened",
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"happening", "absolutely", "definitely", "exactly", "totally",
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"pretty", "little", "whole", "every", "point", "sense", "real",
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"great", "cool", "awesome", "amazing", "crazy", "weird", "funny",
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"tough", "hard", "wrong", "true", "trying", "tried", "works",
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"working", "anymore", "already", "enough", "though", "whatever",
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"theres", "making", "saying", "keeping", "possible", "instead",
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"front", "behind", "course", "talks", "happens", "watch",
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"everybodys", "pants", "husband", "client",
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}
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def extract_keywords(text: str, max_keywords: int = 3) -> list[str]:
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words = text.split()
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if len(words) < 8:
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return [] # Too short to extract meaningful topics
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keywords = []
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# Only look for proper nouns that are likely real topics (not caller names)
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proper_nouns = []
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for i, word in enumerate(words):
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clean = re.sub(r'[^\w]', '', word)
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if not clean or len(clean) < 3:
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continue
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is_sentence_start = i == 0 or (i > 0 and words[i - 1].rstrip()[-1:] in '.!?')
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if clean[0].isupper() and not is_sentence_start and clean.lower() not in STOP_WORDS:
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proper_nouns.append(clean)
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# Only use proper nouns if we found 2+ (single one is probably a name)
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if len(proper_nouns) >= 2:
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for noun in proper_nouns[:max_keywords]:
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if noun not in keywords:
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keywords.append(noun)
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if len(keywords) >= max_keywords:
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return keywords
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# Pass 2: uncommon words (>5 chars, not in stop words)
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for word in words:
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clean = re.sub(r'[^\w]', '', word).lower()
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if len(clean) > 5 and clean not in STOP_WORDS:
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if clean not in [k.lower() for k in keywords]:
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keywords.append(clean)
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if len(keywords) >= max_keywords:
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return keywords
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return keywords
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news_service = NewsService()
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