#!/usr/bin/env python3 """Deep Dive Intelligence Pipeline - Reference Implementation Scaffold This is ARCHITECTURE PROOF code — not production-ready but demonstrates component contracts and data flow. Use as integration target. """ import asyncio import logging from dataclasses import dataclass from datetime import datetime, timedelta from pathlib import Path from typing import List, Optional, Dict, Any # Phase 1: Aggregation @dataclass class FeedItem: title: str summary: str url: str source: str published: datetime raw: Dict[str, Any] class RSSAggregator: """Fetch and normalize RSS feeds.""" def __init__(self, cache_dir: Path = None): self.cache_dir = cache_dir or Path.home() / ".cache" / "deepdive" self.cache_dir.mkdir(parents=True, exist_ok=True) self.logger = logging.getLogger(__name__) async def fetch_feed(self, url: str, etag: str = None) -> List[FeedItem]: """Fetch single feed with caching. Returns normalized items.""" # TODO: Implement with httpx, feedparser # TODO: Respect ETag/Last-Modified for incremental fetch raise NotImplementedError("Phase 1: Implement RSS fetch") async def fetch_all( self, sources: List[Dict[str, str]], since: datetime ) -> List[FeedItem]: """Fetch all configured sources since cutoff time.""" all_items = [] # TODO: asyncio.gather() parallel fetches # TODO: Filter items.published >= since raise NotImplementedError("Phase 1: Implement parallel fetch") # Phase 2: Relevance @dataclass class ScoredItem: item: FeedItem score: float keywords_matched: List[str] class RelevanceScorer: """Score items by relevance to Hermes/Timmy work.""" KEYWORDS = [ "LLM agent", "agent system", "tool use", "function calling", "reinforcement learning", "RLHF", "GRPO", "PPO", "transformer", "attention", "inference optimization", "local LLM", "llama.cpp", "ollama", "vLLM", "Hermes", "LM Studio", "open source AI" ] def __init__(self, model_name: str = "all-MiniLM-L6-v2"): self.model_name = model_name self.model = None # Lazy load sentence-transformers self.keyword_embeddings = None def _load_model(self): from sentence_transformers import SentenceTransformer import numpy as np self.model = SentenceTransformer(self.model_name) self.keyword_embeddings = self.model.encode(self.KEYWORDS) def score(self, item: FeedItem) -> ScoredItem: """Calculate relevance score for item.""" if self.model is None: self._load_model() # TODO: Encode title + summary # TODO: Cosine similarity to keyword embeddings # TODO: Calculate centroid match score # TODO: Boost for high-signal terms in title raise NotImplementedError("Phase 2: Implement scoring") def rank(self, items: List[FeedItem], top_n: int = 10) -> List[ScoredItem]: """Score all items, return top N.""" # TODO: Parallel scoring, cutoff at min_score raise NotImplementedError("Phase 2: Implement ranking") # Phase 3: Synthesis @dataclass class Briefing: date: datetime headline_items: List[ScoredItem] deep_dive: ScoredItem summary_text: str action_items: List[str] class SynthesisEngine: """Generate briefing text via local LLM.""" def __init__(self, model_endpoint: str = "http://localhost:4000/v1"): self.endpoint = model_endpoint self.system_prompt = """You are an intelligence analyst for the Timmy Foundation. Produce concise daily briefings on AI/ML developments relevant to: - LLM agent systems and architecture - Reinforcement learning for LLMs - Local inference and optimization - Open-source AI tooling Tone: Professional, tight, actionable.""" async def synthesize(self, items: List[ScoredItem]) -> Briefing: """Generate structured briefing from top items.""" # TODO: Format prompt with item data # TODO: Call local LLM via OpenAI-compatible API # TODO: Parse response into structured briefing raise NotImplementedError("Phase 3: Implement synthesis") # Phase 4: TTS @dataclass class AudioResult: path: Path duration_seconds: float word_count: int class TTSGenerator: """Generate audio via local Piper TTS.""" def __init__( self, model_path: Path, voice: str = "en_US-amy-medium" ): self.model_path = model_path self.voice = voice async def generate(self, text: str, output_dir: Path) -> AudioResult: """Generate audio file from briefing text.""" # TODO: Split long text into chunks if needed # TODO: Call Piper subprocess # TODO: Optionally combine chunks # TODO: Return path and metadata raise NotImplementedError("Phase 4: Implement TTS") # Phase 5: Delivery class TelegramDelivery: """Deliver briefing via Hermes Telegram gateway.""" def __init__(self, bot_token: str, chat_id: str): self.bot_token = bot_token self.chat_id = chat_id async def deliver_voice(self, audio_path: Path) -> bool: """Send voice message to Telegram.""" # TODO: Use python-telegram-bot or Hermes gateway raise NotImplementedError("Phase 5: Implement voice delivery") async def deliver_text(self, text: str) -> bool: """Send text summary to Telegram.""" # TODO: Truncate if > 4096 chars raise NotImplementedError("Phase 5: Implement text delivery") # Orchestration @dataclass class PipelineResult: success: bool items_considered: int items_selected: int briefing: Optional[Briefing] audio: Optional[AudioResult] errors: List[str] class DeepDivePipeline: """Full pipeline orchestrator.""" def __init__(self, config: Dict[str, Any]): self.config = config self.aggregator = RSSAggregator() self.scorer = RelevanceScorer() self.synthesis = SynthesisEngine() self.tts = TTSGenerator( model_path=Path(config["tts"]["model_path"]) ) self.delivery = TelegramDelivery( bot_token=config["delivery"]["bot_token"], chat_id=config["delivery"]["chat_id"] ) async def run( self, since: Optional[datetime] = None, deliver: bool = True ) -> PipelineResult: """Execute full pipeline.""" errors = [] # Phase 1: Aggregate try: items = await self.aggregator.fetch_all( self.config["sources"], since or datetime.now() - timedelta(days=1) ) except Exception as e: return PipelineResult(False, 0, 0, None, None, [f"Aggregation failed: {e}"]) # Phase 2: Score and rank try: top_items = self.scorer.rank( items, top_n=self.config.get("top_n", 10) ) except Exception as e: return PipelineResult(False, len(items), 0, None, None, [f"Scoring failed: {e}"]) # Phase 3: Synthesize try: briefing = await self.synthesis.synthesize(top_items) except Exception as e: return PipelineResult(False, len(items), len(top_items), None, None, [f"Synthesis failed: {e}"]) # Phase 4: Generate audio try: audio = await self.tts.generate( briefing.summary_text, Path(self.config.get("output_dir", "/tmp/deepdive")) ) except Exception as e: return PipelineResult(False, len(items), len(top_items), briefing, None, [f"TTS failed: {e}"]) # Phase 5: Deliver if deliver: try: await self.delivery.deliver_voice(audio.path) await self.delivery.deliver_text(briefing.summary_text[:4000]) except Exception as e: errors.append(f"Delivery failed: {e}") return PipelineResult( success=len(errors) == 0, items_considered=len(items), items_selected=len(top_items), briefing=briefing, audio=audio, errors=errors ) # CLI entry point async def main(): import argparse import yaml parser = argparse.ArgumentParser() parser.add_argument("--config", default="config.yaml") parser.add_argument("--since", type=lambda s: datetime.fromisoformat(s)) parser.add_argument("--no-deliver", action="store_true") parser.add_argument("--dry-run", action="store_true") args = parser.parse_args() with open(args.config) as f: config = yaml.safe_load(f) pipeline = DeepDivePipeline(config) result = await pipeline.run( since=args.since, deliver=not args.no_deliver ) print(f"Success: {result.success}") print(f"Items: {result.items_selected}/{result.items_considered}") if result.briefing: print(f"Briefing length: {len(result.briefing.summary_text)} chars") if result.audio: print(f"Audio: {result.audio.duration_seconds}s at {result.audio.path}") if result.errors: print(f"Errors: {result.errors}") if __name__ == "__main__": asyncio.run(main())